In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

PubMed Central

Pegorini, Vinicius; Karam, Leandro Zen; Pitta, Christiano Santos Rocha; Cardoso, Rafael; da Silva, Jean Carlos Cardozo; Kalinowski, Hypolito José; Ribeiro, Richardson; Bertotti, Fábio Luiz; Assmann, Tangriani Simioni

2015-01-01

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%. PMID:26569250

Integration of Chinese medicine with Western medicine could lead to future medicine: molecular module medicine.

PubMed

Zhang, Chi; Zhang, Ge; Chen, Ke-ji; Lu, Ai-ping

2016-04-01

The development of an effective classification method for human health conditions is essential for precise diagnosis and delivery of tailored therapy to individuals. Contemporary classification of disease systems has properties that limit its information content and usability. Chinese medicine pattern classification has been incorporated with disease classification, and this integrated classification method became more precise because of the increased understanding of the molecular mechanisms. However, we are still facing the complexity of diseases and patterns in the classification of health conditions. With continuing advances in omics methodologies and instrumentation, we are proposing a new classification approach: molecular module classification, which is applying molecular modules to classifying human health status. The initiative would be precisely defining the health status, providing accurate diagnoses, optimizing the therapeutics and improving new drug discovery strategy. Therefore, there would be no current disease diagnosis, no disease pattern classification, and in the future, a new medicine based on this classification, molecular module medicine, could redefine health statuses and reshape the clinical practice.

Computer-based classification of bacteria species by analysis of their colonies Fresnel diffraction patterns

NASA Astrophysics Data System (ADS)

Suchwalko, Agnieszka; Buzalewicz, Igor; Podbielska, Halina

2012-01-01

In the presented paper the optical system with converging spherical wave illumination for classification of bacteria species, is proposed. It allows for compression of the observation space, observation of Fresnel patterns, diffraction pattern scaling and low level of optical aberrations, which are not possessed by other optical configurations. Obtained experimental results have shown that colonies of specific bacteria species generate unique diffraction signatures. Analysis of Fresnel diffraction patterns of bacteria colonies can be fast and reliable method for classification and recognition of bacteria species. To determine the unique features of bacteria colonies diffraction patterns the image processing analysis was proposed. Classification can be performed by analyzing the spatial structure of diffraction patterns, which can be characterized by set of concentric rings. The characteristics of such rings depends on the bacteria species. In the paper, the influence of basic features and ring partitioning number on the bacteria classification, is analyzed. It is demonstrated that Fresnel patterns can be used for classification of following species: Salmonella enteritidis, Staplyococcus aureus, Proteus mirabilis and Citrobacter freundii. Image processing is performed by free ImageJ software, for which a special macro with human interaction, was written. LDA classification, CV method, ANOVA and PCA visualizations preceded by image data extraction were conducted using the free software R.

The DTW-based representation space for seismic pattern classification

NASA Astrophysics Data System (ADS)

Orozco-Alzate, Mauricio; Castro-Cabrera, Paola Alexandra; Bicego, Manuele; Londoño-Bonilla, John Makario

2015-12-01

Distinguishing among the different seismic volcanic patterns is still one of the most important and labor-intensive tasks for volcano monitoring. This task could be lightened and made free from subjective bias by using automatic classification techniques. In this context, a core but often overlooked issue is the choice of an appropriate representation of the data to be classified. Recently, it has been suggested that using a relative representation (i.e. proximities, namely dissimilarities on pairs of objects) instead of an absolute one (i.e. features, namely measurements on single objects) is advantageous to exploit the relational information contained in the dissimilarities to derive highly discriminant vector spaces, where any classifier can be used. According to that motivation, this paper investigates the suitability of a dynamic time warping (DTW) dissimilarity-based vector representation for the classification of seismic patterns. Results show the usefulness of such a representation in the seismic pattern classification scenario, including analyses of potential benefits from recent advances in the dissimilarity-based paradigm such as the proper selection of representation sets and the combination of different dissimilarity representations that might be available for the same data.

Diagnostic Classification of Schizophrenia Patients on the Basis of Regional Reward-Related fMRI Signal Patterns

PubMed Central

Koch, Stefan P.; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

2015-01-01

Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way. PMID:25799236

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Pattern learning with deep neural networks in EMG-based speech recognition.

PubMed

Wand, Michael; Schultz, Tanja

2014-01-01

We report on classification of phones and phonetic features from facial electromyographic (EMG) data, within the context of our EMG-based Silent Speech interface. In this paper we show that a Deep Neural Network can be used to perform this classification task, yielding a significant improvement over conventional Gaussian Mixture models. Our central contribution is the visualization of patterns which are learned by the neural network. With increasing network depth, these patterns represent more and more intricate electromyographic activity.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

Ecosystem classifications based on summer and winter conditions.

PubMed

Andrew, Margaret E; Nelson, Trisalyn A; Wulder, Michael A; Hobart, George W; Coops, Nicholas C; Farmer, Carson J Q

2013-04-01

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

Patterns of Use of an Agent-Based Model and a System Dynamics Model: The Application of Patterns of Use and the Impacts on Learning Outcomes

ERIC Educational Resources Information Center

Thompson, Kate; Reimann, Peter

2010-01-01

A classification system that was developed for the use of agent-based models was applied to strategies used by school-aged students to interrogate an agent-based model and a system dynamics model. These were compared, and relationships between learning outcomes and the strategies used were also analysed. It was found that the classification system…

Style consistent classification of isogenous patterns.

PubMed

Sarkar, Prateek; Nagy, George

2005-01-01

In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style.

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Hyperspectral image classification based on local binary patterns and PCANet

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Network-based high level data classification.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-06-01

Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Lissencephaly: expanded imaging and clinical classification

PubMed Central

Di Donato, Nataliya; Chiari, Sara; Mirzaa, Ghayda M.; Aldinger, Kimberly; Parrini, Elena; Olds, Carissa; Barkovich, A. James; Guerrini, Renzo; Dobyns, William B.

2017-01-01

Lissencephaly (“smooth brain”, LIS) is a malformation of cortical development associated with deficient neuronal migration and abnormal formation of cerebral convolutions or gyri. The LIS spectrum includes agyria, pachygyria, and subcortical band heterotopia. Our first classification of LIS and subcortical band heterotopia (SBH) was developed to distinguish between the first two genetic causes of LIS – LIS1 (PAFAH1B1) and DCX. However, progress in molecular genetics has led to identification of 19 LIS-associated genes, leaving the existing classification system insufficient to distinguish the increasingly diverse patterns of LIS. To address this challenge, we reviewed clinical, imaging and molecular data on 188 patients with LIS-SBH ascertained during the last five years, and reviewed selected archival data on another ~1,400 patients. Using these data plus published reports, we constructed a new imaging based classification system with 21 recognizable patterns that reliably predict the most likely causative genes. These patterns do not correlate consistently with the clinical outcome, leading us to also develop a new scale useful for predicting clinical severity and outcome. Taken together, our work provides new tools that should prove useful for clinical management and genetic counselling of patients with LIS-SBH (imaging and severity based classifications), and guidance for prioritizing and interpreting genetic testing results (imaging based classification). PMID:28440899

Hybrid approach for robust diagnostics of cutting tools

NASA Astrophysics Data System (ADS)

Ramamurthi, K.; Hough, C. L., Jr.

1994-03-01

A new multisensor based hybrid technique has been developed for robust diagnosis of cutting tools. The technique combines the concepts of pattern classification and real-time knowledge based systems (RTKBS) and draws upon their strengths; learning facility in the case of pattern classification and a higher level of reasoning in the case of RTKBS. It eliminates some of their major drawbacks: false alarms or delayed/lack of diagnosis in case of pattern classification and tedious knowledge base generation in case of RTKBS. It utilizes a dynamic distance classifier, developed upon a new separability criterion and a new definition of robust diagnosis for achieving these benefits. The promise of this technique has been proven concretely through an on-line diagnosis of drill wear. Its suitability for practical implementation is substantiated by the use of practical, inexpensive, machine-mounted sensors and low-cost delivery systems.

Automated classification of articular cartilage surfaces based on surface texture.

PubMed

Stachowiak, G P; Stachowiak, G W; Podsiadlo, P

2006-11-01

In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.

A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats.

PubMed

Tejedor, Javier; Macias-Guarasa, Javier; Martins, Hugo F; Piote, Daniel; Pastor-Graells, Juan; Martin-Lopez, Sonia; Corredera, Pedro; Gonzalez-Herraez, Miguel

2017-02-12

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Mapping the Natchez Trace Parkway

USGS Publications Warehouse

Rangoonwala, Amina; Bannister, Terri; Ramsey, Elijah W.

2011-01-01

Based on a National Park Service (NPS) landcover classification, a landcover map of the 715-km (444-mile) NPS Natchez Trace Parkway (hereafter referred to as the "Parkway") was created. The NPS landcover classification followed National Vegetation Classification (NVC) protocols. The landcover map, which extended the initial landcover classification to the entire Parkway, was based on color-infrared photography converted to 1-m raster-based digital orthophoto quarter quadrangles, according to U.S. Geological Survey mapping standards. Our goal was to include as many alliance classes as possible in the Parkway landcover map. To reach this goal while maintaining a consistent and quantifiable map product throughout the Parkway extent, a mapping strategy was implemented based on the migration of class-based spectral textural signatures and the congruent progressive refinement of those class signatures along the Parkway. Progressive refinement provided consistent mapping by evaluating the spectral textural distinctiveness of the alliance-association classes, and where necessary, introducing new map classes along the Parkway. By following this mapping strategy, the use of raster-based image processing and geographic information system analyses for the map production provided a quantitative and reproducible product. Although field-site classification data were severely limited, the combination of spectral migration of class membership along the Parkway and the progressive classification strategy produced an organization of alliances that was internally highly consistent. The organization resulted from the natural patterns or alignments of spectral variance and the determination of those spectral patterns that were compositionally similar in the dominant species as NVC alliances. Overall, the mapped landcovers represented the existent spectral textural patterns that defined and encompassed the complex variety of compositional alliances and associations of the Parkway. Based on that mapped representation, forests dominate the Parkway landscape. Grass is the second largest Parkway land cover, followed by scrub-shrub and shrubland classes and pine plantations. The map provides a good representation of the landcover patterns and their changes over the extent of the Parkway, south to north.

Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-11-30

Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

Cognitive approaches for patterns analysis and security applications

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Ogiela, Lidia

2017-08-01

In this paper will be presented new opportunities for developing innovative solutions for semantic pattern classification and visual cryptography, which will base on cognitive and bio-inspired approaches. Such techniques can be used for evaluation of the meaning of analyzed patterns or encrypted information, and allow to involve such meaning into the classification task or encryption process. It also allows using some crypto-biometric solutions to extend personalized cryptography methodologies based on visual pattern analysis. In particular application of cognitive information systems for semantic analysis of different patterns will be presented, and also a novel application of such systems for visual secret sharing will be described. Visual shares for divided information can be created based on threshold procedure, which may be dependent on personal abilities to recognize some image details visible on divided images.

Protein classification using sequential pattern mining.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2006-01-01

Protein classification in terms of fold recognition can be employed to determine the structural and functional properties of a newly discovered protein. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. One of the most efficient SPM algorithms, cSPADE, is employed for protein primary structure analysis. Then a classifier uses the extracted sequential patterns for classifying proteins of unknown structure in the appropriate fold category. The proposed methodology exhibited an overall accuracy of 36% in a multi-class problem of 17 candidate categories. The classification performance reaches up to 65% when the three most probable protein folds are considered.

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

NASA Technical Reports Server (NTRS)

Huntsberger, Terry

2011-01-01

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification

PubMed Central

Xie, Jin; Zhang, Lei; You, Jane; Zhang, David; Qu, Xiaofeng

2012-01-01

Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed to capture HBST images, and an HBST image database was established, which consists of 1,920 images from 80 persons (160 hands). An efficient texton learning based method is then presented to classify the HBST patterns. First, textons are learned in the space of filter bank responses from a set of training images using the l1 -minimization based sparse representation (SR) technique. Then, under the SR framework, we represent the feature vector at each pixel over the learned dictionary to construct a representation coefficient histogram. Finally, the coefficient histogram is used as skin texture feature for classification. Experiments on personal identification and gender classification are performed by using the established HBST database. The results show that HBST can be used to assist human identification and gender classification. PMID:23012512

Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

PubMed Central

Lee, You-Yun; Hsieh, Shulan

2014-01-01

This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states. PMID:24743695

Computer-implemented land use classification with pattern recognition software and ERTS digital data. [Mississippi coastal plains

NASA Technical Reports Server (NTRS)

Joyce, A. T.

1974-01-01

Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy.

Pattern recognition of satellite cloud imagery for improved weather prediction

NASA Technical Reports Server (NTRS)

Gautier, Catherine; Somerville, Richard C. J.; Volfson, Leonid B.

1986-01-01

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees

PubMed Central

2012-01-01

Background Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts. Methods With five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated. Results With EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms. Conclusions The performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance. PMID:23036049

Classification of cancerous cells based on the one-class problem approach

NASA Astrophysics Data System (ADS)

Murshed, Nabeel A.; Bortolozzi, Flavio; Sabourin, Robert

1996-03-01

One of the most important factors in reducing the effect of cancerous diseases is the early diagnosis, which requires a good and a robust method. With the advancement of computer technologies and digital image processing, the development of a computer-based system has become feasible. In this paper, we introduce a new approach for the detection of cancerous cells. This approach is based on the one-class problem approach, through which the classification system need only be trained with patterns of cancerous cells. This reduces the burden of the training task by about 50%. Based on this approach, a computer-based classification system is developed, based on the Fuzzy ARTMAP neural networks. Experimental results were performed using a set of 542 patterns taken from a sample of breast cancer. Results of the experiment show 98% correct identification of cancerous cells and 95% correct identification of non-cancerous cells.

Neuroimaging classification of progression patterns in glioblastoma: a systematic review.

PubMed

Piper, Rory J; Senthil, Keerthi K; Yan, Jiun-Lin; Price, Stephen J

2018-03-30

Our primary objective was to report the current neuroimaging classification systems of spatial patterns of progression in glioblastoma. In addition, we aimed to report the terminology used to describe 'progression' and to assess the compliance with the Response Assessment in Neuro-Oncology (RANO) Criteria. We conducted a systematic review to identify all neuroimaging studies of glioblastoma that have employed a categorical classification system of spatial progression patterns. Our review was registered with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) registry. From the included 157 results, we identified 129 studies that used labels of spatial progression patterns that were not based on radiation volumes (Group 1) and 50 studies that used labels that were based on radiation volumes (Group 2). In Group 1, we found 113 individual labels and the most frequent were: local/localised (58%), distant/distal (51%), diffuse (20%), multifocal (15%) and subependymal/subventricular zone (15%). We identified 13 different labels used to refer to 'progression', of which the most frequent were 'recurrence' (99%) and 'progression' (92%). We identified that 37% (n = 33/90) of the studies published following the release of the RANO classification were adherent compliant with the RANO criteria. Our review reports significant heterogeneity in the published systems used to classify glioblastoma spatial progression patterns. Standardization of terminology and classification systems used in studying progression would increase the efficiency of our research in our attempts to more successfully treat glioblastoma.

Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces.

PubMed

Dai, Shengfa; Wei, Qingguo

2017-01-01

Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. A population of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.

[Utilization of nursing diagnosis according to Nanda's classification for the systematization of nursing care in breast feeding].

PubMed

Abrão, A C; de Gutiérrez, M R; Marin, H F

1997-04-01

The present study aimed at describing the reformulated instrument used in the puerperal woman nursing consultation based on the identified diagnoses classification according to the Taxonomy-I reviewed by NANDA, and the identification of the most frequent nursing diagnoses concerning maternal breastfeeding, based on the reformulated instrument. The diagnoses found as being over 50% were: knowledge deficit (100%); sleep pattern disturbance (75%), altered sexuality patterns (75%), ineffective breastfeeding (66.6%) and impaired physical mobility (66.6%).

Bloodstain pattern classification: Accuracy, effect of contextual information and the role of analyst characteristics.

PubMed

Osborne, Nikola K P; Taylor, Michael C; Healey, Matthew; Zajac, Rachel

2016-03-01

It is becoming increasingly apparent that contextual information can exert a considerable influence on decisions about forensic evidence. Here, we explored accuracy and contextual influence in bloodstain pattern classification, and how these variables might relate to analyst characteristics. Thirty-nine bloodstain pattern analysts with varying degrees of experience each completed measures of compliance, decision-making style, and need for closure. Analysts then examined a bloodstain pattern without any additional contextual information, and allocated votes to listed pattern types according to favoured and less favoured classifications. Next, if they believed it would assist with their classification, analysts could request items of contextual information - from commonly encountered sources of information in bloodstain pattern analysis - and update their vote allocation. We calculated a shift score for each item of contextual information based on vote reallocation. Almost all forms of contextual information influenced decision-making, with medical findings leading to the highest shift scores. Although there was a small positive association between shift scores and the degree to which analysts displayed an intuitive decision-making style, shift scores did not vary meaningfully as a function of experience or the other characteristics measured. Almost all of the erroneous classifications were made by novice analysts. Copyright © 2016 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

Employing wavelet-based texture features in ammunition classification

NASA Astrophysics Data System (ADS)

Borzino, Ángelo M. C. R.; Maher, Robert C.; Apolinário, José A.; de Campos, Marcello L. R.

2017-05-01

Pattern recognition, a branch of machine learning, involves classification of information in images, sounds, and other digital representations. This paper uses pattern recognition to identify which kind of ammunition was used when a bullet was fired based on a carefully constructed set of gunshot sound recordings. To do this task, we show that texture features obtained from the wavelet transform of a component of the gunshot signal, treated as an image, and quantized in gray levels, are good ammunition discriminators. We test the technique with eight different calibers and achieve a classification rate better than 95%. We also compare the performance of the proposed method with results obtained by standard temporal and spectrographic techniques

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Statistical Parametric Mapping to Identify Differences between Consensus-Based Joint Patterns during Gait in Children with Cerebral Palsy.

PubMed

Nieuwenhuys, Angela; Papageorgiou, Eirini; Desloovere, Kaat; Molenaers, Guy; De Laet, Tinne

2017-01-01

Experts recently identified 49 joint motion patterns in children with cerebral palsy during a Delphi consensus study. Pattern definitions were therefore the result of subjective expert opinion. The present study aims to provide objective, quantitative data supporting the identification of these consensus-based patterns. To do so, statistical parametric mapping was used to compare the mean kinematic waveforms of 154 trials of typically developing children (n = 56) to the mean kinematic waveforms of 1719 trials of children with cerebral palsy (n = 356), which were classified following the classification rules of the Delphi study. Three hypotheses stated that: (a) joint motion patterns with 'no or minor gait deviations' (n = 11 patterns) do not differ significantly from the gait pattern of typically developing children; (b) all other pathological joint motion patterns (n = 38 patterns) differ from typically developing gait and the locations of difference within the gait cycle, highlighted by statistical parametric mapping, concur with the consensus-based classification rules. (c) all joint motion patterns at the level of each joint (n = 49 patterns) differ from each other during at least one phase of the gait cycle. Results showed that: (a) ten patterns with 'no or minor gait deviations' differed somewhat unexpectedly from typically developing gait, but these differences were generally small (≤3°); (b) all other joint motion patterns (n = 38) differed from typically developing gait and the significant locations within the gait cycle that were indicated by the statistical analyses, coincided well with the classification rules; (c) joint motion patterns at the level of each joint significantly differed from each other, apart from two sagittal plane pelvic patterns. In addition to these results, for several joints, statistical analyses indicated other significant areas during the gait cycle that were not included in the pattern definitions of the consensus study. Based on these findings, suggestions to improve pattern definitions were made.

Statistical Parametric Mapping to Identify Differences between Consensus-Based Joint Patterns during Gait in Children with Cerebral Palsy

PubMed Central

Papageorgiou, Eirini; Desloovere, Kaat; Molenaers, Guy; De Laet, Tinne

2017-01-01

Experts recently identified 49 joint motion patterns in children with cerebral palsy during a Delphi consensus study. Pattern definitions were therefore the result of subjective expert opinion. The present study aims to provide objective, quantitative data supporting the identification of these consensus-based patterns. To do so, statistical parametric mapping was used to compare the mean kinematic waveforms of 154 trials of typically developing children (n = 56) to the mean kinematic waveforms of 1719 trials of children with cerebral palsy (n = 356), which were classified following the classification rules of the Delphi study. Three hypotheses stated that: (a) joint motion patterns with ‘no or minor gait deviations’ (n = 11 patterns) do not differ significantly from the gait pattern of typically developing children; (b) all other pathological joint motion patterns (n = 38 patterns) differ from typically developing gait and the locations of difference within the gait cycle, highlighted by statistical parametric mapping, concur with the consensus-based classification rules. (c) all joint motion patterns at the level of each joint (n = 49 patterns) differ from each other during at least one phase of the gait cycle. Results showed that: (a) ten patterns with ‘no or minor gait deviations’ differed somewhat unexpectedly from typically developing gait, but these differences were generally small (≤3°); (b) all other joint motion patterns (n = 38) differed from typically developing gait and the significant locations within the gait cycle that were indicated by the statistical analyses, coincided well with the classification rules; (c) joint motion patterns at the level of each joint significantly differed from each other, apart from two sagittal plane pelvic patterns. In addition to these results, for several joints, statistical analyses indicated other significant areas during the gait cycle that were not included in the pattern definitions of the consensus study. Based on these findings, suggestions to improve pattern definitions were made. PMID:28081229

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Pattern recognition for passive polarimetric data using nonparametric classifiers

NASA Astrophysics Data System (ADS)

Thilak, Vimal; Saini, Jatinder; Voelz, David G.; Creusere, Charles D.

2005-08-01

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Segmentation and classification of cell cycle phases in fluorescence imaging.

PubMed

Ersoy, Ilker; Bunyak, Filiz; Chagin, Vadim; Cardoso, M Christina; Palaniappan, Kannappan

2009-01-01

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

Correlation-based pattern recognition for implantable defibrillators.

PubMed Central

Wilkins, J.

1996-01-01

An estimated 300,000 Americans die each year from cardiac arrhythmias. Historically, drug therapy or surgery were the only treatment options available for patients suffering from arrhythmias. Recently, implantable arrhythmia management devices have been developed. These devices allow abnormal cardiac rhythms to be sensed and corrected in vivo. Proper arrhythmia classification is critical to selecting the appropriate therapeutic intervention. The classification problem is made more challenging by the power/computation constraints imposed by the short battery life of implantable devices. Current devices utilize heart rate-based classification algorithms. Although easy to implement, rate-based approaches have unacceptably high error rates in distinguishing supraventricular tachycardia (SVT) from ventricular tachycardia (VT). Conventional morphology assessment techniques used in ECG analysis often require too much computation to be practical for implantable devices. In this paper, a computationally-efficient, arrhythmia classification architecture using correlation-based morphology assessment is presented. The architecture classifies individuals heart beats by assessing similarity between an incoming cardiac signal vector and a series of prestored class templates. A series of these beat classifications are used to make an overall rhythm assessment. The system makes use of several new results in the field of pattern recognition. The resulting system achieved excellent accuracy in discriminating SVT and VT. PMID:8947674

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for the Pattern Consistency of Multi Scale Landcover

NASA Astrophysics Data System (ADS)

Juniati, E.; Arrofiqoh, E. N.

2017-09-01

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

Individually adapted imagery improves brain-computer interface performance in end-users with disability.

PubMed

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V C; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability

PubMed Central

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V. C.; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R.

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage. PMID:25992718

Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay.

PubMed

Smith, Lauren H; Hargrove, Levi J; Lock, Blair A; Kuiken, Todd A

2011-04-01

Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01 ). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p < 0.01 ) and was reduced with longer controller delay (p < 0.01 ), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multistate amplitude controllers.

Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.

PubMed

Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling

2016-05-01

Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches. Copyright © 2016 Elsevier Ltd. All rights reserved.

The morphology and classification of α ganglion cells in the rat retinae: a fractal analysis study.

PubMed

Jelinek, Herbert F; Ristanović, Dušan; Milošević, Nebojša T

2011-09-30

Rat retinal ganglion cells have been proposed to consist of a varying number of subtypes. Dendritic morphology is an essential aspect of classification and a necessary step toward understanding structure-function relationships of retinal ganglion cells. This study aimed at using a heuristic classification procedure in combination with the box-counting analysis to classify the alpha ganglion cells in the rat retinae based on the dendritic branching pattern and to investigate morphological changes with retinal eccentricity. The cells could be divided into two groups: cells with simple dendritic pattern (box dimension lower than 1.390) and cells with complex dendritic pattern (box dimension higher than 1.390) according to their dendritic branching pattern complexity. Both were further divided into two subtypes due to the stratification within the inner plexiform layer. In the present study we have shown that the alpha rat RCGs can be classified further by their dendritic branching complexity and thus extend those of previous reports that fractal analysis can be successfully used in neuronal classification, particularly that the fractal dimension represents a robust and sensitive tool for the classification of retinal ganglion cells. A hypothesis of possible functional significance of our classification scheme is also discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Real-time detection and classification of anomalous events in streaming data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.

2016-04-19

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

Binary Classification using Decision Tree based Genetic Programming and Its Application to Analysis of Bio-mass Data

NASA Astrophysics Data System (ADS)

To, Cuong; Pham, Tuan D.

2010-01-01

In machine learning, pattern recognition may be the most popular task. "Similar" patterns identification is also very important in biology because first, it is useful for prediction of patterns associated with disease, for example cancer tissue (normal or tumor); second, similarity or dissimilarity of the kinetic patterns is used to identify coordinately controlled genes or proteins involved in the same regulatory process. Third, similar genes (proteins) share similar functions. In this paper, we present an algorithm which uses genetic programming to create decision tree for binary classification problem. The application of the algorithm was implemented on five real biological databases. Base on the results of comparisons with well-known methods, we see that the algorithm is outstanding in most of cases.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Hepatic Arterial Configuration in Relation to the Segmental Anatomy of the Liver; Observations on MDCT and DSA Relevant to Radioembolization Treatment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoven, Andor F. van den, E-mail: a.f.vandenhoven@umcutrecht.nl; Leeuwen, Maarten S. van, E-mail: m.s.vanleeuwen@umcutrecht.nl; Lam, Marnix G. E. H., E-mail: m.lam@umcutrecht.nl

PurposeCurrent anatomical classifications do not include all variants relevant for radioembolization (RE). The purpose of this study was to assess the individual hepatic arterial configuration and segmental vascularization pattern and to develop an individualized RE treatment strategy based on an extended classification.MethodsThe hepatic vascular anatomy was assessed on MDCT and DSA in patients who received a workup for RE between February 2009 and November 2012. Reconstructed MDCT studies were assessed to determine the hepatic arterial configuration (origin of every hepatic arterial branch, branching pattern and anatomical course) and the hepatic segmental vascularization territory of all branches. Aberrant hepatic arteries weremore » defined as hepatic arterial branches that did not originate from the celiac axis/CHA/PHA. Early branching patterns were defined as hepatic arterial branches originating from the celiac axis/CHA.ResultsThe hepatic arterial configuration and segmental vascularization pattern could be assessed in 110 of 133 patients. In 59 patients (54 %), no aberrant hepatic arteries or early branching was observed. Fourteen patients without aberrant hepatic arteries (13 %) had an early branching pattern. In the 37 patients (34 %) with aberrant hepatic arteries, five also had an early branching pattern. Sixteen different hepatic arterial segmental vascularization patterns were identified and described, differing by the presence of aberrant hepatic arteries, their respective vascular territory, and origin of the artery vascularizing segment four.ConclusionsThe hepatic arterial configuration and segmental vascularization pattern show marked individual variability beyond well-known classifications of anatomical variants. We developed an individualized RE treatment strategy based on an extended anatomical classification.« less

Weather patterns as a downscaling tool - evaluating their skill in stratifying local climate variables

NASA Astrophysics Data System (ADS)

Murawski, Aline; Bürger, Gerd; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

The use of a weather pattern based approach for downscaling of coarse, gridded atmospheric data, as usually obtained from the output of general circulation models (GCM), allows for investigating the impact of anthropogenic greenhouse gas emissions on fluxes and state variables of the hydrological cycle such as e.g. on runoff in large river catchments. Here we aim at attributing changes in high flows in the Rhine catchment to anthropogenic climate change. Therefore we run an objective classification scheme (simulated annealing and diversified randomisation - SANDRA, available from the cost733 classification software) on ERA20C reanalyses data and apply the established classification to GCMs from the CMIP5 project. After deriving weather pattern time series from GCM runs using forcing from all greenhouse gases (All-Hist) and using natural greenhouse gas forcing only (Nat-Hist), a weather generator will be employed to obtain climate data time series for the hydrological model. The parameters of the weather pattern classification (i.e. spatial extent, number of patterns, classification variables) need to be selected in a way that allows for good stratification of the meteorological variables that are of interest for the hydrological modelling. We evaluate the skill of the classification in stratifying meteorological data using a multi-variable approach. This allows for estimating the stratification skill for all meteorological variables together, not separately as usually done in existing similar work. The advantage of the multi-variable approach is to properly account for situations where e.g. two patterns are associated with similar mean daily temperature, but one pattern is dry while the other one is related to considerable amounts of precipitation. Thus, the separation of these two patterns would not be justified when considering temperature only, but is perfectly reasonable when accounting for precipitation as well. Besides that, the weather patterns derived from reanalyses data should be well represented in the All-Hist GCM runs in terms of e.g. frequency, seasonality, and persistence. In this contribution we show how to select the most appropriate weather pattern classification and how the classes derived from it are reflected in the GCMs.

Real-time and simultaneous control of artificial limbs based on pattern recognition algorithms.

PubMed

Ortiz-Catalan, Max; Håkansson, Bo; Brånemark, Rickard

2014-07-01

The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigate different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions, such as the multi-layer perceptron (MLP), were found to be more cost effective, as they can be successfully employed in their simplest form. In the prediction of individual movements, the one-vs-one (OVO) topology was found to improve classification accuracy across different classifiers and it was therefore used to benchmark the benefits of simultaneous control. As opposed to previous work reporting only offline accuracy, the classification performance and the resulting controllability are evaluated in real time using the motion test and target achievement control (TAC) test, respectively. We propose a simultaneous classification strategy based on MLP that outperformed a top classifier for individual movements (LDA-OVO), thus improving the state-of-the-art classification approach. Furthermore, all the presented classification strategies and data collected in this study are freely available in BioPatRec, an open source platform for the development of advanced prosthetic control strategies.

Comprehensive classification test of scapular dyskinesis: A reliability study.

PubMed

Huang, Tsun-Shun; Huang, Han-Yi; Wang, Tyng-Guey; Tsai, Yung-Shen; Lin, Jiu-Jenq

2015-06-01

Assessment of scapular dyskinesis (SD) is of clinical interest, as SD is believed to be related to shoulder pathology. However, no clinical assessment with sufficient reliability to identify SD and provide treatment strategies is available. The purpose of this study was to investigate the reliability of the comprehensive SD classification method. Cross-sectional reliability study. Sixty subjects with unilateral shoulder pain were evaluated by two independent physiotherapists with a visual-based palpation method. SD was classified as single abnormal scapular pattern [inferior angle (pattern I), medial border (pattern II), superior border of scapula prominence or abnormal scapulohumeral rhythm (pattern III)], a mixture of the above abnormal scapular patterns, or normal pattern (pattern IV). The assessment of SD was evaluated as subjects performed bilateral arm raising/lowering movements with a weighted load in the scapular plane. Percentage of agreement and kappa coefficients were calculated to determine reliability. Agreement between the 2 independent physiotherapists was 83% (50/60, 6 subjects as pattern III and 44 subjects as pattern IV) in the raising phase and 68% (41/60, 5 subjects as pattern I, 12 subjects as pattern II, 12 subjects as pattern IV, 12 subjects as mixed patterns I and II) in the lowering phase. The kappa coefficients were 0.49-0.64. We concluded that the visual-based palpation classification method for SD had moderate to substantial inter-rater reliability. The appearance of different types of SD was more pronounced in the lowering phase than in the raising phase of arm movements. Copyright © 2014 Elsevier Ltd. All rights reserved.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Cellular-automata-based learning network for pattern recognition

NASA Astrophysics Data System (ADS)

Tzionas, Panagiotis G.; Tsalides, Phillippos G.; Thanailakis, Adonios

1991-11-01

Most classification techniques either adopt an approach based directly on the statistical characteristics of the pattern classes involved, or they transform the patterns in a feature space and try to separate the point clusters in this space. An alternative approach based on memory networks has been presented, its novelty being that it can be implemented in parallel and it utilizes direct features of the patterns rather than statistical characteristics. This study presents a new approach for pattern classification using pseudo 2-D binary cellular automata (CA). This approach resembles the memory network classifier in the sense that it is based on an adaptive knowledge based formed during a training phase, and also in the fact that both methods utilize pattern features that are directly available. The main advantage of this approach is that the sensitivity of the pattern classifier can be controlled. The proposed pattern classifier has been designed using 1.5 micrometers design rules for an N-well CMOS process. Layout has been achieved using SOLO 1400. Binary pseudo 2-D hybrid additive CA (HACA) is described in the second section of this paper. The third section describes the operation of the pattern classifier and the fourth section presents some possible applications. The VLSI implementation of the pattern classifier is presented in the fifth section and, finally, the sixth section draws conclusions from the results obtained.

Pattern of Cortical Fracture following Corticotomy for Distraction Osteogenesis.

PubMed

Luvan, M; Kanthan, S R; Roshan, G; Saw, A

2015-11-01

Corticotomy is an essential procedure for deformity correction and there are many techniques described. However there is no proper classification of the fracture pattern resulting from corticotomies to enable any studies to be conducted. We performed a retrospective study of corticotomy fracture patterns in 44 patients (34 tibias and 10 femurs) performed for various indications. We identified four distinct fracture patterns, Type I through IV classification based on the fracture propagation following percutaneous corticotomy. Type I transverse fracture, Type II transverse fracture with a winglet, Type III presence of butterfly fragment and Type IV fracture propagation to a fixation point. No significant correlation was noted between the fracture pattern and the underlying pathology or region of corticotomy.

Classification of crystal structure using a convolutional neural network

PubMed Central

Park, Woon Bae; Chung, Jiyong; Sohn, Keemin; Pyo, Myoungho

2017-01-01

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds. PMID:28875035

Classification of crystal structure using a convolutional neural network.

PubMed

Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee-Sun

2017-07-01

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

EEG classification of emotions using emotion-specific brain functional network.

PubMed

Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

2015-08-01

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

Mining sequential patterns for protein fold recognition.

PubMed

Exarchos, Themis P; Papaloukas, Costas; Lampros, Christos; Fotiadis, Dimitrios I

2008-02-01

Protein data contain discriminative patterns that can be used in many beneficial applications if they are defined correctly. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. Protein classification in terms of fold recognition plays an important role in computational protein analysis, since it can contribute to the determination of the function of a protein whose structure is unknown. Specifically, one of the most efficient SPM algorithms, cSPADE, is employed for the analysis of protein sequence. A classifier uses the extracted sequential patterns to classify proteins in the appropriate fold category. For training and evaluating the proposed method we used the protein sequences from the Protein Data Bank and the annotation of the SCOP database. The method exhibited an overall accuracy of 25% in a classification problem with 36 candidate categories. The classification performance reaches up to 56% when the five most probable protein folds are considered.

Correlation of AO and Lauge-Hansen classification systems for ankle fractures to the mechanism of injury.

PubMed

Rodriguez, Edward K; Kwon, John Y; Herder, Lindsay M; Appleton, Paul T

2013-11-01

Our aim was to assess whether the Lauge-Hansen (LH) and the Muller AO classification systems for ankle fractures radiographically correlate with in vivo injuries based on observed mechanism of injury. Videos of potential study candidates were reviewed on YouTube.com. Individuals were recruited for participation if the video could be classified by injury mechanism with a high likelihood of sustaining an ankle fracture. Corresponding injury radiographs were obtained. Injury mechanism was classified using the LH system as supination/external rotation (SER), supination/adduction (SAD), pronation/external rotation (PER), or pronation/abduction (PAB). Corresponding radiographs were classified by the LH system and the AO system. Thirty injury videos with their corresponding radiographs were collected. Of the video clips reviewed, 16 had SAD mechanisms and 14 had PER mechanisms. There were 26 ankle fractures, 3 nonfractures, and 1 subtalar dislocation. Twelve fractures with SAD mechanisms had corresponding SAD fracture patterns. Five PER mechanisms had PER fracture patterns. Eight PER mechanisms had SER fracture patterns and 1 had SAD fracture pattern. When the AO classification was used, all 12 SAD type injuries had a 44A type fracture, whereas the 14 PER injuries resulted in nine 44B fractures, two 44C fractures, and three 43A fractures. When injury video clips of ankle fractures were matched to their corresponding radiographs, the LH system was 65% (17/26) consistent in predicting fracture patterns from the deforming injury mechanism. When the AO classification system was used, consistency was 81% (21/26). The AO classification, despite its development as a purely radiographic system, correlated with in vivo injuries, as based on observed mechanism of injury, more closely than did the LH system. Level IV, case series.

Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

NASA Astrophysics Data System (ADS)

Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

PubMed

Pläschke, Rachel N; Cieslik, Edna C; Müller, Veronika I; Hoffstaedter, Felix; Plachti, Anna; Varikuti, Deepthi P; Goosses, Mareike; Latz, Anne; Caspers, Svenja; Jockwitz, Christiane; Moebus, Susanne; Gruber, Oliver; Eickhoff, Claudia R; Reetz, Kathrin; Heller, Julia; Südmeyer, Martin; Mathys, Christian; Caspers, Julian; Grefkes, Christian; Kalenscher, Tobias; Langner, Robert; Eickhoff, Simon B

2017-12-01

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

PubMed

Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

2016-08-31

Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images.

PubMed

Shanthi, C; Pappa, N

2017-05-01

Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are recorded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

PubMed Central

Kauppi, Jukka-Pekka; Hahne, Janne; Müller, Klaus-Robert; Hyvärinen, Aapo

2015-01-01

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results. PMID:26039100

The classification of frontal sinus pneumatization patterns by CT-based volumetry.

PubMed

Yüksel Aslier, Nesibe Gül; Karabay, Nuri; Zeybek, Gülşah; Keskinoğlu, Pembe; Kiray, Amaç; Sütay, Semih; Ecevit, Mustafa Cenk

2016-10-01

We aimed to define the classification of frontal sinus pneumatization patterns according to three-dimensional volume measurements. Datasets of 148 sides of 74 dry skulls were generated by the computerized tomography-based volumetry to measure frontal sinus volumes. The cutoff points for frontal sinus hypoplasia and hyperplasia were tested by ROC curve analysis and the validity of the diagnostic points was measured. The overall frequencies were 4.1, 14.2, 37.2 and 44.5 % for frontal sinus aplasia, hypoplasia, medium size and hyperplasia, respectively. The aplasia was bilateral in all three skulls. Hypoplasia was seen 76 % at the right side and hyperplasia was seen 56 % at the left side. The cutoff points for diagnosing frontal sinus hypoplasia and hyperplasia were '1131.25 mm(3)' (95.2 % sensitivity and 100 % specificity) and '3328.50 mm(3)' (88 % sensitivity and 86 % specificity), respectively. The findings provided in the present study, which define frontal sinus pneumatization patterns by CT-based volumetry, proved that two opposite sides of the frontal sinuses are asymmetric and three-dimensional classification should be developed by CT-based volumetry, because two-dimensional evaluations lack depth measurement.

Invasive endocervical adenocarcinoma: proposal for a new pattern-based classification system with significant clinical implications: a multi-institutional study.

PubMed

Diaz De Vivar, Andrea; Roma, Andres A; Park, Kay J; Alvarado-Cabrero, Isabel; Rasty, Golnar; Chanona-Vilchis, Jose G; Mikami, Yoshiki; Hong, Sung R; Arville, Brent; Teramoto, Norihiro; Ali-Fehmi, Rouba; Rutgers, Joanne K L; Tabassum, Farah; Barbuto, Denise; Aguilera-Barrantes, Irene; Shaye-Brown, Alexandra; Daya, Dean; Silva, Elvio G

2013-11-01

The management of endocervical adenocarcinoma is largely based on tumor size and depth of invasion (DOI); however, DOI is difficult to measure accurately. The surgical treatment includes resection of regional lymph nodes, even though most lymph nodes are negative and lymphadenectomies can cause significant morbidity. We have investigated alternative parameters to better identify patients at risk of node metastases. Cases of invasive endocervical adenocarcinoma from 12 institutions were reviewed, and clinical/pathologic features assessed: patients' age, tumor size, DOI, differentiation, lymph-vascular invasion, lymph node metastases, recurrences, and stage. Cases were classified according to a new pattern-based system into Pattern A (well-demarcated glands), B (early destructive stromal invasion arising from well-demarcated glands), and C (diffuse destructive invasion). In total, 352 cases (FIGO Stages I-IV) were identified. Patients' age ranged from 20 to 83 years (mean 45), DOI ranged from 0.2 to 27 mm (mean 6.73), and lymph-vascular invasion was present in 141 cases. Forty-nine (13.9%) demonstrated lymph node metastases. Using this new system, 73 patients (20.7%) with Pattern A tumors (all Stage I) were identified. None had lymph node metastases and/or recurrences. Ninety patients (25.6%) had Pattern B tumors, of which 4 (4.4%) had positive nodes; whereas 189 (53.7%) had Pattern C tumors, of which 45 (23.8%) had metastatic nodes. The proposed classification system can spare 20.7% of patients (Pattern A) of unnecessary lymphadenectomy. Patients with Pattern B rarely present with positive nodes. An aggressive approach is justified in patients with Pattern C. This classification system is simple, easy to apply, and clinically significant.

Building a biomedical tokenizer using the token lattice design pattern and the adapted Viterbi algorithm

PubMed Central

2011-01-01

Background Tokenization is an important component of language processing yet there is no widely accepted tokenization method for English texts, including biomedical texts. Other than rule based techniques, tokenization in the biomedical domain has been regarded as a classification task. Biomedical classifier-based tokenizers either split or join textual objects through classification to form tokens. The idiosyncratic nature of each biomedical tokenizer’s output complicates adoption and reuse. Furthermore, biomedical tokenizers generally lack guidance on how to apply an existing tokenizer to a new domain (subdomain). We identify and complete a novel tokenizer design pattern and suggest a systematic approach to tokenizer creation. We implement a tokenizer based on our design pattern that combines regular expressions and machine learning. Our machine learning approach differs from the previous split-join classification approaches. We evaluate our approach against three other tokenizers on the task of tokenizing biomedical text. Results Medpost and our adapted Viterbi tokenizer performed best with a 92.9% and 92.4% accuracy respectively. Conclusions Our evaluation of our design pattern and guidelines supports our claim that the design pattern and guidelines are a viable approach to tokenizer construction (producing tokenizers matching leading custom-built tokenizers in a particular domain). Our evaluation also demonstrates that ambiguous tokenizations can be disambiguated through POS tagging. In doing so, POS tag sequences and training data have a significant impact on proper text tokenization. PMID:21658288

Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control

PubMed Central

Scheme, Erik; Englehart, Kevin

2013-01-01

The performance of pattern recognition based myoelectric control has seen significant interest in the research community for many years. Due to a recent surge in the development of dexterous prosthetic devices, determining the clinical viability of multifunction myoelectric control has become paramount. Several factors contribute to differences between offline classification accuracy and clinical usability, but the overriding theme is that the variability of the elicited patterns increases greatly during functional use. Proportional control has been shown to greatly improve the usability of conventional myoelectric control systems. Typically, a measure of the amplitude of the electromyogram (a rectified and smoothed version) is used to dictate the velocity of control of a device. The discriminatory power of myoelectric pattern classifiers, however, is also largely based on amplitude features of the electromyogram. This work presents an introductory look at the effect of contraction strength and proportional control on pattern recognition based control. These effects are investigated using typical pattern recognition data collection methods as well as a real-time position tracking test. Training with dynamically force varying contractions and appropriate gain selection is shown to significantly improve (p<0.001) the classifier’s performance and tolerance to proportional control. PMID:23894224

Identification and classification of failure modes in laminated composites by using a multivariate statistical analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2017-11-01

Acoustic Emission (AE) is a suitable method to monitor the health of composite structures in real-time. However, AE-based failure mode identification and classification are still complex to apply due to the fact that AE waves are generally released simultaneously from all AE-emitting damage sources. Hence, the use of advanced signal processing techniques in combination with pattern recognition approaches is required. In this paper, AE signals generated from laminated carbon fiber reinforced polymer (CFRP) subjected to indentation test are examined and analyzed. A new pattern recognition approach involving a number of processing steps able to be implemented in real-time is developed. Unlike common classification approaches, here only CWT coefficients are extracted as relevant features. Firstly, Continuous Wavelet Transform (CWT) is applied to the AE signals. Furthermore, dimensionality reduction process using Principal Component Analysis (PCA) is carried out on the coefficient matrices. The PCA-based feature distribution is analyzed using Kernel Density Estimation (KDE) allowing the determination of a specific pattern for each fault-specific AE signal. Moreover, waveform and frequency content of AE signals are in depth examined and compared with fundamental assumptions reported in this field. A correlation between the identified patterns and failure modes is achieved. The introduced method improves the damage classification and can be used as a non-destructive evaluation tool.

Considerations on patent valuation based on patent classification and citation in biotechnological field

NASA Astrophysics Data System (ADS)

Mihara, Kenji

Regarding innovation measurement utilizing patent information, a number of researchers are making great efforts to measure a "patent value (patent quality)." For patent valuation, patent classification and citation are often utilized as patent information. Also, biotechnological field is attracting attention from the viewpoint of application to environmental or medical study, and considerable researches on patent valuation are ongoing in this technical field. However, it is not enough recognized that researchers cannot be too careful when they deal with classification information in the biotech field because patent classification structure in this field is not well-established. And also, it is not known enough that citation patterns of both academic papers and patent documents are so complicated that the patterns cannot be easily generalized. In this article, the issues above were verified from a position based on working experiences of biotech patent examiner at Japan Patent Office, and considerations and implications were given on what patent valuation should be.

Brain-Computer Interface Based on Generation of Visual Images

PubMed Central

Bobrov, Pavel; Frolov, Alexander; Cantor, Charles; Fedulova, Irina; Bakhnyan, Mikhail; Zhavoronkov, Alexander

2011-01-01

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier. PMID:21695206

Foot-strike pattern and performance in a marathon.

PubMed

Kasmer, Mark E; Liu, Xue-Cheng; Roberts, Kyle G; Valadao, Jason M

2013-05-01

To determine prevalence of heel strike in a midsize city marathon, if there is an association between foot-strike classification and race performance, and if there is an association between foot-strike classification and gender. Foot-strike classification (forefoot, midfoot, heel, or split strike), gender, and rank (position in race) were recorded at the 8.1-km mark for 2112 runners at the 2011 Milwaukee Lakefront Marathon. 1991 runners were classified by foot-strike pattern, revealing a heel-strike prevalence of 93.67% (n = 1865). A significant difference between foot-strike classification and performance was found using a Kruskal-Wallis test (P < .0001), with more elite performers being less likely to heel strike. No significant difference between foot-strike classification and gender was found using a Fisher exact test. In addition, subgroup analysis of the 126 non-heel strikers found no significant difference between shoe wear and performance using a Kruskal-Wallis test. The high prevalence of heel striking observed in this study reflects the foot-strike pattern of most mid-distance to long-distance runners and, more important, may predict their injury profile based on the biomechanics of a heel-strike running pattern. This knowledge can help clinicians appropriately diagnose, manage, and train modifications of injured runners.

Pattern of Cortical Fracture following Corticotomy for Distraction Osteogenesis

PubMed Central

Luvan, M; Roshan, G; Saw, A

2015-01-01

Corticotomy is an essential procedure for deformity correction and there are many techniques described. However there is no proper classification of the fracture pattern resulting from corticotomies to enable any studies to be conducted. We performed a retrospective study of corticotomy fracture patterns in 44 patients (34 tibias and 10 femurs) performed for various indications. We identified four distinct fracture patterns, Type I through IV classification based on the fracture propagation following percutaneous corticotomy. Type I transverse fracture, Type II transverse fracture with a winglet, Type III presence of butterfly fragment and Type IV fracture propagation to a fixation point. No significant correlation was noted between the fracture pattern and the underlying pathology or region of corticotomy. PMID:28611907

Classifications of Acute Scaphoid Fractures: A Systematic Literature Review.

PubMed

Ten Berg, Paul W; Drijkoningen, Tessa; Strackee, Simon D; Buijze, Geert A

2016-05-01

Background In the lack of consensus, surgeon-based preference determines how acute scaphoid fractures are classified. There is a great variety of classification systems with considerable controversies. Purposes The purpose of this study was to provide an overview of the different classification systems, clarifying their subgroups and analyzing their popularity by comparing citation indexes. The intention was to improve data comparison between studies using heterogeneous fracture descriptions. Methods We performed a systematic review of the literature based on a search of medical literature from 1950 to 2015, and a manual search using the reference lists in relevant book chapters. Only original descriptions of classifications of acute scaphoid fractures in adults were included. Popularity was based on citation index as reported in the databases of Web of Science (WoS) and Google Scholar. Articles that were cited <10 times in WoS were excluded. Results Our literature search resulted in 308 potentially eligible descriptive reports of which 12 reports met the inclusion criteria. We distinguished 13 different (sub) classification systems based on (1) fracture location, (2) fracture plane orientation, and (3) fracture stability/displacement. Based on citations numbers, the Herbert classification was most popular, followed by the Russe and Mayo classifications. All classification systems were based on plain radiography. Conclusions Most classification systems were based on fracture location, displacement, or stability. Based on the controversy and limited reliability of current classification systems, suggested research areas for an updated classification include three-dimensional fracture pattern etiology and fracture fragment mobility assessed by dynamic imaging.

Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

PubMed

Horst, Fabian; Eekhoff, Alexander; Newell, Karl M; Schöllhorn, Wolfgang I

2017-01-01

Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Automated classification of single airborne particles from two-dimensional angle-resolved optical scattering (TAOS) patterns by non-linear filtering

NASA Astrophysics Data System (ADS)

Crosta, Giovanni Franco; Pan, Yong-Le; Aptowicz, Kevin B.; Casati, Caterina; Pinnick, Ronald G.; Chang, Richard K.; Videen, Gorden W.

2013-12-01

Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized airborne particles. In general, the interpretation of these patterns and the retrieval of the particle refractive index, shape or size alone, are difficult problems. By reformulating the problem in statistical learning terms, a solution is proposed herewith: rather than identifying airborne particles from their scattering patterns, TAOS patterns themselves are classified through a learning machine, where feature extraction interacts with multivariate statistical analysis. Feature extraction relies on spectrum enhancement, which includes the discrete cosine FOURIER transform and non-linear operations. Multivariate statistical analysis includes computation of the principal components and supervised training, based on the maximization of a suitable figure of merit. All algorithms have been combined together to analyze TAOS patterns, organize feature vectors, design classification experiments, carry out supervised training, assign unknown patterns to classes, and fuse information from different training and recognition experiments. The algorithms have been tested on a data set with more than 3000 TAOS patterns. The parameters that control the algorithms at different stages have been allowed to vary within suitable bounds and are optimized to some extent. Classification has been targeted at discriminating aerosolized Bacillus subtilis particles, a simulant of anthrax, from atmospheric aerosol particles and interfering particles, like diesel soot. By assuming that all training and recognition patterns come from the respective reference materials only, the most satisfactory classification result corresponds to 20% false negatives from B. subtilis particles and <11% false positives from all other aerosol particles. The most effective operations have consisted of thresholding TAOS patterns in order to reject defective ones, and forming training sets from three or four pattern classes. The presented automated classification method may be adapted into a real-time operation technique, capable of detecting and characterizing micron-sized airborne particles.

Mayo Clinic/Renal Pathology Society Consensus Report on Pathologic Classification, Diagnosis, and Reporting of GN.

PubMed

Sethi, Sanjeev; Haas, Mark; Markowitz, Glen S; D'Agati, Vivette D; Rennke, Helmut G; Jennette, J Charles; Bajema, Ingeborg M; Alpers, Charles E; Chang, Anthony; Cornell, Lynn D; Cosio, Fernando G; Fogo, Agnes B; Glassock, Richard J; Hariharan, Sundaram; Kambham, Neeraja; Lager, Donna J; Leung, Nelson; Mengel, Michael; Nath, Karl A; Roberts, Ian S; Rovin, Brad H; Seshan, Surya V; Smith, Richard J H; Walker, Patrick D; Winearls, Christopher G; Appel, Gerald B; Alexander, Mariam P; Cattran, Daniel C; Casado, Carmen Avila; Cook, H Terence; De Vriese, An S; Radhakrishnan, Jai; Racusen, Lorraine C; Ronco, Pierre; Fervenza, Fernando C

2016-05-01

Renal pathologists and nephrologists met on February 20, 2015 to establish an etiology/pathogenesis-based system for classification and diagnosis of GN, with a major aim of standardizing the kidney biopsy report of GN. On the basis of etiology/pathogenesis, GN is classified into the following five pathogenic types, each with specific disease entities: immune-complex GN, pauci-immune GN, antiglomerular basement membrane GN, monoclonal Ig GN, and C3 glomerulopathy. The pathogenesis-based classification forms the basis of the kidney biopsy report. To standardize the report, the diagnosis consists of a primary diagnosis and a secondary diagnosis. The primary diagnosis should include the disease entity/pathogenic type (if disease entity is not known) followed in order by pattern of injury (mixed patterns may be present); score/grade/class for disease entities, such as IgA nephropathy, lupus nephritis, and ANCA GN; and additional features as detailed herein. A pattern diagnosis as the sole primary diagnosis is not recommended. Secondary diagnoses should be reported separately and include coexisting lesions that do not form the primary diagnosis. Guidelines for the report format, light microscopy, immunofluorescence microscopy, electron microscopy, and ancillary studies are also provided. In summary, this consensus report emphasizes a pathogenesis-based classification of GN and provides guidelines for the standardized reporting of GN. Copyright © 2016 by the American Society of Nephrology.

Pattern Classification of Endocervical Adenocarcinoma: Reproducibility and Review of Criteria

PubMed Central

Rutgers, Joanne K.L.; Roma, Andres; Park, Kay; Zaino, Richard J.; Johnson, Abbey; Alvarado, Isabel; Daya, Dean; Rasty, Golnar; Longacre, Teri; Ronnett, Brigitte; Silva, Elvio

2017-01-01

Previously, our international team proposed a 3-tiered pattern classification (Pattern Classification) system for endocervical adenocarcinoma of the usual type that correlates with nodal disease and recurrence. Pattern Classification- A have well demarcated glands lacking destructive stromal invasion or lymphovascular invasion (lymphovascular invasion), Pattern Classification- B show localized, limited destructive invasion arising from A-type glands, and Pattern Classification- C have diffuse destructive stromal invasion, significant (filling a 4× field) confluence, or solid architecture. 24 Pattern Classification-A, 22 Pattern Classification-B, 38 Pattern Classification-C from the tumor set used in the original description were chosen using the reference diagnosis (reference diagnosis) originally established. 1 H&E slide per case was reviewed by 7 gynecologic pathologists, 4 from the original study. Kappa statistics were prepared, and cases with discrepancies reviewed. We found a majority agreement with reference diagnosis in 81% of cases, with complete or near complete (6 of 7) agreement in 50%. Overall concordance was 74%. Overall Kappa (agreement among pathologists) was .488 (moderate agreement). Pattern Classification- B has lowest kappa, and agreement is not improved by combining B+C. 6 of 7 reviewers had substantial agreement by weighted kappas (>.6), with one reviewer accounting for the majority of cases under or overcalled by 2 tiers. Confluence filling a 4× field, labyrinthine glands, or solid architecture accounted for undercalling other reference diagnosis-C cases. Missing a few individually infiltrative cells was the most common cause of undercalling reference diagnosis- B. Small foci of inflamed, loose or desmoplastic stroma lacking infiltrative tumor cells in reference diagnosis-A appeared to account for those cases up-graded to Pattern Classification-B. In summary, an overall concordance of 74% indicates that the criteria can be reproducibly applied by gynecologic pathologists. Further refinement of criteria should allow use of this powerful classification system to delineate which cervical adenocarcinomas can be safely treated conservatively. PMID:27255163

A canonical correlation analysis based EMG classification algorithm for eliminating electrode shift effect.

PubMed

Zhe Fan; Zhong Wang; Guanglin Li; Ruomei Wang

2016-08-01

Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.

The ITE Land classification: Providing an environmental stratification of Great Britain.

PubMed

Bunce, R G; Barr, C J; Gillespie, M K; Howard, D C

1996-01-01

The surface of Great Britain (GB) varies continuously in land cover from one area to another. The objective of any environmentally based land classification is to produce classes that match the patterns that are present by helping to define clear boundaries. The more appropriate the analysis and data used, the better the classes will fit the natural patterns. The observation of inter-correlations between ecological factors is the basis for interpreting ecological patterns in the field, and the Institute of Terrestrial Ecology (ITE) Land Classification formalises such subjective ideas. The data inevitably comprise a large number of factors in order to describe the environment adequately. Single factors, such as altitude, would only be useful on a national basis if they were the only dominant causative agent of ecological variation.The ITE Land Classification has defined 32 environmental categories called 'land classes', initially based on a sample of 1-km squares in Great Britain but subsequently extended to all 240 000 1-km squares. The original classification was produced using multivariate analysis of 75 environmental variables. The extension to all squares in GB was performed using a combination of logistic discrimination and discriminant functions. The classes have provided a stratification for successive ecological surveys, the results of which have characterised the classes in terms of botanical, zoological and landscape features.The classification has also been applied to integrate diverse datasets including satellite imagery, soils and socio-economic information. A variety of models have used the structure of the classification, for example to show potential land use change under different economic conditions. The principal data sets relevant for planning purposes have been incorporated into a user-friendly computer package, called the 'Countryside Information System'.

Short-Term Global Horizontal Irradiance Forecasting Based on Sky Imaging and Pattern Recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Feng, Cong; Cui, Mingjian

Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern ismore » determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16% in terms of the normalized mean absolute error and 25% in terms of the normalized root mean square error.« less

Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

PubMed Central

Koutsouleris, Nikolaos; Meisenzahl, Eva M.; Davatzikos, Christos; Bottlender, Ronald; Frodl, Thomas; Scheuerecker, Johanna; Schmitt, Gisela; Zetzsche, Thomas; Decker, Petra; Reiser, Maximilian; Möller, Hans-Jürgen; Gaser, Christian

2014-01-01

Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis. PMID:19581561

The clinical outcomes of deep gray matter injury in children with cerebral palsy in relation with brain magnetic resonance imaging.

PubMed

Choi, Ja Young; Choi, Yoon Seong; Rha, Dong-Wook; Park, Eun Sook

2016-08-01

In the present study we investigated the nature and extent of clinical outcomes using various classifications and analyzed the relationship between brain magnetic resonance imaging (MRI) findings and the extent of clinical outcomes in children with cerebral palsy (CP) with deep gray matter injury. The deep gray matter injuries of 69 children were classified into hypoxic ischemic encephalopathy (HIE) and kernicterus patterns. HIE patterns were divided into four groups (I-IV) based on severity. Functional classification was investigated using the gross motor function classification system-expanded and revised, manual ability classification system, communication function classification system, and tests of cognitive function, and other associated problems. The severity of HIE pattern on brain MRI was strongly correlated with the severity of clinical outcomes in these various domains. Children with a kernicterus pattern showed a wide range of clinical outcomes in these areas. Children with severe HIE are at high risk of intellectual disability (ID) or epilepsy and children with a kernicterus pattern are at risk of hearing impairment and/or ID. Grading severity of HIE pattern on brain MRI is useful for predicting overall outcomes. The clinical outcomes of children with a kernicterus pattern range widely from mild to severe. Delineation of the clinical outcomes of children with deep gray matter injury, which are a common abnormal brain MRI finding in children with CP, is necessary. The present study provides clinical outcomes for various domains in children with deep gray matter injury on brain MRI. The deep gray matter injuries were divided into two major groups; HIE and kernicterus patterns. Our study showed that severity of HIE pattern on brain MRI was strongly associated with the severity of impairments in gross motor function, manual ability, communication function, and cognition. These findings suggest that severity of HIE pattern can be useful for predicting the severity of impairments. Conversely, children with a kernicterus pattern showed a wide range of clinical outcomes in various domains. Children with severe HIE pattern are at high risk of ID or epilepsy and children with kernicterus pattern are at risk of hearing impairment or ID. The strength of our study was the assessment of clinical outcomes after 3 years of age using standardized classification systems in various domains in children with deep gray matter injury. Copyright © 2016 Elsevier Ltd. All rights reserved.

Knowledge-Based Image Analysis.

DTIC Science & Technology

1981-04-01

UNCLASSIF1 ED ETL-025s N IIp ETL-0258 AL Ai01319 S"Knowledge-based image analysis u George C. Stockman Barbara A. Lambird I David Lavine Laveen N. Kanal...extraction, verification, region classification, pattern recognition, image analysis . 3 20. A. CT (Continue on rever.. d. It necessary and Identify by...UNCLgSTFTF n In f SECURITY CLASSIFICATION OF THIS PAGE (When Date Entered) .L1 - I Table of Contents Knowledge Based Image Analysis I Preface

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data: An Approach for Building Effective Clinical Decision-Making System.

PubMed

Jane, Nancy Yesudhas; Nehemiah, Khanna Harichandran; Arputharaj, Kannan

2016-01-01

Clinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging. This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification. In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier. For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset. The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

Parametric classification of handvein patterns based on texture features

NASA Astrophysics Data System (ADS)

Al Mahafzah, Harbi; Imran, Mohammad; Supreetha Gowda H., D.

2018-04-01

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein,which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor, LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

Feature extraction via KPCA for classification of gait patterns.

PubMed

Wu, Jianning; Wang, Jue; Liu, Li

2007-06-01

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.

MRI classification system (MRICS) for children with cerebral palsy: development, reliability, and recommendations.

PubMed

Himmelmann, Kate; Horber, Veronka; De La Cruz, Javier; Horridge, Karen; Mejaski-Bosnjak, Vlatka; Hollody, Katalin; Krägeloh-Mann, Ingeborg

2017-01-01

To develop and evaluate a classification system for magnetic resonance imaging (MRI) findings of children with cerebral palsy (CP) that can be used in CP registers. The classification system was based on pathogenic patterns occurring in different periods of brain development. The MRI classification system (MRICS) consists of five main groups: maldevelopments, predominant white matter injury, predominant grey matter injury, miscellaneous, and normal findings. A detailed manual for the descriptions of these patterns was developed, including test cases (www.scpenetwork.eu/en/my-scpe/rtm/neuroimaging/cp-neuroimaging/). A literature review was performed and MRICS was compared with other classification systems. An exercise was carried out to check applicability and interrater reliability. Professionals working with children with CP or in CP registers were invited to participate in the exercise and chose to classify either 18 MRIs or MRI reports of children with CP. Classification systems in the literature were compatible with MRICS and harmonization possible. Interrater reliability was found to be good overall (k=0.69; 0.54-0.82) among the 41 participants and very good (k=0.81; 0.74-0.92) using the classification based on imaging reports. Surveillance of Cerebral Palsy in Europe (SCPE) proposes the MRICS as a reliable tool. Together with its manual it is simple to apply for CP registers. © 2016 Mac Keith Press.

Non-Invasive Detection of CH-46 AFT Gearbox Faults Using Digital Pattern Recognition and Classification Techniques

DTIC Science & Technology

1999-05-05

processing and artificial neural network (ANN) technology. The detector will classify incipient faults based on real-tine vibration data taken from the...provided the vibration data necessary to develop and test the feasibility of en artificial neural network for fault classification. This research

Estimation and Q-Matrix Validation for Diagnostic Classification Models

ERIC Educational Resources Information Center

Feng, Yuling

2013-01-01

Diagnostic classification models (DCMs) are structured latent class models widely discussed in the field of psychometrics. They model subjects' underlying attribute patterns and classify subjects into unobservable groups based on their mastery of attributes required to answer the items correctly. The effective implementation of DCMs depends…

Identifying bilingual semantic neural representations across languages

PubMed Central

Buchweitz, Augusto; Shinkareva, Svetlana V.; Mason, Robert A.; Mitchell, Tom M.; Just, Marcel Adam

2015-01-01

The goal of the study was to identify the neural representation of a noun's meaning in one language based on the neural representation of that same noun in another language. Machine learning methods were used to train classifiers to identify which individual noun bilingual participants were thinking about in one language based solely on their brain activation in the other language. The study shows reliable (p < .05) pattern-based classification accuracies for the classification of brain activity for nouns across languages. It also shows that the stable voxels used to classify the brain activation were located in areas associated with encoding information about semantic dimensions of the words in the study. The identification of the semantic trace of individual nouns from the pattern of cortical activity demonstrates the existence of a multi-voxel pattern of activation across the cortex for a single noun common to both languages in bilinguals. PMID:21978845

Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton.

PubMed

Mazumder, Oishee; Kundu, Ananda Sankar; Lenka, Prasanna Kumar; Bhaumik, Subhasis

2016-10-01

Ambulatory activity classification is an active area of research for controlling and monitoring state initiation, termination, and transition in mobility assistive devices such as lower-limb exoskeletons. State transition of lower-limb exoskeletons reported thus far are achieved mostly through the use of manual switches or state machine-based logic. In this paper, we propose a postural activity classifier using a 'dendogram-based support vector machine' (DSVM) which can be used to control a lower-limb exoskeleton. A pressure sensor-based wearable insole and two six-axis inertial measurement units (IMU) have been used for recognising two static and seven dynamic postural activities: sit, stand, and sit-to-stand, stand-to-sit, level walk, fast walk, slope walk, stair ascent and stair descent. Most of the ambulatory activities are periodic in nature and have unique patterns of response. The proposed classification algorithm involves the recognition of activity patterns on the basis of the periodic shape of trajectories. Polynomial coefficients extracted from the hip angle trajectory and the centre-of-pressure (CoP) trajectory during an activity cycle are used as features to classify dynamic activities. The novelty of this paper lies in finding suitable instrumentation, developing post-processing techniques, and selecting shape-based features for ambulatory activity classification. The proposed activity classifier is used to identify the activity states of a lower-limb exoskeleton. The DSVM classifier algorithm achieved an overall classification accuracy of 95.2%. Copyright © 2016 Elsevier B.V. All rights reserved.

Entropic One-Class Classifiers.

PubMed

Livi, Lorenzo; Sadeghian, Alireza; Pedrycz, Witold

2015-12-01

The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed nontarget, and therefore, they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation-based approach; we embed the input data into the dissimilarity space (DS) by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented by weighted Euclidean graphs, which we use to determine the entropy of the data distribution in the DS and at the same time to derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking data sets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique.

Inter-comparison of weather and circulation type classifications for hydrological drought development

NASA Astrophysics Data System (ADS)

Fleig, Anne K.; Tallaksen, Lena M.; Hisdal, Hege; Stahl, Kerstin; Hannah, David M.

Classifications of weather and circulation patterns are often applied in research seeking to relate atmospheric state to surface environmental phenomena. However, numerous procedures have been applied to define the patterns, thus limiting comparability between studies. The COST733 Action “ Harmonisation and Applications of Weather Type Classifications for European regions” tests 73 different weather type classifications (WTC) and their associate weather types (WTs) and compares the WTCs’ utility for various applications. The objective of this study is to evaluate the potential of these WTCs for analysis of regional hydrological drought development in north-western Europe. Hydrological drought is defined in terms of a Regional Drought Area Index (RDAI), which is based on deficits derived from daily river flow series. RDAI series (1964-2001) were calculated for four homogeneous regions in Great Britain and two in Denmark. For each region, WTs associated with hydrological drought development were identified based on antecedent and concurrent WT-frequencies for major drought events. The utility of the different WTCs for the study of hydrological drought development was evaluated, and the influence of WTC attributes, i.e. input variables, number of defined WTs and general classification concept, on WTC performance was assessed. The objective Grosswetterlagen (OGWL), the objective Second-Generation Lamb Weather Type Classification (LWT2) with 18 WTs and two implementations of the objective Wetterlagenklassifikation (WLK; with 40 and 28 WTs) outperformed all other WTCs. In general, WTCs with more WTs (⩾27) were found to perform better than WTCs with less (⩽18) WTs. The influence of input variables was not consistent across the different classification procedures, and the performance of a WTC was determined primarily by the classification procedure itself. Overall, classification procedures following the relatively simple general classification concept of predefining WTs based on thresholds, performed better than those based on more sophisticated classification concepts such as deriving WTs by cluster analysis or artificial neural networks. In particular, PCA based WTCs with 9 WTs and automated WTCs with a high number of predefined WTs (subjectively and threshold based) performed well. It is suggested that the explicit consideration of the air flow characteristics of meridionality, zonality and cyclonicity in the definition of WTs is a useful feature for a WTC when analysing regional hydrological drought development.

Heuristic pattern correction scheme using adaptively trained generalized regression neural networks.

PubMed

Hoya, T; Chambers, J A

2001-01-01

In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studies.

Potential risk for healthy siblings to develop schizophrenia: evidence from pattern classification with whole-brain connectivity.

PubMed

Liu, Meijie; Zeng, Ling-Li; Shen, Hui; Liu, Zhening; Hu, Dewen

2012-03-28

Recent resting-state functional connectivity MRI studies using group-level statistical analysis have demonstrated the inheritable characters of schizophrenia. The objective of the present study was to use pattern classification as a means to investigate schizophrenia inheritance based on the whole-brain resting-state functional connectivity at the individual subject level. One-against-one pattern classifications were made amongst three groups (i.e. patients diagnosed with schizophrenia, healthy siblings, and healthy controls after preprocessing), resulting in an 80.4% separation between patients with schizophrenia and healthy controls, a 77.6% separation between schizophrenia patients and their healthy siblings, and a 78.7% separation between healthy siblings and healthy controls, respectively. These results suggest that the healthy siblings of schizophrenia patients have an altered resting-state functional connectivity pattern compared with healthy controls. Thus, healthy siblings may have a potential higher risk for developing schizophrenia compared with the general population. Moreover, this pattern differed from that of schizophrenia patients and may contribute to the normal behavior exhibition of healthy siblings in daily life.

[Characteristic study on village landscape patterns in Sichuan Basin hilly region based on high resolution IKONOS remote sensing].

PubMed

Li, Shoucheng; Liu, Wenquan; Cheng, Xu; Ellis, Erle C

2005-10-01

To realize the landscape programming of agro-ecosystem management, landscape-stratification can provide us the best understanding of landscape ecosystem at very detailed scales. For this purpose, the village landscapes in densely populated Jintang and Jianyang Counties of Sichuan Basin hilly region were mapped from high resolution (1 m) IKONOS satellite imagery by using a standardized 4 level ecological landscape classification and mapping system in a regionally-representative sample of five 500 x 500 m2 landscape quadrats (sample plots). Based on these maps, the spatial patterns were analyzed by landscape indicators, which demonstrated a large variety of landscape types or ecotopes across the village landscape of this region, with diversity indexes ranging from 1.08 to 2.26 at different levels of the landscape classification system. The richness indices ranged from 42.2% to 58.6 %, except that for the landcover at 85 %. About 12.5 % of the ecotopes were distributed in the same way in each landscape sample, and the remaining 87.5% were distributed differently. The landscape fragmentation indices varied from 2.93 to 4.27 across sample plots, and from 2.86 to 5.63 across classification levels. The population density and the road and hamlet areas had strong linear correlations with some landscape indicators, and especially, the correlation coefficients of hamlet areas with fractal indexes and fragmental dimensions were 0.957* and 0.991**, respectively. The differences in most landscape pattern indices across sample plots and landscape classes were statistically significant, indicating that cross-scale mapping and classification of village landscapes could provide more detailed information on landscape patterns than those from a single level of classification.

A semi-automated method for bone age assessment using cervical vertebral maturation.

PubMed

Baptista, Roberto S; Quaglio, Camila L; Mourad, Laila M E H; Hummel, Anderson D; Caetano, Cesar Augusto C; Ortolani, Cristina Lúcia F; Pisa, Ivan T

2012-07-01

To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al. A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naïve Bayes algorithm were built and assessed using a software program. The classifier with the greatest accuracy according to the weighted kappa test was considered best. The classifier showed a weighted kappa coefficient of 0.861 ± 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 ± 0.019. Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice.

Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach

NASA Astrophysics Data System (ADS)

Chang Chien, Kuang-Che; Fetita, Catalin; Brillet, Pierre-Yves; Prêteux, Françoise; Chang, Ruey-Feng

2009-02-01

Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution decomposition, six features are computed, for which fuzzy membership functions define a probability of association with a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned to each membership function and two threshold values are used to decide the final class of the pattern. The proposed approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing: 84% and ground glass: 97%.

Acromioclavicular joint dislocations: radiological correlation between Rockwood classification system and injury patterns in human cadaver species.

PubMed

Eschler, Anica; Rösler, Klaus; Rotter, Robert; Gradl, Georg; Mittlmeier, Thomas; Gierer, Philip

2014-09-01

The classification system of Rockwood and Young is a commonly used classification for acromioclavicular joint separations subdividing types I-VI. This classification hypothesizes specific lesions to anatomical structures (acromioclavicular and coracoclavicular ligaments, capsule, attached muscles) leading to the injury. In recent literature, our understanding for anatomical correlates leading to the radiological-based Rockwood classification is questioned. The goal of this experimental-based investigation was to approve the correlation between the anatomical injury pattern and the Rockwood classification. In four human cadavers (seven shoulders), the acromioclavicular and coracoclavicular ligaments were transected stepwise. Radiological correlates were recorded (Zanca view) with 15-kg longitudinal tension applied at the wrist. The resulting acromio- and coracoclavicular distances were measured. Radiographs after acromioclavicular ligament transection showed joint space enlargement (8.6 ± 0.3 vs. 3.1 ± 0.5 mm, p < 0.05) and no significant change in coracoclavicular distance (10.4 ± 0.9 vs. 10.0 ± 0.8 mm). According to the Rockwood classification only type I and II lesions occurred. After additional coracoclavicular ligament cut, the acromioclavicular joint space width increased to 16.7 ± 2.7 vs. 8.6 ± 0.3 mm, p < 0.05. The mean coracoclavicular distance increased to 20.6 ± 2.1 mm resulting in type III-V lesions concerning the Rockwood classification. Trauma with intact coracoclavicular ligaments did not result in acromioclavicular joint lesions higher than Rockwood type I and II. The clinical consequence for reconstruction of low-grade injuries might be a solely surgical approach for the acromioclavicular ligaments or conservative treatment. High-grade injuries were always based on additional structural damage to the coracoclavicular ligaments. Rockwood type V lesions occurred while muscle attachments were intact.

Imaging features predict prognosis of patients with combined hepatocellular-cholangiocarcinoma.

PubMed

Mao, Y; Xu, S; Hu, W; Huang, J; Wang, J; Zhang, R; Li, S

2017-02-01

To evaluate the prognostic value of imaging patterns in combined hepatocellular-cholangiocarcinoma. A total of 36 patients with histopathologically confirmed combined hepatocellular-cholangiocarcinoma were enrolled. Pretreatment imaging was conducted to evaluate the tumour enhancement patterns, based on which the disease was classified as two subtypes: radiographic hepatocellular carcinoma-dominant (n=26) and radiographic cholangiocarcinoma-dominant (n=10). Moreover, based on the proportion of components, all combined hepatocellular-cholangiocarcinoma cases were divided into histopathological hepatocellular carcinoma-dominant (n=26) or histopathological cholangiocarcinoma-dominant (n=10). The Kaplan-Meier method was used to compare patient outcome between the two subtypes of each classification. Univariate Cox regression analysis were employed to evaluate the prognostic relevance of the imaging and histopathological classification. Consistency between histopathological and imaging classification was not high. Only 66.7% of patients had consistent classification. Moreover, the median overall survival of the radiographic cholangiocarcinoma-dominant and radiographic hepatocellular carcinoma-dominant population was 15.03 and 40.4 months, respectively (p=0.012); however, no significant difference was observed between histopathological type, with median overall survival being 32.07 and 40.4 months in the histopathological cholangiocarcinoma-dominant group and histopathological hepatocellular carcinoma-dominant group, respectively (p=0.784). There was an association between imaging patterns and overall survival in combined hepatocellular-cholangiocarcinoma. Postoperative re-evaluation of imaging patterns could help to assess patient outcome. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach.

PubMed

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Changsen; Liu, Feixiang

2017-02-15

Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. The proposed approach is a promising candidate for future BCI systems. Copyright © 2016 Elsevier B.V. All rights reserved.

Automating the expert consensus paradigm for robust lung tissue classification

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Karwoski, Ronald A.; Raghunath, Sushravya; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.

Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines

NASA Astrophysics Data System (ADS)

Lama, Norsang; Umbaugh, Scott E.; Mishra, Deependra; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

Anterior cruciate ligament (ACL) rupture in canines is a common orthopedic injury in veterinary medicine. Veterinarians use both imaging and non-imaging methods to diagnose the disease. Common imaging methods such as radiography, computed tomography (CT scan) and magnetic resonance imaging (MRI) have some disadvantages: expensive setup, high dose of radiation, and time-consuming. In this paper, we present an alternative diagnostic method based on feature extraction and pattern classification (FEPC) to diagnose abnormal patterns in ACL thermograms. The proposed method was experimented with a total of 30 thermograms for each camera view (anterior, lateral and posterior) including 14 disease and 16 non-disease cases provided from Long Island Veterinary Specialists. The normal and abnormal patterns in thermograms are analyzed in two steps: feature extraction and pattern classification. Texture features based on gray level co-occurrence matrices (GLCM), histogram features and spectral features are extracted from the color normalized thermograms and the computed feature vectors are applied to Nearest Neighbor (NN) classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier with leave-one-out validation method. The algorithm gives the best classification success rate of 86.67% with a sensitivity of 85.71% and a specificity of 87.5% in ACL rupture detection using NN classifier for the lateral view and Norm-RGB-Lum color normalization method. Our results show that the proposed method has the potential to detect ACL rupture in canines.

Foot-strike pattern and performance in a marathon

PubMed Central

Kasmer, Mark E.; Liu, Xue-cheng; Roberts, Kyle G.; Valadao, Jason M.

2016-01-01

Purpose To: 1) determine prevalence of heel-strike in a mid-size city marathon, 2) determine if there is an association between foot-strike classification and race performance, and 3) determine if there is an association between foot-strike classification and gender. Methods Foot-strike classification (fore-foot strike, mid-foot strike, heel strike, or split-strike), gender, and rank (position in race) were recorded at the 8.1 kilometer (km) mark for 2,112 runners at the 2011 Milwaukee Lakefront Marathon. Results 1,991 runners were classified by foot-strike pattern, revealing a heel-strike prevalence of 93.67% (n=1,865). A significant difference between foot-strike classification and performance was found using a Kruskal-Wallis test (p < 0.0001), with more elite performers being less likely to heel-strike. No significant difference between foot-strike classification and gender was found using a Fisher’s exact test. Additionally, subgroup analysis of the 126 non-heel strikers found no significant difference between shoe wear and performance using a Kruskal-Wallis test. Conclusions The high prevalence of heel-striking observed in this study reflects the foot-strike pattern of the majority of mid- to long-distance runners and more importantly, may predict their injury profile based on the biomechanics of a heel strike running pattern. This knowledge can aid the clinician in the appropriate diagnosis, management, and training modifications of the injured runner. PMID:23006790

Using ecological zones to increase the detail of Landsat classifications

NASA Technical Reports Server (NTRS)

Fox, L., III; Mayer, K. E.

1981-01-01

Changes in classification detail of forest species descriptions were made for Landsat data on 2.2 million acres in northwestern California. Because basic forest canopy structures may exhibit very similar E-M energy reflectance patterns in different environmental regions, classification labels based on Landsat spectral signatures alone become very generalized when mapping large heterogeneous ecological regions. By adding a seven ecological zone stratification, a 167% improvement in classification detail was made over the results achieved without it. The seven zone stratification is a less costly alternative to the inclusion of complex collateral information, such as terrain data and soil type, into the Landsat data base when making inventories of areas greater than 500,000 acres.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

PubMed Central

Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

2015-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. PMID:25987366

Recurring patterns in the songs of humpback whales (Megaptera novaeangliae).

PubMed

Green, Sean R; Mercado, Eduardo; Pack, Adam A; Herman, Louis M

2011-02-01

Humpback whales, unlike most mammalian species, learn new songs as adults. Populations of singers progressively and collectively change the sounds and patterns within their songs throughout their lives and across generations. In this study, humpback whale songs recorded in Hawaii from 1985 to 1995 were analyzed using self-organizing maps (SOMs) to classify the sounds within songs, and to identify sound patterns that were present across multiple years. These analyses supported the hypothesis that recurring, persistent patterns exist within whale songs, and that these patterns are defined at least in part by acoustic relationships between adjacent sounds within songs. Sound classification based on acoustic differences between adjacent sounds yielded patterns within songs that were more consistent from year to year than classifications based on the properties of single sounds. Maintenance of fixed ratios of acoustic modulation across sounds, despite large variations in individual sounds, suggests intrinsic constraints on how sounds change within songs. Such acoustically invariant cues may enable whales to recognize and assess variations in songs despite propagation-related distortion of individual sounds and yearly changes in songs. Copyright © 2011 Elsevier B.V. All rights reserved.

Scanning electron microscope automatic defect classification of process induced defects

NASA Astrophysics Data System (ADS)

Wolfe, Scott; McGarvey, Steve

2017-03-01

With the integration of high speed Scanning Electron Microscope (SEM) based Automated Defect Redetection (ADR) in both high volume semiconductor manufacturing and Research and Development (R and D), the need for reliable SEM Automated Defect Classification (ADC) has grown tremendously in the past few years. In many high volume manufacturing facilities and R and D operations, defect inspection is performed on EBeam (EB), Bright Field (BF) or Dark Field (DF) defect inspection equipment. A comma separated value (CSV) file is created by both the patterned and non-patterned defect inspection tools. The defect inspection result file contains a list of the inspection anomalies detected during the inspection tools' examination of each structure, or the examination of an entire wafers surface for non-patterned applications. This file is imported into the Defect Review Scanning Electron Microscope (DRSEM). Following the defect inspection result file import, the DRSEM automatically moves the wafer to each defect coordinate and performs ADR. During ADR the DRSEM operates in a reference mode, capturing a SEM image at the exact position of the anomalies coordinates and capturing a SEM image of a reference location in the center of the wafer. A Defect reference image is created based on the Reference image minus the Defect image. The exact coordinates of the defect is calculated based on the calculated defect position and the anomalies stage coordinate calculated when the high magnification SEM defect image is captured. The captured SEM image is processed through either DRSEM ADC binning, exporting to a Yield Analysis System (YAS), or a combination of both. Process Engineers, Yield Analysis Engineers or Failure Analysis Engineers will manually review the captured images to insure that either the YAS defect binning is accurately classifying the defects or that the DRSEM defect binning is accurately classifying the defects. This paper is an exploration of the feasibility of the utilization of a Hitachi RS4000 Defect Review SEM to perform Automatic Defect Classification with the objective of the total automated classification accuracy being greater than human based defect classification binning when the defects do not require multiple process step knowledge for accurate classification. The implementation of DRSEM ADC has the potential to improve the response time between defect detection and defect classification. Faster defect classification will allow for rapid response to yield anomalies that will ultimately reduce the wafer and/or the die yield.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

NASA Astrophysics Data System (ADS)

Bianconi, Francesco; Bello-Cerezo, Raquel; Napoletano, Paolo

2018-01-01

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images.

Two notions of conspicuity and the classification of phyllotaxis.

PubMed

Reick, Christian H

2002-04-07

Invoking cylindrical Bravais lattices, Adler (1974, 1977) proposed a mathematically precise definition for the botanical classification of phyllotaxis. It is based on opposed pairs of parastichy families, that are conspicuous and visible. Jean (1988) generalized this concept to non-opposed pairs of parastichy families. In the present paper it is shown that this generalization implies a notion of conspicuity different from Adler's. This is made obvious by redefining the key terms of the two approaches. Both classifications are well defined. For Adler's, this is shown by presenting a general proof for his conjecture that conspicuous (in the sense of Adler) opposed pairs of parastichy families are visible. There are indications that in applications to models of phyllotaxis (van Iterson model, inhibitor models) their solutions are better characterized by Jean's classification. The differences between Adler's and Jean's classification show up only in very rare cases, so that the practice of pattern determination is only insignificantly touched by the present results. It turns out that the widely used contact parastichy method to determine phyllotactic patterns gives results according to Jean's classification rather than Adler's. Copyright 2002 Elsevier Science Ltd. All rights reserved.

Towards exaggerated emphysema stereotypes

NASA Astrophysics Data System (ADS)

Chen, C.; Sørensen, L.; Lauze, F.; Igel, C.; Loog, M.; Feragen, A.; de Bruijne, M.; Nielsen, M.

2012-03-01

Classification is widely used in the context of medical image analysis and in order to illustrate the mechanism of a classifier, we introduce the notion of an exaggerated image stereotype based on training data and trained classifier. The stereotype of some image class of interest should emphasize/exaggerate the characteristic patterns in an image class and visualize the information the employed classifier relies on. This is useful for gaining insight into the classification and serves for comparison with the biological models of disease. In this work, we build exaggerated image stereotypes by optimizing an objective function which consists of a discriminative term based on the classification accuracy, and a generative term based on the class distributions. A gradient descent method based on iterated conditional modes (ICM) is employed for optimization. We use this idea with Fisher's linear discriminant rule and assume a multivariate normal distribution for samples within a class. The proposed framework is applied to computed tomography (CT) images of lung tissue with emphysema. The synthesized stereotypes illustrate the exaggerated patterns of lung tissue with emphysema, which is underpinned by three different quantitative evaluation methods.

Algorithms for Hyperspectral Endmember Extraction and Signature Classification with Morphological Dendritic Networks

NASA Astrophysics Data System (ADS)

Schmalz, M.; Ritter, G.

Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination [1]. Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra [3]. Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs [4]. However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and simulated camera optical distortions. In particular, we examine two critical cases: (1) classification of multiple closely spaced signatures that are difficult to separate using distance measures, and (2) classification of materials in simulated hyperspectral images of spaceborne satellites. In each case, test data are derived from a NASA database of space material signatures. Additional analysis pertains to computational complexity and noise sensitivity, which are superior to classical NN based techniques.

Biomedical image classification based on a cascade of an SVM with a reject option and subspace analysis.

PubMed

Lin, Dongyun; Sun, Lei; Toh, Kar-Ann; Zhang, Jing Bo; Lin, Zhiping

2018-05-01

Automated biomedical image classification could confront the challenges of high level noise, image blur, illumination variation and complicated geometric correspondence among various categorical biomedical patterns in practice. To handle these challenges, we propose a cascade method consisting of two stages for biomedical image classification. At stage 1, we propose a confidence score based classification rule with a reject option for a preliminary decision using the support vector machine (SVM). The testing images going through stage 1 are separated into two groups based on their confidence scores. Those testing images with sufficiently high confidence scores are classified at stage 1 while the others with low confidence scores are rejected and fed to stage 2. At stage 2, the rejected images from stage 1 are first processed by a subspace analysis technique called eigenfeature regularization and extraction (ERE), and then classified by another SVM trained in the transformed subspace learned by ERE. At both stages, images are represented based on two types of local features, i.e., SIFT and SURF, respectively. They are encoded using various bag-of-words (BoW) models to handle biomedical patterns with and without geometric correspondence, respectively. Extensive experiments are implemented to evaluate the proposed method on three benchmark real-world biomedical image datasets. The proposed method significantly outperforms several competing state-of-the-art methods in terms of classification accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Rock classification based on resistivity patterns in electrical borehole wall images

NASA Astrophysics Data System (ADS)

Linek, Margarete; Jungmann, Matthias; Berlage, Thomas; Pechnig, Renate; Clauser, Christoph

2007-06-01

Electrical borehole wall images represent grey-level-coded micro-resistivity measurements at the borehole wall. Different scientific methods have been implemented to transform image data into quantitative log curves. We introduce a pattern recognition technique applying texture analysis, which uses second-order statistics based on studying the occurrence of pixel pairs. We calculate so-called Haralick texture features such as contrast, energy, entropy and homogeneity. The supervised classification method is used for assigning characteristic texture features to different rock classes and assessing the discriminative power of these image features. We use classifiers obtained from training intervals to characterize the entire image data set recovered in ODP hole 1203A. This yields a synthetic lithology profile based on computed texture data. We show that Haralick features accurately classify 89.9% of the training intervals. We obtained misclassification for vesicular basaltic rocks. Hence, further image analysis tools are used to improve the classification reliability. We decompose the 2D image signal by the application of wavelet transformation in order to enhance image objects horizontally, diagonally and vertically. The resulting filtered images are used for further texture analysis. This combined classification based on Haralick features and wavelet transformation improved our classification up to a level of 98%. The application of wavelet transformation increases the consistency between standard logging profiles and texture-derived lithology. Texture analysis of borehole wall images offers the potential to facilitate objective analysis of multiple boreholes with the same lithology.

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

Cities through the Prism of People’s Spending Behavior

PubMed Central

Hawelka, Bartosz; Murillo Arias, Juan; Ratti, Carlo

2016-01-01

Scientific studies of society increasingly rely on digital traces produced by various aspects of human activity. In this paper, we exploit a relatively unexplored source of data–anonymized records of bank card transactions collected in Spain by a big European bank, and propose a new classification scheme of cities based on the economic behavior of their residents. First, we study how individual spending behavior is qualitatively and quantitatively affected by various factors such as customer’s age, gender, and size of his/her home city. We show that, similar to other socioeconomic urban quantities, individual spending activity exhibits a statistically significant superlinear scaling with city size. With respect to the general trends, we quantify the distinctive signature of each city in terms of residents’ spending behavior, independently from the effects of scale and demographic heterogeneity. Based on the comparison of city signatures, we build a novel classification of cities across Spain in three categories. That classification exhibits a substantial stability over different city definitions and connects with a meaningful socioeconomic interpretation. Furthermore, it corresponds with the ability of cities to attract foreign visitors, which is a particularly remarkable finding given that the classification was based exclusively on the behavioral patterns of city residents. This highlights the far-reaching applicability of the presented classification approach and its ability to discover patterns that go beyond the quantities directly involved in it. PMID:26849218

Cities through the Prism of People's Spending Behavior.

PubMed

Sobolevsky, Stanislav; Sitko, Izabela; Tachet des Combes, Remi; Hawelka, Bartosz; Murillo Arias, Juan; Ratti, Carlo

2016-01-01

Scientific studies of society increasingly rely on digital traces produced by various aspects of human activity. In this paper, we exploit a relatively unexplored source of data-anonymized records of bank card transactions collected in Spain by a big European bank, and propose a new classification scheme of cities based on the economic behavior of their residents. First, we study how individual spending behavior is qualitatively and quantitatively affected by various factors such as customer's age, gender, and size of his/her home city. We show that, similar to other socioeconomic urban quantities, individual spending activity exhibits a statistically significant superlinear scaling with city size. With respect to the general trends, we quantify the distinctive signature of each city in terms of residents' spending behavior, independently from the effects of scale and demographic heterogeneity. Based on the comparison of city signatures, we build a novel classification of cities across Spain in three categories. That classification exhibits a substantial stability over different city definitions and connects with a meaningful socioeconomic interpretation. Furthermore, it corresponds with the ability of cities to attract foreign visitors, which is a particularly remarkable finding given that the classification was based exclusively on the behavioral patterns of city residents. This highlights the far-reaching applicability of the presented classification approach and its ability to discover patterns that go beyond the quantities directly involved in it.

Walking pattern analysis and SVM classification based on simulated gaits.

PubMed

Mao, Yuxiang; Saito, Masaru; Kanno, Takehiro; Wei, Daming; Muroi, Hiroyasu

2008-01-01

Three classes of walking patterns, normal, caution and danger, were simulated by tying elastic bands to joints of lower body. In order to distinguish one class from another, four local motions suggested by doctors were investigated stepwise, and differences between levels were evaluated using t-tests. The human adaptability in the tests was also evaluated. We improved average classification accuracy to 84.50% using multiclass support vector machine classifier and concluded that human adaptability is a factor that can cause obvious bias in contiguous data collections.

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

PubMed Central

Rana, Mohit; Prasad, Vinod A.; Guan, Cuntai; Birbaumer, Niels; Sitaram, Ranganatha

2016-01-01

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. PMID:27467528

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

Sunspot Pattern Classification using PCA and Neural Networks (Poster)

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Thompson, D. E.; Slater, G. L.

2005-01-01

The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.

Stygoregions – a promising approach to a bioregional classification of groundwater systems

PubMed Central

Stein, Heide; Griebler, Christian; Berkhoff, Sven; Matzke, Dirk; Fuchs, Andreas; Hahn, Hans Jürgen

2012-01-01

Linked to diverse biological processes, groundwater ecosystems deliver essential services to mankind, the most important of which is the provision of drinking water. In contrast to surface waters, ecological aspects of groundwater systems are ignored by the current European Union and national legislation. Groundwater management and protection measures refer exclusively to its good physicochemical and quantitative status. Current initiatives in developing ecologically sound integrative assessment schemes by taking groundwater fauna into account depend on the initial classification of subsurface bioregions. In a large scale survey, the regional and biogeographical distribution patterns of groundwater dwelling invertebrates were examined for many parts of Germany. Following an exploratory approach, our results underline that the distribution patterns of invertebrates in groundwater are not in accordance with any existing bioregional classification system established for surface habitats. In consequence, we propose to develope a new classification scheme for groundwater ecosystems based on stygoregions. PMID:22993698

DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis.

PubMed

Sahm, Felix; Schrimpf, Daniel; Stichel, Damian; Jones, David T W; Hielscher, Thomas; Schefzyk, Sebastian; Okonechnikov, Konstantin; Koelsche, Christian; Reuss, David E; Capper, David; Sturm, Dominik; Wirsching, Hans-Georg; Berghoff, Anna Sophie; Baumgarten, Peter; Kratz, Annekathrin; Huang, Kristin; Wefers, Annika K; Hovestadt, Volker; Sill, Martin; Ellis, Hayley P; Kurian, Kathreena M; Okuducu, Ali Fuat; Jungk, Christine; Drueschler, Katharina; Schick, Matthias; Bewerunge-Hudler, Melanie; Mawrin, Christian; Seiz-Rosenhagen, Marcel; Ketter, Ralf; Simon, Matthias; Westphal, Manfred; Lamszus, Katrin; Becker, Albert; Koch, Arend; Schittenhelm, Jens; Rushing, Elisabeth J; Collins, V Peter; Brehmer, Stefanie; Chavez, Lukas; Platten, Michael; Hänggi, Daniel; Unterberg, Andreas; Paulus, Werner; Wick, Wolfgang; Pfister, Stefan M; Mittelbronn, Michel; Preusser, Matthias; Herold-Mende, Christel; Weller, Michael; von Deimling, Andreas

2017-05-01

The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups. In this multicentre, retrospective analysis, we investigated genome-wide DNA methylation patterns of meningiomas from ten European academic neuro-oncology centres to identify distinct methylation classes of meningiomas. The methylation classes were further characterised by DNA copy number analysis, mutational profiling, and RNA sequencing. Methylation classes were analysed for progression-free survival outcomes by the Kaplan-Meier method. The DNA methylation-based and WHO classification schema were compared using the Brier prediction score, analysed in an independent cohort with WHO grading, progression-free survival, and disease-specific survival data available, collected at the Medical University Vienna (Vienna, Austria), assessing methylation patterns with an alternative methylation chip. We retrospectively collected 497 meningiomas along with 309 samples of other extra-axial skull tumours that might histologically mimic meningioma variants. Unsupervised clustering of DNA methylation data clearly segregated all meningiomas from other skull tumours. We generated genome-wide DNA methylation profiles from all 497 meningioma samples. DNA methylation profiling distinguished six distinct clinically relevant methylation classes associated with typical mutational, cytogenetic, and gene expression patterns. Compared with WHO grading, classification by individual and combined methylation classes more accurately identifies patients at high risk of disease progression in tumours with WHO grade I histology, and patients at lower risk of recurrence among WHO grade II tumours (p=0·0096) from the Brier prediction test). We validated this finding in our independent cohort of 140 patients with meningioma. DNA methylation-based meningioma classification captures clinically more homogenous groups and has a higher power for predicting tumour recurrence and prognosis than the WHO classification. The approach presented here is potentially very useful for stratifying meningioma patients to observation-only or adjuvant treatment groups. We consider methylation-based tumour classification highly relevant for the future diagnosis and treatment of meningioma. German Cancer Aid, Else Kröner-Fresenius Foundation, and DKFZ/Heidelberg Institute of Personalized Oncology/Precision Oncology Program. Copyright © 2017 Elsevier Ltd. All rights reserved.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

PubMed

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Impervious surface mapping with Quickbird imagery

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio

2010-01-01

This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of “salt-and-pepper” pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance. PMID:21643434

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug

2013-05-15

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessedmore » using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI data obtained from both scanners, the classification accuracies with the SVM and Bayesian classifiers were 92% and 77%, respectively. The selected features resulting from the classification process differed by scanner, with more features included for the classification of the integrated HRCT data than for the classification of the HRCT data from each scanner. For the integrated data, consisting of HRCT images of both scanners, the classification accuracy based on the SVM was statistically similar to the accuracy of the data obtained from each scanner. However, the classification accuracy of the integrated data using the Bayesian classifier was significantly lower than the classification accuracy of the ROI data of each scanner. Conclusions: The use of an integrated dataset along with a SVM classifier rather than a Bayesian classifier has benefits in terms of the classification accuracy of HRCT images acquired with more than one scanner. This finding is of relevance in studies involving large number of images, as is the case in a multicenter trial with different scanners.« less

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

A Comparison of the Effects of Electrode Implantation and Targeting on Pattern Classification Accuracy for Prosthesis Control

PubMed Central

Farrell, Todd R.; Weir, Richard F. ff.

2011-01-01

The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy. PMID:18713689

Optical Neural Classification Of Binary Patterns

NASA Astrophysics Data System (ADS)

Gustafson, Steven C.; Little, Gordon R.

1988-05-01

Binary pattern classification that may be implemented using optical hardware and neural network algorithms is considered. Pattern classification problems that have no concise description (as in classifying handwritten characters) or no concise computation (as in NP-complete problems) are expected to be particularly amenable to this approach. For example, optical processors that efficiently classify binary patterns in accordance with their Boolean function complexity might be designed. As a candidate for such a design, an optical neural network model is discussed that is designed for binary pattern classification and that consists of an optical resonator with a dynamic multiplex-recorded reflection hologram and a phase conjugate mirror with thresholding and gain. In this model, learning or training examples of binary patterns may be recorded on the hologram such that one bit in each pattern marks the pattern class. Any input pattern, including one with an unknown class or marker bit, will be modified by a large number of parallel interactions with the reflection hologram and nonlinear mirror. After perhaps several seconds and 100 billion interactions, a steady-state pattern may develop with a marker bit that represents a minimum-Boolean-complexity classification of the input pattern. Computer simulations are presented that illustrate progress in understanding the behavior of this model and in developing a processor design that could have commanding and enduring performance advantages compared to current pattern classification techniques.

One-year persistence of individual gait patterns identified in a follow-up study - A call for individualised diagnose and therapy.

PubMed

Horst, F; Mildner, M; Schöllhorn, W I

2017-10-01

Although a hunch about the individuality of human movements generally exists, differences in gait patterns between individuals are often neglected. To date, only a few studies distinguished individual gait patterns in terms of uniqueness and emphasised the relevance of individualised diagnoses and therapy. However, small sample sizes have been a limitation on identifying subjects based on gait patterns, and little is known about the permanence of subject-specific characteristics over time. The purpose of this study was (1) to prove the uniqueness of individual gait patterns within a larger sample and (2) to prove the long-term permanence of individual gait patterns. A sample of 128 healthy participants each walked a distance of 10m barefoot 10 times. Two force plates recorded the ground reaction forces during a double step at a self-selected walking speed. A subsample of 46 participants repeated this procedure after a period of 7-16 months. The application of support vector machines resulted in classification rates of 99.8% (1278 out of 1280) and 99.4% (914 out of 920) for the initial subject-classification and the subsample follow-up-classification, respectively. The results showed that gait patterns based on time-continuous ground reaction forces were unique to an individual and could be differentiated from those of other individuals. Support vector machines classified gait patterns to the corresponding individual almost error-free. Hence, human gait is not only different between individuals but also exhibits unique individual characteristics that are persistent over years. Our findings provide evidence for the individual nature of human walking and emphasise the need to evaluate individualised clinical approaches for diagnoses and therapy. Copyright © 2017 Elsevier B.V. All rights reserved.

Integrating Vegetation Classification, Mapping, and Strategic Inventory for Forest Management

Treesearch

C. K. Brewer; R. Bush; D. Berglund; J. A. Barber; S. R. Brown

2006-01-01

Many of the analyses needed to address multiple resource issues are focused on vegetation pattern and process relationships and most rely on the data models produced from vegetation classification, mapping, and/or inventory. The Northern Region Vegetation Mapping Project (R1-VMP) data models are based on these three integrally related, yet separate processes. This...

Idiopathic interstitial pneumonias and emphysema: detection and classification using a texture-discriminative approach

NASA Astrophysics Data System (ADS)

Fetita, C.; Chang-Chien, K. C.; Brillet, P. Y.; Pr"teux, F.; Chang, R. F.

2012-03-01

Our study aims at developing a computer-aided diagnosis (CAD) system for fully automatic detection and classification of pathological lung parenchyma patterns in idiopathic interstitial pneumonias (IIP) and emphysema using multi-detector computed tomography (MDCT). The proposed CAD system is based on three-dimensional (3-D) mathematical morphology, texture and fuzzy logic analysis, and can be divided into four stages: (1) a multi-resolution decomposition scheme based on a 3-D morphological filter was exploited to discriminate the lung region patterns at different analysis scales. (2) An additional spatial lung partitioning based on the lung tissue texture was introduced to reinforce the spatial separation between patterns extracted at the same resolution level in the decomposition pyramid. Then, (3) a hierarchic tree structure was exploited to describe the relationship between patterns at different resolution levels, and for each pattern, six fuzzy membership functions were established for assigning a probability of association with a normal tissue or a pathological target. Finally, (4) a decision step exploiting the fuzzy-logic assignments selects the target class of each lung pattern among the following categories: normal (N), emphysema (EM), fibrosis/honeycombing (FHC), and ground glass (GDG). According to a preliminary evaluation on an extended database, the proposed method can overcome the drawbacks of a previously developed approach and achieve higher sensitivity and specificity.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

Clustering Of Left Ventricular Wall Motion Patterns

NASA Astrophysics Data System (ADS)

Bjelogrlic, Z.; Jakopin, J.; Gyergyek, L.

1982-11-01

A method for detection of wall regions with similar motion was presented. A model based on local direction information was used to measure the left ventricular wall motion from cineangiographic sequence. Three time functions were used to define segmental motion patterns: distance of a ventricular contour segment from the mean contour, the velocity of a segment and its acceleration. Motion patterns were clustered by the UPGMA algorithm and by an algorithm based on K-nearest neighboor classification rule.

Classification and global distribution of ocean precipitation types based on satellite passive microwave signatures

NASA Astrophysics Data System (ADS)

Gautam, Nitin

The main objectives of this thesis are to develop a robust statistical method for the classification of ocean precipitation based on physical properties to which the SSM/I is sensitive and to examine how these properties vary globally and seasonally. A two step approach is adopted for the classification of oceanic precipitation classes from multispectral SSM/I data: (1)we subjectively define precipitation classes using a priori information about the precipitating system and its possible distinct signature on SSM/I data such as scattering by ice particles aloft in the precipitating cloud, emission by liquid rain water below freezing level, the difference of polarization at 19 GHz-an indirect measure of optical depth, etc.; (2)we then develop an objective classification scheme which is found to reproduce the subjective classification with high accuracy. This hybrid strategy allows us to use the characteristics of the data to define and encode classes and helps retain the physical interpretation of classes. The classification methods based on k-nearest neighbor and neural network are developed to objectively classify six precipitation classes. It is found that the classification method based neural network yields high accuracy for all precipitation classes. An inversion method based on minimum variance approach was used to retrieve gross microphysical properties of these precipitation classes such as column integrated liquid water path, column integrated ice water path, and column integrated min water path. This classification method is then applied to 2 years (1991-92) of SSM/I data to examine and document the seasonal and global distribution of precipitation frequency corresponding to each of these objectively defined six classes. The characteristics of the distribution are found to be consistent with assumptions used in defining these six precipitation classes and also with well known climatological patterns of precipitation regions. The seasonal and global distribution of these six classes is also compared with the earlier results obtained from Comprehensive Ocean Atmosphere Data Sets (COADS). It is found that the gross pattern of the distributions obtained from SSM/I and COADS data match remarkably well with each other.

Assessing paedophilia based on the haemodynamic brain response to face images.

PubMed

Ponseti, Jorge; Granert, Oliver; Van Eimeren, Thilo; Jansen, Olav; Wolff, Stephan; Beier, Klaus; Deuschl, Günther; Huchzermeier, Christian; Stirn, Aglaja; Bosinski, Hartmut; Roman Siebner, Hartwig

2016-01-01

Objective assessment of sexual preferences may be of relevance in the treatment and prognosis of child sexual offenders. Previous research has indicated that this can be achieved by pattern classification of brain responses to sexual child and adult images. Our recent research showed that human face processing is tuned to sexual age preferences. This observation prompted us to test whether paedophilia can be inferred based on the haemodynamic brain responses to adult and child faces. Twenty-four men sexually attracted to prepubescent boys or girls (paedophiles) and 32 men sexually attracted to men or women (teleiophiles) were exposed to images of child and adult, male and female faces during a functional magnetic resonance imaging (fMRI) session. A cross-validated, automatic pattern classification algorithm of brain responses to facial stimuli yielded four misclassified participants (three false positives), corresponding to a specificity of 91% and a sensitivity of 95%. These results indicate that the functional response to facial stimuli can be reliably used for fMRI-based classification of paedophilia, bypassing the problem of showing child sexual stimuli to paedophiles.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

PubMed

Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

2016-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. Copyright © 2015 Elsevier Inc. All rights reserved.

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

PubMed

Zhang, Jianhua; Li, Sunan; Wang, Rubin

2017-01-01

In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

A Novel Classification System for Injuries After Electronic Cigarette Explosions.

PubMed

Patterson, Scott B; Beckett, Allison R; Lintner, Alicia; Leahey, Carly; Greer, Ashley; Brevard, Sidney B; Simmons, Jon D; Kahn, Steven A

Electronic cigarettes (e-cigarettes) contain lithium batteries that have been known to explode and/or cause fires that have resulted in burn injury. The purpose of this article is to present a case study, review injuries caused by e-cigarettes, and present a novel classification system from the newly emerging patterns of burns. A case study was presented and online media reports for e-cigarette burns were queried with search terms "e-cigarette burns" and "electronic cigarette burns." The reports and injury patterns were tabulated. Analysis was then performed to create a novel classification system based on the distinct injury patterns seen in the study. Two patients were seen at our regional burn center after e-cigarette burns. One had an injury to his thigh and penis that required operative intervention after ignition of this device in his pocket. The second had a facial burn and corneal abrasions when the device exploded while he was inhaling vapor. The Internet search and case studies resulted in 26 cases for evaluation. The burn patterns were divided in direct injury from the device igniting and indirect injury when the device caused a house or car fire. A numerical classification was created: direct injury: type 1 (hand injury) 7 cases, type 2 (face injury) 8 cases, type 3 (waist/groin injury) 11 cases, and type 5a (inhalation injury from using device) 2 cases; indirect injury: type 4 (house fire injury) 7 cases and type 5b (inhalation injury from fire started by the device) 4 cases. Multiple e-cigarette injuries are occurring in the United States and distinct patterns of burns are emerging. The classification system developed in this article will aid in further study and future regulation of these dangerous devices.

Structure-based classification and ontology in chemistry

PubMed Central

2012-01-01

Background Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies. Results We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches. Conclusion Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research. PMID:22480202

Application of Sensor Fusion to Improve Uav Image Classification

NASA Astrophysics Data System (ADS)

Jabari, S.; Fathollahi, F.; Zhang, Y.

2017-08-01

Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

Design of partially supervised classifiers for multispectral image data

NASA Technical Reports Server (NTRS)

Jeon, Byeungwoo; Landgrebe, David

1993-01-01

A partially supervised classification problem is addressed, especially when the class definition and corresponding training samples are provided a priori only for just one particular class. In practical applications of pattern classification techniques, a frequently observed characteristic is the heavy, often nearly impossible requirements on representative prior statistical class characteristics of all classes in a given data set. Considering the effort in both time and man-power required to have a well-defined, exhaustive list of classes with a corresponding representative set of training samples, this 'partially' supervised capability would be very desirable, assuming adequate classifier performance can be obtained. Two different classification algorithms are developed to achieve simplicity in classifier design by reducing the requirement of prior statistical information without sacrificing significant classifying capability. The first one is based on optimal significance testing, where the optimal acceptance probability is estimated directly from the data set. In the second approach, the partially supervised classification is considered as a problem of unsupervised clustering with initially one known cluster or class. A weighted unsupervised clustering procedure is developed to automatically define other classes and estimate their class statistics. The operational simplicity thus realized should make these partially supervised classification schemes very viable tools in pattern classification.

Towards automated human gait disease classification using phase space representation of intrinsic mode functions

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Patra, Sayantani; Pratiher, Souvik

2017-06-01

A novel analytical methodology for segregating healthy and neurological disorders from gait patterns is proposed by employing a set of oscillating components called intrinsic mode functions (IMF's). These IMF's are generated by the Empirical Mode Decomposition of the gait time series and the Hilbert transformed analytic signal representation forms the complex plane trace of the elliptical shaped analytic IMFs. The area measure and the relative change in the centroid position of the polygon formed by the Convex Hull of these analytic IMF's are taken as the discriminative features. Classification accuracy of 79.31% with Ensemble learning based Adaboost classifier validates the adequacy of the proposed methodology for a computer aided diagnostic (CAD) system for gait pattern identification. Also, the efficacy of several potential biomarkers like Bandwidth of Amplitude Modulation and Frequency Modulation IMF's and it's Mean Frequency from the Fourier-Bessel expansion from each of these analytic IMF's has been discussed for its potency in diagnosis of gait pattern identification and classification.

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation.

PubMed

Cong, Rui; Li, Jing; Guo, Song

2017-02-01

To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all P<0.05). When applying Qual1=Color pattern 1 for downgrading and Qual1=Color pattern 5 for upgrading the BI-RADS categories, we obtained the highest Az value (0.951), and achieved a significantly higher specificity (86.56%, P=0.002) than that of the US (81.18%) with the same sensitivity (94.96%). The qualitative classification proposed in this study may be representative of SWE parameters and has potential to be relevant assistance in breast mass diagnoses. Copyright © 2016. Published by Elsevier B.V.

Classification of Topographical Pattern of Spasticity in Cerebral Palsy: A Registry Perspective

ERIC Educational Resources Information Center

Reid, Susan M.; Carlin, John B.; Reddihough, Dinah S.

2011-01-01

This study used data from a population-based cerebral palsy (CP) registry and systematic review to assess the amount of heterogeneity between registries in topographical patterns when dichotomised into unilateral (USCP) and bilateral spastic CP (BSCP), and whether the terms diplegia and quadriplegia provide useful additional epidemiological…

A Dynamic Bayesian Network Based Structural Learning towards Automated Handwritten Digit Recognition

NASA Astrophysics Data System (ADS)

Pauplin, Olivier; Jiang, Jianmin

Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. In this paper, we present DBN models trained for classification of handwritten digit characters. The structure of these models is partly inferred from the training data of each class of digit before performing parameter learning. Classification results are presented for the four described models.

Doppler Feature Based Classification of Wind Profiler Data

NASA Astrophysics Data System (ADS)

Sinha, Swati; Chandrasekhar Sarma, T. V.; Lourde. R, Mary

2017-01-01

Wind Profilers (WP) are coherent pulsed Doppler radars in UHF and VHF bands. They are used for vertical profiling of wind velocity and direction. This information is very useful for weather modeling, study of climatic patterns and weather prediction. Observations at different height and different wind velocities are possible by changing the operating parameters of WP. A set of Doppler power spectra is the standard form of WP data. Wind velocity, direction and wind velocity turbulence at different heights can be derived from it. Modern wind profilers operate for long duration and generate approximately 4 megabytes of data per hour. The radar data stream contains Doppler power spectra from different radar configurations with echoes from different atmospheric targets. In order to facilitate systematic study, this data needs to be segregated according the type of target. A reliable automated target classification technique is required to do this job. Classical techniques of radar target identification use pattern matching and minimization of mean squared error, Euclidean distance etc. These techniques are not effective for the classification of WP echoes, as these targets do not have well-defined signature in Doppler power spectra. This paper presents an effective target classification technique based on range-Doppler features.

Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field

PubMed Central

Luo, Gang; Min, Wanli

2007-01-01

Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages. This task is one of the most important steps in sleep analysis. It is crucial for the diagnosis and treatment of various sleep disorders, and also relates closely to brain-machine interfaces. We report an automatic, online sleep stager using electroencephalogram (EEG) signal based on a recently-developed statistical pattern recognition method, conditional random field, and novel potential functions that have explicit physical meanings. Using sleep recordings from human subjects, we show that the average classification accuracy of our sleep stager almost approaches the theoretical limit and is about 8% higher than that of existing systems. Moreover, for a new subject snew with limited training data Dnew, we perform subject adaptation to improve classification accuracy. Our idea is to use the knowledge learned from old subjects to obtain from Dnew a regulated estimate of CRF’s parameters. Using sleep recordings from human subjects, we show that even without any Dnew, our sleep stager can achieve an average classification accuracy of 70% on snew. This accuracy increases with the size of Dnew and eventually becomes close to the theoretical limit. PMID:18693884

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

PubMed Central

Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

2017-01-01

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.

PubMed

Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

2013-01-01

We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

Long-range dismount activity classification: LODAC

NASA Astrophysics Data System (ADS)

Garagic, Denis; Peskoe, Jacob; Liu, Fang; Cuevas, Manuel; Freeman, Andrew M.; Rhodes, Bradley J.

2014-06-01

Continuous classification of dismount types (including gender, age, ethnicity) and their activities (such as walking, running) evolving over space and time is challenging. Limited sensor resolution (often exacerbated as a function of platform standoff distance) and clutter from shadows in dense target environments, unfavorable environmental conditions, and the normal properties of real data all contribute to the challenge. The unique and innovative aspect of our approach is a synthesis of multimodal signal processing with incremental non-parametric, hierarchical Bayesian machine learning methods to create a new kind of target classification architecture. This architecture is designed from the ground up to optimally exploit correlations among the multiple sensing modalities (multimodal data fusion) and rapidly and continuously learns (online self-tuning) patterns of distinct classes of dismounts given little a priori information. This increases classification performance in the presence of challenges posed by anti-access/area denial (A2/AD) sensing. To fuse multimodal features, Long-range Dismount Activity Classification (LODAC) develops a novel statistical information theoretic approach for multimodal data fusion that jointly models multimodal data (i.e., a probabilistic model for cross-modal signal generation) and discovers the critical cross-modal correlations by identifying components (features) with maximal mutual information (MI) which is efficiently estimated using non-parametric entropy models. LODAC develops a generic probabilistic pattern learning and classification framework based on a new class of hierarchical Bayesian learning algorithms for efficiently discovering recurring patterns (classes of dismounts) in multiple simultaneous time series (sensor modalities) at multiple levels of feature granularity.

Application of local binary pattern and human visual Fibonacci texture features for classification different medical images

NASA Astrophysics Data System (ADS)

Sanghavi, Foram; Agaian, Sos

2017-05-01

The goal of this paper is to (a) test the nuclei based Computer Aided Cancer Detection system using Human Visual based system on the histopathology images and (b) Compare the results of the proposed system with the Local Binary Pattern and modified Fibonacci -p pattern systems. The system performance is evaluated using different parameters such as accuracy, specificity, sensitivity, positive predictive value, and negative predictive value on 251 prostate histopathology images. The accuracy of 96.69% was observed for cancer detection using the proposed human visual based system compared to 87.42% and 94.70% observed for Local Binary patterns and the modified Fibonacci p patterns.

Classification of US hydropower dams by their modes of operation

DOE PAGES

McManamay, Ryan A.; Oigbokie, II, Clement O.; Kao, Shih -Chieh; ...

2016-02-19

A key challenge to understanding ecohydrologic responses to dam regulation is the absence of a universally transferable classification framework for how dams operate. In the present paper, we develop a classification system to organize the modes of operation (MOPs) for U.S. hydropower dams and powerplants. To determine the full diversity of MOPs, we mined federal documents, open-access data repositories, and internet sources. W then used CART classification trees to predict MOPs based on physical characteristics, regulation, and project generation. Finally, we evaluated how much variation MOPs explained in sub-daily discharge patterns for stream gages downstream of hydropower dams. After reviewingmore » information for 721 dams and 597 power plants, we developed a 2-tier hierarchical classification based on 1) the storage and control of flows to powerplants, and 2) the presence of a diversion around the natural stream bed. This resulted in nine tier-1 MOPs representing a continuum of operations from strictly peaking, to reregulating, to run-of-river, and two tier-2 MOPs, representing diversion and integral dam-powerhouse configurations. Although MOPs differed in physical characteristics and energy production, classification trees had low accuracies (<62%), which suggested accurate evaluations of MOPs may require individual attention. MOPs and dam storage explained 20% of the variation in downstream subdaily flow characteristics and showed consistent alterations in subdaily flow patterns from reference streams. Lastly, this standardized classification scheme is important for future research including estimating reservoir operations for large-scale hydrologic models and evaluating project economics, environmental impacts, and mitigation.« less

Classification of US hydropower dams by their modes of operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

McManamay, Ryan A.; Oigbokie, II, Clement O.; Kao, Shih -Chieh

A key challenge to understanding ecohydrologic responses to dam regulation is the absence of a universally transferable classification framework for how dams operate. In the present paper, we develop a classification system to organize the modes of operation (MOPs) for U.S. hydropower dams and powerplants. To determine the full diversity of MOPs, we mined federal documents, open-access data repositories, and internet sources. W then used CART classification trees to predict MOPs based on physical characteristics, regulation, and project generation. Finally, we evaluated how much variation MOPs explained in sub-daily discharge patterns for stream gages downstream of hydropower dams. After reviewingmore » information for 721 dams and 597 power plants, we developed a 2-tier hierarchical classification based on 1) the storage and control of flows to powerplants, and 2) the presence of a diversion around the natural stream bed. This resulted in nine tier-1 MOPs representing a continuum of operations from strictly peaking, to reregulating, to run-of-river, and two tier-2 MOPs, representing diversion and integral dam-powerhouse configurations. Although MOPs differed in physical characteristics and energy production, classification trees had low accuracies (<62%), which suggested accurate evaluations of MOPs may require individual attention. MOPs and dam storage explained 20% of the variation in downstream subdaily flow characteristics and showed consistent alterations in subdaily flow patterns from reference streams. Lastly, this standardized classification scheme is important for future research including estimating reservoir operations for large-scale hydrologic models and evaluating project economics, environmental impacts, and mitigation.« less

Classifying Lower Extremity Muscle Fatigue during Walking using Machine Learning and Inertial Sensors

PubMed Central

Zhang, Jian; Lockhart, Thurmon E.; Soangra, Rahul

2013-01-01

Fatigue in lower extremity musculature is associated with decline in postural stability, motor performance and alters normal walking patterns in human subjects. Automated recognition of lower extremity muscle fatigue condition may be advantageous in early detection of fall and injury risks. Supervised machine learning methods such as Support Vector Machines (SVM) have been previously used for classifying healthy and pathological gait patterns and also for separating old and young gait patterns. In this study we explore the classification potential of SVM in recognition of gait patterns utilizing an inertial measurement unit associated with lower extremity muscular fatigue. Both kinematic and kinetic gait patterns of 17 participants (29±11 years) were recorded and analyzed in normal and fatigued state of walking. Lower extremities were fatigued by performance of a squatting exercise until the participants reached 60% of their baseline maximal voluntary exertion level. Feature selection methods were used to classify fatigue and no-fatigue conditions based on temporal and frequency information of the signals. Additionally, influences of three different kernel schemes (i.e., linear, polynomial, and radial basis function) were investigated for SVM classification. The results indicated that lower extremity muscle fatigue condition influenced gait and loading responses. In terms of the SVM classification results, an accuracy of 96% was reached in distinguishing the two gait patterns (fatigue and no-fatigue) within the same subject using the kinematic, time and frequency domain features. It is also found that linear kernel and RBF kernel were equally good to identify intra-individual fatigue characteristics. These results suggest that intra-subject fatigue classification using gait patterns from an inertial sensor holds considerable potential in identifying “at-risk” gait due to muscle fatigue. PMID:24081829

Fatigue level estimation of monetary bills based on frequency band acoustic signals with feature selection by supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued monetary bills adversely affect the daily operation of automated teller machines (ATMs). In order to make the classification of fatigued bills more efficient, the development of an automatic fatigued monetary bill classification method is desirable. We propose a new method by which to estimate the fatigue level of monetary bills from the feature-selected frequency band acoustic energy pattern of banking machines. By using a supervised self-organizing map (SOM), we effectively estimate the fatigue level using only the feature-selected frequency band acoustic energy pattern. Furthermore, the feature-selected frequency band acoustic energy pattern improves the estimation accuracy of the fatigue level of monetary bills by adding frequency domain information to the acoustic energy pattern. The experimental results with real monetary bill samples reveal the effectiveness of the proposed method.

A novel collaborative representation and SCAD based classification method for fibrosis and inflammatory activity analysis of chronic hepatitis C

NASA Astrophysics Data System (ADS)

Cai, Jiaxin; Chen, Tingting; Li, Yan; Zhu, Nenghui; Qiu, Xuan

2018-03-01

In order to analysis the fibrosis stage and inflammatory activity grade of chronic hepatitis C, a novel classification method based on collaborative representation (CR) with smoothly clipped absolute deviation penalty (SCAD) penalty term, called CR-SCAD classifier, is proposed for pattern recognition. After that, an auto-grading system based on CR-SCAD classifier is introduced for the prediction of fibrosis stage and inflammatory activity grade of chronic hepatitis C. The proposed method has been tested on 123 clinical cases of chronic hepatitis C based on serological indexes. Experimental results show that the performance of the proposed method outperforms the state-of-the-art baselines for the classification of fibrosis stage and inflammatory activity grade of chronic hepatitis C.

Fuzzy logic based on-line fault detection and classification in transmission line.

PubMed

Adhikari, Shuma; Sinha, Nidul; Dorendrajit, Thingam

2016-01-01

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.

Attribute-based Decision Graphs: A framework for multiclass data classification.

PubMed

Bertini, João Roberto; Nicoletti, Maria do Carmo; Zhao, Liang

2017-01-01

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances. Aiming at exploring alternative ways to build graphs from data, this paper proposes an algorithm for constructing a new type of graph, called Attribute-based Decision Graph-AbDG. Given a vector-based data set, an AbDG is built by partitioning each data attribute range into disjoint intervals and representing each interval as a vertex. The edges are then established between vertices from different attributes according to a pre-defined pattern. Classification is performed through a matching process among the attribute values of the new instance and AbDG. Moreover, AbDG provides an inner mechanism to handle missing attribute values, which contributes for expanding its applicability. Results of classification tasks have shown that AbDG is a competitive approach when compared to well-known multiclass algorithms. The main contribution of the proposed framework is the combination of the advantages of attribute-based and graph-based techniques to perform robust pattern matching data classification, while permitting the analysis the input data considering only a subset of its attributes. Copyright © 2016 Elsevier Ltd. All rights reserved.

CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns.

PubMed

Goodson, Summer G; White, Sarah; Stevans, Alicia M; Bhat, Sanjana; Kao, Chia-Yu; Jaworski, Scott; Marlowe, Tamara R; Kohlmeier, Martin; McMillan, Leonard; Zeisel, Steven H; O'Brien, Deborah A

2017-11-01

The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile percentage and kinematic parameters at the population level, and uses kinematic gating methods to identify subpopulations such as progressive or hyperactivated sperm. The goal of this study was to develop an automated method that classifies all patterns of human sperm motility during in vitro capacitation following the removal of seminal plasma. We visually classified CASA tracks of 2817 sperm from 18 individuals and used a support vector machine-based decision tree to compute four hyperplanes that separate five classes based on their kinematic parameters. We then developed a web-based program, CASAnova, which applies these equations sequentially to assign a single classification to each motile sperm. Vigorous sperm are classified as progressive, intermediate, or hyperactivated, and nonvigorous sperm as slow or weakly motile. This program correctly classifies sperm motility into one of five classes with an overall accuracy of 89.9%. Application of CASAnova to capacitating sperm populations showed a shift from predominantly linear patterns of motility at initial time points to more vigorous patterns, including hyperactivated motility, as capacitation proceeds. Both intermediate and hyperactivated motility patterns were largely eliminated when sperm were incubated in noncapacitating medium, demonstrating the sensitivity of this method. The five CASAnova classifications are distinctive and reflect kinetic parameters of washed human sperm, providing an accurate, quantitative, and high-throughput method for monitoring alterations in motility. © The Authors 2017. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.

A review of contrast pattern based data mining

NASA Astrophysics Data System (ADS)

Zhu, Shiwei; Ju, Meilong; Yu, Junfeng; Cai, Binlei; Wang, Aiping

2015-07-01

Contrast pattern based data mining is concerned with the mining of patterns and models that contrast two or more datasets. Contrast patterns can describe similarities or differences between the datasets. They represent strong contrast knowledge and have been shown to be very successful for constructing accurate and robust clusters and classifiers. The increasing use of contrast pattern data mining has initiated a great deal of research and development attempts in the field of data mining. A comprehensive revision on the existing contrast pattern based data mining research is given in this paper. They are generally categorized into background and representation, definitions and mining algorithms, contrast pattern based classification, clustering, and other applications, the research trends in future. The primary of this paper is to server as a glossary for interested researchers to have an overall picture on the current contrast based data mining development and identify their potential research direction to future investigation.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

PubMed

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-12-16

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Patient prognosis based on feature extraction, selection and classification of EEG periodic activity.

PubMed

Sánchez-González, Alain; García-Zapirain, Begoña; Maestro Saiz, Iratxe; Yurrebaso Santamaría, Izaskun

2015-01-01

Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.

Patterns of brain structural connectivity differentiate normal weight from overweight subjects

PubMed Central

Gupta, Arpana; Mayer, Emeran A.; Sanmiguel, Claudia P.; Van Horn, John D.; Woodworth, Davis; Ellingson, Benjamin M.; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S.

2015-01-01

Background Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network. Conclusions 1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity. PMID:25737959

Multiple Scale Landscape Pattern Index Interpretation for the Persistent Monitoring of Land-Cover and Land-Use

NASA Astrophysics Data System (ADS)

Spivey, Alvin J.

Mapping land-cover land-use change (LCLUC) over regional and continental scales, and long time scales (years and decades), can be accomplished using thematically identified classification maps of a landscape---a LCLU class map. Observations of a landscape's LCLU class map pattern can indicate the most relevant process, like hydrologic or ecologic function, causing landscape scale environmental change. Quantified as Landscape Pattern Metrics (LPM), emergent landscape patterns act as Landscape Indicators (LI) when physically interpreted. The common mathematical approach to quantifying observed landscape scale pattern is to have LPM measure how connected a class exists within the landscape, through nonlinear local kernel operations of edges and gradients in class maps. Commonly applied kernel-based LPM that consistently reveal causal processes are Dominance, Contagion, and Fractal Dimension. These kernel-based LPM can be difficult to interpret. The emphasis on an image pixel's edge by gradient operations and dependence on an image pixel's existence according to classification accuracy limit the interpretation of LPM. For example, the Dominance and Contagion kernel-based LPM very similarly measure how connected a landscape is. Because of this, their reported edge measurements of connected pattern correlate strongly, making their results ambiguous. Additionally, each of these kernel-based LPM are unscalable when comparing class maps from separate imaging system sensor scenarios that change the image pixel's edge position (i.e. changes in landscape extent, changes in pixel size, changes in orientation, etc), and can only interpret landscape pattern as accurately as the LCLU map classification will allow. This dissertation discusses the reliability of common LPM in light of imaging system effects such as: algorithm classification likelihoods, LCLU classification accuracy due to random image sensor noise, and image scale. A description of an approach to generating well behaved LPM through a Fourier system analysis of the entire class map, or any subset of the class map (e.g. the watershed) is the focus of this work. The Fourier approach provides four improvements for LPM. First, the approach reduces any correlation between metrics by developing them within an independent (i.e. orthogonal) Fourier vector space; a Fourier vector space that includes relevant physically representative parameters ( i.e. between class Euclidean distance). Second, accounting for LCLU classification accuracy the LPM measurement precision and measurement accuracy are reported. Third, the mathematics of this approach makes it possible to compare image data captured at separate pixel resolutions or even from separate landscape scenes. Fourth, Fourier interpreted landscape pattern measurement can be a measure of the entire landscape shape, of individual landscape cover change, or as exchanges between class map subsets by operating on the entire class map, subset of class map, or separate subsets of class map[s] respectively. These LCLUC LPM are examined along the 1991-1992 and 2000-2001 records of National Land Cover Database Landsat data products. Those LPM results are used in a predictive fecal coliform model at the South Carolina watershed level in the context of past (validation study) change. Finally, the proposed LPM ability to be used as ecologically relevant environmental indicators is tested by correlating metrics with other, well known LI that consistently reveal causal processes in the literature.

Interrater reliability of a Pilates movement-based classification system.

PubMed

Yu, Kwan Kenny; Tulloch, Evelyn; Hendrick, Paul

2015-01-01

To determine the interrater reliability for identification of a specific movement pattern using a Pilates Classification system. Videos of 5 subjects performing specific movement tasks were sent to raters trained in the DMA-CP classification system. Ninety-six raters completed the survey. Interrater reliability for the detection of a directional bias was excellent (Pi = 0.92, and K(free) = 0.89). Interrater reliability for classifying an individual into a specific subgroup was moderate (Pi = 0.64, K(free) = 0.55) however raters who had completed levels 1-4 of the DMA-CP training and reported using the assessment daily demonstrated excellent reliability (Pi = 0.89 and K(free) = 0.87). The reliability of the classification system demonstrated almost perfect agreement in determining the existence of a specific movement pattern and classifying into a subgroup for experienced raters. There was a trend for greater reliability associated with increased levels of training and experience of the raters. Copyright © 2014 Elsevier Ltd. All rights reserved.

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

PubMed Central

Geng, Yanjuan; Wei, Yue

2017-01-01

Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees. PMID:28523276

Mapping Crop Patterns in Central US Agricultural Systems from 2000 to 2014 Based on Landsat Data: To What Degree Does Fusing MODIS Data Improve Classification Accuracies?

NASA Astrophysics Data System (ADS)

Zhu, L.; Radeloff, V.; Ives, A. R.; Barton, B.

2015-12-01

Deriving crop pattern with high accuracy is of great importance for characterizing landscape diversity, which affects the resilience of food webs in agricultural systems in the face of climatic and land cover changes. Landsat sensors were originally designed to monitor agricultural areas, and both radiometric and spatial resolution are optimized for monitoring large agricultural fields. Unfortunately, few clear Landsat images per year are available, which has limited the use of Landsat for making crop classification, and this situation is worse in cloudy areas of the Earth. Meanwhile, the MODerate Resolution Imaging Spectroradiometer (MODIS) data has better temporal resolution but cannot capture fine spatial heterogeneity of agricultural systems. Our question was to what extent fusing imagery from both sensors could improve crop classifications. We utilized the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to simulate Landsat-like images at MODIS temporal resolution. Based on Random Forests (RF) classifier, we tested whether and by what degree crop maps from 2000 to 2014 of the Arlington Agricultural Research Station (Wisconsin, USA) were improved by integrating available clear Landsat images each year with synthetic images. We predicted that the degree to which classification accuracy can be improved by incorporating synthetic imagery depends on the number and acquisition time of clear Landsat images. Moreover, multi-season data are essential for mapping crop types by capturing their phenological dynamics, and STARFM-simulated images can be used to compensate for missing Landsat observations. Our study is helpful for eliminating the limits of the use of Landsat data in mapping crop patterns, and can provide a benchmark of accuracy when choosing STARFM-simulated images to make crop classification at broader scales.

Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography.

PubMed

Sugimoto, Katsutoshi; Shiraishi, Junji; Moriyasu, Fuminori; Doi, Kunio

2009-04-01

To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.

Molecular Classification of Melanoma

Cancer.gov

Tissue-based analyses of precursors, melanoma tumors and metastases within existing study populations to further understanding of the heterogeneity of melanoma and determine a predictive pattern of progression for dysplastic nevi.

Attribute-Level and Pattern-Level Classification Consistency and Accuracy Indices for Cognitive Diagnostic Assessment

ERIC Educational Resources Information Center

Wang, Wenyi; Song, Lihong; Chen, Ping; Meng, Yaru; Ding, Shuliang

2015-01-01

Classification consistency and accuracy are viewed as important indicators for evaluating the reliability and validity of classification results in cognitive diagnostic assessment (CDA). Pattern-level classification consistency and accuracy indices were introduced by Cui, Gierl, and Chang. However, the indices at the attribute level have not yet…

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

PubMed

Cho, Youngsang; Seong, Joon-Kyung; Jeong, Yong; Shin, Sung Yong

2012-02-01

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

A SOFTWARE PACKAGE FOR UNSUPERVISED PATTERN RECOGNITION AND SYNOPTIC REPRESENTATION OF RESULTS: APPLICATION TO VOLCANIC TREMOR DATA OF MT ETNA

NASA Astrophysics Data System (ADS)

Langer, H. K.; Falsaperla, S. M.; Behncke, B.; Messina, A.; Spampinato, S.

2009-12-01

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a scheme based on a metrics accounting for correlation between components of the feature vector. We show examples of applications of this tool to volcanic tremor data recorded at Mt Etna between 2007 and 2009. This time span - during which Strombolian explosions, 7 episodes of lava fountaining and effusive activity occurred - is particularly interesting, as it encompassed different states of volcanic activity (i.e., non-eruptive, eruptive according to different styles) for the unsupervised classifier to identify, highlighting their development in time. Even subtle changes in the signal characteristics allow the unsupervised classifier to recognize features belonging to the different classes and stages of volcanic activity. A convenient color-code representation shows up the temporal development of the different classes of signal, making this method extremely helpful for monitoring purposes and surveillance. Though being developed for volcanic tremor classification, KKAnalysis is generally applicable to any type of physical or chemical pattern, provided that feature vectors are given in numerical form. References: Falsaperla, S., S. Graziani, G. Nunnari, and S. Spampinato (1996). Automatic classification of volcanic earthquakes by using multy-layered neural networks. Natural Hazard, 13, 205-228. Langer, H., S. Falsaperla, M. Masotti, R. Campanini, S. Spampinato, and A. Messina (2008). Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna, Italy. Geophys. J. Int., doi:10.1111/j.1365-246X.2009.04179.x.

A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples.

PubMed

Boskamp, Tobias; Lachmund, Delf; Oetjen, Janina; Cordero Hernandez, Yovany; Trede, Dennis; Maass, Peter; Casadonte, Rita; Kriegsmann, Jörg; Warth, Arne; Dienemann, Hendrik; Weichert, Wilko; Kriegsmann, Mark

2017-07-01

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann. Copyright © 2016 Elsevier B.V. All rights reserved.

Refined 4-group classification of left ventricular hypertrophy based on ventricular concentricity and volume dilatation outlines distinct noninvasive hemodynamic profiles in a large contemporary echocardiographic population.

PubMed

Barbieri, Andrea; Rossi, Andrea; Gaibazzi, Nicola; Erlicher, Andrea; Mureddu, Gian Francesco; Frattini, Silvia; Faden, Giacomo; Manicardi, Marcella; Beraldi, Monica; Agostini, Francesco; Lazzarini, Valentina; Moreo, Antonella; Temporelli, Pier Luigi; Faggiano, Pompilio

2018-05-23

Left ventricular hypertrophy (LVH) may reflect a wide variety of physiologic and pathologic conditions. Thus, it can be misleading to consider all LVH to be homogenous or similar. Refined 4-group classification of LVH based on ventricular concentricity and dilatation may be identified. To determine whether the 4-group classification of LVH identified distinct phenotypes, we compared their association with various noninvasive markers of cardiac stress. Cohort of unselected adult outpatients referred to a seven tertiary care echocardiographic laboratory for any indication in a 2-week period. We evaluated the LV geometric patterns using validated echocardiographic indexation methods and partition values. Standard echocardiography was performed in 1137 consecutive subjects, and LVH was found in 42%. The newly proposed 4-group classification of LVH was applicable in 88% of patients. The most common pattern resulted in concentric LVH (19%). The worst functional and hemodynamic profile was associated with eccentric LVH and those with mixed LVH had a higher prevalence of reduced EF than those with concentric LVH (P < .001 for all). The new 4-group classification of LVH system showed distinct differences in cardiac function and noninvasive hemodynamics allowing clinicians to distinguish different LV hemodynamic stress adaptations in patients with LVH. © 2018 Wiley Periodicals, Inc.

Relationship between AOD and synoptic circulation over the Eastern Mediterranean: A comparison between subjective and objective classifications

NASA Astrophysics Data System (ADS)

Bodenheimer, Shalev; Nirel, Ronit; Lensky, Itamar M.; Dayan, Uri

2018-03-01

The Eastern Mediterranean (EM) Basin is strongly affected by dust originating from two of the largest world sources: The Sahara Desert and the Arabian Peninsula. Climatologically, the distribution pattern of aerosol optical depth (AOD), as proxy to particulate matter (PM), is known to be correlated with synoptic circulation. The climatological relationship between circulation type classifications (CTCs) and AOD levels over the EM Basin ("synoptic skill") was examined for the years 2000-2014. We compared the association between subjective (expert-based) and objective (fully automated) classifications and AOD using autoregressive models. After seasonal adjustment, the mean values of R2 for the different methods were similar. However, the distinct spatial pattern of the R2 values suggests that subjective classifications perform better in their area of expertise, specifically in the southeast region of the study area, while, objective CTCs had better synoptic skill over the northern part of the EM. This higher synoptic skill of subjective CTCs stem from their ability to identify distinct circulation types (e.g. Sharav lows and winter lows) that are infrequent but are highly correlated with AOD. Notably, a simple CTC based on seasonality rather than meteorological parameters predicted well AOD levels, especially over the south-eastern part of the domain. Synoptic classifications that are area-oriented are likely better predictors of AOD and possibly other environmental variables.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Mission-Based Scenario Research: Experimental Design and Analysis

DTIC Science & Technology

2011-08-10

with Army Reserach Lab; DCS Corporation, Alexandria, Va and Warren, Mi 14. ABSTRACT In this paper , we discuss a neuroimaging experiment that...Development and Engineering Center Warren, MI Kelvin S. Oie, PhD Army Research Laboratory Aberdeen Proving Ground, MD ABSTRACT In this paper , we...experiment, this paper will emphasize analyses that employ a pattern classification analysis approach. These classification examples aim to identify

A software package for interactive motor unit potential classification using fuzzy k-NN classifier.

PubMed

Rasheed, Sarbast; Stashuk, Daniel; Kamel, Mohamed

2008-01-01

We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-based classification that takes into account a combination of motor unit potential (MUP) shapes and two modes of use of motor unit firing pattern information: the passive and the active modes. The developed package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using assertion-based classifiers.

Seismic data classification and artificial neural networks: can software replace eyeballs?

NASA Astrophysics Data System (ADS)

Reusch, D. B.; Larson, A. M.

2006-05-01

Modern seismic datasets are providing many new opportunities for furthering our understanding of our planet, ranging from the deep earth to the sub-ice sheet interface. With many geophysical applications, the large volume of these datasets raises issues of manageability in areas such as quality control (QC) and event identification (EI). While not universally true, QC can be a labor intensive, subjective (and thus not entirely reproducible) and uninspiring task when such datasets are involved. The EI process shares many of these drawbacks but has the benefit of (usually) being closer to interesting science-based questions. Here we explore two techniques from the field of artificial neural networks (ANNs) that seek to reduce the time requirements and increase the objectivity of QC and EI on seismic datasets. In particular, we focus on QC of receiver functions from broadband seismic data collected by the 2000-2003 Transantarctic Mountains Seismic Experiment (TAMSEIS). Self-organizing maps (SOMs) enable unsupervised classification of large, complex geophysical data sets (e.g., time series of the atmospheric circulation) into a fixed number of distinct generalized patterns or modes representing the probability distribution function of the input data. These patterns are organized spatially as a two-dimensional grid such that distances represent similarity (adjacent patterns will be most similar). After training, input data are matched to their most similar generalized pattern to produce frequency maps, i.e., what fraction of the data is represented best by each individual SOM pattern. Given a priori information on data quality (from previous manual grading) or event type, a probabilistic classification can be developed that gives a likelihood for each category of interest for each SOM pattern. New data are classified by identifying the closest matching pattern (without retraining) and examining the associated probabilities. Feed-forward ANNs (FFNNs) are a supervised classification tool that has been successfully used in a number of seismic applications (e.g., Langer et al, 2003; Del Pezzo et al 2003). FFNNs require a correct answer for each training record so that the transfer functions between input predictors and output predictions can be developed during training. After training, applying new input data to the FFNNs classifies the input based on the existing transfer functions. Key to the success of both approaches is the selection of proper predictor variables that reflect, to varying degrees, the criteria humans use when doing these tasks manually. SOMs also have the potential to assist in this selection process. Because SOMs and FFNNs are used in different ways, they can address different aspects of the overall data classification problem in complementary ways. While not the first application of computers to these problems, ANN-based tools bring unique characteristics to the problem of capturing human decision-making processes.

Classifying Human Activity Patterns from Smartphone Collected GPS data: a Fuzzy Classification and Aggregation Approach.

PubMed

Wan, Neng; Lin, Ge

2016-12-01

Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone-measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone-collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g., up to 96% in activity identification) with use of this method. Two examples were provided in the appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.

Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern.

PubMed

Cheng, Jiao; Jin, Jing; Wang, Xingyu

2017-01-01

Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i.e., face expression, face familiarity, and multifaces) on the BCI performance. Studies on the influence of face transparency differences are scarce. Therefore, we investigated the effect of semitransparent face pattern (STF-P) (the subject could see the target character when the stimuli were flashed) and traditional face pattern (F-P) (the subject could not see the target character when the stimuli were flashed) on the BCI performance from the transparency perspective. Results showed that STF-P obtained significantly higher classification accuracy and ITR than those of F-P ( p < 0.05).

Extraction and Analysis of Mega Cities’ Impervious Surface on Pixel-based and Object-oriented Support Vector Machine Classification Technology: A case of Bombay

NASA Astrophysics Data System (ADS)

Yu, S. S.; Sun, Z. C.; Sun, L.; Wu, M. F.

2017-02-01

The object of this paper is to study the impervious surface extraction method using remote sensing imagery and monitor the spatiotemporal changing patterns of mega cities. Megacity Bombay was selected as the interesting area. Firstly, the pixel-based and object-oriented support vector machine (SVM) classification methods were used to acquire the land use/land cover (LULC) products of Bombay in 2010. Consequently, the overall accuracy (OA) and overall Kappa (OK) of the pixel-based method were 94.97% and 0.96 with a running time of 78 minutes, the OA and OK of the object-oriented method were 93.72% and 0.94 with a running time of only 17s. Additionally, OA and OK of the object-oriented method after a post-classification were improved up to 95.8% and 0.94. Then, the dynamic impervious surfaces of Bombay in the period 1973-2015 were extracted and the urbanization pattern of Bombay was analysed. Results told that both the two SVM classification methods could accomplish the impervious surface extraction, but the object-oriented method should be a better choice. Urbanization of Bombay experienced a fast extending during the past 42 years, implying a dramatically urban sprawl of mega cities in the developing countries along the One Belt and One Road (OBOR).

A New Classification of Diabetic Gait Pattern Based on Cluster Analysis of Biomechanical Data

PubMed Central

Sawacha, Zimi; Guarneri, Gabriella; Avogaro, Angelo; Cobelli, Claudio

2010-01-01

Background The diabetic foot, one of the most serious complications of diabetes mellitus and a major risk factor for plantar ulceration, is determined mainly by peripheral neuropathy. Neuropathic patients exhibit decreased stability while standing as well as during dynamic conditions. A new methodology for diabetic gait pattern classification based on cluster analysis has been proposed that aims to identify groups of subjects with similar patterns of gait and verify if three-dimensional gait data are able to distinguish diabetic gait patterns from one of the control subjects. Method The gait of 20 nondiabetic individuals and 46 diabetes patients with and without peripheral neuropathy was analyzed [mean age 59.0 (2.9) and 61.1(4.4) years, mean body mass index (BMI) 24.0 (2.8), and 26.3 (2.0)]. K-means cluster analysis was applied to classify the subjects' gait patterns through the analysis of their ground reaction forces, joints and segments (trunk, hip, knee, ankle) angles, and moments. Results Cluster analysis classification led to definition of four well-separated clusters: one aggregating just neuropathic subjects, one aggregating both neuropathics and non-neuropathics, one including only diabetes patients, and one including either controls or diabetic and neuropathic subjects. Conclusions Cluster analysis was useful in grouping subjects with similar gait patterns and provided evidence that there were subgroups that might otherwise not be observed if a group ensemble was presented for any specific variable. In particular, we observed the presence of neuropathic subjects with a gait similar to the controls and diabetes patients with a long disease duration with a gait as altered as the neuropathic one. PMID:20920432

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

Development of a reproducible method for determining quantity of water and its configuration in a marsh landscape

USGS Publications Warehouse

Suir, Glenn M.; Evers, D. Elaine; Steyer, Gregory D.; Sasser, Charles E.

2013-01-01

Coastal Louisiana is a dynamic and ever-changing landscape. From 1956 to 2010, over 3,734 km2 of Louisiana's coastal wetlands have been lost due to a combination of natural and human-induced activities. The resulting landscape constitutes a mosaic of conditions from highly deteriorated to relatively stable with intact landmasses. Understanding how and why coastal landscapes change over time is critical to restoration and rehabilitation efforts. Historically, changes in marsh pattern (i.e., size and spatial distribution of marsh landmasses and water bodies) have been distinguished using visual identification by individual researchers. Difficulties associated with this approach include subjective interpretation, uncertain reproducibility, and laborious techniques. In order to minimize these limitations, this study aims to expand existing tools and techniques via a computer-based method, which uses geospatial technologies for determining shifts in landscape patterns. Our method is based on a raster framework and uses landscape statistics to develop conditions and thresholds for a marsh classification scheme. The classification scheme incorporates land and water classified imagery and a two-part classification system: (1) ratio of water to land, and (2) configuration and connectivity of water within wetland landscapes to evaluate changes in marsh patterns. This analysis system can also be used to trace trajectories in landscape patterns through space and time. Overall, our method provides a more automated means of quantifying landscape patterns and may serve as a reliable landscape evaluation tool for future investigations of wetland ecosystem processes in the northern Gulf of Mexico.

Pressure-flow characteristics of normal and disordered esophageal motor patterns.

PubMed

Singendonk, Maartje M J; Kritas, Stamatiki; Cock, Charles; Ferris, Lara F; McCall, Lisa; Rommel, Nathalie; van Wijk, Michiel P; Benninga, Marc A; Moore, David; Omari, Taher I

2015-03-01

To perform pressure-flow analysis (PFA) in a cohort of pediatric patients who were referred for diagnostic manometric investigation. PFA was performed using purpose designed Matlab-based software. The pressure-flow index (PFI), a composite measure of bolus pressurization relative to flow and the impedance ratio, a measure of the extent of bolus clearance failure were calculated. Tracings of 76 pediatric patients (32 males; 9.1 ± 0.7 years) and 25 healthy adult controls (7 males; 36.1 ± 2.2 years) were analyzed. Patients mostly had normal motility (50%) or a category 4 disorder and usually weak peristalsis (31.5%) according to the Chicago Classification. PFA of healthy controls defined reference ranges for PFI ≤142 and impedance ratio ≤0.49. Pediatric patients with pressure-flow (PF) characteristics within these limits had normal motility (62%), most patients with PF characteristics outside these limits also had an abnormal Chicago Classification (61%). Patients with high PFI and disordered motor patterns all had esophagogastric junction outflow obstruction. Disordered PF characteristics are associated with disordered esophageal motor patterns. By defining the degree of over-pressurization and/or extent of clearance failure, PFA may be a useful adjunct to esophageal pressure topography-based classification. Copyright © 2015 Elsevier Inc. All rights reserved.

Selection-Fusion Approach for Classification of Datasets with Missing Values

PubMed Central

Ghannad-Rezaie, Mostafa; Soltanian-Zadeh, Hamid; Ying, Hao; Dong, Ming

2010-01-01

This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values. PMID:20212921

Classification of full-thickness rotator cuff lesions: a review

PubMed Central

Lädermann, Alexandre; Burkhart, Stephen S.; Hoffmeyer, Pierre; Neyton, Lionel; Collin, Philippe; Yates, Evan; Denard, Patrick J.

2016-01-01

Rotator cuff lesions (RCL) have considerable variability in location, tear pattern, functional impairment, and repairability. Historical classifications for differentiating these lesions have been based upon factors such as the size and shape of the tear, and the degree of atrophy and fatty infiltration. Additional recent descriptions include bipolar rotator cuff insufficiency, ‘Fosbury flop tears’, and musculotendinous lesions. Recommended treatment is based on the location of the lesion, patient factors and associated pathology, and often includes personal experience and data from case series. Development of a more comprehensive classification which integrates historical and newer descriptions of RCLs may help to guide treatment further. Cite this article: Lädermann A, Burkhart SS, Hoffmeyer P, et al. Classification of full thickness rotator cuff lesions: a review. EFORT Open Rev 2016;1:420-430. DOI: 10.1302/2058-5241.1.160005. PMID:28461921

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

PubMed Central

Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function. PMID:26121466

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

PubMed

Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Toward Automated Cochlear Implant Fitting Procedures Based on Event-Related Potentials.

PubMed

Finke, Mareike; Billinger, Martin; Büchner, Andreas

Cochlear implants (CIs) restore hearing to the profoundly deaf by direct electrical stimulation of the auditory nerve. To provide an optimal electrical stimulation pattern the CI must be individually fitted to each CI user. To date, CI fitting is primarily based on subjective feedback from the user. However, not all CI users are able to provide such feedback, for example, small children. This study explores the possibility of using the electroencephalogram (EEG) to objectively determine if CI users are able to hear differences in tones presented to them, which has potential applications in CI fitting or closed loop systems. Deviant and standard stimuli were presented to 12 CI users in an active auditory oddball paradigm. The EEG was recorded in two sessions and classification of the EEG data was performed with shrinkage linear discriminant analysis. Also, the impact of CI artifact removal on classification performance and the possibility to reuse a trained classifier in future sessions were evaluated. Overall, classification performance was above chance level for all participants although performance varied considerably between participants. Also, artifacts were successfully removed from the EEG without impairing classification performance. Finally, reuse of the classifier causes only a small loss in classification performance. Our data provide first evidence that EEG can be automatically classified on single-trial basis in CI users. Despite the slightly poorer classification performance over sessions, classifier and CI artifact correction appear stable over successive sessions. Thus, classifier and artifact correction weights can be reused without repeating the set-up procedure in every session, which makes the technique easier applicable. With our present data, we can show successful classification of event-related cortical potential patterns in CI users. In the future, this has the potential to objectify and automate parts of CI fitting procedures.

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

PubMed

Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

2018-05-24

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

Optimizing Input/Output Using Adaptive File System Policies

NASA Technical Reports Server (NTRS)

Madhyastha, Tara M.; Elford, Christopher L.; Reed, Daniel A.

1996-01-01

Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to select appropriate caching and pre-fetching policies, while performance sensors provide feedback used to tune policy parameters for specific system environments. To illustrate the potential performance improvements possible using adaptive file system policies, we present results from experiments involving classification-based and performance-based steering.

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

PubMed

Dottori, Martin; Sedeño, Lucas; Martorell Caro, Miguel; Alifano, Florencia; Hesse, Eugenia; Mikulan, Ezequiel; García, Adolfo M; Ruiz-Tagle, Amparo; Lillo, Patricia; Slachevsky, Andrea; Serrano, Cecilia; Fraiman, Daniel; Ibanez, Agustin

2017-06-19

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer's disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.

An Intelligent Pattern Recognition System Based on Neural Network and Wavelet Decomposition for Interpretation of Heart Sounds

DTIC Science & Technology

2001-10-25

wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a...Proceedings. pp. 415–418, 1990. [3] G. Ergun, “An intelligent diagnostic system for interpretation of arterpartum fetal heart rate tracings based on ANNs and...AN INTELLIGENT PATTERN RECOGNITION SYSTEM BASED ON NEURAL NETWORK AND WAVELET DECOMPOSITION FOR INTERPRETATION OF HEART SOUNDS I. TURKOGLU1, A

Novel Strength Test Battery to Permit Evidence-Based Paralympic Classification

PubMed Central

Beckman, Emma M.; Newcombe, Peter; Vanlandewijck, Yves; Connick, Mark J.; Tweedy, Sean M.

2014-01-01

Abstract Ordinal-scale strength assessment methods currently used in Paralympic athletics classification prevent the development of evidence-based classification systems. This study evaluated a battery of 7, ratio-scale, isometric tests with the aim of facilitating the development of evidence-based methods of classification. This study aimed to report sex-specific normal performance ranges, evaluate test–retest reliability, and evaluate the relationship between the measures and body mass. Body mass and strength measures were obtained from 118 participants—63 males and 55 females—ages 23.2 years ± 3.7 (mean ± SD). Seventeen participants completed the battery twice to evaluate test–retest reliability. The body mass–strength relationship was evaluated using Pearson correlations and allometric exponents. Conventional patterns of force production were observed. Reliability was acceptable (mean intraclass correlation = 0.85). Eight measures had moderate significant correlations with body size (r = 0.30–61). Allometric exponents were higher in males than in females (mean 0.99 vs 0.30). Results indicate that this comprehensive and parsimonious battery is an important methodological advance because it has psychometric properties critical for the development of evidence-based classification. Measures were interrelated with body size, indicating further research is required to determine whether raw measures require normalization in order to be validly applied in classification. PMID:25068950

Classification

ERIC Educational Resources Information Center

Clary, Renee; Wandersee, James

2013-01-01

In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

PubMed

Zeng, Ling-Li; Wang, Huaning; Hu, Panpan; Yang, Bo; Pu, Weidan; Shen, Hui; Chen, Xingui; Liu, Zhening; Yin, Hong; Tan, Qingrong; Wang, Kai; Hu, Dewen

2018-04-01

A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Copyright © 2018 German Center for Neurodegenerative Diseases (DZNE). Published by Elsevier B.V. All rights reserved.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination.

PubMed

Jee Keen Raymond, Wong; Illias, Hazlee Azil; Abu Bakar, Ab Halim

2017-01-01

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach.

PubMed

Wu, Mon-Ju; Wu, Hanjing Emily; Mwangi, Benson; Sanches, Marsal; Selvaraj, Sudhakar; Zunta-Soares, Giovana B; Soares, Jair C

2015-03-01

Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment - without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level. We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was 'trained' to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated. The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls. These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Moraes, Diego R.; Reche, Gustavo A.; Borges, Lucas R.; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2017-03-01

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

A quantitative index for classification of plantar thermal changes in the diabetic foot

NASA Astrophysics Data System (ADS)

Hernandez-Contreras, D.; Peregrina-Barreto, H.; Rangel-Magdaleno, J.; Gonzalez-Bernal, J. A.; Altamirano-Robles, L.

2017-03-01

One of the main complications caused by diabetes mellitus is the development of diabetic foot, which in turn, can lead to ulcerations. Because ulceration risks are linked to an increase in plantar temperatures, recent approaches analyze thermal changes. These approaches try to identify spatial patterns of temperature that could be characteristic of a diabetic group. However, this is a difficult task since thermal patterns have wide variations resulting on complex classification. Moreover, the measurement of contralateral plantar temperatures is important to determine whether there is an abnormal difference but, this only provides information when thermal changes are asymmetric and in absence of ulceration or amputation. Therefore, in this work is proposed a quantitative index for measuring the thermal change in the plantar region of participants diagnosed diabetes mellitus regards to a reliable reference (control) or regards to the contralateral foot (as usual). Also, a classification of the thermal changes based on a quantitative index is proposed. Such classification demonstrate the wide diversity of spatial distributions in the diabetic foot but also demonstrate that it is possible to identify common characteristics. An automatic process, based on the analysis of plantar angiosomes and image processing, is presented to quantify these thermal changes and to provide valuable information to the medical expert.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

PubMed Central

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-01-01

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

The Effect of Achievement Test Selection on Identification of Learning Disabilities within a Patterns of Strengths and Weaknesses Framework

ERIC Educational Resources Information Center

Miciak, Jeremy; Taylor, W. Pat; Denton, Carolyn A.; Fletcher, Jack M.

2015-01-01

Few empirical investigations have evaluated learning disabilities (LD) identification methods based on a pattern of cognitive strengths and weaknesses (PSW). This study investigated the reliability of LD classification decisions of the concordance/discordance method (C/DM) across different psychoeducational assessment batteries. C/DM criteria were…

Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes

NASA Astrophysics Data System (ADS)

Ng, Theam Foo; Pham, Tuan D.; Zhou, Xiaobo

2010-01-01

With the fast development of multi-dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering-based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype-image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ-HMM) and a well-known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG-HMM) will be carried out. The experimental results show that the performances of both FDVQ-HMM and LBG-HMM are almost similar. Finally, we have justified the competitiveness of FDVQ-HMM in classification of cellular phenotype image database by using hypotheses t-test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome-wide screening image data.

Directional Multi-scale Modeling of High-Resolution Computed Tomography (HRCT) Lung Images for Diffuse Lung Disease Classification

NASA Astrophysics Data System (ADS)

Vo, Kiet T.; Sowmya, Arcot

A directional multi-scale modeling scheme based on wavelet and contourlet transforms is employed to describe HRCT lung image textures for classifying four diffuse lung disease patterns: normal, emphysema, ground glass opacity (GGO) and honey-combing. Generalized Gaussian density parameters are used to represent the detail sub-band features obtained by wavelet and contourlet transforms. In addition, support vector machines (SVMs) with excellent performance in a variety of pattern classification problems are used as classifier. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512x512, 16 bits/pixel in DICOM format. The dataset contains 70,000 ROIs of those slices marked by experienced radiologists. We employ this technique at different wavelet and contourlet transform scales for diffuse lung disease classification. The technique presented here has best overall sensitivity 93.40% and specificity 98.40%.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Pattern recognition and image processing for environmental monitoring

NASA Astrophysics Data System (ADS)

Siddiqui, Khalid J.; Eastwood, DeLyle

1999-12-01

Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

PubMed

Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

2014-01-01

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

HEp-2 cell image classification method based on very deep convolutional networks with small datasets

NASA Astrophysics Data System (ADS)

Lu, Mengchi; Gao, Long; Guo, Xifeng; Liu, Qiang; Yin, Jianping

2017-07-01

Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

Formalization of the classification pattern: survey of classification modeling in information systems engineering.

PubMed

Partridge, Chris; de Cesare, Sergio; Mitchell, Andrew; Odell, James

2018-01-01

Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the "one and the many." Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor's work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus to the ISE literature. The literature survey follows the evolution of ISE's understanding of how to formalize the classification pattern. The various proposals are assessed using the classical example of classification; the Linnaean taxonomy formalized using powersets as a benchmark for formal expressiveness. The broad conclusion of the survey is that (1) the ISE community is currently in the early stages of the process of understanding how to formalize the classification pattern, particularly in the requirements for expressiveness exemplified by powersets, and (2) that there is an opportunity to intervene and speed up the process of adoption by clarifying this expressiveness. Given the central place that the classification pattern has in domain modeling, this intervention has the potential to lead to significant improvements.

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

VTOL shipboard letdown guidance system analysis

NASA Technical Reports Server (NTRS)

Phatak, A. V.; Karmali, M. S.

1983-01-01

Alternative letdown guidance strategies are examined for landing of a VTOL aircraft onboard a small aviation ship under adverse environmental conditions. Off line computer simulation of shipboard landing task is utilized for assessing the relative merits of the proposed guidance schemes. The touchdown performance of a nominal constant rate of descent (CROD) letdown strategy serves as a benchmark for ranking the performance of the alternative letdown schemes. Analysis of ship motion time histories indicates the existence of an alternating sequence of quiescent and rough motions called lulls and swells. A real time algorithms lull/swell classification based upon ship motion pattern features is developed. The classification algorithm is used to command a go/no go signal to indicate the initiation and termination of an acceptable landing window. Simulation results show that such a go/no go pattern based letdown guidance strategy improves touchdown performance.

Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets.

PubMed

Park, Inho; Lee, Kwang H; Lee, Doheon

2010-06-15

Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material. Supplementary data are available at Bioinformatics online.

Classification of autistic individuals and controls using cross-task characterization of fMRI activity

PubMed Central

Chanel, Guillaume; Pichon, Swann; Conty, Laurence; Berthoz, Sylvie; Chevallier, Coralie; Grèzes, Julie

2015-01-01

Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations. PMID:26793434

Robust pattern decoding in shape-coded structured light

NASA Astrophysics Data System (ADS)

Tang, Suming; Zhang, Xu; Song, Zhan; Song, Lifang; Zeng, Hai

2017-09-01

Decoding is a challenging and complex problem in a coded structured light system. In this paper, a robust pattern decoding method is proposed for the shape-coded structured light in which the pattern is designed as grid shape with embedded geometrical shapes. In our decoding method, advancements are made at three steps. First, a multi-template feature detection algorithm is introduced to detect the feature point which is the intersection of each two orthogonal grid-lines. Second, pattern element identification is modelled as a supervised classification problem and the deep neural network technique is applied for the accurate classification of pattern elements. Before that, a training dataset is established, which contains a mass of pattern elements with various blurring and distortions. Third, an error correction mechanism based on epipolar constraint, coplanarity constraint and topological constraint is presented to reduce the false matches. In the experiments, several complex objects including human hand are chosen to test the accuracy and robustness of the proposed method. The experimental results show that our decoding method not only has high decoding accuracy, but also owns strong robustness to surface color and complex textures.

A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering.

PubMed

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-05-18

Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary. In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine-learning algorithms. Finally, the class label is predicted using the trained classifiers. Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features' provider of support vector machine (SVM) lead to the highest accuracy of 89.40 %. The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.

Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns

PubMed Central

Dijksterhuis, Chris; de Waard, Dick; Brookhuis, Karel A.; Mulder, Ben L. J. M.; de Jong, Ritske

2013-01-01

A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications. PMID:23970851

A comparison of blood vessel features and local binary patterns for colorectal polyp classification

NASA Astrophysics Data System (ADS)

Gross, Sebastian; Stehle, Thomas; Behrens, Alexander; Auer, Roland; Aach, Til; Winograd, Ron; Trautwein, Christian; Tischendorf, Jens

2009-02-01

Colorectal cancer is the third leading cause of cancer deaths in the United States of America for both women and men. By means of early detection, the five year survival rate can be up to 90%. Polyps can to be grouped into three different classes: hyperplastic, adenomatous, and carcinomatous polyps. Hyperplastic polyps are benign and are not likely to develop into cancer. Adenomas, on the other hand, are known to grow into cancer (adenoma-carcinoma sequence). Carcinomas are fully developed cancers and can be easily distinguished from adenomas and hyperplastic polyps. A recent narrow band imaging (NBI) study by Tischendorf et al. has shown that hyperplastic polyps and adenomas can be discriminated by their blood vessel structure. We designed a computer-aided system for the differentiation between hyperplastic and adenomatous polyps. Our development aim is to provide the medical practitioner with an additional objective interpretation of the available image data as well as a confidence measure for the classification. We propose classification features calculated on the basis of the extracted blood vessel structure. We use the combined length of the detected blood vessels, the average perimeter of the vessels and their average gray level value. We achieve a successful classification rate of more than 90% on 102 polyps from our polyp data base. The classification results based on these features are compared to the results of Local Binary Patterns (LBP). The results indicate that the implemented features are superior to LBP.

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

NASA Technical Reports Server (NTRS)

Anastasiadis, Stergios

1991-01-01

Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.

Group-Based Active Learning of Classification Models.

PubMed

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

Classification of spontaneous EEG signals in migraine

NASA Astrophysics Data System (ADS)

Bellotti, R.; De Carlo, F.; de Tommaso, M.; Lucente, M.

2007-08-01

We set up a classification system able to detect patients affected by migraine without aura, through the analysis of their spontaneous EEG patterns. First, the signals are characterized by means of wavelet-based features, than a supervised neural network is used to classify the multichannel data. For the feature extraction, scale-dependent and scale-independent methods are considered with a variety of wavelet functions. Both the approaches provide very high and almost comparable classification performances. A complete separation of the two groups is obtained when the data are plotted in the plane spanned by two suitable neural outputs.

A classification of ecological boundaries

USGS Publications Warehouse

Strayer, D.L.; Power, M.E.; Fagan, W.F.; Pickett, S.T.A.; Belnap, J.

2003-01-01

Ecologists use the term boundary to refer to a wide range of real and conceptual structures. Because imprecise terminology may impede the search for general patterns and theories about ecological boundaries, we present a classification of the attributes of ecological boundaries to aid in communication and theory development. Ecological boundaries may differ in their origin and maintenance, their spatial structure, their function, and their temporal dynamics. A classification system based on these attributes should help ecologists determine whether boundaries are truly comparable. This system can be applied when comparing empirical studies, comparing theories, and testing theoretical predictions against empirical results.

Three forms of immune myasthenia.

PubMed

Agius, Mark A; Richman, David P; Fairclough, Robert H; Aarli, Johan; Gilhus, Nils Erik; Romi, Fredrik

2003-09-01

We propose a new classification for immune myasthenia based on antibody pattern. The types of immune myasthenia presently characterized by known antibody targets segregate into three groups: type 1, in which the muscle target is the acetylcholine receptor only; type 2, in which titin antibodies are present in addition to acetylcholine receptor antibodies; and type 3, in which muscle-specific kinase antibodies are present in the absence of acetylcholine receptor antibodies. The immune target is unknown in the patients with immune myasthenia not associated with these antibodies. This classification has advantages over the present classifications as regards homogeneity of groups, etiology, mechanism of disease, and prognosis.

Methods for Monitoring the Detection of Multi-Temporal Land Use Change Through the Classification of Urban Areas

NASA Astrophysics Data System (ADS)

Alhaddad, B. I.; Burns, M. C.; Roca, J.

2011-08-01

Urban areas are complicated due to the mix of man-made features and natural features. A higher level of structural information plays an important role in land cover/use classification of urban areas. Additional spatial indicators have to be extracted based on structural analysis in order to understand and identify spatial patterns or the spatial organization of features, especially for man-made feature. It's very difficult to extract such spatial patterns by using only classification approaches. Clusters of urban patterns which are integral parts of other uses may be difficult to identify. A lot of public resources have been directed towards seeking to develop a standardized classification system and to provide as much compatibility as possible to ensure the widespread use of such categorized data obtained from remote sensor sources. In this paper different methods applied to understand the change in the land use areas by understanding and monitoring the change in urban areas and as its hard to apply those methods to classification results for high elements quantities, dusts and scratches (Roca and Alhaddad, 2005). This paper focuses on a methodology developed based relation between urban elements and how to join this elements in zones or clusters have commune behaviours such as form, pattern, size. The main objective is to convert urban class category in to various structure densities depend on conjunction of pixel and shortest distance between them, Delaunay triangulation has been widely used in spatial analysis and spatial modelling. To identify these different zones, a spatial density-based clustering technique was adopted. In highly urban zones, the spatial density of the pixels is high, while in sparsely areas the density of points is much lower. Once the groups of pixels are identified, the calculation of the boundaries of the areas containing each group of pixels defines the new regions indicate the different contains inside such as high or low urban areas. Multi-temporal datasets from 1986, 1995 and 2004 used to urban region centroid to be our reference in this study which allow us to follow the urban movement, increase and decrease by the time. Kernel Density function used to Calculates urban magnitude, Voronoi algorithm is proposed for deriving explicit boundaries between objects units. To test the approach, we selected a site in a suburban area in Barcelona Municipality, the Spain.

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

PubMed

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Phenomenology and classification of dystonia: a consensus update

PubMed Central

Albanese, Alberto; Bhatia, Kailash; Bressman, Susan B.; DeLong, Mahlon R.; Fahn, Stanley; Fung, Victor S.C.; Hallett, Mark; Jankovic, Joseph; Jinnah, H.A.; Klein, Christine; Lang, Anthony E.; Mink, Jonathan W.; Teller, Jan K.

2013-01-01

This report describes the consensus outcome of an international panel consisting of investigators with years of experience in this field that reviewed the definition and classification of dystonia. Agreement was obtained based on a consensus development methodology during three in-person meetings and manuscript review by mail. Dystonia is defined as a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive, movements, postures, or both. Dystonic movements are typically patterned and twisting, and may be tremulous. Dystonia is often initiated or worsened by voluntary action and associated with overflow muscle activation. Dystonia is classified along two axes: clinical characteristics, including age at onset, body distribution, temporal pattern and associated features (additional movement disorders or neurological features), and etiology, which includes nervous system pathology and inheritance. The clinical characteristics fall into several specific dystonia syndromes that help to guide diagnosis and treatment. We provide here a new general definition of dystonia and propose a new classification. We encourage clinicians and researchers to use these innovative definition and classification and test them in the clinical setting on a variety of patients with dystonia. PMID:23649720

Optimal design of a bank of spatio-temporal filters for EEG signal classification.

PubMed

Higashi, Hiroshi; Tanaka, Toshihisa

2011-01-01

The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery.

HVM die yield improvement as a function of DRSEM ADC

NASA Astrophysics Data System (ADS)

Maheshwary, Sonu; Haas, Terry; McGarvey, Steve

2010-03-01

Given the current manufacturing technology roadmap and the competitiveness of the global semiconductor manufacturing environment in conjunction with the semiconductor manufacturing market dynamics, the market place continues to demand a reduced die manufacturing cost. This continuous pressure on lowering die cost in turn drives an aggressive yield learning curve, a key component of which is defect reduction of manufacturing induced anomalies. In order to meet and even exceed line and die yield targets there is a need to revamp defect classification strategies and place a greater emphasize on increasing the accuracy and purity of the Defect Review Scanning Electron Microscope (DRSEM) Automated Defect Classification (ADC) results while placing less emphasis on the ADC results of patterned/un-patterned wafer inspection systems. The increased emphasis on DRSEM ADC results allows for a high degree of automation and consistency in the classification data and eliminates variance induced by the manufacturing staff. This paper examines the use of SEM based Auto Defect Classification in a high volume manufacturing environment as a key driver in the reduction of defect limited yields.

Multiscale corner detection and classification using local properties and semantic patterns

NASA Astrophysics Data System (ADS)

Gallo, Giovanni; Giuoco, Alessandro L.

2002-05-01

A new technique to detect, localize and classify corners in digital closed curves is proposed. The technique is based on correct estimation of support regions for each point. We compute multiscale curvature to detect and to localize corners. As a further step, with the aid of some local features, it's possible to classify corners into seven distinct types. Classification is performed using a set of rules, which describe corners according to preset semantic patterns. Compared with existing techniques, the proposed approach inscribes itself into the family of algorithms that try to explain the curve, instead of simple labeling. Moreover, our technique works in manner similar to what is believed are typical mechanisms of human perception.

Abnormal fronto-striatal activation as a marker of threshold and subthreshold Bulimia Nervosa.

PubMed

Cyr, Marilyn; Yang, Xiao; Horga, Guillermo; Marsh, Rachel

2018-04-01

This study aimed to determine whether functional disturbances in fronto-striatal control circuits characterize adolescents with Bulimia Nervosa (BN) spectrum eating disorders regardless of clinical severity. FMRI was used to assess conflict-related brain activations during performance of a Simon task in two samples of adolescents with BN symptoms compared with healthy adolescents. The BN samples differed in the severity of their clinical presentation, illness duration and age. Multi-voxel pattern analyses (MVPAs) based on machine learning were used to determine whether patterns of fronto-striatal activation characterized adolescents with BN spectrum disorders regardless of clinical severity, and whether accurate classification of less symptomatic adolescents (subthreshold BN; SBN) could be achieved based on patterns of activation in adolescents who met DSM5 criteria for BN. MVPA classification analyses revealed that both BN and SBN adolescents could be accurately discriminated from healthy adolescents based on fronto-striatal activation. Notably, the patterns detected in more severely ill BN compared with healthy adolescents accurately discriminated less symptomatic SBN from healthy adolescents. Deficient activation of fronto-striatal circuits can characterize BN early in its course, when clinical presentations are less severe, perhaps pointing to circuit-based disturbances as useful biomarker or risk factor for the disorder, and a tool for understanding its developmental trajectory, as well as the development of early interventions. © 2018 Wiley Periodicals, Inc.

Content Classification and Context-Based Retrieval System for E-Learning

ERIC Educational Resources Information Center

Mittal, Ankush; Krishnan, Pagalthivarthi V.; Altman, Edward

2006-01-01

A recent focus in web based learning systems has been the development of reusable learning materials that can be delivered as personalized courses depending of a number of factors such as the user's background, his/her learning preferences, current knowledge based on previous assessments, or previous browsing patterns. The student is often…

Real-Time Classification of Hand Motions Using Ultrasound Imaging of Forearm Muscles.

PubMed

Akhlaghi, Nima; Baker, Clayton A; Lahlou, Mohamed; Zafar, Hozaifah; Murthy, Karthik G; Rangwala, Huzefa S; Kosecka, Jana; Joiner, Wilsaan M; Pancrazio, Joseph J; Sikdar, Siddhartha

2016-08-01

Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle-computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle-computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.

Classification of Regional Radiographic Emphysematous Patterns Using Low-Attenuation Gap Length Matrix

NASA Astrophysics Data System (ADS)

Tan, Kok Liang; Tanaka, Toshiyuki; Nakamura, Hidetoshi; Shirahata, Toru; Sugiura, Hiroaki

The standard computer-tomography-based method for measuring emphysema uses percentage of area of low attenuation which is called the pixel index (PI). However, the PI method is susceptible to the problem of averaging effect and this causes the discrepancy between what the PI method describes and what radiologists observe. Knowing that visual recognition of the different types of regional radiographic emphysematous tissues in a CT image can be fuzzy, this paper proposes a low-attenuation gap length matrix (LAGLM) based algorithm for classifying the regional radiographic lung tissues into four emphysema types distinguishing, in particular, radiographic patterns that imply obvious or subtle bullous emphysema from those that imply diffuse emphysema or minor destruction of airway walls. Neural network is used for discrimination. The proposed LAGLM method is inspired by, but different from, former texture-based methods like gray level run length matrix (GLRLM) and gray level gap length matrix (GLGLM). The proposed algorithm is successfully validated by classifying 105 lung regions that are randomly selected from 270 images. The lung regions are hand-annotated by radiologists beforehand. The average four-class classification accuracies in the form of the proposed algorithm/PI/GLRLM/GLGLM methods are: 89.00%/82.97%/52.90%/51.36%, respectively. The p-values from the correlation analyses between the classification results of 270 images and pulmonary function test results are generally less than 0.01. The classification results are useful for a followup study especially for monitoring morphological changes with progression of pulmonary disease.

Joint sparse coding based spatial pyramid matching for classification of color medical image.

PubMed

Shi, Jun; Li, Yi; Zhu, Jie; Sun, Haojie; Cai, Yin

2015-04-01

Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid matching (ScSPM) and its variants are popular for grayscale image classification, but cannot extract color information. In this paper, we propose a joint sparse coding based SPM (JScSPM) method for the classification of color medical images. A joint dictionary can represent both the color information in each color channel and the correlation between channels. Consequently, the joint sparse codes calculated from a joint dictionary can carry color information, and therefore this method can easily transform a feature descriptor originally designed for grayscale images to a color descriptor. A color hepatocellular carcinoma histological image dataset was used to evaluate the performance of the proposed JScSPM algorithm. Experimental results show that JScSPM provides significant improvements as compared with the majority voting based ScSPM and the original ScSPM for color medical image classification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Creating a three level building classification using topographic and address-based data for Manchester

NASA Astrophysics Data System (ADS)

Hussain, M.; Chen, D.

2014-11-01

Buildings, the basic unit of an urban landscape, host most of its socio-economic activities and play an important role in the creation of urban land-use patterns. The spatial arrangement of different building types creates varied urban land-use clusters which can provide an insight to understand the relationships between social, economic, and living spaces. The classification of such urban clusters can help in policy-making and resource management. In many countries including the UK no national-level cadastral database containing information on individual building types exists in public domain. In this paper, we present a framework for inferring functional types of buildings based on the analysis of their form (e.g. geometrical properties, such as area and perimeter, layout) and spatial relationship from large topographic and address-based GIS database. Machine learning algorithms along with exploratory spatial analysis techniques are used to create the classification rules. The classification is extended to two further levels based on the functions (use) of buildings derived from address-based data. The developed methodology was applied to the Manchester metropolitan area using the Ordnance Survey's MasterMap®, a large-scale topographic and address-based data available for the UK.

Identification of autism spectrum disorder using deep learning and the ABIDE dataset.

PubMed

Heinsfeld, Anibal Sólon; Franco, Alexandre Rosa; Craddock, R Cameron; Buchweitz, Augusto; Meneguzzi, Felipe

2018-01-01

The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.

Iris indexing based on local intensity order pattern

NASA Astrophysics Data System (ADS)

Emerich, Simina; Malutan, Raul; Crisan, Septimiu; Lefkovits, Laszlo

2017-03-01

In recent years, iris biometric systems have increased in popularity and have been proven that are capable of handling large-scale databases. The main advantage of these systems is accuracy and reliability. A proper iris patterns classification is expected to reduce the matching time in huge databases. This paper presents an iris indexing technique based on Local Intensity Order Pattern. The performance of the present approach is evaluated on UPOL database and is compared with other recent systems designed for iris indexing. The results illustrate the potential of the proposed method for large scale iris identification.

Classification of circulation type sequences applied to snow avalanches over the eastern Pyrenees (Andorra and Catalonia)

NASA Astrophysics Data System (ADS)

Esteban, Pere; Beck, Christoph; Philipp, Andreas

2010-05-01

Using data associated with accidents or damages caused by snow avalanches over the eastern Pyrenees (Andorra and Catalonia) several atmospheric circulation type catalogues have been obtained. For this purpose, different circulation type classification methods based on Principal Component Analysis (T-mode and S-mode using the extreme scores) and on optimization procedures (Improved K-means and SANDRA) were applied . Considering the characteristics of the phenomena studied, not only single day circulation patterns were taken into account but also sequences of circulation types of varying length. Thus different classifications with different numbers of types and for different sequence lengths were obtained using the different classification methods. Simple between type variability, within type variability, and outlier detection procedures have been applied for selecting the best result concerning snow avalanches type classifications. Furthermore, days without occurrence of the hazards were also related to the avalanche centroids using pattern-correlations, facilitating the calculation of the anomalies between hazardous and no hazardous days, and also frequencies of occurrence of hazardous events for each circulation type. Finally, the catalogues statistically considered the best results are evaluated using the avalanche forecaster expert knowledge. Consistent explanation of snow avalanches occurrence by means of circulation sequences is obtained, but always considering results from classifications with different sequence length. This work has been developed in the framework of the COST Action 733 (Harmonisation and Applications of Weather Type Classifications for European regions).

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID:29209190

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

The distance function effect on k-nearest neighbor classification for medical datasets.

PubMed

Hu, Li-Yu; Huang, Min-Wei; Ke, Shih-Wen; Tsai, Chih-Fong

2016-01-01

K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually. The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets. In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

How should children with speech sound disorders be classified? A review and critical evaluation of current classification systems.

PubMed

Waring, R; Knight, R

2013-01-01

Children with speech sound disorders (SSD) form a heterogeneous group who differ in terms of the severity of their condition, underlying cause, speech errors, involvement of other aspects of the linguistic system and treatment response. To date there is no universal and agreed-upon classification system. Instead, a number of theoretically differing classification systems have been proposed based on either an aetiological (medical) approach, a descriptive-linguistic approach or a processing approach. To describe and review the supporting evidence, and to provide a critical evaluation of the current childhood SSD classification systems. Descriptions of the major specific approaches to classification are reviewed and research papers supporting the reliability and validity of the systems are evaluated. Three specific paediatric SSD classification systems; the aetiologic-based Speech Disorders Classification System, the descriptive-linguistic Differential Diagnosis system, and the processing-based Psycholinguistic Framework are identified as potentially useful in classifying children with SSD into homogeneous subgroups. The Differential Diagnosis system has a growing body of empirical support from clinical population studies, across language error pattern studies and treatment efficacy studies. The Speech Disorders Classification System is currently a research tool with eight proposed subgroups. The Psycholinguistic Framework is a potential bridge to linking cause and surface level speech errors. There is a need for a universally agreed-upon classification system that is useful to clinicians and researchers. The resulting classification system needs to be robust, reliable and valid. A universal classification system would allow for improved tailoring of treatments to subgroups of SSD which may, in turn, lead to improved treatment efficacy. © 2012 Royal College of Speech and Language Therapists.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

In the (sub)tropics allergic rhinitis and its impact on asthma classification of allergic rhinitis is more useful than perennial-seasonal classification.

PubMed

Larenas-Linnemann, Désirée; Michels, Alexandra; Dinger, Hanna; Arias-Cruz, Alfredo; Ambriz Moreno, Marichuy; Bedolla Barajas, Martin; Javier, Ruth Cerino; Cid Del Prado, Maria de la Luz; Cruz Moreno, Manuel Alejandro; Vergara, Laura Diego; García Almaráz, Roberto; García-Cobas, Cecilia Y; Garcia Imperial, Daniel Alberto; Muñoz, Rosa Garcia; Hernandez Colín, Dante; Linares Zapien, Francisco Javier; Luna Pech, Jorge Agustín; Matta Campos, Juan Jose; Martinez Jimenez, Norma; Avalos, Miguel Medina; Medina Hernandez, Alejandra; Maldonado, Albero Monteverde; López, Doris Nereida; Pizano Nazara, Luis Julian; Sanchez, Emanuel Ramirez; Ramos López, José Domingo; Rodriguez-Pérez, Noel; Rodriguez Ortiz, Pablo G; Shah-Hosseini, Kijawasch; Mösges, Ralph

2014-01-01

Two different allergic rhinitis (AR) symptom phenotype classifications exist. Treatment recommendations are based on intermittent-persistent (INT-PER) cataloging, but clinical trials still use the former seasonal AR-perennial AR (SAR-PAR) classification. This study was designed to describe how INT-PER, mild-moderate/severe and SAR-PAR of patients seen by allergists are distributed over the different climate zones in a (sub)tropical country and how these phenotypes relate to allergen sensitization patterns. Six climate zones throughout Mexico were determined, based on National Geographic Institute (Instituto Nacional de Estadística y Geografía) data. Subsequent AR patients (2-68 years old) underwent a blinded, standardized skin-prick test and filled out a validated questionnaire phenotyping AR. Five hundred twenty-nine subjects participated in this study. In the tropical zone with 87% house-dust mite sensitization, INT (80.9%; p < 0.001) and PAR (91%; p = 0.04) were more frequent than in the subtropics. In the central high-pollen areas, there was less moderate/severe AR (65.5%; p < 0.005). Frequency of comorbid asthma showed a clear north-south gradient, from 25% in the dry north to 59% in the tropics (p < 0.005). No differences exist in AR cataloging among patients with different sensitization patterns, with two minor exceptions (more PER in tree sensitized and more PAR in mold positives; p < 0.05). In a (sub)tropical country the SAR-PAR classification seems of limited value and bears poor relation with the INT-PER classification. INT is more frequent in the tropical zone. Because PER has been shown to relate to AR severity, clinical trials should select patients based on INT-PER combined with the severity cataloging because these make for a better treatment guide than SAR-PAR.

Novel pathomorphologic classification of capsulo-articular lesions of the pubic symphysis in athletes to predict treatment and outcome.

PubMed

Hopp, Sascha; Ojodu, Ishaq; Jain, Atul; Fritz, Tobias; Pohlemann, Tim; Kelm, Jens

2018-05-01

Radiographic abnormalities of the symphysis as well as the formation of accessory clefts, indicating injury at the rectus-adductor aponeurosis, reportedly relate to longstanding groin pain in athletes. However, yet, no systematic classification for clinical and scientific purposes exists. We aimed to (1) create a radiographic classification based on symphysography; (2) test intra- and interobserver reliability; (3) characterise clinical significance of the morphologic patterns by evaluating success of injection therapy. We retrospectively reviewed symphysography, AP radiographs, and MRI of the pelvis from 70 consecutive competitive athletes, with chronic groin pain. Symphysographs were evaluated for intra- and interobserver variance using cohen's kappa statistics. Morphologic studies of the different contrast distribution patterns and their clinical and radiological correlation with symptom relief were investigated. All patients were followed up to evaluate immediate and long-term response to the initial therapeutic injection with steroid. Four reproducible symphysographic patterns were identified: type 0, no changes; type 1, symphyseal disk degeneration; types 2a with unilateral clefts, bilateral clefts (2b), suprapubic clefts (2c); and type 3, with expanded or multidirectional clefts. Analysis revealed excellent intra (0.94)-and interobserver (0.90) reliability. Our findings showed that 78.6% of our patients had significant short-term improvement enabling early resumption of physiotherapy, only in types 1 and 2 (p = 0.001), while type 0 and 3 did not respond. At follow-up, only 21.8% had permanent pain relief. Regarding the detection of pathologic clefts with symphysography, sensitivity (88%) and specifity (77%) were superior to that of MRI. A reproducible symphysography-based classification of distinct morphologic patterns is proposed. It serves as a predictive tool for response to injection therapy in a select group of pathologic lesions. Complete recovery after injection can only be expected in a lesser percentage, as this might indicate surgical treatment for long-term non-responders.

Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

PubMed

Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

2014-06-01

To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Differentiating between bipolar and unipolar depression in functional and structural MRI studies.

PubMed

Han, Kyu-Man; De Berardis, Domenico; Fornaro, Michele; Kim, Yong-Ku

2018-03-28

Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification. Copyright © 2018 Elsevier Inc. All rights reserved.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Iris Image Classification Based on Hierarchical Visual Codebook.

PubMed

Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

2014-06-01

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

PubMed

Zhang, Jianhua; Yin, Zhong; Wang, Rubin

2017-01-01

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

PubMed Central

2017-01-01

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination. PMID:28085953

Validation of a selective ensemble-based classification scheme for myoelectric control using a three-dimensional Fitts' Law test.

PubMed

Scheme, Erik J; Englehart, Kevin B

2013-07-01

When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform other popular classification schemes when presented with muscle contractions that did not correspond to desired actions. In this work, a 3-D Fitts' Law test is proposed as a suitable alternative to using virtual limb environments for evaluating real-time myoelectric control performance. The test is used to compare the selective approach to a state-of-the-art linear discriminant analysis classification based scheme. The framework is shown to obey Fitts' Law for both control schemes, producing linear regression fittings with high coefficients of determination (R(2) > 0.936). Additional performance metrics focused on quality of control are discussed and incorporated in the evaluation. Using this framework the selective classification based scheme is shown to produce significantly higher efficiency and completion rates, and significantly lower overshoot and stopping distances, with no significant difference in throughput.

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification.

PubMed

Dai, Mengxi; Zheng, Dezhi; Liu, Shucong; Zhang, Pengju

2018-01-01

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

PubMed Central

Dai, Mengxi; Liu, Shucong; Zhang, Pengju

2018-01-01

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods. PMID:29743934

Early differential processing of material images: Evidence from ERP classification.

PubMed

Wiebel, Christiane B; Valsecchi, Matteo; Gegenfurtner, Karl R

2014-06-24

Investigating the temporal dynamics of natural image processing using event-related potentials (ERPs) has a long tradition in object recognition research. In a classical Go-NoGo task two characteristic effects have been emphasized: an early task independent category effect and a later task-dependent target effect. Here, we set out to use this well-established Go-NoGo paradigm to study the time course of material categorization. Material perception has gained more and more interest over the years as its importance in natural viewing conditions has been ignored for a long time. In addition to analyzing standard ERPs, we conducted a single trial ERP pattern analysis. To validate this procedure, we also measured ERPs in two object categories (people and animals). Our linear classification procedure was able to largely capture the overall pattern of results from the canonical analysis of the ERPs and even extend it. We replicate the known target effect (differential Go-NoGo potential at frontal sites) for the material images. Furthermore, we observe task-independent differential activity between the two material categories as early as 140 ms after stimulus onset. Using our linear classification approach, we show that material categories can be differentiated consistently based on the ERP pattern in single trials around 100 ms after stimulus onset, independent of the target-related status. This strengthens the idea of early differential visual processing of material categories independent of the task, probably due to differences in low-level image properties and suggests pattern classification of ERP topographies as a strong instrument for investigating electrophysiological brain activity. © 2014 ARVO.

RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

NASA Astrophysics Data System (ADS)

Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

2017-03-01

Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes.

PubMed

Das, Barnan; Cook, Diane J; Krishnan, Narayanan C; Schmitter-Edgecombe, Maureen

2016-08-01

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

Study on store-space assignment based on logistic AGV in e-commerce goods to person picking pattern

NASA Astrophysics Data System (ADS)

Xu, Lijuan; Zhu, Jie

2017-10-01

This paper studied on the store-space assignment based on logistic AGV in E-commerce goods to person picking pattern, and established the store-space assignment model based on the lowest picking cost, and design for store-space assignment algorithm after the cluster analysis based on similarity coefficient. And then through the example analysis, compared the picking cost between store-space assignment algorithm this paper design and according to item number and storage according to ABC classification allocation, and verified the effectiveness of the design of the store-space assignment algorithm.

Classification and Mapping of Agricultural Land for National Water-Quality Assessment

USGS Publications Warehouse

Gilliom, Robert J.; Thelin, Gail P.

1997-01-01

Agricultural land use is one of the most important influences on water quality at national and regional scales. Although there is great diversity in the character of agricultural land, variations follow regional patterns that are influenced by environmental setting and economics. These regional patterns can be characterized by the distribution of crops. A new approach to classifying and mapping agricultural land use for national water-quality assessment was developed by combining information on general land-use distribution with information on crop patterns from agricultural census data. Separate classification systems were developed for row crops and for orchards, vineyards, and nurseries. These two general categories of agricultural land are distinguished from each other in the land-use classification system used in the U.S. Geological Survey national Land Use and Land Cover database. Classification of cropland was based on the areal extent of crops harvested. The acreage of each crop in each county was divided by total row-crop area or total orchard, vineyard, and nursery area, as appropriate, thus normalizing the crop data and making the classification independent of total cropland area. The classification system was developed using simple percentage criteria to define combinations of 1 to 3 crops that account for 50 percent or more or harvested acreage in a county. The classification system consists of 21 level I categories and 46 level II subcategories for row crops, and 26 level I categories and 19 level II subcategories for orchards, vineyards, and nurseries. All counties in the United States with reported harvested acreage are classified in these categories. The distribution of agricultural land within each county, however, must be evaluated on the basis of general land-use data. This can be done at the national scale using 'Major Land Uses of the United States,' at the regional scale using data from the national Land Use and Land Cover database, or at smaller scales using locally available data.

A novel single neuron perceptron with universal approximation and XOR computation properties.

PubMed

Lotfi, Ehsan; Akbarzadeh-T, M-R

2014-01-01

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.

Handwritten digits recognition based on immune network

NASA Astrophysics Data System (ADS)

Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

2011-11-01

With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

PubMed

Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

2015-11-01

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers

NASA Technical Reports Server (NTRS)

Li, Winston; Leung, Henry; Kwan, Chiman; Linnell, Bruce R.

2005-01-01

Electronic nose (e-nose) vapor identification is an efficient approach to monitor air contaminants in space stations and shuttles in order to ensure the health and safety of astronauts. Data preprocessing (measurement denoising and feature extraction) and pattern classification are important components of an e-nose system. In this paper, a wavelet-based denoising method is applied to filter the noisy sensor measurements. Transient-state features are then extracted from the denoised sensor measurements, and are used to train multiple classifiers such as multi-layer perceptions (MLP), support vector machines (SVM), k nearest neighbor (KNN), and Parzen classifier. The Dempster-Shafer (DS) technique is used at the end to fuse the results of the multiple classifiers to get the final classification. Experimental analysis based on real vapor data shows that the wavelet denoising method can remove both random noise and outliers successfully, and the classification rate can be improved by using classifier fusion.

The Chicago classification of motility disorders: an update.

PubMed

Roman, Sabine; Gyawali, C Prakash; Xiao, Yinglian; Pandolfino, John E; Kahrilas, Peter J

2014-10-01

The Chicago Classification defines esophageal motility disorders in high resolution manometry. This is based on individual scoring of 10 swallows performed in supine position. Disorders of esophago-gastric junction (EGJ) outflow obstruction are defined by a median integrated relaxation pressure above the limit of normal and divided into 3 achalasia subtypes and EGJ outflow obstruction. Major motility disorders (aperistalsis, distal esophageal spasm, and hypercontractile esophagus) are patterns not encountered in controls in the context of normal EGJ relaxation. Finally with the latest version of the Chicago Classification, only two minor motor disorders are considered: ineffective esophageal motility and fragmented peristalsis. Copyright © 2014 Elsevier Inc. All rights reserved.

Integrated Remote Sensing Modalities for Classification at a Legacy Test Site

NASA Astrophysics Data System (ADS)

Lee, D. J.; Anderson, D.; Craven, J.

2016-12-01

Detecting, locating, and characterizing suspected underground nuclear test sites is of interest to the worldwide nonproliferation monitoring community. Remote sensing provides both cultural and surface geological information over a large search area in a non-intrusive manner. We have characterized a legacy nuclear test site at the Nevada National Security Site (NNSS) using an aerial system based on RGB imagery, light detection and ranging, and hyperspectral imaging. We integrate these different remote sensing modalities to perform pattern recognition and classification tasks on the test site. These tasks include detecting cultural artifacts and exotic materials. We evaluate if the integration of different remote sensing modalities improves classification performance.

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

2018-02-01

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

Tone classification of syllable-segmented Thai speech based on multilayer perception

NASA Astrophysics Data System (ADS)

Satravaha, Nuttavudh; Klinkhachorn, Powsiri; Lass, Norman

2002-05-01

Thai is a monosyllabic tonal language that uses tone to convey lexical information about the meaning of a syllable. Thus to completely recognize a spoken Thai syllable, a speech recognition system not only has to recognize a base syllable but also must correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system. Thai has five distinctive tones (``mid,'' ``low,'' ``falling,'' ``high,'' and ``rising'') and each tone is represented by a single fundamental frequency (F0) pattern. However, several factors, including tonal coarticulation, stress, intonation, and speaker variability, affect the F0 pattern of a syllable in continuous Thai speech. In this study, an efficient method for tone classification of syllable-segmented Thai speech, which incorporates the effects of tonal coarticulation, stress, and intonation, as well as a method to perform automatic syllable segmentation, were developed. Acoustic parameters were used as the main discriminating parameters. The F0 contour of a segmented syllable was normalized by using a z-score transformation before being presented to a tone classifier. The proposed system was evaluated on 920 test utterances spoken by 8 speakers. A recognition rate of 91.36% was achieved by the proposed system.

Patterns and correlates of co-occurrence among multiple types of child maltreatment

PubMed Central

Kim, Kihyun; Mennen, Ferol E.; Trickett, Penelope K.

2017-01-01

This study examined the patterns and correlates of the types of maltreatment experienced by adolescents aged 9–12, participating in an ongoing longitudinal study on the impact of neglect on children’s development. Using case record abstraction, the study compared the child protection classification and findings from the case record abstraction with regard to the rates of four types of maltreatment (i.e. physical, sexual, emotional abuse and neglect) as well as co-occurrence across multiple types of maltreatment. Next, the study examined the frequently observed patterns of child maltreatment. Finally, the study investigated whether aspects of caretaker functioning and the detailed incident characteristics in the cases of neglect differed by the number of different types of maltreatment the children experienced. Results showed significant discrepancies between the Child Protective Service classification and case record abstraction. Child Protective Service classification considerably underestimated the rate of co-occurrence across multiple types of maltreatment. Neglect accompanied by physical and emotional abuse was the most common form. Some of the caretaker functioning variables distinguished the number of types of maltreatment. Based on the findings, future-research directions and practice implication were discussed. PMID:29225485

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

Adaptive Classification of Landscape Process and Function: An Integration of Geoinformatics and Self-Organizing Maps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coleman, Andre M.

2009-07-17

The advanced geospatial information extraction and analysis capabilities of a Geographic Information System (GISs) and Artificial Neural Networks (ANNs), particularly Self-Organizing Maps (SOMs), provide a topology-preserving means for reducing and understanding complex data relationships in the landscape. The Adaptive Landscape Classification Procedure (ALCP) is presented as an adaptive and evolutionary capability where varying types of data can be assimilated to address different management needs such as hydrologic response, erosion potential, habitat structure, instrumentation placement, and various forecast or what-if scenarios. This paper defines how the evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight intomore » complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Establishing relationships among high-dimensional datasets through neurocomputing based pattern recognition methods can help 1) resolve large volumes of data into a structured and meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape. Classification of hydrologic patterns in the landscape is demonstrated.« less

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Patterns and Predictors of Sleep Quality Before, During, and After Hospitalization in Older Adults

PubMed Central

Dzierzewski, Joseph M.; Mitchell, Michael; Rodriguez, Juan Carlos; Fung, Constance H.; Jouldjian, Stella; Alessi, Cathy A.; Martin, Jennifer L.

2015-01-01

Study Objectives: The impact of hospitalization on sleep in late-life is underexplored. The current study examined patterns of sleep quality before, during, and following hospitalization, investigated predictors of sleep quality patterns, and examined predictors of classification discordance between two suggested clinical cutoffs used to demarcate poor/good sleep. Methods: This study included older adults (n = 163; mean age 79.7 ± 6.9 years, 31% female) undergoing inpatient post-acute rehabilitation. Upon admission to inpatient post-acute rehabilitation, patients completed the Pittsburgh Sleep Quality Index (PSQI) retrospectively regarding their sleep prior to hospitalization. They subsequently completed the PSQI at discharge, and 3 months, 6 months, 9 months, and 1 year post discharge. Patient demographic and clinical characteristics (pain, depression, cognition, comorbidity) were collected upon admission. Results: Using latent class analysis methods, older adults could be classified into (1) Consistently Good Sleepers and (2) Chronically Poor Sleepers based on patterns of self-reported sleep quality pre-illness, during, and up to 1 year following inpatient rehabilitation. This pattern was maintained regardless of the clinical cutoff employed (> 5 or > 8). Logistic regression analyses indicated that higher pain and depressive symptoms were consistently associated with an increased likelihood of being classified as a chronic poor sleeper. While there was substantial classification discordance based on clinical cutoff employed, no significant predictors of this discordance emerged. Conclusions: Clinicians should exercise caution in assessing sleep quality in inpatient settings. Alterations in the cutoffs employed may result in discordant clinical classifications of older adults. Pain and depression warrant detailed considerations when working with older adults on inpatient units when poor sleep is a concern. Citation: Dzierzewski JM, Mitchell M, Rodriguez JC, Fung CH, Jouldjian S, Alessi CA, Martin JL. Patterns and predictors of sleep quality before, during, and after hospitalization in older adults. J Clin Sleep Med 2015;11(1):45–51. PMID:25325580

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

PubMed

Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-05-30

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 Elsevier B.V. All rights reserved.

The construction of support vector machine classifier using the firefly algorithm.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

Using Gaussian mixture models to detect and classify dolphin whistles and pulses.

PubMed

Peso Parada, Pablo; Cardenal-López, Antonio

2014-06-01

In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.

Multilayer perceptron, fuzzy sets, and classification

NASA Technical Reports Server (NTRS)

Pal, Sankar K.; Mitra, Sushmita

1992-01-01

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data

PubMed Central

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-01-01

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification. PMID:28025525

HIV classification using the coalescent theory

PubMed Central

Bulla, Ingo; Schultz, Anne-Kathrin; Schreiber, Fabian; Zhang, Ming; Leitner, Thomas; Korber, Bette; Morgenstern, Burkhard; Stanke, Mario

2010-01-01

Motivation: Existing coalescent models and phylogenetic tools based on them are not designed for studying the genealogy of sequences like those of HIV, since in HIV recombinants with multiple cross-over points between the parental strains frequently arise. Hence, ambiguous cases in the classification of HIV sequences into subtypes and circulating recombinant forms (CRFs) have been treated with ad hoc methods in lack of tools based on a comprehensive coalescent model accounting for complex recombination patterns. Results: We developed the program ARGUS that scores classifications of sequences into subtypes and recombinant forms. It reconstructs ancestral recombination graphs (ARGs) that reflect the genealogy of the input sequences given a classification hypothesis. An ARG with maximal probability is approximated using a Markov chain Monte Carlo approach. ARGUS was able to distinguish the correct classification with a low error rate from plausible alternative classifications in simulation studies with realistic parameters. We applied our algorithm to decide between two recently debated alternatives in the classification of CRF02 of HIV-1 and find that CRF02 is indeed a recombinant of Subtypes A and G. Availability: ARGUS is implemented in C++ and the source code is available at http://gobics.de/software Contact: ibulla@uni-goettingen.de Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:20400454

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data.

PubMed

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-12-22

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.

Novel layered clustering-based approach for generating ensemble of classifiers.

PubMed

Rahman, Ashfaqur; Verma, Brijesh

2011-05-01

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

Classification of Farmland Landscape Structure in Multiple Scales

NASA Astrophysics Data System (ADS)

Jiang, P.; Cheng, Q.; Li, M.

2017-12-01

Farmland is one of the basic terrestrial resources that support the development and survival of human beings and thus plays a crucial role in the national security of every country. Pattern change is the intuitively spatial representation of the scale and quality variation of farmland. Through the characteristic development of spatial shapes as well as through changes in system structures, functions and so on, farmland landscape patterns may indicate the landscape health level. Currently, it is still difficult to perform positioning analyses of landscape pattern changes that reflect the landscape structure variations of farmland with an index model. Depending on a number of spatial properties such as locations and adjacency relations, distance decay, fringe effect, and on the model of patch-corridor-matrix that is applied, this study defines a type system of farmland landscape structure on the national, provincial, and city levels. According to such a definition, the classification model of farmland landscape-structure type at the pixel scale is developed and validated based on mathematical-morphology concepts and on spatial-analysis methods. Then, the laws that govern farmland landscape-pattern change in multiple scales are analyzed from the perspectives of spatial heterogeneity, spatio-temporal evolution, and function transformation. The result shows that the classification model of farmland landscape-structure type can reflect farmland landscape-pattern change and its effects on farmland production function. Moreover, farmland landscape change in different scales displayed significant disparity in zonality, both within specific regions and in urban-rural areas.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

PubMed

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

2016-09-01

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

NASA Technical Reports Server (NTRS)

Key, J.

1990-01-01

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

Some sequential, distribution-free pattern classification procedures with applications

NASA Technical Reports Server (NTRS)

Poage, J. L.

1971-01-01

Some sequential, distribution-free pattern classification techniques are presented. The decision problem to which the proposed classification methods are applied is that of discriminating between two kinds of electroencephalogram responses recorded from a human subject: spontaneous EEG and EEG driven by a stroboscopic light stimulus at the alpha frequency. The classification procedures proposed make use of the theory of order statistics. Estimates of the probabilities of misclassification are given. The procedures were tested on Gaussian samples and the EEG responses.

Spatial scale and distribution of neurovascular signals underlying decoding of orientation and eye of origin from fMRI data

PubMed Central

Harrison, Charlotte; Jackson, Jade; Oh, Seung-Mock; Zeringyte, Vaida

2016-01-01

Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1–V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1–V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas. NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex. PMID:27903637

Boosting CNN performance for lung texture classification using connected filtering

NASA Astrophysics Data System (ADS)

Tarando, Sebastián. Roberto; Fetita, Catalin; Kim, Young-Wouk; Cho, Hyoun; Brillet, Pierre-Yves

2018-02-01

Infiltrative lung diseases describe a large group of irreversible lung disorders requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. This paper presents an original image pre-processing framework based on locally connected filtering applied in multiresolution, which helps improving the learning process and boost the performance of CNN for lung texture classification. By removing the dense vascular network from images used by the CNN for lung classification, locally connected filters provide a better discrimination between different lung patterns and help regularizing the classification output. The approach was tested in a preliminary evaluation on a 10 patient database of various lung pathologies, showing an increase of 10% in true positive rate (on average for all the cases) with respect to the state of the art cascade of CNNs for this task.

An eye tracking study of bloodstain pattern analysts during pattern classification.

PubMed

Arthur, R M; Hoogenboom, J; Green, R D; Taylor, M C; de Bruin, K G

2018-05-01

Bloodstain pattern analysis (BPA) is the forensic discipline concerned with the classification and interpretation of bloodstains and bloodstain patterns at the crime scene. At present, it is unclear exactly which stain or pattern properties and their associated values are most relevant to analysts when classifying a bloodstain pattern. Eye tracking technology has been widely used to investigate human perception and cognition. Its application to forensics, however, is limited. This is the first study to use eye tracking as a tool for gaining access to the mindset of the bloodstain pattern expert. An eye tracking method was used to follow the gaze of 24 bloodstain pattern analysts during an assigned task of classifying a laboratory-generated test bloodstain pattern. With the aid of an automated image-processing methodology, the properties of selected features of the pattern were quantified leading to the delineation of areas of interest (AOIs). Eye tracking data were collected for each AOI and combined with verbal statements made by analysts after the classification task to determine the critical range of values for relevant diagnostic features. Eye-tracking data indicated that there were four main regions of the pattern that analysts were most interested in. Within each region, individual elements or groups of elements that exhibited features associated with directionality, size, colour and shape appeared to capture the most interest of analysts during the classification task. The study showed that the eye movements of trained bloodstain pattern experts and their verbal descriptions of a pattern were well correlated.

Phenomenology and classification of dystonia: a consensus update.

PubMed

Albanese, Alberto; Bhatia, Kailash; Bressman, Susan B; Delong, Mahlon R; Fahn, Stanley; Fung, Victor S C; Hallett, Mark; Jankovic, Joseph; Jinnah, Hyder A; Klein, Christine; Lang, Anthony E; Mink, Jonathan W; Teller, Jan K

2013-06-15

This report describes the consensus outcome of an international panel consisting of investigators with years of experience in this field that reviewed the definition and classification of dystonia. Agreement was obtained based on a consensus development methodology during 3 in-person meetings and manuscript review by mail. Dystonia is defined as a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive, movements, postures, or both. Dystonic movements are typically patterned and twisting, and may be tremulous. Dystonia is often initiated or worsened by voluntary action and associated with overflow muscle activation. Dystonia is classified along 2 axes: clinical characteristics, including age at onset, body distribution, temporal pattern and associated features (additional movement disorders or neurological features); and etiology, which includes nervous system pathology and inheritance. The clinical characteristics fall into several specific dystonia syndromes that help to guide diagnosis and treatment. We provide here a new general definition of dystonia and propose a new classification. We encourage clinicians and researchers to use these innovative definition and classification and test them in the clinical setting on a variety of patients with dystonia. © 2013 Movement Disorder Society. © 2013 Movement Disorder Society.

An assessment of the Jenkinson and Collison synoptic classification to a continental mid-latitude location

NASA Astrophysics Data System (ADS)

Spellman, Greg

2017-05-01

A weather-type catalogue based on the Jenkinson and Collison method was developed for an area in south-west Russia for the period 1961-2010. Gridded sea level pressure data was obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. The resulting catalogue was analysed for frequency of individual types and groups of weather types to characterise long-term atmospheric circulation in this region. Overall, the most frequent type is anticyclonic (A) (23.3 %) followed by cyclonic (C) (11.9 %); however, there are some key seasonal patterns with westerly circulation being significantly more common in winter than summer. The utility of this synoptic classification is evaluated by modelling daily rainfall amounts. A low level of error is found using a simple model based on the prevailing weather type. Finally, characteristics of the circulation classification are compared to those for the original JC British Isles catalogue and a much more equal distribution of flow types is seen in the former classification.

Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

PubMed Central

Javadi, Mehrdad; Ebrahimpour, Reza; Sajedin, Atena; Faridi, Soheil; Zakernejad, Shokoufeh

2011-01-01

This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization. PMID:22046232

Reflected scatterometry for noninvasive interrogation of bacterial colonies

USDA-ARS?s Scientific Manuscript database

A phenotyping of bacterial colonies on agar plates using forward-scattering diffraction-pattern analysis provided promising classification of several different bacteria such as Salmonella, Vibrio, Listeria, and E. coli. Since the technique is based on forward-scattering phenomena, light transmittanc...

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification

PubMed Central

Zhao, Yuwei; Han, Jiuqi; Chen, Yushu; Sun, Hongji; Chen, Jiayun; Ke, Ang; Han, Yao; Zhang, Peng; Zhang, Yi; Zhou, Jin; Wang, Changyong

2018-01-01

Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB) with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP) methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems. PMID:29867307

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

Cloud-scale genomic signals processing classification analysis for gene expression microarray data.

PubMed

Harvey, Benjamin; Soo-Yeon Ji

2014-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring inference though analysis of DNA/mRNA sequence data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological inference by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale classification analysis of microarray data using Wavelet thresholding in a Cloud environment to identify significantly expressed features. This paper proposes a novel methodology that uses Wavelet based Denoising to initialize a threshold for determination of significantly expressed genes for classification. Additionally, this research was implemented and encompassed within cloud-based distributed processing environment. The utilization of Cloud computing and Wavelet thresholding was used for the classification 14 tumor classes from the Global Cancer Map (GCM). The results proved to be more accurate than using a predefined p-value for differential expression classification. This novel methodology analyzed Wavelet based threshold features of gene expression in a Cloud environment, furthermore classifying the expression of samples by analyzing gene patterns, which inform us of biological processes. Moreover, enabling researchers to face the present and forthcoming challenges that may arise in the analysis of data in functional genomics of large microarray datasets.

Maximum likelihood estimation of label imperfections and its use in the identification of mislabeled patterns

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of estimating label imperfections and the use of the estimation in identifying mislabeled patterns is presented. Expressions for the maximum likelihood estimates of classification errors and a priori probabilities are derived from the classification of a set of labeled patterns. Expressions also are given for the asymptotic variances of probability of correct classification and proportions. Simple models are developed for imperfections in the labels and for classification errors and are used in the formulation of a maximum likelihood estimation scheme. Schemes are presented for the identification of mislabeled patterns in terms of threshold on the discriminant functions for both two-class and multiclass cases. Expressions are derived for the probability that the imperfect label identification scheme will result in a wrong decision and are used in computing thresholds. The results of practical applications of these techniques in the processing of remotely sensed multispectral data are presented.

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Classification of topographical pattern of spasticity in cerebral palsy: a registry perspective.

PubMed

Reid, Susan M; Carlin, John B; Reddihough, Dinah S

2011-01-01

This study used data from a population-based cerebral palsy (CP) registry and systematic review to assess the amount of heterogeneity between registries in topographical patterns when dichotomised into unilateral (USCP) and bilateral spastic CP (BSCP), and whether the terms diplegia and quadriplegia provide useful additional epidemiological information. From the Victorian CP Register, 2956 individuals (1658 males, 1298 females), born 1970-2003, with spastic CP were identified. The proportions with each topographical pattern were analysed overall and by gestational age. Binary logistic regression analysis was used to assess temporal trends. For the review, data were systematically collected on topographical patterns from 27 registries. Estimates of heterogeneity were obtained, overall and by region, reporting period and definition of quadriplegia. Among individuals born <32 weeks, 48% had diplegia, whereas the proportion for children born ≥ 32 weeks was 24% (p < 0.001). Evidence was weak for a temporal trend in the relative proportions of USCP and BSCP (p = 0.038), but much clearer for an increase in the proportion of spastic diplegia relative to quadriplegia (p < 0.001). The review revealed wide variations across studies in the proportion of diplegia (range 34-90%) and BSCP (range 51-86%). These findings argue against a topographical classification based solely on laterality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Deep learning application: rubbish classification with aid of an android device

NASA Astrophysics Data System (ADS)

Liu, Sijiang; Jiang, Bo; Zhan, Jie

2017-06-01

Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don't know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish's classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

Deep learning based classification of morphological patterns in RCM to guide noninvasive diagnosis of melanocytic lesions (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kose, Kivanc; Bozkurt, Alican; Ariafar, Setareh; Alessi-Fox, Christi A.; Gill, Melissa; Dy, Jennifer G.; Brooks, Dana H.; Rajadhyaksha, Milind

2017-02-01

In this study we present a deep learning based classification algorithm for discriminating morphological patterns that appear in RCM mosaics of melanocytic lesions collected at the dermal epidermal junction (DEJ). These patterns are classified into 6 distinct types in the literature: background, meshwork, ring, clod, mixed, and aspecific. Clinicians typically identify these morphological patterns by examination of their textural appearance at 10X magnification. To mimic this process we divided mosaics into smaller regions, which we call tiles, and classify each tile in a deep learning framework. We used previously acquired DEJ mosaics of lesions deemed clinically suspicious, from 20 different patients, which were then labelled according to those 6 types by 2 expert users. We tried three different approaches for classification, all starting with a publicly available convolutional neural network (CNN) trained on natural image, consisting of a series of convolutional layers followed by a series of fully connected layers: (1) We fine-tuned this network using training data from the dataset. (2) Instead, we added an additional fully connected layer before the output layer network and then re-trained only last two layers, (3) We used only the CNN convolutional layers as a feature extractor, encoded the features using a bag of words model, and trained a support vector machine (SVM) classifier. Sensitivity and specificity were generally comparable across the three methods, and in the same ranges as our previous work using SURF features with SVM . Approach (3) was less computationally intensive to train but more sensitive to unbalanced representation of the 6 classes in the training data. However we expect CNN performance to improve as we add more training data because both the features and the classifier are learned jointly from the data. *First two authors share first authorship.

Multi-voxel pattern classification differentiates personally experienced event memories from secondhand event knowledge.

PubMed

Chow, Tiffany E; Westphal, Andrew J; Rissman, Jesse

2018-04-11

Studies of autobiographical memory retrieval often use photographs to probe participants' memories for past events. Recent neuroimaging work has shown that viewing photographs depicting events from one's own life evokes a characteristic pattern of brain activity across a network of frontal, parietal, and medial temporal lobe regions that can be readily distinguished from brain activity associated with viewing photographs from someone else's life (Rissman, Chow, Reggente, and Wagner, 2016). However, it is unclear whether the neural signatures associated with remembering a personally experienced event are distinct from those associated with recognizing previously encountered photographs of an event. The present experiment used a novel functional magnetic resonance imaging (fMRI) paradigm to investigate putative differences in brain activity patterns associated with these distinct expressions of memory retrieval. Eighteen participants wore necklace-mounted digital cameras to capture events from their everyday lives over the course of three weeks. One week later, participants underwent fMRI scanning, where on each trial they viewed a sequence of photographs depicting either an event from their own life or from another participant's life and judged their memory for this event. Importantly, half of the trials featured photographic sequences that had been shown to participants during a laboratory session administered the previous day. Multi-voxel pattern analyses assessed the sensitivity of two brain networks of interest-as identified by a meta-analysis of prior autobiographical and laboratory-based memory retrieval studies-to the original source of the photographs (own life or other's life) and their experiential history as stimuli (previewed or non-previewed). The classification analyses revealed a striking dissociation: activity patterns within the autobiographical memory network were significantly more diagnostic than those within the laboratory-based network as to whether photographs depicted one's own personal experience (regardless of whether they had been previously seen), whereas activity patterns within the laboratory-based memory network were significantly more diagnostic than those within the autobiographical memory network as to whether photographs had been previewed (regardless of whether they were from the participant's own life). These results, also apparent in whole-brain searchlight classifications, provide evidence for dissociable patterns of activation across two putative memory networks as a function of whether real-world photographs trigger the retrieval of firsthand experiences or secondhand event knowledge. Copyright © 2018 Elsevier Inc. All rights reserved.

New developments in demographic targeting--the implications of 1991.

PubMed

Humby, C R

1989-01-01

This paper examines benefits that systems such as ACORN, a demographic marketing system that classifies neighborhoods, offer today and monitors some of the trends. It then considers the impact of the 1992 UK census and gives a view of what marketeers can expect in the next 5 years. Neighborhood classifications represent a summary of the consumption patterns of a set of neighbors. If we could reach individuals based on the current life stage the gains to be had would be as great again as that offered by the neighborhood classifications themselves. The greatest weakness of all the neighborhood-based systems is their inability to target at life stage or age.

A novel approach to describing and detecting performance anti-patterns

NASA Astrophysics Data System (ADS)

Sheng, Jinfang; Wang, Yihan; Hu, Peipei; Wang, Bin

2017-08-01

Anti-pattern, as an extension to pattern, describes a widely used poor solution which can bring negative influence to application systems. Aiming at the shortcomings of the existing anti-pattern descriptions, an anti-pattern description method based on first order predicate is proposed. This method synthesizes anti-pattern forms and symptoms, which makes the description more accurate and has good scalability and versatility as well. In order to improve the accuracy of anti-pattern detection, a Bayesian classification method is applied in validation for detection results, which can reduce false negatives and false positives of anti-pattern detection. Finally, the proposed approach in this paper is applied to a small e-commerce system, the feasibility and effectiveness of the approach is demonstrated further through experiments.

What are the most fire-dangerous atmospheric circulations in the Eastern-Mediterranean? Analysis of the synoptic wildfire climatology.

PubMed

Paschalidou, A K; Kassomenos, P A

2016-01-01

Wildfire management is closely linked to robust forecasts of changes in wildfire risk related to meteorological conditions. This link can be bridged either through fire weather indices or through statistical techniques that directly relate atmospheric patterns to wildfire activity. In the present work the COST-733 classification schemes are applied in order to link wildfires in Greece with synoptic circulation patterns. The analysis reveals that the majority of wildfire events can be explained by a small number of specific synoptic circulations, hence reflecting the synoptic climatology of wildfires. All 8 classification schemes used, prove that the most fire-dangerous conditions in Greece are characterized by a combination of high atmospheric pressure systems located N to NW of Greece, coupled with lower pressures located over the very Eastern part of the Mediterranean, an atmospheric pressure pattern closely linked to the local Etesian winds over the Aegean Sea. During these events, the atmospheric pressure has been reported to be anomalously high, while anomalously low 500hPa geopotential heights and negative total water column anomalies were also observed. Among the various classification schemes used, the 2 Principal Component Analysis-based classifications, namely the PCT and the PXE, as well as the Leader Algorithm classification LND proved to be the best options, in terms of being capable to isolate the vast amount of fire events in a small number of classes with increased frequency of occurrence. It is estimated that these 3 schemes, in combination with medium-range to seasonal climate forecasts, could be used by wildfire risk managers to provide increased wildfire prediction accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

NASA Astrophysics Data System (ADS)

Alexander, Cici; Deák, Balázs; Kania, Adam; Mücke, Werner; Heilmeier, Hermann

2015-09-01

Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels - Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) - based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen's kappa coefficient, κ). The accuracies at Levels 2-4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy (κ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data.

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

NASA Astrophysics Data System (ADS)

Nitze, Ingmar; Barrett, Brian; Cawkwell, Fiona

2015-02-01

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8-10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.

Atmospheric circulation classification comparison based on wildfires in Portugal

NASA Astrophysics Data System (ADS)

Pereira, M. G.; Trigo, R. M.

2009-04-01

Atmospheric circulation classifications are not a simple description of atmospheric states but a tool to understand and interpret the atmospheric processes and to model the relation between atmospheric circulation and surface climate and other related variables (Radan Huth et al., 2008). Classifications were initially developed with weather forecasting purposes, however with the progress in computer processing capability, new and more robust objective methods were developed and applied to large datasets prompting atmospheric circulation classification methods to one of the most important fields in synoptic and statistical climatology. Classification studies have been extensively used in climate change studies (e.g. reconstructed past climates, recent observed changes and future climates), in bioclimatological research (e.g. relating human mortality to climatic factors) and in a wide variety of synoptic climatological applications (e.g. comparison between datasets, air pollution, snow avalanches, wine quality, fish captures and forest fires). Likewise, atmospheric circulation classifications are important for the study of the role of weather in wildfire occurrence in Portugal because the daily synoptic variability is the most important driver of local weather conditions (Pereira et al., 2005). In particular, the objective classification scheme developed by Trigo and DaCamara (2000) to classify the atmospheric circulation affecting Portugal have proved to be quite useful in discriminating the occurrence and development of wildfires as well as the distribution over Portugal of surface climatic variables with impact in wildfire activity such as maximum and minimum temperature and precipitation. This work aims to present: (i) an overview the existing circulation classification for the Iberian Peninsula, and (ii) the results of a comparison study between these atmospheric circulation classifications based on its relation with wildfires and relevant meteorological variables. To achieve these objectives we consider the main classifications for Iberia developed within the framework of COST action 733 (Radan Huth et al., 2008). This European project aims to provide a wide range of atmospheric circulation classifications for Europe and sub-regions (http://www.cost733.org/) with an ambitious objective of assessing, comparing and classifying all relevant weather situations in Europe. Pereira et al. (2005) "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology,129, 11-25. Radan Huth et al. (2008) "Classifications of Atmospheric circulation patterns. Recent advances and applications". Trends and Directions in Climate Research: Ann. N.Y. Acad. Sci. 1146: 105-152. doi: 10.1196/annals.1446.019. Trigo R.M., DaCamara C. (2000) "Circulation Weather Types and their impact on the precipitation regime in Portugal". Int J of Climatology, 20, 1559-1581.

Combining feature extraction and classification for fNIRS BCIs by regularized least squares optimization.

PubMed

Heger, Dominic; Herff, Christian; Schultz, Tanja

2014-01-01

In this paper, we show that multiple operations of the typical pattern recognition chain of an fNIRS-based BCI, including feature extraction and classification, can be unified by solving a convex optimization problem. We formulate a regularized least squares problem that learns a single affine transformation of raw HbO(2) and HbR signals. We show that this transformation can achieve competitive results in an fNIRS BCI classification task, as it significantly improves recognition of different levels of workload over previously published results on a publicly available n-back data set. Furthermore, we visualize the learned models and analyze their spatio-temporal characteristics.

A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG.

PubMed

Roy, Rinku; Sikdar, Debdeep; Mahadevappa, Manjunatha; Kumar, C S

2018-05-19

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with "one against one" multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.

A multivariate pattern analysis study of the HIV-related white matter anatomical structural connections alterations

NASA Astrophysics Data System (ADS)

Tang, Zhenchao; Liu, Zhenyu; Li, Ruili; Cui, Xinwei; Li, Hongjun; Dong, Enqing; Tian, Jie

2017-03-01

It's widely known that HIV infection would cause white matter integrity impairments. Nevertheless, it is still unclear that how the white matter anatomical structural connections are affected by HIV infection. In the current study, we employed a multivariate pattern analysis to explore the HIV-related white matter connections alterations. Forty antiretroviraltherapy- naïve HIV patients and thirty healthy controls were enrolled. Firstly, an Automatic Anatomical Label (AAL) atlas based white matter structural network, a 90 × 90 FA-weighted matrix, was constructed for each subject. Then, the white matter connections deprived from the structural network were entered into a lasso-logistic regression model to perform HIV-control group classification. Using leave one out cross validation, a classification accuracy (ACC) of 90% (P=0.002) and areas under the receiver operating characteristic curve (AUC) of 0.96 was obtained by the classification model. This result indicated that the white matter anatomical structural connections contributed greatly to HIV-control group classification, providing solid evidence that the white matter connections were affected by HIV infection. Specially, 11 white matter connections were selected in the classification model, mainly crossing the regions of frontal lobe, Cingulum, Hippocampus, and Thalamus, which were reported to be damaged in previous HIV studies. This might suggest that the white matter connections adjacent to the HIV-related impaired regions were prone to be damaged.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

PubMed

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

An evaluation of object-oriented image analysis techniques to identify motorized vehicle effects in semi-arid to arid ecosystems of the American West

USGS Publications Warehouse

Mladinich, C.

2010-01-01

Human disturbance is a leading ecosystem stressor. Human-induced modifications include transportation networks, areal disturbances due to resource extraction, and recreation activities. High-resolution imagery and object-oriented classification rather than pixel-based techniques have successfully identified roads, buildings, and other anthropogenic features. Three commercial, automated feature-extraction software packages (Visual Learning Systems' Feature Analyst, ENVI Feature Extraction, and Definiens Developer) were evaluated by comparing their ability to effectively detect the disturbed surface patterns from motorized vehicle traffic. Each package achieved overall accuracies in the 70% range, demonstrating the potential to map the surface patterns. The Definiens classification was more consistent and statistically valid. Copyright ?? 2010 by Bellwether Publishing, Ltd. All rights reserved.

Threat Based Risk Assessment for Enterprise Networks

DTIC Science & Technology

2016-02-15

served as the program chair of the Research in Attacks, Intrusions , and Defenses workshop; the Neural Information Processing Systems (NIPS) annual...Threat- Based Risk Assessment for Enterprise Networks Richard P. Lippmann and James F. Riordan Protecting enterprise networks requires...include aids for the hearing impaired, speech recognition, pattern classification, neural networks , and cybersecurity. He has taught three courses

SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

PubMed

Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

2017-01-01

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis.

PubMed

Fu, Chen; Zhang, Nevin Lianwen; Chen, Bao-Xin; Chen, Zhou Rong; Jin, Xiang Lan; Guo, Rong-Juan; Chen, Zhi-Gang; Zhang, Yun-Ling

2017-05-01

To treat patients with vascular mild cognitive impairment (VMCI) using traditional Chinese medicine (TCM), it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis (LTA). A cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA was performed on the data to reveal symptom co-occurrence patterns, and the patients were partitioned into clusters in multiple ways based on the patterns. The patient clusters were matched up with syndrome types, and population statistics of the clusters are used to quantify the syndrome types and to establish classification rules. Eight syndrome types are identified: Qi deficiency, Qi stagnation, Blood deficiency, Blood stasis, Phlegm-dampness, Fire-heat, Yang deficiency, and Yin deficiency. The prevalence and symptom occurrence characteristics of each syndrome type are determined. Quantitative classification rules are established for determining whether a patient belongs to each of the syndrome types. A solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA of unlabeled symptom survey data. The results can be used as a reference in clinic practice to improve the quality of syndrome differentiation and to reduce diagnosis variances across physicians. They can also be used for patient selection in research projects aimed at finding biomarkers for the syndrome types and in randomized control trials aimed at determining the efficacy of TCM treatments of VMCI.

Biogeographic classification of the Caspian Sea

NASA Astrophysics Data System (ADS)

Fendereski, F.; Vogt, M.; Payne, M. R.; Lachkar, Z.; Gruber, N.; Salmanmahiny, A.; Hosseini, S. A.

2014-11-01

Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the hierarchical agglomerative clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow nearshore waters from those offshore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (one-way ANOVA, P < 0.05). In particular, we found differences in phytoplankton phenology, with differences in the date of bloom initiation, its duration and amplitude between ecoregions. A first qualitative evaluation of differences in community composition based on recorded presence-absence patterns of 25 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

PubMed

Kumar, Shiu; Mamun, Kabir; Sharma, Alok

2017-12-01

Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bio-geographic classification of the Caspian Sea

NASA Astrophysics Data System (ADS)

Fendereski, F.; Vogt, M.; Payne, M. R.; Lachkar, Z.; Gruber, N.; Salmanmahiny, A.; Hosseini, S. A.

2014-03-01

Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in-situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the Hierarchical Agglomerative Clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow near-shore waters from those off-shore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (Kruskal-Wallis rank test, P < 0.05). In particular, we found differences in phytoplankton phenology, with differences in the date of bloom initiation, its duration and amplitude between ecoregions. A first qualitative evaluation of differences in community composition based on recorded presence-absence patterns of 27 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

General method of pattern classification using the two-domain theory

NASA Technical Reports Server (NTRS)

Rorvig, Mark E. (Inventor)

1993-01-01

Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.

General method of pattern classification using the two-domain theory

NASA Technical Reports Server (NTRS)

Rorvig, Mark E. (Inventor)

1990-01-01

Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data

PubMed Central

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2013-01-01

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. PMID:25309815

An analysis of the synoptic and climatological applicability of circulation type classifications for Ireland

NASA Astrophysics Data System (ADS)

Broderick, Ciaran; Fealy, Rowan

2013-04-01

Circulation type classifications (CTCs) compiled as part of the COST733 Action, entitled 'Harmonisation and Application of Weather Type Classifications for European Regions', are examined for their synoptic and climatological applicability to Ireland based on their ability to characterise surface temperature and precipitation. In all 16 different objective classification schemes, representative of four different methodological approaches to circulation typing (optimization algorithms, threshold based methods, eigenvector techniques and leader algorithms) are considered. Several statistical metrics which variously quantify the ability of CTCs to discretize daily data into well-defined homogeneous groups are used to evaluate and compare different approaches to synoptic typing. The records from 14 meteorological stations located across the island of Ireland are used in the study. The results indicate that while it was not possible to identify a single optimum classification or approach to circulation typing - conditional on the location and surface variables considered - a number of general assertions regarding the performance of different schemes can be made. The findings for surface temperature indicate that that those classifications based on predefined thresholds (e.g. Litynski, GrossWetterTypes and original Lamb Weather Type) perform well, as do the Kruizinga and Lund classification schemes. Similarly for precipitation predefined type classifications return high skill scores, as do those classifications derived using some optimization procedure (e.g. SANDRA, Self Organizing Maps and K-Means clustering). For both temperature and precipitation the results generally indicate that the classifications perform best for the winter season - reflecting the closer coupling between large-scale circulation and surface conditions during this period. In contrast to the findings for temperature, spatial patterns in the performance of classifications were more evident for precipitation. In the case of this variable those more westerly synoptic stations open to zonal airflow and less influenced by regional scale forcings generally exhibited a stronger link with large-scale circulation.

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

Protein subcellular location pattern classification in cellular images using latent discriminative models.

PubMed

Li, Jieyue; Xiong, Liang; Schneider, Jeff; Murphy, Robert F

2012-06-15

Knowledge of the subcellular location of a protein is crucial for understanding its functions. The subcellular pattern of a protein is typically represented as the set of cellular components in which it is located, and an important task is to determine this set from microscope images. In this article, we address this classification problem using confocal immunofluorescence images from the Human Protein Atlas (HPA) project. The HPA contains images of cells stained for many proteins; each is also stained for three reference components, but there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of the stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. This region-based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient-based methods to maximize the data likelihood. In the experiments, we show that the proposed models help improve the classification accuracies on synthetic data and real cellular images. The best overall accuracy we report in this article for classifying 942 proteins into 13 classes of patterns is about 84.6%, which to our knowledge is the best so far. In addition, the dependencies learned are consistent with prior knowledge of cell organization. http://murphylab.web.cmu.edu/software/.

Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

A study of morphology, provenance, and movement of desert sand seas in Africa, Asia, and Australia

NASA Technical Reports Server (NTRS)

Mckee, E. D.; Breed, C. S.

1973-01-01

A description and classification of major types of sand seas on the basis of morphological pattern and lineation are discussed. The steps involved in analyzing the patterns of deposits on ERTS-1 imagery, where the visible forms are mostly dune complexes rather than individual dunes are outlined. After completion of thematic maps portraying the pattern and lineation of the sand bodies, data on directions and intensity of prevailing and other winds are plotted on corresponding bases, as a preliminary to determination of internal structures through ground truth.

The road to 11th edition of the International Classification of Diseases: trajectories of scientific consensus and contested science in the classification of intellectual disability/intellectual developmental disorders.

PubMed

Salvador-Carulla, Luis; Bertelli, Marco; Martinez-Leal, Rafael

2018-03-01

To increase the expert knowledge-base on intellectual developmental disorders (IDDs) by investigating the typology trajectories of consensus formation in the classification systems up to the 11th edition of the International Classification of Diseases (ICD-11). This expert review combines an analysis of key recent literature and the revision of the consensus formation and contestation in the expert committees contributing to the classification systems since the 1950s. Historically two main approaches have contributed to the development of this knowledge-base: a neurodevelopmental-clinical approach and a psychoeducational-social approach. These approaches show a complex interaction throughout the history of IDD and have had a diverse influence on its classification. Although in theory Diagnostic and Statistical Manual (DSM)-5 and ICD adhere to the neurodevelopmental-clinical model, the new definition in the ICD-11 follows a restrictive normality approach to intellectual quotient and to the measurement of adaptive behaviour. On the contrary DSM-5 is closer to the recommendations made by the WHO 'Working Group on Mental Retardation' for ICD-11 for an integrative approach. A cyclical pattern of consensus formation has been identified in IDD. The revision of the three major classification systems in the last decade has increased the terminological and conceptual variability and the overall scientific contestation on IDD.

Similarity-Dissimilarity Competition in Disjunctive Classification Tasks

PubMed Central

Mathy, Fabien; Haladjian, Harry H.; Laurent, Eric; Goldstone, Robert L.

2013-01-01

Typical disjunctive artificial classification tasks require participants to sort stimuli according to rules such as “x likes cars only when black and coupe OR white and SUV.” For categories like this, increasing the salience of the diagnostic dimensions has two simultaneous effects: increasing the distance between members of the same category and increasing the distance between members of opposite categories. Potentially, these two effects respectively hinder and facilitate classification learning, leading to competing predictions for learning. Increasing saliency may lead to members of the same category to be considered less similar, while the members of separate categories might be considered more dissimilar. This implies a similarity-dissimilarity competition between two basic classification processes. When focusing on sub-category similarity, one would expect more difficult classification when members of the same category become less similar (disregarding the increase of between-category dissimilarity); however, the between-category dissimilarity increase predicts a less difficult classification. Our categorization study suggests that participants rely more on using dissimilarities between opposite categories than finding similarities between sub-categories. We connect our results to rule- and exemplar-based classification models. The pattern of influences of within- and between-category similarities are challenging for simple single-process categorization systems based on rules or exemplars. Instead, our results suggest that either these processes should be integrated in a hybrid model, or that category learning operates by forming clusters within each category. PMID:23403979

The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

NASA Technical Reports Server (NTRS)

Hogan, Christine A.

1996-01-01

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation composition was noted in the change detection image.

A Multi-Modal Face Recognition Method Using Complete Local Derivative Patterns and Depth Maps

PubMed Central

Yin, Shouyi; Dai, Xu; Ouyang, Peng; Liu, Leibo; Wei, Shaojun

2014-01-01

In this paper, we propose a multi-modal 2D + 3D face recognition method for a smart city application based on a Wireless Sensor Network (WSN) and various kinds of sensors. Depth maps are exploited for the 3D face representation. As for feature extraction, we propose a new feature called Complete Local Derivative Pattern (CLDP). It adopts the idea of layering and has four layers. In the whole system, we apply CLDP separately on Gabor features extracted from a 2D image and depth map. Then, we obtain two features: CLDP-Gabor and CLDP-Depth. The two features weighted by the corresponding coefficients are combined together in the decision level to compute the total classification distance. At last, the probe face is assigned the identity with the smallest classification distance. Extensive experiments are conducted on three different databases. The results demonstrate the robustness and superiority of the new approach. The experimental results also prove that the proposed multi-modal 2D + 3D method is superior to other multi-modal ones and CLDP performs better than other Local Binary Pattern (LBP) based features. PMID:25333290

Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

NASA Astrophysics Data System (ADS)

Srimani, P. K.; Parimala, Y. G.

2011-12-01

A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

PubMed

Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

Proposal for a new classification of variations in the iliac venous system based on internal iliac veins: a case series and a review of double and left inferior vena cava.

PubMed

Hayashi, Shogo; Naito, Munekazu; Hirai, Shuichi; Terayama, Hayato; Miyaki, Takayoshi; Itoh, Masahiro; Fukuzawa, Yoshitaka; Nakano, Takashi

2013-09-01

There are many reports on variations in the inferior vena cava (IVC), particularly double IVC (DIVC) and left IVC (LIVC). However, no systematic report has recorded iliac vein (IV) flow patterns in the DIVC and LIVC. In this study, we examined IV flow patterns in both DIVC and LIVC observed during gross anatomy courses conducted for medical students and in previously reported cases. During the gross anatomy courses, three cases of DIVC and one case of LIVC were found in 618 cadavers. The IV flow pattern from these four cases and all other previously reported cases can be classified into one of the following three types according to the vein into which the internal iliac vein drained: the ipsilateral external IV; confluence of the ipsilateral external IV and IVC; and the communicating vein, which connects the IVC and the contralateral IVC or its iliac branch. This classification, which is based on the internal IV course, is considered to be useful because IV variations have the potential to cause clinical problems during related retroperitoneal surgery, venous interventional radiology, and diagnostic procedures for pelvic cancer.

Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.

PubMed

Liu, Wenbo; Li, Ming; Yi, Li

2016-08-01

The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888-898. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

2018-02-01

In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.

Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

PubMed Central

Hussain, Shaista; Basu, Arindam

2016-01-01

The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike classifiers. We show that our system can achieve classification accuracy within 1 − 2% of other reported spike-based classifiers while using much less synaptic resources (only 7%) compared to that used by other methods. Further, an ensemble classifier created with adaptively learned sizes can attain accuracy of 96.4% which is at par with the best reported performance of spike-based classifiers. Moreover, the proposed method achieves this by using about 20% of the synapses used by other spike algorithms. We also present results of applying our algorithm to classify the MNIST-DVS dataset collected from a real spike-based image sensor and show results comparable to the best reported ones (88.1% accuracy). For VLSI implementations, we show that the reduced synaptic memory can save upto 4X area compared to conventional crossbar topologies. Finally, we also present a biologically realistic spike-based version for calculating the correlations required by the structural learning rule and demonstrate the correspondence between the rate-based and spike-based methods of learning. PMID:27065782

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Pipeline Processing with an Iterative, Context-based Detection Model

DTIC Science & Technology

2014-04-19

stripping the incoming data stream of repeating and irrelevant signals prior to running primary detectors , adaptive beamforming and matched field processing...framework, pattern detectors , correlation detectors , subspace detectors , matched field detectors , nuclear explosion monitoring 16. SECURITY CLASSIFICATION...10 5. Teleseismic paths from earthquakes in

Discriminative illumination: per-pixel classification of raw materials based on optimal projections of spectral BRDF.

PubMed

Liu, Chao; Gu, Jinwei

2014-01-01

Classifying raw, unpainted materials--metal, plastic, ceramic, fabric, and so on--is an important yet challenging task for computer vision. Previous works measure subsets of surface spectral reflectance as features for classification. However, acquiring the full spectral reflectance is time consuming and error-prone. In this paper, we propose to use coded illumination to directly measure discriminative features for material classification. Optimal illumination patterns--which we call "discriminative illumination"--are learned from training samples, after projecting to which the spectral reflectance of different materials are maximally separated. This projection is automatically realized by the integration of incident light for surface reflection. While a single discriminative illumination is capable of linear, two-class classification, we show that multiple discriminative illuminations can be used for nonlinear and multiclass classification. We also show theoretically that the proposed method has higher signal-to-noise ratio than previous methods due to light multiplexing. Finally, we construct an LED-based multispectral dome and use the discriminative illumination method for classifying a variety of raw materials, including metal (aluminum, alloy, steel, stainless steel, brass, and copper), plastic, ceramic, fabric, and wood. Experimental results demonstrate its effectiveness.

Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

PubMed

Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

2016-09-01

Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Auditory “bubbles”: Efficient classification of the spectrotemporal modulations essential for speech intelligibility

PubMed Central

Venezia, Jonathan H.; Hickok, Gregory; Richards, Virginia M.

2016-01-01

Speech intelligibility depends on the integrity of spectrotemporal patterns in the signal. The current study is concerned with the speech modulation power spectrum (MPS), which is a two-dimensional representation of energy at different combinations of temporal and spectral (i.e., spectrotemporal) modulation rates. A psychophysical procedure was developed to identify the regions of the MPS that contribute to successful reception of auditory sentences. The procedure, based on the two-dimensional image classification technique known as “bubbles” (Gosselin and Schyns (2001). Vision Res. 41, 2261–2271), involves filtering (i.e., degrading) the speech signal by removing parts of the MPS at random, and relating filter patterns to observer performance (keywords identified) over a number of trials. The result is a classification image (CImg) or “perceptual map” that emphasizes regions of the MPS essential for speech intelligibility. This procedure was tested using normal-rate and 2×-time-compressed sentences. The results indicated: (a) CImgs could be reliably estimated in individual listeners in relatively few trials, (b) CImgs tracked changes in spectrotemporal modulation energy induced by time compression, though not completely, indicating that “perceptual maps” deviated from physical stimulus energy, and (c) the bubbles method captured variance in intelligibility not reflected in a common modulation-based intelligibility metric (spectrotemporal modulation index or STMI). PMID:27586738

Component analysis of somatosensory evoked potentials for identifying spinal cord injury location.

PubMed

Wang, Yazhou; Li, Guangsheng; Luk, Keith D K; Hu, Yong

2017-05-24

This study aims to determine whether the time-frequency components (TFCs) of somatosensory evoked potentials (SEPs) can be used to identify the specific location of a compressive spinal cord injury using a classification technique. Waveforms of SEPs after compressive injuries at various locations (C4, C5 and C6) in rat spinal cords were decomposed into a series of TFCs using a high-resolution time-frequency analysis method. A classification method based on support vector machine (SVM) was applied to the distributions of these TFCs among different pathological locations. The difference among injury locations manifests itself in different categories of SEP TFCs. High-energy TFCs of normal-state SEPs have significantly higher power and frequency than those of injury-state SEPs. The location of C5 is characterized by a unique distribution pattern of middle-energy TFCs. The difference between C4 and C6 is evidenced by the distribution pattern of low-energy TFCs. The proposed classification method based on SEP TFCs offers a discrimination accuracy of 80.2%. In this study, meaningful information contained in various SEP components was investigated and used to propose a new application of SEPs for identification of the location of pathological changes in the cervical spinal cord.

Noninvasive forward-scattering system for rapid detection, characterization, and identification of Listeria colonies: image processing and data analysis

NASA Astrophysics Data System (ADS)

Rajwa, Bartek; Bayraktar, Bulent; Banada, Padmapriya P.; Huff, Karleigh; Bae, Euiwon; Hirleman, E. Daniel; Bhunia, Arun K.; Robinson, J. Paul

2006-10-01

Bacterial contamination by Listeria monocytogenes puts the public at risk and is also costly for the food-processing industry. Traditional methods for pathogen identification require complicated sample preparation for reliable results. Previously, we have reported development of a noninvasive optical forward-scattering system for rapid identification of Listeria colonies grown on solid surfaces. The presented system included application of computer-vision and patternrecognition techniques to classify scatter pattern formed by bacterial colonies irradiated with laser light. This report shows an extension of the proposed method. A new scatterometer equipped with a high-resolution CCD chip and application of two additional sets of image features for classification allow for higher accuracy and lower error rates. Features based on Zernike moments are supplemented by Tchebichef moments, and Haralick texture descriptors in the new version of the algorithm. Fisher's criterion has been used for feature selection to decrease the training time of machine learning systems. An algorithm based on support vector machines was used for classification of patterns. Low error rates determined by cross-validation, reproducibility of the measurements, and robustness of the system prove that the proposed technology can be implemented in automated devices for detection and classification of pathogenic bacteria.

Citizen science land cover classification based on ground and satellite imagery: Case study Day River in Vietnam

NASA Astrophysics Data System (ADS)

Nguyen, Son Tung; Minkman, Ellen; Rutten, Martine

2016-04-01

Citizen science is being increasingly used in the context of environmental research, thus there are needs to evaluate cognitive ability of humans in classifying environmental features. With the focus on land cover, this study explores the extent to which citizen science can be applied in sensing and measuring the environment that contribute to the creation and validation of land cover data. The Day Basin in Vietnam was selected to be the study area. Different methods to examine humans' ability to classify land cover were implemented using different information sources: ground based photos - satellite images - field observation and investigation. Most of the participants were solicited from local people and/or volunteers. Results show that across methods and sources of information, there are similar patterns of agreement and disagreement on land cover classes among participants. Understanding these patterns is critical to create a solid basis for implementing human sensors in earth observation. Keywords: Land cover, classification, citizen science, Landsat 8

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

NASA Astrophysics Data System (ADS)

Assaleh, Khaled; Al-Rousan, M.

2005-12-01

Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

KIPSE1: A Knowledge-based Interactive Problem Solving Environment for data estimation and pattern classification

NASA Technical Reports Server (NTRS)

Han, Chia Yung; Wan, Liqun; Wee, William G.

1990-01-01

A knowledge-based interactive problem solving environment called KIPSE1 is presented. The KIPSE1 is a system built on a commercial expert system shell, the KEE system. This environment gives user capability to carry out exploratory data analysis and pattern classification tasks. A good solution often consists of a sequence of steps with a set of methods used at each step. In KIPSE1, solution is represented in the form of a decision tree and each node of the solution tree represents a partial solution to the problem. Many methodologies are provided at each node to the user such that the user can interactively select the method and data sets to test and subsequently examine the results. Otherwise, users are allowed to make decisions at various stages of problem solving to subdivide the problem into smaller subproblems such that a large problem can be handled and a better solution can be found.

A wavelet-based approach for a continuous analysis of phonovibrograms.

PubMed

Unger, Jakob; Meyer, Tobias; Doellinger, Michael; Hecker, Dietmar J; Schick, Bernhard; Lohscheller, Joerg

2012-01-01

Recently, endoscopic high-speed laryngoscopy has been established for commercial use and constitutes a state-of-the-art technique to examine vocal fold dynamics. Despite overcoming many limitations of commonly applied stroboscopy it has not gained widespread clinical application, yet. A major drawback is a missing methodology of extracting valuable features to support visual assessment or computer-aided diagnosis. In this paper a compact and descriptive feature set is presented. The feature extraction routines are based on two-dimensional color graphs called phonovibrograms (PVG). These graphs contain the full spatio-temporal pattern of vocal fold dynamics and are therefore suited to derive features that comprehensively describe the vibration pattern of vocal folds. Within our approach, clinically relevant features such as glottal closure type, symmetry and periodicity are quantified in a set of 10 descriptive features. The suitability for classification tasks is shown using a clinical data set comprising 50 healthy and 50 paralytic subjects. A classification accuracy of 93.2% has been achieved.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

PubMed

Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t -test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

PubMed

Ji, Jiadong; He, Di; Feng, Yang; He, Yong; Xue, Fuzhong; Xie, Lei

2017-10-01

A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. R scripts available at https://github.com/jijiadong/JDINAC. lxie@iscb.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

The PLATINO study: description of the distribution, stability, and mortality according to the Global Initiative for Chronic Obstructive Lung Disease classification from 2007 to 2017.

PubMed

Menezes, Ana M; Wehrmeister, Fernando C; Perez-Padilla, Rogelio; Viana, Karynna P; Soares, Claudia; Müllerova, Hana; Valdivia, Gonzalo; Jardim, José R; Montes de Oca, Maria

2017-01-01

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) report provides a framework for classifying COPD reflecting the impacts of disease on patients and for targeting treatment recommendations. The GOLD 2017 introduced a new classification with 16 subgroups based on a composite of spirometry and symptoms/exacerbations. Data from the population-based PLATINO study, collected at baseline and at follow-up, in three sites in Latin America were analyzed to compare the following: 1) the distribution of COPD patients according to GOLD 2007, 2013, and 2017; 2) the stability of the 2007 and 2013 classifications; and 3) the mortality rate over time stratified by GOLD 2007, 2013, and 2017. Of the 524 COPD patients evaluated, most of them were classified as Grade I or II (GOLD 2007) and Group A or B (GOLD 2013), with ≈70% of those classified as Group A in GOLD 2013 also classified as Grade I in GOLD 2007 and the highest percentage (41%) in Group D (2013) classified as Grade III (2007). According to GOLD 2017, among patients with Grade I airflow limitation, 69% of them were categorized into Group A, whereas Grade IV patients were more evenly distributed among Groups A-D. Most of the patients classified by GOLD 2007 remained in the same airflow limitation group at the follow-up; a greater temporal variability was observed with GOLD 2013 classification. Incidence-mortality rate in patients classified by GOLD 2007 was positively associated with increasing severity of airflow obstruction; for GOLD 2013 and GOLD 2017 (Groups A-D), highest mortality rates were observed in Groups C and D. No clear pattern was observed for mortality across the GOLD 2017 subgroups. The PLATINO study data suggest that GOLD 2007 classification shows more stability over time compared with GOLD 2013. No clear patterns with respect to the distribution of patients or incidence-mortality rates were observed according to GOLD 2013/2017 classification.

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

PubMed

Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

2016-01-01

Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

PubMed Central

Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation. PMID:26681933

On Utilizing Optimal and Information Theoretic Syntactic Modeling for Peptide Classification

NASA Astrophysics Data System (ADS)

Aygün, Eser; Oommen, B. John; Cataltepe, Zehra

Syntactic methods in pattern recognition have been used extensively in bioinformatics, and in particular, in the analysis of gene and protein expressions, and in the recognition and classification of bio-sequences. These methods are almost universally distance-based. This paper concerns the use of an Optimal and Information Theoretic (OIT) probabilistic model [11] to achieve peptide classification using the information residing in their syntactic representations. The latter has traditionally been achieved using the edit distances required in the respective peptide comparisons. We advocate that one can model the differences between compared strings as a mutation model consisting of random Substitutions, Insertions and Deletions (SID) obeying the OIT model. Thus, in this paper, we show that the probability measure obtained from the OIT model can be perceived as a sequence similarity metric, using which a Support Vector Machine (SVM)-based peptide classifier, referred to as OIT_SVM, can be devised.

Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

PubMed

Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

2006-01-01

In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

Study on a pattern classification method of soil quality based on simplified learning sample dataset

USGS Publications Warehouse

Zhang, Jiahua; Liu, S.; Hu, Y.; Tian, Y.

2011-01-01

Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

PubMed Central

Ren, Xianwen; Wang, Yong; Zhang, Xiang-Sun; Jin, Qi

2013-01-01

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles. PMID:23761440

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

PubMed

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Comparing the predictive value of the pelvic ring injury classification systems by Tile and by Young and Burgess.

PubMed

Osterhoff, Georg; Scheyerer, Max J; Fritz, Yannick; Bouaicha, Samy; Wanner, Guido A; Simmen, Hans-Peter; Werner, Clément M L

2014-04-01

Radiology-based classifications of pelvic ring injuries and their relevance for the prognosis of morbidity and mortality are disputed in the literature. The purpose of this study was to evaluate potential differences between the pelvic ring injury classification systems by Tile and by Young and Burgess with regard to their predictive value on mortality, transfusion/infusion requirement and concomitant injuries. Two-hundred-and-eighty-five consecutive patients with pelvic ring fractures were analyzed for mortality within 30 days after admission, number of blood units and total volume of fluid infused during the first 24h after trauma, the Abbreviated Injury Severity (AIS) scores for head, chest, spine, abdomen and extremities as a function of the Tile and the Young-Burgess classifications. There was no significant relationship between occurrence of death and fracture pattern but a significant relationship between fracture pattern and need for blood units/total fluid volume for Tile (p<.001/p<.001) and Young-Burgess (p<.001/p<.001). In both classifications, open book fractures were associated with more fluid requirement and more severe injuries of the abdomen, spine and extremities (p<.05). When divided into the larger subgroups "partially stable" and "unstable", unstable fractures were associated with a higher mortality rate in the Young-Burgess system (p=.036). In both classifications, patients with unstable fractures required significantly more blood transfusions (p<.001) and total fluid infusion (p<.001) and higher AIS scores. In this first direct comparison of both classifications, we found no clinical relevant differences with regard to their predictive value on mortality, transfusion/infusion requirement and concomitant injuries. Copyright © 2013 Elsevier Ltd. All rights reserved.

An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

NASA Astrophysics Data System (ADS)

Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

2017-03-01

Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.

Optical classification for quality and defect analysis of train brakes

NASA Astrophysics Data System (ADS)

Glock, Stefan; Hausmann, Stefan; Gerke, Sebastian; Warok, Alexander; Spiess, Peter; Witte, Stefan; Lohweg, Volker

2009-06-01

In this paper we present an optical measurement system approach for quality analysis of brakes which are used in high-speed trains. The brakes consist of the so called brake discs and pads. In a deceleration process the discs will be heated up to 500°C. The quality measure is based on the fact that the heated brake discs should not generate hot spots inside the brake material. Instead, the brake disc should be heated homogeneously by the deceleration. Therefore, it makes sense to analyze the number of hot spots and their relative gradients to create a quality measure for train brakes. In this contribution we present a new approach for a quality measurement system which is based on an image analysis and classification of infra-red based heat images. Brake images which are represented in pseudo-color are first transformed in a linear grayscale space by a hue-saturation-intensity (HSI) space. This transform is necessary for the following gradient analysis which is based on gray scale gradient filters. Furthermore, different features based on Haralick's measures are generated from the gray scale and gradient images. A following Fuzzy-Pattern-Classifier is used for the classification of good and bad brakes. It has to be pointed out that the classifier returns a score value for each brake which is between 0 and 100% good quality. This fact guarantees that not only good and bad bakes can be distinguished, but also their quality can be labeled. The results show that all critical thermal patterns of train brakes can be sensed and verified.

Computer discrimination procedures applicable to aerial and ERTS multispectral data

NASA Technical Reports Server (NTRS)

Richardson, A. J.; Torline, R. J.; Allen, W. A.

1970-01-01

Two statistical models are compared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

NASA Astrophysics Data System (ADS)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.

Nonlinear channel equalization for QAM signal constellation using artificial neural networks.

PubMed

Patra, J C; Pal, R N; Baliarsingh, R; Panda, G

1999-01-01

Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.

Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

PubMed

Becker, A S; Blüthgen, C; Phi van, V D; Sekaggya-Wiltshire, C; Castelnuovo, B; Kambugu, A; Fehr, J; Frauenfelder, T

2018-03-01

To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients. In this prospective, observational study, patients with previously diagnosed TB were enrolled. Photographs of their CXRs were taken using a consumer-grade digital still camera. The images were stratified by pathological patterns into classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal examination. Image analysis was performed with commercially available Deep Learning software in two steps. Pathological areas were first localised; detected areas were then classified. Detection was assessed using receiver operating characteristics (ROC) analysis, and classification using a confusion matrix. The study cohort was 138 patients with human immunodeficiency virus (HIV) and TB co-infection (median age 34 years, IQR 28-40); 54 patients were female. Localisation of pathological areas was excellent (area under the ROC curve 0.82). The software could perfectly distinguish pleural effusions from intraparenchymal changes. The most frequent misclassifications were consolidations as cavitations, and miliary patterns as interstitial patterns (and vice versa). Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.

Neural attractor network for application in visual field data classification.

PubMed

Fink, Wolfgang

2004-07-07

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Evaluation of modified crystal violet chromoendoscopy procedure using new mucosal pit pattern classification for detection of Barrett's dysplastic lesions.

PubMed

Yuki, T; Amano, Y; Kushiyama, Y; Takahashi, Y; Ose, T; Moriyama, I; Fukuhara, H; Ishimura, N; Koshino, K; Furuta, K; Ishihara, S; Adachi, K; Kinoshita, Y

2006-05-01

Pit pattern diagnosis is important for endoscopic detection of dysplastic Barrett's lesions, though using magnification endoscopy can be difficult and laborious. We investigated the usefulness of a modified crystal violet chromoendoscopy procedure and utilised a new pit pattern classification for diagnosis of dysplastic Barrett's lesions. A total of 1,030 patients suspected of having a columnar lined oesophagus were examined, of whom 816 demonstrated a crystal violet-stained columnar lined oesophagus. The early group of patients underwent 0.05% crystal violet chromoendoscopy, while the later group was examined using 0.03% crystal violet with 3.0% acetate. A targeted biopsy of the columnar lined oesophagus was performed using crystal violet staining after making a diagnosis of closed or open type pit pattern with a newly proposed system of classification. The relationship between type of pit pattern and histologically identified dysplastic Barrett's lesions was evaluated. Dysplastic Barrett's lesions were identified in biopsy samples with an open type pit pattern with a sensitivity of 96.0%. Further, Barrett's mucosa with the intestinal predominant mucin phenotype was closely associated with the open type pit pattern (sensitivity 81.9%, specificity 95.6%). The new pit pattern classification for diagnosis of Barrett's mucosa was found to be useful for identification of cases with dysplastic lesions and possible malignant potential using a crystal violet chromoendoscopic procedure.

New classification of geometric ventricular patterns in severe aortic stenosis: Could it be clinically useful?

PubMed

Di Nora, Concetta; Cervesato, Eugenio; Cosei, Iulian; Ravasel, Andreea; Popescu, Bogdan A; Zito, Concetta; Carerj, Scipione; Antonini-Canterin, Francesco; Popescu, Andreea C

2018-04-16

In severe aortic stenosis, different left ventricle (LV) remodeling patterns as a response to pressure overload have distinct hemodynamic profiles, cardiac function, and outcomes. The most common classification considers LV relative wall thickness and LV mass index to create 4 different groups. A new classification including also end-diastolic volume index has been recently proposed. To describe the prevalence of the newly identified remodeling patterns in patients with severe aortic stenosis and to evaluate their clinical relevance according to symptoms. We analyzed 286 consecutive patients with isolated severe aortic stenosis. Current guidelines were used for echocardiographic evaluation. Symptoms were defined as the presence of angina, syncope, or NYHA class III-IV. The mean age was 75 ± 9 years, 156 patients (54%) were men, while 158 (55%) were symptomatic. According to the new classification, the most frequent remodeling pattern was concentric hypertrophy (57.3%), followed by mixed (18.9%) and dilated hypertrophy (8.4%). There were no patients with eccentric remodeling; only 4 patients had a normalLV geometry. Symptomatic patients showed significantly more mixed hypertrophy (P < .05), while the difference regarding the prevalence of the other patterns was not statistically significant. When we analyzed the distribution of the classic 4 patterns stratified by the presence of symptoms, however, we did not find a significant difference (P = .157). The new classification had refined the description of different cardiac geometric phenotypes that develop as a response to pressure overload. It might be superior to the classic 4 patterns in terms of association with symptoms. © 2018 Wiley Periodicals, Inc.

Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

NASA Astrophysics Data System (ADS)

Zamora Ramos, Ernesto

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures, multilayer percepterons and convolutional neural networks. Our research with neural networks has encountered a great deal of difficulties regarding hyperparameter estimation for good training convergence rate and accuracy. Most hyperparameters, including architecture, learning rate, regularization, trainable parameters (or weights) initialization, and so on, are chosen via a trial and error process with some educated guesses. However, we developed the first quantitative method to compare weight initialization strategies, a critical hyperparameter choice during training, to estimate among a group of candidate strategies which would make the network converge to the highest classification accuracy faster with high probability. Our method provides a quick, objective measure to compare initialization strategies to select the best possible among them beforehand without having to complete multiple training sessions for each candidate strategy to compare final results.

Targeting an efficient target-to-target interval for P300 speller brain–computer interfaces

PubMed Central

Sellers, Eric W.; Wang, Xingyu

2013-01-01

Longer target-to-target intervals (TTI) produce greater P300 event-related potential amplitude, which can increase brain–computer interface (BCI) classification accuracy and decrease the number of flashes needed for accurate character classification. However, longer TTIs requires more time for each trial, which will decrease the information transfer rate of BCI. In this paper, a P300 BCI using a 7 × 12 matrix explored new flash patterns (16-, 18- and 21-flash pattern) with different TTIs to assess the effects of TTI on P300 BCI performance. The new flash patterns were designed to minimize TTI, decrease repetition blindness, and examine the temporal relationship between each flash of a given stimulus by placing a minimum of one (16-flash pattern), two (18-flash pattern), or three (21-flash pattern) non-target flashes between each target flashes. Online results showed that the 16-flash pattern yielded the lowest classification accuracy among the three patterns. The results also showed that the 18-flash pattern provides a significantly higher information transfer rate (ITR) than the 21-flash pattern; both patterns provide high ITR and high accuracy for all subjects. PMID:22350331

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

NASA Technical Reports Server (NTRS)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

Classification of skin cancer images using local binary pattern and SVM classifier

NASA Astrophysics Data System (ADS)

Adjed, Faouzi; Faye, Ibrahima; Ababsa, Fakhreddine; Gardezi, Syed Jamal; Dass, Sarat Chandra

2016-11-01

In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1% with sensitivity of 75.6% and specificity of 76.7%.

Local binary pattern texture-based classification of solid masses in ultrasound breast images

NASA Astrophysics Data System (ADS)

Matsumoto, Monica M. S.; Sehgal, Chandra M.; Udupa, Jayaram K.

2012-03-01

Breast cancer is one of the leading causes of cancer mortality among women. Ultrasound examination can be used to assess breast masses, complementarily to mammography. Ultrasound images reveal tissue information in its echoic patterns. Therefore, pattern recognition techniques can facilitate classification of lesions and thereby reduce the number of unnecessary biopsies. Our hypothesis was that image texture features on the boundary of a lesion and its vicinity can be used to classify masses. We have used intensity-independent and rotation-invariant texture features, known as Local Binary Patterns (LBP). The classifier selected was K-nearest neighbors. Our breast ultrasound image database consisted of 100 patient images (50 benign and 50 malignant cases). The determination of whether the mass was benign or malignant was done through biopsy and pathology assessment. The training set consisted of sixty images, randomly chosen from the database of 100 patients. The testing set consisted of forty images to be classified. The results with a multi-fold cross validation of 100 iterations produced a robust evaluation. The highest performance was observed for feature LBP with 24 symmetrically distributed neighbors over a circle of radius 3 (LBP24,3) with an accuracy rate of 81.0%. We also investigated an approach with a score of malignancy assigned to the images in the test set. This approach provided an ROC curve with Az of 0.803. The analysis of texture features over the boundary of solid masses showed promise for malignancy classification in ultrasound breast images.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

PubMed

de Pierrefeu, Amicie; Fovet, Thomas; Hadj-Selem, Fouad; Löfstedt, Tommy; Ciuciu, Philippe; Lefebvre, Stephanie; Thomas, Pierre; Lopes, Renaud; Jardri, Renaud; Duchesnay, Edouard

2018-04-01

Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback. © 2018 Wiley Periodicals, Inc.

An optimized ERP brain-computer interface based on facial expression changes.

PubMed

Jin, Jing; Daly, Ian; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej

2014-06-01

Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern. Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures. The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05). The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.

An optimized ERP brain-computer interface based on facial expression changes

NASA Astrophysics Data System (ADS)

Jin, Jing; Daly, Ian; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej

2014-06-01

Objective. Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern. Approach. Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures. Main results. The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05). Significance. The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.

Application of support vector machines for copper potential mapping in Kerman region, Iran

NASA Astrophysics Data System (ADS)

Shabankareh, Mahdi; Hezarkhani, Ardeshir

2017-04-01

The first step in systematic exploration studies is mineral potential mapping, which involves classification of the study area to favorable and unfavorable parts. Support vector machines (SVM) are designed for supervised classification based on statistical learning theory. This method named support vector classification (SVC). This paper describes SVC model, which combine exploration data in the regional-scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps were in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration and geochemistry. The SVC modeling result selected 2220 pixels as favorable zones, approximately 25 percent of the study area. Besides, 66 out of 86 copper indices, approximately 78.6% of all, were located in favorable zones. Other main goal of this study was to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data to its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer had a certain pattern. These patterns of SVC results could be considered as regional copper exploration characteristics.

Climate and landscape explain richness patterns depending on the type of species' distribution data

NASA Astrophysics Data System (ADS)

Tsianou, Mariana A.; Koutsias, Nikolaos; Mazaris, Antonios D.; Kallimanis, Athanasios S.

2016-07-01

Understanding the patterns of species richness and their environmental drivers, remains a central theme in ecological research and especially in the continental scales where many conservation decisions are made. Here, we analyzed the patterns of species richness from amphibians, reptiles and mammals at the EU level. We used two different data sources for each taxon: expert-drawn species range maps, and presence/absence atlases. As environmental drivers, we considered climate and land cover. Land cover is increasingly the focus of research, but there still is no consensus on how to classify land cover to distinct habitat classes, so we analyzed the CORINE land cover data with three different levels of thematic resolution (resolution of classification scheme ˗ less to more detailed). We found that the two types of species richness data explored in this study yielded different richness maps. Although, we expected expert-drawn range based estimates of species richness to exceed those from atlas data (due to the assumption that species are present in all locations throughout their region), we found that in many cases the opposite is true (the extreme case is the reptiles where more than half of the atlas based estimates were greater than the expert-drawn range based estimates). Also, we detected contrasting information on the richness drivers of biodiversity patterns depending on the dataset used. For atlas based richness estimates, landscape attributes played more important role than climate while for expert-drawn range based richness estimates climatic variables were more important (for the ectothermic amphibians and reptiles). Finally we found that the thematic resolution of the land cover classification scheme, also played a role in quantifying the effect of land cover diversity, with more detailed thematic resolution increasing the relative contribution of landscape attributes in predicting species richness.

Ecoregions as a level of ecological analysis

USGS Publications Warehouse

Wright, R.G.; Murray, M.P.; Merrill, T.

1998-01-01

There have been many attempts to classify geographic areas into zones of similar characteristics. Recent focus has been on ecoregions. We examined how well the boundaries of the most commonly used ecoregion classifications for the US matched the boundaries of existing vegetation cover mapped at three levels of classification, fine, mid- and coarse scale. We analyzed ecoregions in Idaho, Oregon and Washington. The results were similar among the two ecoregion classifications. For both ecoregion delineations and all three vegetation classifications, the patterns of existing vegetation did not correspond well with the patterns of ecoregions. Most vegetation types had a small proportion of their total area in a given ecoregion. There was also no dominance by one or more vegetation types in any ecoregion and contrary to our hypothesis, the level of congruence of vegetation patterns with ecoregion boundaries decreased as the level of classification became more general. The implications of these findings on the use of ecoregions as a planning tool and in the development of land conservation efforts are discussed.

CIFAR10-DVS: An Event-Stream Dataset for Object Classification

PubMed Central

Li, Hongmin; Liu, Hanchao; Ji, Xiangyang; Li, Guoqi; Shi, Luping

2017-01-01

Neuromorphic vision research requires high-quality and appropriately challenging event-stream datasets to support continuous improvement of algorithms and methods. However, creating event-stream datasets is a time-consuming task, which needs to be recorded using the neuromorphic cameras. Currently, there are limited event-stream datasets available. In this work, by utilizing the popular computer vision dataset CIFAR-10, we converted 10,000 frame-based images into 10,000 event streams using a dynamic vision sensor (DVS), providing an event-stream dataset of intermediate difficulty in 10 different classes, named as “CIFAR10-DVS.” The conversion of event-stream dataset was implemented by a repeated closed-loop smooth (RCLS) movement of frame-based images. Unlike the conversion of frame-based images by moving the camera, the image movement is more realistic in respect of its practical applications. The repeated closed-loop image movement generates rich local intensity changes in continuous time which are quantized by each pixel of the DVS camera to generate events. Furthermore, a performance benchmark in event-driven object classification is provided based on state-of-the-art classification algorithms. This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification. PMID:28611582

CIFAR10-DVS: An Event-Stream Dataset for Object Classification.

PubMed

Li, Hongmin; Liu, Hanchao; Ji, Xiangyang; Li, Guoqi; Shi, Luping

2017-01-01

Neuromorphic vision research requires high-quality and appropriately challenging event-stream datasets to support continuous improvement of algorithms and methods. However, creating event-stream datasets is a time-consuming task, which needs to be recorded using the neuromorphic cameras. Currently, there are limited event-stream datasets available. In this work, by utilizing the popular computer vision dataset CIFAR-10, we converted 10,000 frame-based images into 10,000 event streams using a dynamic vision sensor (DVS), providing an event-stream dataset of intermediate difficulty in 10 different classes, named as "CIFAR10-DVS." The conversion of event-stream dataset was implemented by a repeated closed-loop smooth (RCLS) movement of frame-based images. Unlike the conversion of frame-based images by moving the camera, the image movement is more realistic in respect of its practical applications. The repeated closed-loop image movement generates rich local intensity changes in continuous time which are quantized by each pixel of the DVS camera to generate events. Furthermore, a performance benchmark in event-driven object classification is provided based on state-of-the-art classification algorithms. This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification.

A conceptual weather-type classification procedure for the Philadelphia, Pennsylvania, area

USGS Publications Warehouse

McCabe, Gregory J.

1990-01-01

A simple method of weather-type classification, based on a conceptual model of pressure systems that pass through the Philadelphia, Pennsylvania, area, has been developed. The only inputs required for the procedure are daily mean wind direction and cloud cover, which are used to index the relative position of pressure systems and fronts to Philadelphia.Daily mean wind-direction and cloud-cover data recorded at Philadelphia, Pennsylvania, from January 1954 through August 1988 were used to categorize daily weather conditions. The conceptual weather types reflect changes in daily air and dew-point temperatures, and changes in monthly mean temperature and monthly and annual precipitation. The weather-type classification produced by using the conceptual model was similar to a classification produced by using a multivariate statistical classification procedure. Even though the conceptual weather types are derived from a small amount of data, they appear to account for the variability of daily weather patterns sufficiently to describe distinct weather conditions for use in environmental analyses of weather-sensitive processes.

Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

PubMed Central

Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

2014-01-01

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples. PMID:24688361

Geographical classification of apple based on hyperspectral imaging

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Zhao, Chunjiang; Peng, Yankun

2013-05-01

Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

PubMed

Kebschull, Moritz; Papapanou, Panos N

2017-01-01

Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional patterns, beyond the mere identification of lists of features that differ between groups.Here, we demonstrate the utility of (1) supervised classification algorithms in class validation, and (2) unsupervised clustering in class discovery. We use data from our previous work that described the transcriptional profiles of gingival tissue samples obtained from subjects suffering from chronic or aggressive periodontitis (1) to test whether the two diagnostic entities were also characterized by differences on the molecular level, and (2) to search for a novel, alternative classification of periodontitis based on the tissue transcriptomes.Using machine learning technology, we provide evidence for diagnostic imprecision in the currently accepted classification of periodontitis, and demonstrate that a novel, alternative classification based on differences in gingival tissue transcriptomes is feasible. The outlined procedures allow for the unbiased interrogation of high-dimensional datasets for characteristic underlying classes, and are applicable to a broad range of -omics data.

On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification

NASA Astrophysics Data System (ADS)

Gao, Liang; Li, Fuhai; Thrall, Michael J.; Yang, Yaliang; Xing, Jiong; Hammoudi, Ahmad A.; Zhao, Hong; Massoud, Yehia; Cagle, Philip T.; Fan, Yubo; Wong, Kelvin K.; Wang, Zhiyong; Wong, Stephen T. C.

2011-09-01

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

A thyroid nodule classification method based on TI-RADS

NASA Astrophysics Data System (ADS)

Wang, Hao; Yang, Yang; Peng, Bo; Chen, Qin

2017-07-01

Thyroid Imaging Reporting and Data System(TI-RADS) is a valuable tool for differentiating the benign and the malignant thyroid nodules. In clinic, doctors can determine the extent of being benign or malignant in terms of different classes by using TI-RADS. Classification represents the degree of malignancy of thyroid nodules. TI-RADS as a classification standard can be used to guide the ultrasonic doctor to examine thyroid nodules more accurately and reliably. In this paper, we aim to classify the thyroid nodules with the help of TI-RADS. To this end, four ultrasound signs, i.e., cystic and solid, echo pattern, boundary feature and calcification of thyroid nodules are extracted and converted into feature vectors. Then semi-supervised fuzzy C-means ensemble (SS-FCME) model is applied to obtain the classification results. The experimental results demonstrate that the proposed method can help doctors diagnose the thyroid nodules effectively.

Classification of hyperlipidaemias and hyperlipoproteinaemias*

PubMed Central

1970-01-01

Many studies of atherosclerosis have indicated hyperlipidaemia as a predisposing factor to vascular disease. The relationship holds even for mild degrees of hyperlipidaemia, a fact that underlines the importance of this category of disorders. Both primary and secondary hyperlipidaemias represent such a variety of abnormalities that an internationally acceptable provisional classification is highly desirable in order to facilitate communication between scientists with different backgrounds. The present memorandum presents such a classification; it briefly describes the criteria for diagnosis of the main types of hyperlipidaemia as well as the methods of their determination. Because lipoproteins offer more information than analysis of plasma lipids (most of the plasma lipids being bound to various proteins), the classification is based on lipoprotein analyses by electrophoresis and ultracentrifugation. Simpler methods, however, such as the observation of plasma and measurements of cholesterol and triglycerides, are used to the fullest possible extent in determining the lipoprotein patterns. PMID:4930042

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine.

PubMed

Yan, Jian-Jun; Guo, Rui; Wang, Yi-Qin; Liu, Guo-Ping; Yan, Hai-Xia; Xia, Chun-Ming; Shen, Xiaojing

2014-01-01

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine

PubMed Central

Yan, Jian-Jun; Wang, Yi-Qin; Liu, Guo-Ping; Yan, Hai-Xia; Xia, Chun-Ming; Shen, Xiaojing

2014-01-01

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered. PMID:24883068

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

PubMed Central

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-01-01

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood. PMID:28294963

Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development

PubMed Central

Sertel, O.; Kong, J.; Shimada, H.; Catalyurek, U.V.; Saltz, J.H.; Gurcan, M.N.

2009-01-01

We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%. PMID:20161324

Food Classification Systems Based on Food Processing: Significance and Implications for Policies and Actions: A Systematic Literature Review and Assessment.

PubMed

Moubarac, Jean-Claude; Parra, Diana C; Cannon, Geoffrey; Monteiro, Carlos A

2014-06-01

This paper is the first to make a systematic review and assessment of the literature that attempts methodically to incorporate food processing into classification of diets. The review identified 1276 papers, of which 110 were screened and 21 studied, derived from five classification systems. This paper analyses and assesses the five systems, one of which has been devised and developed by a research team that includes co-authors of this paper. The quality of the five systems is assessed and scored according to how specific, coherent, clear, comprehensive and workable they are. Their relevance to food, nutrition and health, and their use in various settings, is described. The paper shows that the significance of industrial food processing in shaping global food systems and supplies and thus dietary patterns worldwide, and its role in the pandemic of overweight and obesity, remains overlooked and underestimated. Once food processing is systematically incorporated into food classifications, they will be more useful in assessing and monitoring dietary patterns. Food classification systems that emphasize industrial food processing, and that define and distinguish relevant different types of processing, will improve understanding of how to prevent and control overweight, obesity and related chronic non-communicable diseases, and also malnutrition. They will also be a firmer basis for rational policies and effective actions designed to protect and improve public health at all levels from global to local.

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

PubMed

Zare, Marzieh; Rezvani, Zahra; Benasich, April A

2016-07-01

This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

PubMed

Siu, Ho Chit; Shah, Julie A; Stirling, Leia A

2016-10-25

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

PubMed Central

Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

Minimal perceptrons for memorizing complex patterns

NASA Astrophysics Data System (ADS)

Pastor, Marissa; Song, Juyong; Hoang, Danh-Tai; Jo, Junghyo

2016-11-01

Feedforward neural networks have been investigated to understand learning and memory, as well as applied to numerous practical problems in pattern classification. It is a rule of thumb that more complex tasks require larger networks. However, the design of optimal network architectures for specific tasks is still an unsolved fundamental problem. In this study, we consider three-layered neural networks for memorizing binary patterns. We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity. We formulated the minimal network size for regular, random, and complex patterns. In particular, the minimal size for complex patterns, which are neither ordered nor disordered, was predicted by measuring their Hamming distances from known ordered patterns. Our predictions agree with simulations based on the back-propagation algorithm.

Quantification of Reflection Patterns in Ground-Penetrating Radar Data

NASA Astrophysics Data System (ADS)

Moysey, S.; Knight, R. J.; Jol, H. M.; Allen-King, R. M.; Gaylord, D. R.

2005-12-01

Radar facies analysis provides a way of interpreting the large-scale structure of the subsurface from ground-penetrating radar (GPR) data. Radar facies are often distinguished from each other by the presence of patterns, such as flat-lying, dipping, or chaotic reflections, in different regions of a radar image. When these patterns can be associated with radar facies in a repeated and predictable manner we refer to them as `radar textures'. While it is often possible to qualitatively differentiate between radar textures visually, pattern recognition tools, like neural networks, require a quantitative measure to discriminate between them. We investigate whether currently available tools, such as instantaneous attributes or metrics adapted from standard texture analysis techniques, can be used to improve the classification of radar facies. To this end, we use a neural network to perform cross-validation tests that assess the efficacy of different textural measures for classifying radar facies in GPR data collected from the William River delta, Saskatchewan, Canada. We found that the highest classification accuracies (>93%) were obtained for measures of texture that preserve information about the spatial arrangement of reflections in the radar image, e.g., spatial covariance. Lower accuracy (87%) was obtained for classifications based directly on windows of amplitude data extracted from the radar image. Measures that did not account for the spatial arrangement of reflections in the image, e.g., instantaneous attributes and amplitude variance, yielded classification accuracies of less than 65%. Optimal classifications were obtained for textural measures that extracted sufficient information from the radar data to discriminate between radar facies but were insensitive to other facies specific characteristics. For example, the rotationally invariant Fourier-Mellin transform delivered better classification results than the spatial covariance because dip angle of the reflections, but not dip direction, was an important discriminator between radar facies at the William River delta. To extend the use of radar texture beyond the identification of radar facies to sedimentary facies we are investigating how sedimentary features are encoded in GPR data at Borden, Ontario, Canada. At this site, we have collected extensive sedimentary and hydrologic data over the area imaged by GPR. Analysis of this data coupled with synthetic modeling of the radar signal has allowed us to develop insight into the generation of radar texture in complex geologic environments.

FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort.

PubMed

Caminiti, Silvia Paola; Ballarini, Tommaso; Sala, Arianna; Cerami, Chiara; Presotto, Luca; Santangelo, Roberto; Fallanca, Federico; Vanoli, Emilia Giovanna; Gianolli, Luigi; Iannaccone, Sandro; Magnani, Giuseppe; Perani, Daniela

2018-01-01

In this multicentre study in clinical settings, we assessed the accuracy of optimized procedures for FDG-PET brain metabolism and CSF classifications in predicting or excluding the conversion to Alzheimer's disease (AD) dementia and non-AD dementias. We included 80 MCI subjects with neurological and neuropsychological assessments, FDG-PET scan and CSF measures at entry, all with clinical follow-up. FDG-PET data were analysed with a validated voxel-based SPM method. Resulting single-subject SPM maps were classified by five imaging experts according to the disease-specific patterns, as "typical-AD", "atypical-AD" (i.e. posterior cortical atrophy, asymmetric logopenic AD variant, frontal-AD variant), "non-AD" (i.e. behavioural variant FTD, corticobasal degeneration, semantic variant FTD; dementia with Lewy bodies) or "negative" patterns. To perform the statistical analyses, the individual patterns were grouped either as "AD dementia vs. non-AD dementia (all diseases)" or as "FTD vs. non-FTD (all diseases)". Aβ42, total and phosphorylated Tau CSF-levels were classified dichotomously, and using the Erlangen Score algorithm. Multivariate logistic models tested the prognostic accuracy of FDG-PET-SPM and CSF dichotomous classifications. Accuracy of Erlangen score and Erlangen Score aided by FDG-PET SPM classification was evaluated. The multivariate logistic model identified FDG-PET "AD" SPM classification (Expβ = 19.35, 95% C.I. 4.8-77.8, p < 0.001) and CSF Aβ42 (Expβ = 6.5, 95% C.I. 1.64-25.43, p < 0.05) as the best predictors of conversion from MCI to AD dementia. The "FTD" SPM pattern significantly predicted conversion to FTD dementias at follow-up (Expβ = 14, 95% C.I. 3.1-63, p < 0.001). Overall, FDG-PET-SPM classification was the most accurate biomarker, able to correctly differentiate either the MCI subjects who converted to AD or FTD dementias, and those who remained stable or reverted to normal cognition (Expβ = 17.9, 95% C.I. 4.55-70.46, p < 0.001). Our results support the relevant role of FDG-PET-SPM classification in predicting progression to different dementia conditions in prodromal MCI phase, and in the exclusion of progression, outperforming CSF biomarkers.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2014 CFR

2014-10-01

... discharge is based, as appropriate, on the patient's age, sex, principal diagnosis (that is, the diagnosis... whether a change in the DRG assignment is appropriate. If the intermediary decides that a higher-weighted... (b) of this section at least annually to reflect changes in treatment patterns, technology, and other...

Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

ERIC Educational Resources Information Center

Ye, Qiang

2010-01-01

Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

Active learning strategies for the deduplication of electronic patient data using classification trees.

PubMed

Sariyar, M; Borg, A; Pommerening, K

2012-10-01

Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

Intra- and interobserver agreement in the classification and treatment of distal third clavicle fractures.

PubMed

Bishop, Julie Y; Jones, Grant L; Lewis, Brian; Pedroza, Angela

2015-04-01

In treatment of distal third clavicle fractures, the Neer classification system, based on the location of the fracture in relation to the coracoclavicular ligaments, has traditionally been used to determine fracture pattern stability. To determine the intra- and interobserver reliability in the classification of distal third clavicle fractures via standard plain radiographs and the intra- and interobserver agreement in the preferred treatment of these fractures. Cohort study (Diagnosis); Level of evidence, 3. Thirty radiographs of distal clavicle fractures were randomly selected from patients treated for distal clavicle fractures between 2006 and 2011. The radiographs were distributed to 22 shoulder/sports medicine fellowship-trained orthopaedic surgeons. Fourteen surgeons responded and took part in the study. The evaluators were asked to measure the size of the distal fragment, classify the fracture pattern as stable or unstable, assign the Neer classification, and recommend operative versus nonoperative treatment. The radiographs were reordered and redistributed 3 months later. Inter- and intrarater agreement was determined for the distal fragment size, stability of the fracture, Neer classification, and decision to operate. Single variable logistic regression was performed to determine what factors could most accurately predict the decision for surgery. Interrater agreement was fair for distal fragment size, moderate for stability, fair for Neer classification, slight for type IIB and III fractures, and moderate for treatment approach. Intrarater agreement was moderate for distal fragment size categories (κ = 0.50, P < .001) and Neer classification (κ = 0.42, P < .001) and substantial for stable fracture (κ = 0.65, P < .001) and decision to operate (κ = 0.65, P < .001). Fracture stability was the best predictor of treatment, with 89% accuracy (P < .001). Fracture stability determination and the decision to operate had the highest interobserver agreement. Fracture stability was the key determinant of treatment, rather than the Neer classification system or the size of the distal fragment. © 2015 The Author(s).

Patterns recognition of electric brain activity using artificial neural networks

NASA Astrophysics Data System (ADS)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Texture operator for snow particle classification into snowflake and graupel

NASA Astrophysics Data System (ADS)

Nurzyńska, Karolina; Kubo, Mamoru; Muramoto, Ken-ichiro

2012-11-01

In order to improve the estimation of precipitation, the coefficients of Z-R relation should be determined for each snow type. Therefore, it is necessary to identify the type of falling snow. Consequently, this research addresses a problem of snow particle classification into snowflake and graupel in an automatic manner (as these types are the most common in the study region). Having correctly classified precipitation events, it is believed that it will be possible to estimate the related parameters accurately. The automatic classification system presented here describes the images with texture operators. Some of them are well-known from the literature: first order features, co-occurrence matrix, grey-tone difference matrix, run length matrix, and local binary pattern, but also a novel approach to design simple local statistic operators is introduced. In this work the following texture operators are defined: mean histogram, min-max histogram, and mean-variance histogram. Moreover, building a feature vector, which is based on the structure created in many from mentioned algorithms is also suggested. For classification, the k-nearest neighbourhood classifier was applied. The results showed that it is possible to achieve correct classification accuracy above 80% by most of the techniques. The best result of 86.06%, was achieved for operator built from a structure achieved in the middle stage of the co-occurrence matrix calculation. Next, it was noticed that describing an image with two texture operators does not improve the classification results considerably. In the best case the correct classification efficiency was 87.89% for a pair of texture operators created from local binary pattern and structure build in a middle stage of grey-tone difference matrix calculation. This also suggests that the information gathered by each texture operator is redundant. Therefore, the principal component analysis was applied in order to remove the unnecessary information and additionally reduce the length of the feature vectors. The improvement of the correct classification efficiency for up to 100% is possible for methods: min-max histogram, texture operator built from structure achieved in a middle stage of co-occurrence matrix calculation, texture operator built from a structure achieved in a middle stage of grey-tone difference matrix creation, and texture operator based on a histogram, when the feature vector stores 99% of initial information.

Analysis of the hand vein pattern for people recognition

NASA Astrophysics Data System (ADS)

Castro-Ortega, R.; Toxqui-Quitl, C.; Cristóbal, G.; Marcos, J. Victor; Padilla-Vivanco, A.; Hurtado Pérez, R.

2015-09-01

The shape of the hand vascular pattern contains useful and unique features that can be used for identifying and authenticating people, with applications in access control, medicine and financial services. In this work, an optical system for the image acquisition of the hand vascular pattern is implemented. It consists of a CCD camera with sensitivity in the IR and a light source with emission in the 880 nm. The IR radiation interacts with the desoxyhemoglobin, hemoglobin and water present in the blood of the veins, making possible to see the vein pattern underneath skin. The segmentation of the Region Of Interest (ROI) is achieved using geometrical moments locating the centroid of an image. For enhancement of the vein pattern we use the technique of Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE). In order to remove unnecessary information such as body hair and skinfolds, a low pass filter is implemented. A method based on geometric moments is used to obtain the invariant descriptors of the input images. The classification task is achieved using Artificial Neural Networks (ANN) and K-Nearest Neighbors (K-nn) algorithms. Experimental results using our database show a percentage of correct classification, higher of 86.36% with ANN for 912 images of 38 people with 12 versions each one.

Decoding task-based attentional modulation during face categorization.

PubMed

Chiu, Yu-Chin; Esterman, Michael; Han, Yuefeng; Rosen, Heather; Yantis, Steven

2011-05-01

Attention is a neurocognitive mechanism that selects task-relevant sensory or mnemonic information to achieve current behavioral goals. Attentional modulation of cortical activity has been observed when attention is directed to specific locations, features, or objects. However, little is known about how high-level categorization task set modulates perceptual representations. In the current study, observers categorized faces by gender (male vs. female) or race (Asian vs. White). Each face was perceptually ambiguous in both dimensions, such that categorization of one dimension demanded selective attention to task-relevant information within the face. We used multivoxel pattern classification to show that task-specific modulations evoke reliably distinct spatial patterns of activity within three face-selective cortical regions (right fusiform face area and bilateral occipital face areas). This result suggests that patterns of activity in these regions reflect not only stimulus-specific (i.e., faces vs. houses) responses but also task-specific (i.e., race vs. gender) attentional modulation. Furthermore, exploratory whole-brain multivoxel pattern classification (using a searchlight procedure) revealed a network of dorsal fronto-parietal regions (left middle frontal gyrus and left inferior and superior parietal lobule) that also exhibit distinct patterns for the two task sets, suggesting that these regions may represent abstract goals during high-level categorization tasks.

Perceptrons and Pattern Recognition

DTIC Science & Technology

1967-09-01

invariant ~isual patterns. Rosenblatt, F •. , !t_i~ip_les of Neurodynamics , Spartan Books, 1962. Introg~ces and discusses many parallel-network...classification ;of the patterns -th~selves, and their r~ cognition by per~eptrons. It· would b~ too much to ask for an absolute classification of "patt~’""’ls...function-of the level of existing cognitive structure. 0.5 Seductive Aspects of Perceptro~a, II: Parallel Computation The perceptron was conceived

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.

PubMed

Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing

2015-11-01

Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.

Parsing the roles of the frontal lobes and basal ganglia in task control using multivoxel pattern analysis

PubMed Central

Kehagia, Angie A.; Ye, Rong; Joyce, Dan W.; Doyle, Orla M.; Rowe, James B.; Robbins, Trevor W.

2017-01-01

Cognitive control has traditionally been associated with the prefrontal cortex, based on observations of deficits in patients with frontal lesions. However, evidence from patients with Parkinson’s disease (PD) indicates that subcortical regions also contribute to control under certain conditions. We scanned 17 healthy volunteers while they performed a task switching paradigm that previously dissociated performance deficits arising from frontal lesions in comparison with PD, as a function of the abstraction of the rules that are switched. From a multivoxel pattern analysis by Gaussian Process Classification (GPC), we then estimated the forward (generative) model to infer regional patterns of activity that predict Switch / Repeat behaviour between rule conditions. At 1000 permutations, Switch / Repeat classification accuracy for concrete rules was significant in the basal ganglia, but at chance in the frontal lobe. The inverse pattern was obtained for abstract rules, whereby the conditions were successfully discriminated in the frontal lobe but not in the basal ganglia. This double dissociation highlights the difference between cortical and subcortical contributions to cognitive control and demonstrates the utility of multivariate approaches in investigations of functions that rely on distributed and overlapping neural substrates. PMID:28387585

Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Huiying; Hou, Zhangshuan; Huang, Maoyi

The Community Land Model (CLM) represents physical, chemical, and biological processes of the terrestrial ecosystems that interact with climate across a range of spatial and temporal scales. As CLM includes numerous sub-models and associated parameters, the high-dimensional parameter space presents a formidable challenge for quantifying uncertainty and improving Earth system predictions needed to assess environmental changes and risks. This study aims to evaluate the potential of transferring hydrologic model parameters in CLM through sensitivity analyses and classification across watersheds from the Model Parameter Estimation Experiment (MOPEX) in the United States. The sensitivity of CLM-simulated water and energy fluxes to hydrologicalmore » parameters across 431 MOPEX basins are first examined using an efficient stochastic sampling-based sensitivity analysis approach. Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening. The basins are then classified accordingly to their parameter sensitivity patterns (internal attributes), as well as their hydrologic indices/attributes (external hydrologic factors) separately, using a Principal component analyses (PCA) and expectation-maximization (EM) –based clustering approach. Similarities and differences among the parameter sensitivity-based classification system (S-Class), the hydrologic indices-based classification (H-Class), and the Koppen climate classification systems (K-Class) are discussed. Within each S-class with similar parameter sensitivity characteristics, similar inversion modeling setups can be used for parameter calibration, and the parameters and their contribution or significance to water and energy cycling may also be more transferrable. This classification study provides guidance on identifiable parameters, and on parameterization and inverse model design for CLM but the methodology is applicable to other models. Inverting parameters at representative sites belonging to the same class can significantly reduce parameter calibration efforts.« less

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Similarity-dissimilarity plot for visualization of high dimensional data in biomedical pattern classification.

PubMed

Arif, Muhammad

2012-06-01

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

Symbolic dynamic filtering and language measure for behavior identification of mobile robots.

PubMed

Mallapragada, Goutham; Ray, Asok; Jin, Xin

2012-06-01

This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.

The Effect of Involuntary Motor Activity on Myoelectric Pattern Recognition: A Case Study with Chronic Stroke Patients

PubMed Central

Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Rymer, William Zev; Zhou, Ping

2013-01-01

This study investigates the effect of involuntary motor activity of paretic-spastic muscles on classification of surface electromyography (EMG) signals. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at a relatively slow and fast speed. For each stroke subject, the degree of involuntary motor activity present in voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from slow and fast sessions. Across all tested stroke subjects, our results revealed that when involuntary surface EMG was absent or present in both training and testing datasets, high accuracies (> 96%, > 98%, respectively, averaged over all the subjects) can be achieved in classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either training or testing datasets, the classification accuracies were dramatically reduced (< 89%, < 85%, respectively). However, if both training and testing datasets contained EMG signals with presence and absence of involuntary EMG interference, high accuracies were still achieved (> 97%). The findings of this study can be used to guide appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation. PMID:23860192

Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen)

NASA Astrophysics Data System (ADS)

Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello

2013-01-01

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.

Large-area settlement pattern recognition from Landsat-8 data

NASA Astrophysics Data System (ADS)

Wieland, Marc; Pittore, Massimiliano

2016-09-01

The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties.

Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy

PubMed Central

Acosta-Mesa, Héctor Gabriel; Cruz-Ramírez, Nicandro; Hernández-Jiménez, Rodolfo

2017-01-01

Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4.5), in addition to different data models that take full advantage of this information and improve the diagnostic performance of colposcopy based on acetowhite temporal patterns. Based on the ROC and PRC area scores, the k-Nearest Neighbors and discrete PLA representation performed better than other methods. The values of sensitivity, specificity, and accuracy reached using this method were 60% (95% CI 50–70), 79% (95% CI 71–86), and 70% (95% CI 60–80), respectively. The acetowhitening phenomenon is not exclusive to high-grade lesions, and we have found acetowhite temporal patterns of epithelial changes that are not precancerous lesions but that are similar to positive ones. These findings need to be considered when developing more robust computing systems in the future. PMID:28744318

Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.

PubMed

Amanpour, Behzad; Erfanian, Abbas

2013-01-01

An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI. It is desirable to select a mental task to be consistent with the desired action to be performed by BCI. In this paper, we investigated the detecting the imagination of the hand grasping, hand opening, and hand reaching in one hand using electroencephalographic (EEG) signals. The results show that the ERD/ERS patterns, associated with the imagination of hand grasping, opening, and reaching are different. For classification of brain signals associated with these mental tasks and feature extraction, a method based on wavelet packet, regularized common spatial pattern (CSP), and mutual information is proposed. The results of an offline analysis on five subjects show that the two-class mental tasks can be classified with an average accuracy of 77.6% using proposed method. In addition, we examine the proposed method on datasets IVa from BCI Competition III and IIa from BCI Competition IV.

Anatomical classification of breast sentinel lymph nodes using computed tomography-lymphography.

PubMed

Fujita, Tamaki; Miura, Hiroyuki; Seino, Hiroko; Ono, Shuichi; Nishi, Takashi; Nishimura, Akimasa; Hakamada, Kenichi; Aoki, Masahiko

2018-05-03

To evaluate the anatomical classification and location of breast sentinel lymph nodes, preoperative computed tomography-lymphography examinations were retrospectively reviewed for sentinel lymph nodes in 464 cases clinically diagnosed with node-negative breast cancer between July 2007 and June 2016. Anatomical classification was performed based on the numbers of lymphatic routes and sentinel lymph nodes, the flow direction of lymphatic routes, and the location of sentinel lymph nodes. Of the 464 cases reviewed, anatomical classification could be performed in 434 (93.5 %). The largest number of cases showed single route/single sentinel lymph node (n = 296, 68.2 %), followed by multiple routes/multiple sentinel lymph nodes (n = 59, 13.6 %), single route/multiple sentinel lymph nodes (n = 53, 12.2 %), and multiple routes/single sentinel lymph node (n = 26, 6.0 %). Classification based on the flow direction of lymphatic routes showed that 429 cases (98.8 %) had outward flow on the superficial fascia toward axillary lymph nodes, whereas classification based on the height of sentinel lymph nodes showed that 323 cases (74.4 %) belonged to the upper pectoral group of axillary lymph nodes. There was wide variation in the number of lymphatic routes and their branching patterns and in the number, location, and direction of flow of sentinel lymph nodes. It is clinically very important to preoperatively understand the anatomical morphology of lymphatic routes and sentinel lymph nodes for optimal treatment of breast cancer, and computed tomography-lymphography is suitable for this purpose.

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

PubMed

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Qualitative pattern classification of shear wave elastography for breast masses: how it correlates to quantitative measurements.

PubMed

Yoon, Jung Hyun; Ko, Kyung Hee; Jung, Hae Kyoung; Lee, Jong Tae

2013-12-01

To determine the correlation of qualitative shear wave elastography (SWE) pattern classification to quantitative SWE measurements and whether it is representative of quantitative SWE values with similar performances. From October 2012 to January 2013, 267 breast masses of 236 women (mean age: 45.12 ± 10.54 years, range: 21-88 years) who had undergone ultrasonography (US), SWE, and subsequent biopsy were included. US BI-RADS final assessment and qualitative and quantitative SWE measurements were recorded. Correlation between pattern classification and mean elasticity, maximum elasticity, elasticity ratio and standard deviation were evaluated. Diagnostic performances of grayscale US, SWE parameters, and US combined to SWE values were calculated and compared. Of the 267 breast masses, 208 (77.9%) were benign and 59 (22.1%) were malignant. Pattern classifications significantly correlated with all quantitative SWE measurements, showing highest correlation with maximum elasticity, r = 0.721 (P<0.001). Sensitivity was significantly decreased in US combined to SWE measurements to grayscale US: 69.5-89.8% to 100.0%, while specificity was significantly improved: 62.5-81.7% to 13.9% (P<0.001). Area under the ROC curve (Az) did not show significant differences between grayscale US to US combined to SWE (P>0.05). Pattern classification shows high correlation to maximum stiffness and may be representative of quantitative SWE values. When combined to grayscale US, SWE improves specificity of US. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Physiotherapy movement based classification approaches to low back pain: comparison of subgroups through review and developer/expert survey.

PubMed

Karayannis, Nicholas V; Jull, Gwendolen A; Hodges, Paul W

2012-02-20

Several classification schemes, each with its own philosophy and categorizing method, subgroup low back pain (LBP) patients with the intent to guide treatment. Physiotherapy derived schemes usually have a movement impairment focus, but the extent to which other biological, psychological, and social factors of pain are encompassed requires exploration. Furthermore, within the prevailing 'biological' domain, the overlap of subgrouping strategies within the orthopaedic examination remains unexplored. The aim of this study was "to review and clarify through developer/expert survey, the theoretical basis and content of physical movement classification schemes, determine their relative reliability and similarities/differences, and to consider the extent of incorporation of the bio-psycho-social framework within the schemes". A database search for relevant articles related to LBP and subgrouping or classification was conducted. Five dominant movement-based schemes were identified: Mechanical Diagnosis and Treatment (MDT), Treatment Based Classification (TBC), Pathoanatomic Based Classification (PBC), Movement System Impairment Classification (MSI), and O'Sullivan Classification System (OCS) schemes. Data were extracted and a survey sent to the classification scheme developers/experts to clarify operational criteria, reliability, decision-making, and converging/diverging elements between schemes. Survey results were integrated into the review and approval obtained for accuracy. Considerable diversity exists between schemes in how movement informs subgrouping and in the consideration of broader neurosensory, cognitive, emotional, and behavioural dimensions of LBP. Despite differences in assessment philosophy, a common element lies in their objective to identify a movement pattern related to a pain reduction strategy. Two dominant movement paradigms emerge: (i) loading strategies (MDT, TBC, PBC) aimed at eliciting a phenomenon of centralisation of symptoms; and (ii) modified movement strategies (MSI, OCS) targeted towards documenting the movement impairments associated with the pain state. Schemes vary on: the extent to which loading strategies are pursued; the assessment of movement dysfunction; and advocated treatment approaches. A biomechanical assessment predominates in the majority of schemes (MDT, PBC, MSI), certain psychosocial aspects (fear-avoidance) are considered in the TBC scheme, certain neurophysiologic (central versus peripherally mediated pain states) and psychosocial (cognitive and behavioural) aspects are considered in the OCS scheme.

A microfluidic laser scattering sensor for label-free detection of waterborne pathogens

NASA Astrophysics Data System (ADS)

Wei, Huang; Yang, Limei; Li, Feng

2016-10-01

A microfluidic-based multi-angle laser scattering (MALS) sensor capable of acquiring scattering pattern of single particle is demonstrated. The size and relative refractive index (RI) of polystyrene (PS) microspheres were deduced with accuracies of 60 nm and 0.001 by analyzing the scattering patterns. We measured scattering patterns of waterborne parasites i.e., cryptosporidium parvum (c.parvum) and giardia lamblia (g.lamblia), and some other representative species in 1 L water within 1 hour, and the waterborne parasites were identified with accuracy better than 96% by classification of distinctive scattering patterns with a support-vector-machine (SVM) algorithm. The system provides a promising tool for label-free and rapid detection of waterborne parasites.

Neural architecture design based on extreme learning machine.

PubMed

Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

2013-12-01

Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

LBP and SIFT based facial expression recognition

NASA Astrophysics Data System (ADS)

Sumer, Omer; Gunes, Ece O.

2015-02-01

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

"In my opinion, work would be in first place and family in second": young women's imagined gender-work relations in post-Soviet Lithuania.

PubMed

Reiter, Herwig

2010-01-01

This article explores young women's orientation to work and motherhood in the post-communist context of radical socio-economic transformation in Europe. Based on a qualitative-explorative study into meanings of work and unemployment among young people in post-Soviet Lithuania, the paper introduces an empirically grounded classification of imagined gender-work arrangements. The single patterns of the classification are based on the three configurations of work and motherhood, work and partnership, and work and provision. The findings inform the reconstruction of the 'landscape' of imagined gendered adulthoods in Europe as well as the analysis of emerging gender relations under conditions of rapid social change.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

NASA Astrophysics Data System (ADS)

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-01

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system.

PubMed

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-06

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

Classification of Animal Movement Behavior through Residence in Space and Time.

PubMed

Torres, Leigh G; Orben, Rachael A; Tolkova, Irina; Thompson, David R

2017-01-01

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.

PubMed

Zich, Catharina; Debener, Stefan; De Vos, Maarten; Frerichs, Stella; Maurer, Stefanie; Kranczioch, Cornelia

2015-08-01

The mental practice of movements has been suggested as a promising add-on therapy to facilitate motor recovery after stroke. In the case of mentally practised movements, electroencephalogram (EEG) can be utilized to provide feedback about an otherwise covert act. The main target group for such an intervention are elderly patients, though research so far is largely focused on young populations (<30 years). The present study therefore aimed to examine the influence of age on the neural correlates of covert movements (CMs) in a real-time EEG neurofeedback framework. CM-induced event-related desynchronization (ERD) was studied in young (mean age: 23.6 years) and elderly (mean age: 62.7 years) healthy adults. Participants performed covert and overt hand movements. CMs were based on kinesthetic motor imagery (MI) or quasi-movements (QM). Based on previous studies investigating QM in the mu frequency range (8-13Hz) QM were expected to result in more lateralized ERD% patterns and accordingly higher classification accuracies. Independent of CM strategy the elderly were characterized by a significantly reduced lateralization of ERD%, due to stronger ipsilateral ERD%, and in consequence, reduced classification accuracies. QM were generally perceived as more vivid, but no differences were evident between MI and QM in ERD% or classification accuracies. EEG feedback enhanced task-related activity independently of strategy and age. ERD% measures of overt and covert movements were strongly related in young adults, whereas in the elderly ERD% lateralization is dissociated. In summary, we did not find evidence in support of more pronounced ERD% lateralization patterns in QM. Our finding of a less lateralized activation pattern in the elderly is in accordance to previous research and with the idea that compensatory processes help to overcome neurodegenerative changes related to normal ageing. Importantly, it indicates that EEG neurofeedback studies should place more emphasis on the age of the potential end-users. Copyright © 2015 Elsevier Inc. All rights reserved.

Differentiation Between Organic and Non-Organic Apples Using Diffraction Grating and Image Processing-A Cost-Effective Approach.

PubMed

Jiang, Nanfeng; Song, Weiran; Wang, Hui; Guo, Gongde; Liu, Yuanyuan

2018-05-23

As the expectation for higher quality of life increases, consumers have higher demands for quality food. Food authentication is the technical means of ensuring food is what it says it is. A popular approach to food authentication is based on spectroscopy, which has been widely used for identifying and quantifying the chemical components of an object. This approach is non-destructive and effective but expensive. This paper presents a computer vision-based sensor system for food authentication, i.e., differentiating organic from non-organic apples. This sensor system consists of low-cost hardware and pattern recognition software. We use a flashlight to illuminate apples and capture their images through a diffraction grating. These diffraction images are then converted into a data matrix for classification by pattern recognition algorithms, including k -nearest neighbors ( k -NN), support vector machine (SVM) and three partial least squares discriminant analysis (PLS-DA)- based methods. We carry out experiments on a reasonable collection of apple samples and employ a proper pre-processing, resulting in a highest classification accuracy of 94%. Our studies conclude that this sensor system has the potential to provide a viable solution to empower consumers in food authentication.

The maximum vector-angular margin classifier and its fast training on large datasets using a core vector machine.

PubMed

Hu, Wenjun; Chung, Fu-Lai; Wang, Shitong

2012-03-01

Although pattern classification has been extensively studied in the past decades, how to effectively solve the corresponding training on large datasets is a problem that still requires particular attention. Many kernelized classification methods, such as SVM and SVDD, can be formulated as the corresponding quadratic programming (QP) problems, but computing the associated kernel matrices requires O(n2)(or even up to O(n3)) computational complexity, where n is the size of the training patterns, which heavily limits the applicability of these methods for large datasets. In this paper, a new classification method called the maximum vector-angular margin classifier (MAMC) is first proposed based on the vector-angular margin to find an optimal vector c in the pattern feature space, and all the testing patterns can be classified in terms of the maximum vector-angular margin ρ, between the vector c and all the training data points. Accordingly, it is proved that the kernelized MAMC can be equivalently formulated as the kernelized Minimum Enclosing Ball (MEB), which leads to a distinctive merit of MAMC, i.e., it has the flexibility of controlling the sum of support vectors like v-SVC and may be extended to a maximum vector-angular margin core vector machine (MAMCVM) by connecting the core vector machine (CVM) method with MAMC such that the corresponding fast training on large datasets can be effectively achieved. Experimental results on artificial and real datasets are provided to validate the power of the proposed methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Image Augmentation for Object Image Classification Based On Combination of Pre-Trained CNN and SVM

NASA Astrophysics Data System (ADS)

Shima, Yoshihiro

2018-04-01

Neural networks are a powerful means of classifying object images. The proposed image category classification method for object images combines convolutional neural networks (CNNs) and support vector machines (SVMs). A pre-trained CNN, called Alex-Net, is used as a pattern-feature extractor. Alex-Net is pre-trained for the large-scale object-image dataset ImageNet. Instead of training, Alex-Net, pre-trained for ImageNet is used. An SVM is used as trainable classifier. The feature vectors are passed to the SVM from Alex-Net. The STL-10 dataset are used as object images. The number of classes is ten. Training and test samples are clearly split. STL-10 object images are trained by the SVM with data augmentation. We use the pattern transformation method with the cosine function. We also apply some augmentation method such as rotation, skewing and elastic distortion. By using the cosine function, the original patterns were left-justified, right-justified, top-justified, or bottom-justified. Patterns were also center-justified and enlarged. Test error rate is decreased by 0.435 percentage points from 16.055% by augmentation with cosine transformation. Error rates are increased by other augmentation method such as rotation, skewing and elastic distortion, compared without augmentation. Number of augmented data is 30 times that of the original STL-10 5K training samples. Experimental test error rate for the test 8k STL-10 object images was 15.620%, which shows that image augmentation is effective for image category classification.

Attachment classification, psychophysiology and frontal EEG asymmetry across the lifespan: a review

PubMed Central

Gander, Manuela; Buchheim, Anna

2015-01-01

In recent years research on physiological response and frontal electroencephalographic (EEG) asymmetry in different patterns of infant and adult attachment has increased. We review research findings regarding associations between attachment classifications and frontal EEG asymmetry, the autonomic nervous system (ANS) and the hypothalamic-pituitary-adrenocortical axis (HPA). Studies indicate that insecure attachment is related to a heightened adrenocortical activity, heart rate and skin conductance in response to stress, which is consistent with the hypothesis that attachment insecurity leads to impaired emotion regulation. Research on frontal EEG asymmetry also shows a clear difference in the emotional arousal between the attachment groups evidenced by specific frontal asymmetry changes. Furthermore, we discuss neurophysiological evidence of attachment organization and present up-to-date findings of EEG-research with adults. Based on the overall patterns of results presented in this article we identify some major areas of interest and directions for future research. PMID:25745393

Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

NASA Astrophysics Data System (ADS)

Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

2018-03-01

As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

[A cold/heat property classification strategy based on bio-effects of herbal medicines].

PubMed

Jiang, Miao; Lv, Ai-Ping

2014-06-01

The property theory of Chinese herbal medicine (CHM) is regarded as the core and basic of Chinese medical theory, however, the underlying mechanism of the properties in CHMs remains unclear, which impedes a barrier for the modernization of Chinese herbal medicine. The properties of CHM are often categorized into cold and heat according to the theory of Chinese medicine, which are essential to guide the clinical application of CHMs. There is an urgent demand to build a cold/heat property classification model to facilitate the property theory of Chinese herbal medicine, as well as to clarify the controversial properties of some herbs. Based on previous studies on the cold/heat properties of CHM, in this paper, we described a novel strategy on building a cold/heat property classification model based on herbal bio-effect. The interdisciplinary cooperation of systems biology, pharmacological network, and pattern recognition technique might lighten the study on cold/heat property theory, provide a scientific model for determination the cold/heat property of herbal medicines, and a new strategy for expanding the Chinese herbal medicine resources as well.

Multi-q pattern classification of polarization curves

NASA Astrophysics Data System (ADS)

Fabbri, Ricardo; Bastos, Ivan N.; Neto, Francisco D. Moura; Lopes, Francisco J. P.; Gonçalves, Wesley N.; Bruno, Odemir M.

2014-02-01

Several experimental measurements are expressed in the form of one-dimensional profiles, for which there is a scarcity of methodologies able to classify the pertinence of a given result to a specific group. The polarization curves that evaluate the corrosion kinetics of electrodes in corrosive media are applications where the behavior is chiefly analyzed from profiles. Polarization curves are indeed a classic method to determine the global kinetics of metallic electrodes, but the strong nonlinearity from different metals and alloys can overlap and the discrimination becomes a challenging problem. Moreover, even finding a typical curve from replicated tests requires subjective judgment. In this paper, we used the so-called multi-q approach based on the Tsallis statistics in a classification engine to separate the multiple polarization curve profiles of two stainless steels. We collected 48 experimental polarization curves in an aqueous chloride medium of two stainless steel types, with different resistance against localized corrosion. Multi-q pattern analysis was then carried out on a wide potential range, from cathodic up to anodic regions. An excellent classification rate was obtained, at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and both potential ranges, respectively, using only 2% of the original profile data. These results show the potential of the proposed approach towards efficient, robust, systematic and automatic classification of highly nonlinear profile curves.

Instance-Based Ontology Matching for Open and Distance Learning Materials

ERIC Educational Resources Information Center

Cerón-Figueroa, Sergio; López-Yáñez, Itzamá; Villuendas-Rey, Yenny; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Yáñez-Márquez, Cornelio

2017-01-01

The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is…

Classification of Chemical Substances Using Particulate Representations of Matter: An Analysis of Student Thinking

ERIC Educational Resources Information Center

Stains, Marilyne; Talanquer, Vicente

2007-01-01

We applied a mixed-method research design to investigate the patterns of reasoning used by novice undergraduate chemistry students to classify chemical substances as elements, compounds, or mixtures based on their particulate representations. We were interested in the identification of the representational features that students use to build a…

[Specifics of bio-controlled training in directed relaxation].

PubMed

Baranov, V M; Sentiabrev, N N; Solopov, I N

2005-01-01

Studies of personal and general patterns of acquisition of skills in biocontrolled relaxation based on biological feedback (EMG) permitted classification of human subjects by the ability to relax voluntarily muscles. In the process of skill acquisition changes were minimal at the beginning, grew progressively further on and stabilized on completion of the course of training.

Behavioral Profiles in 4-5 Year-Old Children: Normal and Pathological Variants

ERIC Educational Resources Information Center

Larsson, Jan-Olov; Bergman, Lars R.; Earls, Felton; Rydelius, Per-Anders

2004-01-01

Normal and psychopathological patterns of behavior symptoms in preschool children were described by a classification approach using cluster analysis. The behavior of 406 children, average age 4 years 9 months, from the general population was evaluated at home visits. Seven clusters were identified based on empirically defined dimensions:…

Geometry-based ensembles: toward a structural characterization of the classification boundary.

PubMed

Pujol, Oriol; Masip, David

2009-06-01

This paper introduces a novel binary discriminative learning technique based on the approximation of the nonlinear decision boundary by a piecewise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points-points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and nonlinear behavior is obtained. The simplicity of the method allows its extension to cope with some of today's machine learning challenges, such as online learning, large-scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database, comparing with several state-of-the-art classification techniques. Finally, we apply our technique in online and large-scale scenarios and in six real-life computer vision and pattern recognition problems: gender recognition based on face images, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease myocardial damage severity detection, old musical scores clef classification, and action recognition using 3D accelerometer data from a wearable device. The results are promising and this paper opens a line of research that deserves further attention.

Spiral wave classification using normalized compression distance: Towards atrial tissue spatiotemporal electrophysiological behavior characterization.

PubMed

Alagoz, Celal; Guez, Allon; Cohen, Andrew; Bullinga, John R

2015-08-01

Analysis of electrical activation patterns such as re-entries during atrial fibrillation (Afib) is crucial in understanding arrhythmic mechanisms and assessment of diagnostic measures. Spiral waves are a phenomena that provide intuitive basis for re-entries occurring in cardiac tissue. Distinct spiral wave behaviors such as stable spiral waves, meandering spiral waves, and spiral wave break-up may have distinct electrogram manifestations on a mapping catheter. Hence, it is desirable to have an automated classification of spiral wave behavior based on catheter recordings for a qualitative characterization of spatiotemporal electrophysiological activity on atrial tissue. In this study, we propose a method for classification of spatiotemporal characteristics of simulated atrial activation patterns in terms of distinct spiral wave behaviors during Afib using two different techniques: normalized compressed distance (NCD) and normalized FFT (NFFTD). We use a phenomenological model for cardiac electrical propagation to produce various simulated spiral wave behaviors on a 2D grid and labeled them as stable, meandering, or breakup. By mimicking commonly used catheter types, a star shaped and a circular shaped both of which do the local readings from atrial wall, monopolar and bipolar intracardiac electrograms are simulated. Virtual catheters are positioned at different locations on the grid. The classification performance for different catheter locations, types and for monopolar or bipolar readings were also compared. We observed that the performance for each case differed slightly. However, we found that NCD performance is superior to NFFTD. Through the simulation study, we showed the theoretical validation of the proposed method. Our findings suggest that a qualitative wavefront activation pattern can be assessed during Afib without the need for highly invasive mapping techniques such as multisite simultaneous electrogram recordings.

Classification of time series patterns from complex dynamic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schryver, J.C.; Rao, N.

1998-07-01

An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately,more » the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.« less

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

PubMed Central

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.

PubMed

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-06-13

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

Changing beliefs for changing movement and pain: Classification-based cognitive functional therapy (CB-CFT) for chronic non-specific low back pain.

PubMed

Meziat Filho, N

2016-02-01

This case report presents the effect of classification-based cognitive functional therapy in a patient with chronic disabling low back pain. The patient was assessed using a multidimensional biopsychosocial classification system and was classified as having flexion pattern of movement impairment disorder. Management of this patient was to change her belief that bending over and sitting would cause damage to her disc, combined with active exercises for graded exposure to lumbar flexion to restore normal movement. Three months after the first appointment, the treatment resulted in reduced pain, the mitigation of fear avoidance beliefs and the remediation of functional disability. The patient returned to work and was walking for one hour a day on a treadmill. The cognitive intervention to change the patient's negative beliefs related to the biomedical model was important to make the graded exercises and the lifestyle changes possible. Copyright © 2015 Elsevier Ltd. All rights reserved.