Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

PubMed Central

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6–7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification. PMID:24086666

15 CFR 30.61 - Statistical classification schedules.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 1 2011-01-01 2011-01-01 false Statistical classification schedules... § 30.61 Statistical classification schedules. The following statistical classification schedules are....census.gov/trade. (a) Schedule B—Statistical Classification for Domestic and Foreign Commodities Exported...

15 CFR 30.61 - Statistical classification schedules.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Statistical classification schedules... § 30.61 Statistical classification schedules. The following statistical classification schedules are....census.gov/trade. (a) Schedule B—Statistical Classification for Domestic and Foreign Commodities Exported...

Network Data: Statistical Theory and New Models

DTIC Science & Technology

2016-02-17

SECURITY CLASSIFICATION OF: During this period of review, Bin Yu worked on many thrusts of high-dimensional statistical theory and methodologies. Her...research covered a wide range of topics in statistics including analysis and methods for spectral clustering for sparse and structured networks...2,7,8,21], sparse modeling (e.g. Lasso) [4,10,11,17,18,19], statistical guarantees for the EM algorithm [3], statistical analysis of algorithm leveraging

Assessing the Effectiveness of Statistical Classification Techniques in Predicting Future Employment of Participants in the Temporary Assistance for Needy Families Program

ERIC Educational Resources Information Center

Montoya, Isaac D.

2008-01-01

Three classification techniques (Chi-square Automatic Interaction Detection [CHAID], Classification and Regression Tree [CART], and discriminant analysis) were tested to determine their accuracy in predicting Temporary Assistance for Needy Families program recipients' future employment. Technique evaluation was based on proportion of correctly…

Quantitative classification of a historic northern Wisconsin (U.S.A.) landscape: mapping forests at regional scales

Treesearch

Lisa A. Schulte; David J. Mladenoff; Erik V. Nordheim

2002-01-01

We developed a quantitative and replicable classification system to improve understanding of historical composition and structure within northern Wisconsin's forests. The classification system was based on statistical cluster analysis and two forest metrics, relative dominance (% basal area) and relative importance (mean of relative dominance and relative density...

A discrimlnant function approach to ecological site classification in northern New England

Treesearch

James M. Fincher; Marie-Louise Smith

1994-01-01

Describes one approach to ecologically based classification of upland forest community types of the White and Green Mountain physiographic regions. The classification approach is based on an intensive statistical analysis of the relationship between the communities and soil-site factors. Discriminant functions useful in distinguishing between types based on soil-site...

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator); Knowlton, D. J.; Dean, M. E.

1981-01-01

A set of training statistics for the 30 meter resolution simulated thematic mapper MSS data was generated based on land use/land cover classes. In addition to this supervised data set, a nonsupervised multicluster block of training statistics is being defined in order to compare the classification results and evaluate the effect of the different training selection methods on classification performance. Two test data sets, defined using a stratified sampling procedure incorporating a grid system with dimensions of 50 lines by 50 columns, and another set based on an analyst supervised set of test fields were used to evaluate the classifications of the TMS data. The supervised training data set generated training statistics, and a per point Gaussian maximum likelihood classification of the 1979 TMS data was obtained. The August 1980 MSS data was radiometrically adjusted. The SAR data was redigitized and the SAR imagery was qualitatively analyzed.

Hydrometeorological application of an extratropical cyclone classification scheme in the southern United States

NASA Astrophysics Data System (ADS)

Senkbeil, J. C.; Brommer, D. M.; Comstock, I. J.; Loyd, T.

2012-07-01

Extratropical cyclones (ETCs) in the southern United States are often overlooked when compared with tropical cyclones in the region and ETCs in the northern United States. Although southern ETCs are significant weather events, there is currently not an operational scheme used for identifying and discussing these nameless storms. In this research, we classified 84 ETCs (1970-2009). We manually identified five distinct formation regions and seven unique ETC types using statistical classification. Statistical classification employed the use of principal components analysis and two methods of cluster analysis. Both manual and statistical storm types generally showed positive (negative) relationships with El Niño (La Niña). Manual storm types displayed precipitation swaths consistent with discrete storm tracks which further legitimizes the existence of multiple modes of southern ETCs. Statistical storm types also displayed unique precipitation intensity swaths, but these swaths were less indicative of track location. It is hoped that by classifying southern ETCs into types, that forecasters, hydrologists, and broadcast meteorologists might be able to better anticipate projected amounts of precipitation at their locations.

Statistical analysis on the concordance of the radiological evaluation of fractures of the distal radius subjected to traction☆

PubMed Central

Machado, Daniel Gonçalves; da Cruz Cerqueira, Sergio Auto; de Lima, Alexandre Fernandes; de Mathias, Marcelo Bezerra; Aramburu, José Paulo Gabbi; Rodarte, Rodrigo Ribeiro Pinho

2016-01-01

Objective The objective of this study was to evaluate the current classifications for fractures of the distal extremity of the radius, since the classifications made using traditional radiographs in anteroposterior and lateral views have been questioned regarding their reproducibility. In the literature, it has been suggested that other options are needed, such as use of preoperative radiographs on fractures of the distal radius subjected to traction, with stratification by the evaluators. The aim was to demonstrate which classification systems present better statistical reliability. Results In the Universal classification, the results from the third-year resident group (R3) and from the group of more experienced evaluators (Staff) presented excellent correlation, with a statistically significant p-value (p < 0.05). Neither of the groups presented a statistically significant result through the Frykman classification. In the AO classification, there were high correlations in the R3 and Staff groups (respectively 0.950 and 0.800), with p-values lower than 0.05 (respectively <0.001 and 0.003). Conclusion It can be concluded that radiographs performed under traction showed good concordance in the Staff group and in the R3 group, and that this is a good tactic for radiographic evaluations of fractures of the distal extremity of the radius. PMID:26962498

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Mapping patent classifications: portfolio and statistical analysis, and the comparison of strengths and weaknesses.

PubMed

Leydesdorff, Loet; Kogler, Dieter Franz; Yan, Bowen

2017-01-01

The Cooperative Patent Classifications (CPC) recently developed cooperatively by the European and US Patent Offices provide a new basis for mapping patents and portfolio analysis. CPC replaces International Patent Classifications (IPC) of the World Intellectual Property Organization. In this study, we update our routines previously based on IPC for CPC and use the occasion for rethinking various parameter choices. The new maps are significantly different from the previous ones, although this may not always be obvious on visual inspection. We provide nested maps online and a routine for generating portfolio overlays on the maps; a new tool is provided for "difference maps" between patent portfolios of organizations or firms. This is illustrated by comparing the portfolios of patents granted to two competing firms-Novartis and MSD-in 2016. Furthermore, the data is organized for the purpose of statistical analysis.

Oregon ground-water quality and its relation to hydrogeological factors; a statistical approach

USGS Publications Warehouse

Miller, T.L.; Gonthier, J.B.

1984-01-01

An appraisal of Oregon ground-water quality was made using existing data accessible through the U.S. Geological Survey computer system. The data available for about 1,000 sites were separated by aquifer units and hydrologic units. Selected statistical moments were described for 19 constituents including major ions. About 96 percent of all sites in the data base were sampled only once. The sample data were classified by aquifer unit and hydrologic unit and analysis of variance was run to determine if significant differences exist between the units within each of these two classifications for the same 19 constituents on which statistical moments were determined. Results of the analysis of variance indicated both classification variables performed about the same, but aquifer unit did provide more separation for some constituents. Samples from the Rogue River basin were classified by location within the flow system and type of flow system. The samples were then analyzed using analysis of variance on 14 constituents to determine if there were significant differences between subsets classified by flow path. Results of this analysis were not definitive, but classification as to the type of flow system did indicate potential for segregating water-quality data into distinct subsets. (USGS)

Three-column classification and Schatzker classification: a three- and two-dimensional computed tomography characterisation and analysis of tibial plateau fractures.

PubMed

Patange Subba Rao, Sheethal Prasad; Lewis, James; Haddad, Ziad; Paringe, Vishal; Mohanty, Khitish

2014-10-01

The aim of the study was to evaluate inter-observer reliability and intra-observer reproducibility between the three-column classification and Schatzker classification systems using 2D and 3D CT models. Fifty-two consecutive patients with tibial plateau fractures were evaluated by five orthopaedic surgeons. All patients were classified into Schatzker and three-column classification systems using x-rays and 2D and 3D CT images. The inter-observer reliability was evaluated in the first round and the intra-observer reliability was determined during the second round 2 weeks later. The average intra-observer reproducibility for the three-column classification was from substantial to excellent in all sub classifications, as compared with Schatzker classification. The inter-observer kappa values increased from substantial to excellent in three-column classification and to moderate in Schatzker classification The average values for three-column classification for all the categories are as follows: (I-III) k2D = 0.718, 95% CI 0.554-0.864, p < 0.0001 and average 3D = 0.874, 95% CI 0.754-0.890, p < 0.0001. For Schatzker classification system, the average values for all six categories are as follows: (I-VI) k2D = 0.536, 95% CI 0.365-0.685, p < 0.0001 and average k3D = 0.552 95% CI 0.405-0.700, p < 0.0001. The values are statistically significant. Statistically significant inter-observer values in both rounds were noted with the three-column classification, making it statistically an excellent agreement. The intra-observer reproducibility for the three-column classification improved as compared with the Schatzker classification. The three-column classification seems to be an effective way to characterise and classify fractures of tibial plateau.

Comparing ecoregional classifications for natural areas management in the Klamath Region, USA

USGS Publications Warehouse

Sarr, Daniel A.; Duff, Andrew; Dinger, Eric C.; Shafer, Sarah L.; Wing, Michael; Seavy, Nathaniel E.; Alexander, John D.

2015-01-01

We compared three existing ecoregional classification schemes (Bailey, Omernik, and World Wildlife Fund) with two derived schemes (Omernik Revised and Climate Zones) to explore their effectiveness in explaining species distributions and to better understand natural resource geography in the Klamath Region, USA. We analyzed presence/absence data derived from digital distribution maps for trees, amphibians, large mammals, small mammals, migrant birds, and resident birds using three statistical analyses of classification accuracy (Analysis of Similarity, Canonical Analysis of Principal Coordinates, and Classification Strength). The classifications were roughly comparable in classification accuracy, with Omernik Revised showing the best overall performance. Trees showed the strongest fidelity to the classifications, and large mammals showed the weakest fidelity. We discuss the implications for regional biogeography and describe how intermediate resolution ecoregional classifications may be appropriate for use as natural areas management domains.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, George

1993-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, Stanislav

1992-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

Effects of two classification strategies on a Benthic Community Index for streams in the Northern Lakes and Forests Ecoregion

USGS Publications Warehouse

Butcher, Jason T.; Stewart, Paul M.; Simon, Thomas P.

2003-01-01

Ninety-four sites were used to analyze the effects of two different classification strategies on the Benthic Community Index (BCI). The first, a priori classification, reflected the wetland status of the streams; the second, a posteriori classification, used a bio-environmental analysis to select classification variables. Both classifications were examined by measuring classification strength and testing differences in metric values with respect to group membership. The a priori (wetland) classification strength (83.3%) was greater than the a posteriori (bio-environmental) classification strength (76.8%). Both classifications found one metric that had significant differences between groups. The original index was modified to reflect the wetland classification by re-calibrating the scoring criteria for percent Crustacea and Mollusca. A proposed refinement to the original Benthic Community Index is suggested. This study shows the importance of using hypothesis-driven classifications, as well as exploratory statistical analysis, to evaluate alternative ways to reveal environmental variability in biological assessment tools.

Mathematical and Statistical Software Index.

DTIC Science & Technology

1986-08-01

geometric) mean HMEAN - harmonic mean MEDIAN - median MODE - mode QUANT - quantiles OGIVE - distribution curve IQRNG - interpercentile range RANGE ... range mutliphase pivoting algorithm cross-classification multiple discriminant analysis cross-tabul ation mul tipl e-objecti ve model curve fitting...Statistics). .. .. .... ...... ..... ...... ..... .. 21 *RANGEX (Correct Correlations for Curtailment of Range ). .. .. .... ...... ... 21 *RUMMAGE II (Analysis

The Effect of Unequal Samples, Heterogeneity of Covariance Matrices, and Number of Variables on Discriminant Analysis Classification Tables and Related Statistics.

ERIC Educational Resources Information Center

Spearing, Debra; Woehlke, Paula

To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…

Chance-corrected classification for use in discriminant analysis: Ecological applications

USGS Publications Warehouse

Titus, K.; Mosher, J.A.; Williams, B.K.

1984-01-01

A method for evaluating the classification table from a discriminant analysis is described. The statistic, kappa, is useful to ecologists in that it removes the effects of chance. It is useful even with equal group sample sizes although the need for a chance-corrected measure of prediction becomes greater with more dissimilar group sample sizes. Examples are presented.

Texture as a basis for acoustic classification of substrate in the nearshore region

NASA Astrophysics Data System (ADS)

Dennison, A.; Wattrus, N. J.

2016-12-01

Segmentation and classification of substrate type from two locations in Lake Superior, are predicted using multivariate statistical processing of textural measures derived from shallow-water, high-resolution multibeam bathymetric data. During a multibeam sonar survey, both bathymetric and backscatter data are collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on substrate type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. Preliminary results from an analysis of bathymetric data and ground-truth samples collected from the Amnicon River, Superior, Wisconsin, and the Lester River, Duluth, Minnesota, demonstrate the ability to process and develop a novel classification scheme of the bottom type in two geomorphologically distinct areas.

Modeling time-to-event (survival) data using classification tree analysis.

PubMed

Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Transforming Graph Data for Statistical Relational Learning

DTIC Science & Technology

2012-10-01

Jordan, 2003), PLSA (Hofmann, 1999), ? Classification via RMN (Taskar et al., 2003) or SVM (Hasan, Chaoji, Salem , & Zaki, 2006) ? Hierarchical...dimensionality reduction methods such as Principal 407 Rossi, McDowell, Aha, & Neville Component Analysis (PCA), Principal Factor Analysis ( PFA ), and...clustering algorithm. Journal of the Royal Statistical Society. Series C, Applied statistics, 28, 100–108. Hasan, M. A., Chaoji, V., Salem , S., & Zaki, M

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry

NASA Astrophysics Data System (ADS)

Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y.; Drake, Steven K.; Gucek, Marjan; Sacks, David B.; Yu, Yi-Kuo

2018-06-01

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry.

PubMed

Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y; Drake, Steven K; Gucek, Marjan; Sacks, David B; Yu, Yi-Kuo

2018-06-05

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

The classification of anxiety and hysterical states. Part I. Historical review and empirical delineation.

PubMed

Sheehan, D V; Sheehan, K H

1982-08-01

The history of the classification of anxiety, hysterical, and hypochondriacal disorders is reviewed. Problems in the ability of current classification schemes to predict, control, and describe the relationship between the symptoms and other phenomena are outlined. Existing classification schemes failed the first test of a good classification model--that of providing categories that are mutually exclusive. The independence of these diagnostic categories from each other does not appear to hold up on empirical testing. In the absence of inherently mutually exclusive categories, further empirical investigation of these classes is obstructed since statistically valid analysis of the nominal data and any useful multivariate analysis would be difficult if not impossible. It is concluded that the existing classifications are unsatisfactory and require some fundamental reconceptualization.

14 CFR Section 19 - Uniform Classification of Operating Statistics

Code of Federal Regulations, 2011 CFR

2011-01-01

... Statistics Section 19 Section 19 Aeronautics and Space OFFICE OF THE SECRETARY, DEPARTMENT OF TRANSPORTATION... AIR CARRIERS Operating Statistics Classifications Section 19 Uniform Classification of Operating Statistics ...

14 CFR Section 19 - Uniform Classification of Operating Statistics

Code of Federal Regulations, 2010 CFR

2010-01-01

... Statistics Section 19 Section 19 Aeronautics and Space OFFICE OF THE SECRETARY, DEPARTMENT OF TRANSPORTATION... AIR CARRIERS Operating Statistics Classifications Section 19 Uniform Classification of Operating Statistics ...

Differential gene expression detection and sample classification using penalized linear regression models.

PubMed

Wu, Baolin

2006-02-15

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.

Automated Clinical Assessment from Smart home-based Behavior Data

PubMed Central

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-01-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348

On the use of administrative databases to support planning activities: the case of the evaluation of neonatal case-mix in the Emilia-Romagna region using DRG and APR-DRG classification systems.

PubMed

Fantini, M P; Cisbani, L; Manzoli, L; Vertrees, J; Lorenzoni, L

2003-06-01

There are several versions of the Diagnosis Related Group (DRG) classification systems that are used for case-mix analysis, utilization review, prospective payment, and planning applications. The objective of this study was to assess the adequacy of two of these DRG systems--Medicare DRG and All Patient Refined DRG--to classify neonatal patients. The first part of the paper contains a descriptive analysis that outlines the major differences between the two systems in terms of classification logic and variables used in the assignment process. The second part examines the statistical performance of each system on the basis of the administrative data collected in all public hospitals of the Emilia-Romagna region relating to neonates discharged in 1997 and 1998. The Medicare DRG are less developed in terms of classification structure and yield a poorer statistical performance in terms of reduction in variance for length of stay. This is important because, for specific areas, a more refined system can prove useful at regional level to remove systematic biases in the measurement of case-mix due to the structural characteristics of the Medicare DRGs classification system.

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

NASA Technical Reports Server (NTRS)

Sadowski, F. E.; Sarno, J. E.

1976-01-01

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

An online sleep apnea detection method based on recurrence quantification analysis.

PubMed

Nguyen, Hoa Dinh; Wilkins, Brek A; Cheng, Qi; Benjamin, Bruce Allen

2014-07-01

This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.

Provenance establishment of coffee using solution ICP-MS and ICP-AES.

PubMed

Valentin, Jenna L; Watling, R John

2013-11-01

Statistical interpretation of the concentrations of 59 elements, determined using solution based inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma emission spectroscopy (ICP-AES), was used to establish the provenance of coffee samples from 15 countries across five continents. Data confirmed that the harvest year, degree of ripeness and whether the coffees were green or roasted had little effect on the elemental composition of the coffees. The application of linear discriminant analysis and principal component analysis of the elemental concentrations permitted up to 96.9% correct classification of the coffee samples according to their continent of origin. When samples from each continent were considered separately, up to 100% correct classification of coffee samples into their countries, and plantations of origin was achieved. This research demonstrates the potential of using elemental composition, in combination with statistical classification methods, for accurate provenance establishment of coffee. Copyright © 2013 Elsevier Ltd. All rights reserved.

Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models

NASA Astrophysics Data System (ADS)

Giesen, Joachim; Mueller, Klaus; Taneva, Bilyana; Zolliker, Peter

Conjoint analysis is a family of techniques that originated in psychology and later became popular in market research. The main objective of conjoint analysis is to measure an individual's or a population's preferences on a class of options that can be described by parameters and their levels. We consider preference data obtained in choice-based conjoint analysis studies, where one observes test persons' choices on small subsets of the options. There are many ways to analyze choice-based conjoint analysis data. Here we discuss the intuition behind a classification based approach, and compare this approach to one based on statistical assumptions (discrete choice models) and to a regression approach. Our comparison on real and synthetic data indicates that the classification approach outperforms the discrete choice models.

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features

NASA Astrophysics Data System (ADS)

Srinivasan, Yeshwanth; Hernes, Dana; Tulpule, Bhakti; Yang, Shuyu; Guo, Jiangling; Mitra, Sunanda; Yagneswaran, Sriraja; Nutter, Brian; Jeronimo, Jose; Phillips, Benny; Long, Rodney; Ferris, Daron

2005-04-01

Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic markers is extremely image-specific. The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating an archive of 60,000 digitized color images of the uterine cervix. NLM is developing tools for the analysis and dissemination of these images over the Web for the study of visual features correlated with precancerous neoplasia and cancer. To enable indexing of images of the cervix, it is essential to develop algorithms for the segmentation of regions of interest, such as acetowhitened regions, and automatic identification and classification of regions exhibiting mosaicism and punctation. Success of such algorithms depends, primarily, on the selection of relevant features representing the region of interest. We present color and geometric features based statistical classification and segmentation algorithms yielding excellent identification of the regions of interest. The distinct classification of the mosaic regions from the non-mosaic ones has been obtained by clustering multiple geometric and color features of the segmented sections using various morphological and statistical approaches. Such automated classification methodologies will facilitate content-based image retrieval from the digital archive of uterine cervix and have the potential of developing an image based screening tool for cervical cancer.

Analysis of the changes in the tarcrete layer on the desert surface of Kuwait using satellite imagery and cell-based modeling

NASA Astrophysics Data System (ADS)

Al-Doasari, Ahmad E.

The 1991 Gulf War caused massive environmental damage in Kuwait. Deposition of oil and soot droplets from hundreds of burning oil-wells created a layer of tarcrete on the desert surface covering over 900 km2. This research investigates the spatial change in the tarcrete extent from 1991 to 1998 using Landsat Thematic Mapper (TM) imagery and statistical modeling techniques. The pixel structure of TM data allows the spatial analysis of the change in tarcrete extent to be conducted at the pixel (cell) level within a geographical information system (GIS). There are two components to this research. The first is a comparison of three remote sensing classification techniques used to map the tarcrete layer. The second is a spatial-temporal analysis and simulation of tarcrete changes through time. The analysis focuses on an area of 389 km2 located south of the Al-Burgan oil field. Five TM images acquired in 1991, 1993, 1994, 1995, and 1998 were geometrically and atmospherically corrected. These images were classified into six classes: oil lakes; heavy, intermediate, light, and traces of tarcrete; and sand. The classification methods tested were unsupervised, supervised, and neural network supervised (fuzzy ARTMAP). Field data of tarcrete characteristics were collected to support the classification process and to evaluate the classification accuracies. Overall, the neural network method is more accurate (60 percent) than the other two methods; both the unsupervised and the supervised classification accuracy assessments resulted in 46 percent accuracy. The five classifications were used in a lagged autologistic model to analyze the spatial changes of the tarcrete through time. The autologistic model correctly identified overall tarcrete contraction between 1991--1993 and 1995--1998. However, tarcrete contraction between 1993--1994 and 1994--1995 was less well marked, in part because of classification errors in the maps from these time periods. Initial simulations of tarcrete contraction with a cellular automaton model were not very successful. However, more accurate classifications could improve the simulations. This study illustrates how an empirical investigation using satellite images, field data, GIS, and spatial statistics can simulate dynamic land-cover change through the use of a discrete statistical and cellular automaton model.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

PubMed Central

Belgiu, Mariana; Drǎguţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules. PMID:24623959

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Belgiu, Mariana; ǎguţ, Lucian, , Dr; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

NASA Astrophysics Data System (ADS)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

Using classification tree analysis to predict oak wilt distribution in Minnesota and Texas

Treesearch

Marla c. Downing; Vernon L. Thomas; Jennifer Juzwik; David N. Appel; Robin M. Reich; Kim Camilli

2008-01-01

We developed a methodology and compared results for predicting the potential distribution of Ceratocystis fagacearum (causal agent of oak wilt), in both Anoka County, MN, and Fort Hood, TX. The Potential Distribution of Oak Wilt (PDOW) utilizes a binary classification tree statistical technique that incorporates: geographical information systems (GIS...

Validity Evidence in Scale Development: The Application of Cross Validation and Classification-Sequencing Validation

ERIC Educational Resources Information Center

Acar, Tu¨lin

2014-01-01

In literature, it has been observed that many enhanced criteria are limited by factor analysis techniques. Besides examinations of statistical structure and/or psychological structure, such validity studies as cross validation and classification-sequencing studies should be performed frequently. The purpose of this study is to examine cross…

Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease

PubMed Central

Koikkalainen, Juha; Lötjönen, Jyrki; Thurfjell, Lennart; Rueckert, Daniel; Waldemar, Gunhild; Soininen, Hilkka

2012-01-01

In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and comparedtothe conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1%for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM. PMID:21419228

The Warning System in Disaster Situations: A Selective Analysis.

DTIC Science & Technology

DISASTERS, *WARNING SYSTEMS), CIVIL DEFENSE, SOCIAL PSYCHOLOGY, REACTION(PSYCHOLOGY), FACTOR ANALYSIS, CLASSIFICATION, STATISTICAL DATA, TIME ... MANAGEMENT PLANNING AND CONTROL, DAMAGE, CONTROL SYSTEMS, THREAT EVALUATION, DECISION MAKING, DATA PROCESSING, COMMUNICATION SYSTEMS, NUCLEAR EXPLOSIONS

An unsupervised classification technique for multispectral remote sensing data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Cummings, R. E.

1973-01-01

Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

Unsupervised classification of earth resources data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Jayroe, R. R., Jr.; Cummings, R. E.

1972-01-01

A new clustering technique is presented. It consists of two parts: (a) a sequential statistical clustering which is essentially a sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by existing supervised maximum liklihood classification technique.

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

PubMed

Diykh, Mohammed; Li, Yan; Wen, Peng

2016-11-01

The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

Rapid Identification of Candida Species by Using Nuclear Magnetic Resonance Spectroscopy and a Statistical Classification Strategy

PubMed Central

Himmelreich, Uwe; Somorjai, Ray L.; Dolenko, Brion; Lee, Ok Cha; Daniel, Heide-Marie; Murray, Ronan; Mountford, Carolyn E.; Sorrell, Tania C.

2003-01-01

Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms. PMID:12902244

Application of PCA and SIMCA statistical analysis of FT-IR spectra for the classification and identification of different slag types with environmental origin.

PubMed

Stumpe, B; Engel, T; Steinweg, B; Marschner, B

2012-04-03

In the past, different slag materials were often used for landscaping and construction purposes or simply dumped. Nowadays German environmental laws strictly control the use of slags, but there is still a remaining part of 35% which is uncontrolled dumped in landfills. Since some slags have high heavy metal contents and different slag types have typical chemical and physical properties that will influence the risk potential and other characteristics of the deposits, an identification of the slag types is needed. We developed a FT-IR-based statistical method to identify different slags classes. Slags samples were collected at different sites throughout various cities within the industrial Ruhr area. Then, spectra of 35 samples from four different slags classes, ladle furnace (LF), blast furnace (BF), oxygen furnace steel (OF), and zinc furnace slags (ZF), were determined in the mid-infrared region (4000-400 cm(-1)). The spectra data sets were subject to statistical classification methods for the separation of separate spectral data of different slag classes. Principal component analysis (PCA) models for each slag class were developed and further used for soft independent modeling of class analogy (SIMCA). Precise classification of slag samples into four different slag classes were achieved using two different SIMCA models stepwise. At first, SIMCA 1 was used for classification of ZF as well as OF slags over the total spectral range. If no correct classification was found, then the spectrum was analyzed with SIMCA 2 at reduced wavenumbers for the classification of LF as well as BF spectra. As a result, we provide a time- and cost-efficient method based on FT-IR spectroscopy for processing and identifying large numbers of environmental slag samples.

Variance approximations for assessments of classification accuracy

Treesearch

R. L. Czaplewski

1994-01-01

Variance approximations are derived for the weighted and unweighted kappa statistics, the conditional kappa statistic, and conditional probabilities. These statistics are useful to assess classification accuracy, such as accuracy of remotely sensed classifications in thematic maps when compared to a sample of reference classifications made in the field. Published...

Statistical significance of task related deep brain EEG dynamic changes in the time-frequency domain.

PubMed

Chládek, J; Brázdil, M; Halámek, J; Plešinger, F; Jurák, P

2013-01-01

We present an off-line analysis procedure for exploring brain activity recorded from intra-cerebral electroencephalographic data (SEEG). The objective is to determine the statistical differences between different types of stimulations in the time-frequency domain. The procedure is based on computing relative signal power change and subsequent statistical analysis. An example of characteristic statistically significant event-related de/synchronization (ERD/ERS) detected across different frequency bands following different oddball stimuli is presented. The method is used for off-line functional classification of different brain areas.

Classification accuracy on the family planning participation status using kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Kurniawan, Dian; Suparti; Sugito

2018-05-01

Population growth in Indonesia has increased every year. According to the population census conducted by the Central Bureau of Statistics (BPS) in 2010, the population of Indonesia has reached 237.6 million people. Therefore, to control the population growth rate, the government hold Family Planning or Keluarga Berencana (KB) program for couples of childbearing age. The purpose of this program is to improve the health of mothers and children in order to manifest prosperous society by controlling births while ensuring control of population growth. The data used in this study is the updated family data of Semarang city in 2016 that conducted by National Family Planning Coordinating Board (BKKBN). From these data, classifiers with kernel discriminant analysis will be obtained, and also classification accuracy will be obtained from that method. The result of the analysis showed that normal kernel discriminant analysis gives 71.05 % classification accuracy with 28.95 % classification error. Whereas triweight kernel discriminant analysis gives 73.68 % classification accuracy with 26.32 % classification error. Using triweight kernel discriminant for data preprocessing of family planning participation of childbearing age couples in Semarang City of 2016 can be stated better than with normal kernel discriminant.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

A Classification of Statistics Courses (A Framework for Studying Statistical Education)

ERIC Educational Resources Information Center

Turner, J. C.

1976-01-01

A classification of statistics courses in presented, with main categories of "course type,""methods of presentation,""objectives," and "syllabus." Examples and suggestions for uses of the classification are given. (DT)

Assessment of statistical methods used in library-based approaches to microbial source tracking.

PubMed

Ritter, Kerry J; Carruthers, Ethan; Carson, C Andrew; Ellender, R D; Harwood, Valerie J; Kingsley, Kyle; Nakatsu, Cindy; Sadowsky, Michael; Shear, Brian; West, Brian; Whitlock, John E; Wiggins, Bruce A; Wilbur, Jayson D

2003-12-01

Several commonly used statistical methods for fingerprint identification in microbial source tracking (MST) were examined to assess the effectiveness of pattern-matching algorithms to correctly identify sources. Although numerous statistical methods have been employed for source identification, no widespread consensus exists as to which is most appropriate. A large-scale comparison of several MST methods, using identical fecal sources, presented a unique opportunity to assess the utility of several popular statistical methods. These included discriminant analysis, nearest neighbour analysis, maximum similarity and average similarity, along with several measures of distance or similarity. Threshold criteria for excluding uncertain or poorly matched isolates from final analysis were also examined for their ability to reduce false positives and increase prediction success. Six independent libraries used in the study were constructed from indicator bacteria isolated from fecal materials of humans, seagulls, cows and dogs. Three of these libraries were constructed using the rep-PCR technique and three relied on antibiotic resistance analysis (ARA). Five of the libraries were constructed using Escherichia coli and one using Enterococcus spp. (ARA). Overall, the outcome of this study suggests a high degree of variability across statistical methods. Despite large differences in correct classification rates among the statistical methods, no single statistical approach emerged as superior. Thresholds failed to consistently increase rates of correct classification and improvement was often associated with substantial effective sample size reduction. Recommendations are provided to aid in selecting appropriate analyses for these types of data.

A statistically harmonized alignment-classification in image space enables accurate and robust alignment of noisy images in single particle analysis.

PubMed

Kawata, Masaaki; Sato, Chikara

2007-06-01

In determining the three-dimensional (3D) structure of macromolecular assemblies in single particle analysis, a large representative dataset of two-dimensional (2D) average images from huge number of raw images is a key for high resolution. Because alignments prior to averaging are computationally intensive, currently available multireference alignment (MRA) software does not survey every possible alignment. This leads to misaligned images, creating blurred averages and reducing the quality of the final 3D reconstruction. We present a new method, in which multireference alignment is harmonized with classification (multireference multiple alignment: MRMA). This method enables a statistical comparison of multiple alignment peaks, reflecting the similarities between each raw image and a set of reference images. Among the selected alignment candidates for each raw image, misaligned images are statistically excluded, based on the principle that aligned raw images of similar projections have a dense distribution around the correctly aligned coordinates in image space. This newly developed method was examined for accuracy and speed using model image sets with various signal-to-noise ratios, and with electron microscope images of the Transient Receptor Potential C3 and the sodium channel. In every data set, the newly developed method outperformed conventional methods in robustness against noise and in speed, creating 2D average images of higher quality. This statistically harmonized alignment-classification combination should greatly improve the quality of single particle analysis.

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability. PMID:28903223

Neural network approaches versus statistical methods in classification of multisource remote sensing data

NASA Technical Reports Server (NTRS)

Benediktsson, Jon A.; Swain, Philip H.; Ersoy, Okan K.

1990-01-01

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

Statistical Signal Models and Algorithms for Image Analysis

DTIC Science & Technology

1984-10-25

In this report, two-dimensional stochastic linear models are used in developing algorithms for image analysis such as classification, segmentation, and object detection in images characterized by textured backgrounds. These models generate two-dimensional random processes as outputs to which statistical inference procedures can naturally be applied. A common thread throughout our algorithms is the interpretation of the inference procedures in terms of linear prediction

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Quantitation of flavonoid constituents in citrus fruits.

PubMed

Kawaii, S; Tomono, Y; Katase, E; Ogawa, K; Yano, M

1999-09-01

Twenty-four flavonoids have been determined in 66 Citrus species and near-citrus relatives, grown in the same field and year, by means of reversed phase high-performance liquid chromatography analysis. Statistical methods have been applied to find relations among the species. The F ratios of 21 flavonoids obtained by applying ANOVA analysis are significant, indicating that a classification of the species using these variables is reasonable to pursue. Principal component analysis revealed that the distributions of Citrus species belonging to different classes were largely in accordance with Tanaka's classification system.

Singularity and Nonnormality in the Classification of Compositional Data

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.; Olea, R.A.; Harff, Jan

1998-01-01

Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore-Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data.

Back-and-Forth Methodology for Objective Voice Quality Assessment: From/to Expert Knowledge to/from Automatic Classification of Dysphonia

NASA Astrophysics Data System (ADS)

Fredouille, Corinne; Pouchoulin, Gilles; Ghio, Alain; Revis, Joana; Bonastre, Jean-François; Giovanni, Antoine

2009-12-01

This paper addresses voice disorder assessment. It proposes an original back-and-forth methodology involving an automatic classification system as well as knowledge of the human experts (machine learning experts, phoneticians, and pathologists). The goal of this methodology is to bring a better understanding of acoustic phenomena related to dysphonia. The automatic system was validated on a dysphonic corpus (80 female voices), rated according to the GRBAS perceptual scale by an expert jury. Firstly, focused on the frequency domain, the classification system showed the interest of 0-3000 Hz frequency band for the classification task based on the GRBAS scale. Later, an automatic phonemic analysis underlined the significance of consonants and more surprisingly of unvoiced consonants for the same classification task. Submitted to the human experts, these observations led to a manual analysis of unvoiced plosives, which highlighted a lengthening of VOT according to the dysphonia severity validated by a preliminary statistical analysis.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Analysis of select Dalbergia and trade timber using direct analysis in real time and time-of-flight mass spectrometry for CITES enforcement.

PubMed

Lancaster, Cady; Espinoza, Edgard

2012-05-15

International trade of several Dalbergia wood species is regulated by The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). In order to supplement morphological identification of these species, a rapid chemical method of analysis was developed. Using Direct Analysis in Real Time (DART) ionization coupled with Time-of-Flight (TOF) Mass Spectrometry (MS), selected Dalbergia and common trade species were analyzed. Each of the 13 wood species was classified using principal component analysis and linear discriminant analysis (LDA). These statistical data clusters served as reliable anchors for species identification of unknowns. Analysis of 20 or more samples from the 13 species studied in this research indicates that the DART-TOFMS results are reproducible. Statistical analysis of the most abundant ions gave good classifications that were useful for identifying unknown wood samples. DART-TOFMS and LDA analysis of 13 species of selected timber samples and the statistical classification allowed for the correct assignment of unknown wood samples. This method is rapid and can be useful when anatomical identification is difficult but needed in order to support CITES enforcement. Published 2012. This article is a US Government work and is in the public domain in the USA.

Signal analysis techniques for incipient failure detection in turbomachinery

NASA Technical Reports Server (NTRS)

Coffin, T.

1985-01-01

Signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery were developed, implemented and evaluated. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques were implemented on a computer and applied to dynamic signals. A laboratory evaluation of the methods with respect to signal detection capability is described. Plans for further technique evaluation and data base development to characterize turbopump incipient failure modes from Space Shuttle main engine (SSME) hot firing measurements are outlined.

Sexual Dimorphism Analysis and Gender Classification in 3D Human Face

NASA Astrophysics Data System (ADS)

Hu, Yuan; Lu, Li; Yan, Jingqi; Liu, Zhi; Shi, Pengfei

In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method.This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.

Crop Identification Technology Assessment for Remote Sensing (CITARS)

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cary, T. K.; Davis, B. J.; Swain, P. H.

1975-01-01

The results of classifications and experiments performed for the Crop Identification Technology Assessment for Remote Sensing (CITARS) project are summarized. Fifteen data sets were classified using two analysis procedures. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. In addition, 20 data sets were classified using training statistics from another segment or date. The results of both the local and non-local classifications in terms of classification and proportion estimation are presented. Several additional experiments are described which were performed to provide additional understanding of the CITARS results. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, the spectral discriminability of corn, soybeans, and other, and analysis of aircraft multispectral data.

Land use/land cover mapping (1:25000) of Taiwan, Republic of China by automated multispectral interpretation of LANDSAT imagery

NASA Technical Reports Server (NTRS)

Sung, Q. C.; Miller, L. D.

1977-01-01

Three methods were tested for collection of the training sets needed to establish the spectral signatures of the land uses/land covers sought due to the difficulties of retrospective collection of representative ground control data. Computer preprocessing techniques applied to the digital images to improve the final classification results were geometric corrections, spectral band or image ratioing and statistical cleaning of the representative training sets. A minimal level of statistical verification was made based upon the comparisons between the airphoto estimates and the classification results. The verifications provided a further support to the selection of MSS band 5 and 7. It also indicated that the maximum likelihood ratioing technique can achieve more agreeable classification results with the airphoto estimates than the stepwise discriminant analysis.

Characteristics and Classification of Least Altered Streamflows in Massachusetts

USGS Publications Warehouse

Armstrong, David S.; Parker, Gene W.; Richards, Todd A.

2008-01-01

Streamflow records from 85 streamflow-gaging stations at which streamflows were considered to be least altered were used to characterize natural streamflows within southern New England. Period-of-record streamflow data were used to determine annual hydrographs of median monthly flows. The shapes and magnitudes of annual hydrographs of median monthly flows, normalized by drainage area, differed among stations in different geographic areas of southern New England. These differences were gradational across southern New England and were attributed to differences in basin and climate characteristics. Period-of-record streamflow data were also used to analyze the statistical properties of daily streamflows at 61 stations across southern New England by using L-moment ratios. An L-moment ratio diagram of L-skewness and L-kurtosis showed a continuous gradation in these properties between stations and indicated differences between base-flow dominated and runoff-dominated rivers. Streamflow records from a concurrent period (1960-2004) for 61 stations were used in a multivariate statistical analysis to develop a hydrologic classification of rivers in southern New England. Missing records from 46 of these stations were extended by using a Maintenance of Variation Extension technique. The concurrent-period streamflows were used in the Indicators of Hydrologic Alteration and Hydrologic Index Tool programs to determine 224 hydrologic indices for the 61 stations. Principal-components analysis (PCA) was used to reduce the number of hydrologic indices to 20 that provided nonredundant information. The PCA also indicated that the major patterns of variability in the dataset are related to differences in flow variability and low-flow magnitude among the stations. Hierarchical cluster analysis was used to classify stations into groups with similar hydrologic properties. The cluster analysis classified rivers in southern New England into two broad groups: (1) base-flow dominated rivers, whose statistical properties indicated less flow variability and high magnitudes of low flow, and (2) runoff-dominated rivers, whose statistical properties indicated greater flow variability and lower magnitudes of low flow. A four-cluster classification further classified the runoff-dominated streams into three groups that varied in gradient, elevation, and differences in winter streamflow conditions: high-gradient runoff-dominated rivers, northern runoff-dominated rivers, and southern runoff-dominated rivers. A nine-cluster division indicated that basin size also becomes a distinguishing factor among basins at finer levels of classification. Smaller basins (less than 10 square miles) were classified into different groups than larger basins. A comparison of station classifications indicated that a classification based on multiple hydrologic indices that represent different aspects of the flow regime did not result in the same classification of stations as a classification based on a single type of statistic such as a monthly median. River basins identified by the cluster analysis as having similar hydrologic properties tended to have similar basin and climate characteristics and to be in close proximity to one another. Stations were not classified in the same cluster on the basis of geographic location alone; as a result, boundaries cannot be drawn between geographic regions with similar streamflow characteristics. Rivers with different basin and climate characteristics were classified in different clusters, even if they were in adjacent basins or upstream and downstream within the same basin.

A PROPOSED CHEMICAL INFORMATION AND DATA SYSTEM. VOLUME I.

DTIC Science & Technology

CHEMICAL COMPOUNDS, *DATA PROCESSING, *INFORMATION RETRIEVAL, * CHEMICAL ANALYSIS, INPUT OUTPUT DEVICES, COMPUTER PROGRAMMING, CLASSIFICATION...CONFIGURATIONS, DATA STORAGE SYSTEMS, ATOMS, MOLECULES, PERFORMANCE( ENGINEERING ), MAINTENANCE, SUBJECT INDEXING, MAGNETIC TAPE, AUTOMATIC, MILITARY REQUIREMENTS, TYPEWRITERS, OPTICS, TOPOLOGY, STATISTICAL ANALYSIS, FLOW CHARTING.

Supervised classification in the presence of misclassified training data: a Monte Carlo simulation study in the three group case.

PubMed

Bolin, Jocelyn Holden; Finch, W Holmes

2014-01-01

Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.

Repertoire and classification of non-song calls in Southeast Alaskan humpback whales (Megaptera novaeangliae).

PubMed

Fournet, Michelle E; Szabo, Andy; Mellinger, David K

2015-01-01

On low-latitude breeding grounds, humpback whales produce complex and highly stereotyped songs as well as a range of non-song sounds associated with breeding behaviors. While on their Southeast Alaskan foraging grounds, humpback whales produce a range of previously unclassified non-song vocalizations. This study investigates the vocal repertoire of Southeast Alaskan humpback whales from a sample of 299 non-song vocalizations collected over a 3-month period on foraging grounds in Frederick Sound, Southeast Alaska. Three classification systems were used, including aural spectrogram analysis, statistical cluster analysis, and discriminant function analysis, to describe and classify vocalizations. A hierarchical acoustic structure was identified; vocalizations were classified into 16 individual call types nested within four vocal classes. The combined classification method shows promise for identifying variability in call stereotypy between vocal groupings and is recommended for future classification of broad vocal repertoires.

[Evaluation of the appropriateness of hospital admissions using the iso-gravity classification systems APR-DRG and Disease Staging and the Italian version of Appropriateness Evaluation Protocol (AEP)].

PubMed

D'Andrea, G; Capalbo, G; Volpe, M; Marchetti, M; Vicentini, F; Capelli, G; Cambieri, A; Cicchetti, A; Ricciardi, G; Catananti, C

2006-01-01

Our main purpose was to evaluate the organizational appropriateness of admissions made in a university hospital, by comparing two iso-gravity classification systems, APR-DRG and Disease Staging, with the Italian version of AEP (PRUO). Our analysis focused on admissions made in 2001, related to specific Diagnosis Related Groups (DRGs), which, according an Italian Law, would be considered at high risk of inappropriateness, if treated as ordinary admissions. The results obtained by using the 2 classification systems did not show statistically significant differences with respect to the total number of admissions. On the other hand, some DRGs showed statistically significant differences due to different algorithms of attribution of the severity levels used by the two systems. For almost all of the DRGs studied, the AEP-based analysis of a sample of medical records showed an higher number of inappropriate admissions in comparison with the number expected by iso-gravity classification methods. The difference is possibly due to the percentage limits of tolerability fixed by the Law for each DRG. Therefore, the authors suggest an integrated use of the two methods to evaluate organizational appropriateness of hospital admissions.

IRIS COLOUR CLASSIFICATION SCALES – THEN AND NOW

PubMed Central

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual’s eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale. PMID:27373112

IRIS COLOUR CLASSIFICATION SCALES--THEN AND NOW.

PubMed

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual's eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

Na, X D; Zang, S Y; Wu, C S; Li, W L

2015-11-01

Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.

Automated training site selection for large-area remote-sensing image analysis

NASA Astrophysics Data System (ADS)

McCaffrey, Thomas M.; Franklin, Steven E.

1993-11-01

A computer program is presented to select training sites automatically from remotely sensed digital imagery. The basic ideas are to guide the image analyst through the process of selecting typical and representative areas for large-area image classifications by minimizing bias, and to provide an initial list of potential classes for which training sites are required to develop a classification scheme or to verify classification accuracy. Reducing subjectivity in training site selection is achieved by using a purely statistical selection of homogeneous sites which then can be compared to field knowledge, aerial photography, or other remote-sensing imagery and ancillary data to arrive at a final selection of sites to be used to train the classification decision rules. The selection of the homogeneous sites uses simple tests based on the coefficient of variance, the F-statistic, and the Student's i-statistic. Comparisons of site means are conducted with a linear growing list of previously located homogeneous pixels. The program supports a common pixel-interleaved digital image format and has been tested on aerial and satellite optical imagery. The program is coded efficiently in the C programming language and was developed under AIX-Unix on an IBM RISC 6000 24-bit color workstation.

Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel-based "mouse pup syllable classification calculator".

PubMed

Grimsley, Jasmine M S; Gadziola, Marie A; Wenstrup, Jeffrey J

2012-01-01

Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified 10 syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

Radiographic classifications in Perthes disease

PubMed Central

Huhnstock, Stefan; Svenningsen, Svein; Merckoll, Else; Catterall, Anthony; Terjesen, Terje; Wiig, Ola

2017-01-01

Background and purpose Different radiographic classifications have been proposed for prediction of outcome in Perthes disease. We assessed whether the modified lateral pillar classification would provide more reliable interobserver agreement and prognostic value compared with the original lateral pillar classification and the Catterall classification. Patients and methods 42 patients (38 boys) with Perthes disease were included in the interobserver study. Their mean age at diagnosis was 6.5 (3–11) years. 5 observers classified the radiographs in 2 separate sessions according to the Catterall classification, the original and the modified lateral pillar classifications. Interobserver agreement was analysed using weighted kappa statistics. We assessed the associations between the classifications and femoral head sphericity at 5-year follow-up in 37 non-operatively treated patients in a crosstable analysis (Gamma statistics for ordinal variables, γ). Results The original lateral pillar and Catterall classifications showed moderate interobserver agreement (kappa 0.49 and 0.43, respectively) while the modified lateral pillar classification had fair agreement (kappa 0.40). The original lateral pillar classification was strongly associated with the 5-year radiographic outcome, with a mean γ correlation coefficient of 0.75 (95% CI: 0.61–0.95) among the 5 observers. The modified lateral pillar and Catterall classifications showed moderate associations (mean γ correlation coefficient 0.55 [95% CI: 0.38–0.66] and 0.64 [95% CI: 0.57–0.72], respectively). Interpretation The Catterall classification and the original lateral pillar classification had sufficient interobserver agreement and association to late radiographic outcome to be suitable for clinical use. Adding the borderline B/C group did not increase the interobserver agreement or prognostic value of the original lateral pillar classification. PMID:28613966

A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield

PubMed Central

Ringard, Justine; Seyler, Frederique; Linguet, Laurent

2017-01-01

Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale. PMID:28621723

A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield.

PubMed

Ringard, Justine; Seyler, Frederique; Linguet, Laurent

2017-06-16

Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

On the Implementation of a Land Cover Classification System for SAR Images Using Khoros

NASA Technical Reports Server (NTRS)

Medina Revera, Edwin J.; Espinosa, Ramon Vasquez

1997-01-01

The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.

A Data Analysis of Naval Air Systems Command Funding Documents

DTIC Science & Technology

2017-06-01

Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management ...Business & Financial Managers 15. NUMBER OF PAGES 75 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY...Summary Statistics for Regressions with a Statistically Significant Relationship

The composite sequential clustering technique for analysis of multispectral scanner data

NASA Technical Reports Server (NTRS)

Su, M. Y.

1972-01-01

The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

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

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hydrologic Landscape Regionalisation Using Deductive Classification and Random Forests

PubMed Central

Brown, Stuart C.; Lester, Rebecca E.; Versace, Vincent L.; Fawcett, Jonathon; Laurenson, Laurie

2014-01-01

Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents. PMID:25396410

Hydrologic landscape regionalisation using deductive classification and random forests.

PubMed

Brown, Stuart C; Lester, Rebecca E; Versace, Vincent L; Fawcett, Jonathon; Laurenson, Laurie

2014-01-01

Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents.

Large-scale gene function analysis with the PANTHER classification system.

PubMed

Mi, Huaiyu; Muruganujan, Anushya; Casagrande, John T; Thomas, Paul D

2013-08-01

The PANTHER (protein annotation through evolutionary relationship) classification system (http://www.pantherdb.org/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments. The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs). Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways. The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests. It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. In the 2013 release of PANTHER (v.8.0), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability. This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Superordinate Shape Classification Using Natural Shape Statistics

ERIC Educational Resources Information Center

Wilder, John; Feldman, Jacob; Singh, Manish

2011-01-01

This paper investigates the classification of shapes into broad natural categories such as "animal" or "leaf". We asked whether such coarse classifications can be achieved by a simple statistical classification of the shape skeleton. We surveyed databases of natural shapes, extracting shape skeletons and tabulating their…

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

Statistics and classification of the microwave zebra patterns associated with solar flares

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

Tan, Baolin; Tan, Chengming; Zhang, Yin

2014-01-10

The microwave zebra pattern (ZP) is the most interesting, intriguing, and complex spectral structure frequently observed in solar flares. A comprehensive statistical study will certainly help us to understand the formation mechanism, which is not exactly clear now. This work presents a comprehensive statistical analysis of a big sample with 202 ZP events collected from observations at the Chinese Solar Broadband Radio Spectrometer at Huairou and the Ondŕejov Radiospectrograph in the Czech Republic at frequencies of 1.00-7.60 GHz from 2000 to 2013. After investigating the parameter properties of ZPs, such as the occurrence in flare phase, frequency range, polarization degree,more » duration, etc., we find that the variation of zebra stripe frequency separation with respect to frequency is the best indicator for a physical classification of ZPs. Microwave ZPs can be classified into three types: equidistant ZPs, variable-distant ZPs, and growing-distant ZPs, possibly corresponding to mechanisms of the Bernstein wave model, whistler wave model, and double plasma resonance model, respectively. This statistical classification may help us to clarify the controversies between the existing various theoretical models and understand the physical processes in the source regions.« less

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Overview and Analysis of the Behaviourist Criticism of the "Diagnostic and Statistical Manual of Mental Disorders (DSM)"

ERIC Educational Resources Information Center

Andersson, Gerhard; Ghaderi, Ata

2006-01-01

While a majority of cognitive behavioural researchers and clinicians adhere to the classification system provided in the "Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)," strong objections have been voiced among behaviourists who find the dichotomous allocation of patients into psychiatric diagnoses incompatible with the philosophy…

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

PubMed Central

Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

2016-01-01

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation. PMID:27065832

Cloud field classification based on textural features

NASA Technical Reports Server (NTRS)

Sengupta, Sailes Kumar

1989-01-01

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images.

PubMed

Leontidis, Georgios

2017-11-01

Human retina is a diverse and important tissue, vastly studied for various retinal and other diseases. Diabetic retinopathy (DR), a leading cause of blindness, is one of them. This work proposes a novel and complete framework for the accurate and robust extraction and analysis of a series of retinal vascular geometric features. It focuses on studying the registered bifurcations in successive years of progression from diabetes (no DR) to DR, in order to identify the vascular alterations. Retinal fundus images are utilised, and multiple experimental designs are employed. The framework includes various steps, such as image registration and segmentation, extraction of features, statistical analysis and classification models. Linear mixed models are utilised for making the statistical inferences, alongside the elastic-net logistic regression, boruta algorithm, and regularised random forests for the feature selection and classification phases, in order to evaluate the discriminative potential of the investigated features and also build classification models. A number of geometric features, such as the central retinal artery and vein equivalents, are found to differ significantly across the experiments and also have good discriminative potential. The classification systems yield promising results with the area under the curve values ranging from 0.821 to 0.968, across the four different investigated combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Real-Time Food Authentication Using a Miniature Mass Spectrometer.

PubMed

Gerbig, Stefanie; Neese, Stephan; Penner, Alexander; Spengler, Bernhard; Schulz, Sabine

2017-10-17

Food adulteration is a threat to public health and the economy. In order to determine food adulteration efficiently, rapid and easy-to-use on-site analytical methods are needed. In this study, a miniaturized mass spectrometer in combination with three ambient ionization methods was used for food authentication. The chemical fingerprints of three milk types, five fish species, and two coffee types were measured using electrospray ionization, desorption electrospray ionization, and low temperature plasma ionization. Minimum sample preparation was needed for the analysis of liquid and solid food samples. Mass spectrometric data was processed using the laboratory-built software MS food classifier, which allows for the definition of specific food profiles from reference data sets using multivariate statistical methods and the subsequent classification of unknown data. Applicability of the obtained mass spectrometric fingerprints for food authentication was evaluated using different data processing methods, leave-10%-out cross-validation, and real-time classification of new data. Classification accuracy of 100% was achieved for the differentiation of milk types and fish species, and a classification accuracy of 96.4% was achieved for coffee types in cross-validation experiments. Measurement of two milk mixtures yielded correct classification of >94%. For real-time classification, the accuracies were comparable. Functionality of the software program and its performance is described. Processing time for a reference data set and a newly acquired spectrum was found to be 12 s and 2 s, respectively. These proof-of-principle experiments show that the combination of a miniaturized mass spectrometer, ambient ionization, and statistical analysis is suitable for on-site real-time food authentication.

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

PubMed Central

Ramirez-Villegas, Juan F.; Lam-Espinosa, Eric; Ramirez-Moreno, David F.; Calvo-Echeverry, Paulo C.; Agredo-Rodriguez, Wilfredo

2011-01-01

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. PMID:21386966

Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events

NASA Astrophysics Data System (ADS)

Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prévôt, André S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael

2017-03-01

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

Inferring diagnosis and trajectory of wet age-related macular degeneration from OCT imagery of retina

NASA Astrophysics Data System (ADS)

Irvine, John M.; Ghadar, Nastaran; Duncan, Steve; Floyd, David; O'Dowd, David; Lin, Kristie; Chang, Tom

2017-03-01

Quantitative biomarkers for assessing the presence, severity, and progression of age-related macular degeneration (AMD) would benefit research, diagnosis, and treatment. This paper explores development of quantitative biomarkers derived from OCT imagery of the retina. OCT images for approximately 75 patients with Wet AMD, Dry AMD, and no AMD (healthy eyes) were analyzed to identify image features indicative of the patients' conditions. OCT image features provide a statistical characterization of the retina. Healthy eyes exhibit a layered structure, whereas chaotic patterns indicate the deterioration associated with AMD. Our approach uses wavelet and Frangi filtering, combined with statistical features that do not rely on image segmentation, to assess patient conditions. Classification analysis indicates clear separability of Wet AMD from other conditions, including Dry AMD and healthy retinas. The probability of correct classification of was 95.7%, as determined from cross validation. Similar classification analysis predicts the response of Wet AMD patients to treatment, as measured by the Best Corrected Visual Acuity (BCVA). A statistical model predicts BCVA from the imagery features with R2 = 0.846. Initial analysis of OCT imagery indicates that imagery-derived features can provide useful biomarkers for characterization and quantification of AMD: Accurate assessment of Wet AMD compared to other conditions; image-based prediction of outcome for Wet AMD treatment; and features derived from the OCT imagery accurately predict BCVA; unlike many methods in the literature, our techniques do not rely on segmentation of the OCT image. Next steps include larger scale testing and validation.

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.

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

PubMed

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

Cluster analysis as a prediction tool for pregnancy outcomes.

PubMed

Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

2015-03-01

Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

Morphological image analysis for classification of gastrointestinal tissues using optical coherence tomography

NASA Astrophysics Data System (ADS)

Garcia-Allende, P. Beatriz; Amygdalos, Iakovos; Dhanapala, Hiruni; Goldin, Robert D.; Hanna, George B.; Elson, Daniel S.

2012-01-01

Computer-aided diagnosis of ophthalmic diseases using optical coherence tomography (OCT) relies on the extraction of thickness and size measures from the OCT images, but such defined layers are usually not observed in emerging OCT applications aimed at "optical biopsy" such as pulmonology or gastroenterology. Mathematical methods such as Principal Component Analysis (PCA) or textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric auto-correlation (CSAC) and spatial grey-level dependency matrices (SGLDM), as well as, quantitative measurements of the attenuation coefficient have been previously proposed to overcome this problem. We recently proposed an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technology for feature quantification. OCT images were first segmented in the axial direction in an automated manner according to intensity. Afterwards, a morphological analysis of the segmented OCT images was employed for quantifying the features that served for tissue classification. In this study, a PCA processing of the extracted features is accomplished to combine their discriminative power in a lower number of dimensions. Ready discrimination of gastrointestinal surgical specimens is attained demonstrating that the approach further surpasses the algorithms previously reported and is feasible for tissue classification in the clinical setting.

The classification of gunshot residue using laser electrospray mass spectrometry and offline multivariate statistical analysis

USDA-ARS?s Scientific Manuscript database

Nonresonant laser vaporization combined with high-resolution electrospray time-of-flight mass spectrometry enables analysis of a casing after discharge of a firearm revealing organic signature molecules including methyl centralite (MC), diphenylamine (DPA), N-nitrosodiphenylamine (N-NO-DPA), 4-nitro...

Comparative study of classification algorithms for damage classification in smart composite laminates

NASA Astrophysics Data System (ADS)

Khan, Asif; Ryoo, Chang-Kyung; Kim, Heung Soo

2017-04-01

This paper presents a comparative study of different classification algorithms for the classification of various types of inter-ply delaminations in smart composite laminates. Improved layerwise theory is used to model delamination at different interfaces along the thickness and longitudinal directions of the smart composite laminate. The input-output data obtained through surface bonded piezoelectric sensor and actuator is analyzed by the system identification algorithm to get the system parameters. The identified parameters for the healthy and delaminated structure are supplied as input data to the classification algorithms. The classification algorithms considered in this study are ZeroR, Classification via regression, Naïve Bayes, Multilayer Perceptron, Sequential Minimal Optimization, Multiclass-Classifier, and Decision tree (J48). The open source software of Waikato Environment for Knowledge Analysis (WEKA) is used to evaluate the classification performance of the classifiers mentioned above via 75-25 holdout and leave-one-sample-out cross-validation regarding classification accuracy, precision, recall, kappa statistic and ROC Area.

Automotive System for Remote Surface Classification.

PubMed

Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail

2017-04-01

In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions.

Automotive System for Remote Surface Classification

PubMed Central

Bystrov, Aleksandr; Hoare, Edward; Tran, Thuy-Yung; Clarke, Nigel; Gashinova, Marina; Cherniakov, Mikhail

2017-01-01

In this paper we shall discuss a novel approach to road surface recognition, based on the analysis of backscattered microwave and ultrasonic signals. The novelty of our method is sonar and polarimetric radar data fusion, extraction of features for separate swathes of illuminated surface (segmentation), and using of multi-stage artificial neural network for surface classification. The developed system consists of 24 GHz radar and 40 kHz ultrasonic sensor. The features are extracted from backscattered signals and then the procedures of principal component analysis and supervised classification are applied to feature data. The special attention is paid to multi-stage artificial neural network which allows an overall increase in classification accuracy. The proposed technique was tested for recognition of a large number of real surfaces in different weather conditions with the average accuracy of correct classification of 95%. The obtained results thereby demonstrate that the use of proposed system architecture and statistical methods allow for reliable discrimination of various road surfaces in real conditions. PMID:28368297

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

USGS Publications Warehouse

Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

1980-01-01

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Remote sensing application to regional activities

NASA Technical Reports Server (NTRS)

Shahrokhi, F.; Jones, N. L.; Sharber, L. A.

1976-01-01

Two agencies within the State of Tennessee were identified whereby the transfer of aerospace technology, namely remote sensing, could be applied to their stated problem areas. Their stated problem areas are wetland and land classification and strip mining studies. In both studies, LANDSAT data was analyzed with the UTSI video-input analog/digital automatic analysis and classification facility. In the West Tennessee area three land-use classifications could be distinguished; cropland, wetland, and forest. In the East Tennessee study area, measurements were submitted to statistical tests which verified the significant differences due to natural terrain, stripped areas, various stages of reclamation, water, etc. Classifications for both studies were output in the form of maps of symbols and varying shades of gray.

On the statistical assessment of classifiers using DNA microarray data

PubMed Central

Ancona, N; Maglietta, R; Piepoli, A; D'Addabbo, A; Cotugno, R; Savino, M; Liuni, S; Carella, M; Pesole, G; Perri, F

2006-01-01

Background In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. Results We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed. Conclusions The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required. PMID:16919171

Macro scale models for freight railroad terminals.

DOT National Transportation Integrated Search

2016-03-02

The project has developed a yard capacity model for macro-level analysis. The study considers the detailed sequence and scheduling in classification yards and their impacts on yard capacities simulate typical freight railroad terminals, and statistic...

Railroad Classification Yard Technology Manual. Volume III. Freight Car Rollability

DOT National Transportation Integrated Search

1981-07-01

The report presents a survey of rolling resistance research, histograms of rolling resistance from five yards, a statistical regression analysis of causal factors affecting rolling resistance, procedures for constructing a rolling resistance histogra...

Invariant approach to the character classification

NASA Astrophysics Data System (ADS)

Šariri, Kristina; Demoli, Nazif

2008-04-01

Image moments analysis is a very useful tool which allows image description invariant to translation and rotation, scale change and some types of image distortions. The aim of this work was development of simple method for fast and reliable classification of characters by using Hu's and affine moment invariants. Measure of Eucleidean distance was used as a discrimination feature with statistical parameters estimated. The method was tested in classification of Times New Roman font letters as well as sets of the handwritten characters. It is shown that using all Hu's and three affine invariants as discrimination set improves recognition rate by 30%.

A hybrid sensing approach for pure and adulterated honey classification.

PubMed

Subari, Norazian; Mohamad Saleh, Junita; Md Shakaff, Ali Yeon; Zakaria, Ammar

2012-10-17

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2017-03-01

Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge. In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors' knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature. The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM. Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

PubMed Central

Smolinski, Tomasz G; Buchanan, Roger; Boratyn, Grzegorz M; Milanova, Mariofanna; Prinz, Astrid A

2006-01-01

Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. PMID:17118151

Analysis of dual tree M-band wavelet transform based features for brain image classification.

PubMed

Ayalapogu, Ratna Raju; Pabboju, Suresh; Ramisetty, Rajeswara Rao

2018-04-29

The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach. Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4 th level statistical features of DTMBWT. Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification. © 2018 International Society for Magnetic Resonance in Medicine.

Feature-based classification of amino acid substitutions outside conserved functional protein domains.

PubMed

Gemovic, Branislava; Perovic, Vladimir; Glisic, Sanja; Veljkovic, Nevena

2013-01-01

There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.

An Assessment of Worldview-2 Imagery for the Classification Of a Mixed Deciduous Forest

NASA Astrophysics Data System (ADS)

Carter, Nahid

Remote sensing provides a variety of methods for classifying forest communities and can be a valuable tool for the impact assessment of invasive species. The emerald ash borer (Agrilus planipennis) infestation of ash trees (Fraxinus) in the United States has resulted in the mortality of large stands of ash throughout the Northeast. This study assessed the suitability of multi-temporal Worldview-2 multispectral satellite imagery for classifying a mixed deciduous forest in Upstate New York. Training sites were collected using a Global Positioning System (GPS) receiver, with each training site consisting of a single tree of a corresponding class. Six classes were collected; Ash, Maple, Oak, Beech, Evergreen, and Other. Three different classifications were investigated on four data sets. A six class classification (6C), a two class classification consisting of ash and all other classes combined (2C), and a merging of the ash and maple classes for a five class classification (5C). The four data sets included Worldview-2 multispectral data collection from June 2010 (J-WV2) and September 2010 (S-WV2), a layer stacked data set using J-WV2 and S-WV2 (LS-WV2), and a reduced data set (RD-WV2). RD-WV2 was created using a statistical analysis of the processed and unprocessed imagery. Statistical analysis was used to reduce the dimensionality of the data and identify key bands to create a fourth data set (RD-WV2). Overall accuracy varied considerably depending upon the classification type, but results indicated that ash was confused with maple in a majority of the classifications. Ash was most accurately identified using the 2C classification and RD-WV2 data set (81.48%). A combination of the ash and maple classes yielded an accuracy of 89.41%. Future work should focus on separating the ash and maple classifiers by using data sources such as hyperspectral imagery, LiDAR, or extensive forest surveys.

Landscape of Research Areas for Zeolites and Metal-Organic Frameworks Using Computational Classification Based on Citation Networks.

PubMed

Ogawa, Takaya; Iyoki, Kenta; Fukushima, Tomohiro; Kajikawa, Yuya

2017-12-14

The field of porous materials is widely spreading nowadays, and researchers need to read tremendous numbers of papers to obtain a "bird's eye" view of a given research area. However, it is difficult for researchers to obtain an objective database based on statistical data without any relation to subjective knowledge related to individual research interests. Here, citation network analysis was applied for a comparative analysis of the research areas for zeolites and metal-organic frameworks as examples for porous materials. The statistical and objective data contributed to the analysis of: (1) the computational screening of research areas; (2) classification of research stages to a certain domain; (3) "well-cited" research areas; and (4) research area preferences of specific countries. Moreover, we proposed a methodology to assist researchers to gain potential research ideas by reviewing related research areas, which is based on the detection of unfocused ideas in one area but focused in the other area by a bibliometric approach.

Landscape of Research Areas for Zeolites and Metal-Organic Frameworks Using Computational Classification Based on Citation Networks

PubMed Central

Ogawa, Takaya; Fukushima, Tomohiro; Kajikawa, Yuya

2017-01-01

The field of porous materials is widely spreading nowadays, and researchers need to read tremendous numbers of papers to obtain a “bird’s eye” view of a given research area. However, it is difficult for researchers to obtain an objective database based on statistical data without any relation to subjective knowledge related to individual research interests. Here, citation network analysis was applied for a comparative analysis of the research areas for zeolites and metal-organic frameworks as examples for porous materials. The statistical and objective data contributed to the analysis of: (1) the computational screening of research areas; (2) classification of research stages to a certain domain; (3) “well-cited” research areas; and (4) research area preferences of specific countries. Moreover, we proposed a methodology to assist researchers to gain potential research ideas by reviewing related research areas, which is based on the detection of unfocused ideas in one area but focused in the other area by a bibliometric approach. PMID:29240708

Statistical analysis of spectral data: a methodology for designing an intelligent monitoring system for the diabetic foot

NASA Astrophysics Data System (ADS)

Liu, Chanjuan; van Netten, Jaap J.; Klein, Marvin E.; van Baal, Jeff G.; Bus, Sicco A.; van der Heijden, Ferdi

2013-12-01

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Test of spectral/spatial classifier

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Kast, J. L.; Davis, B. J.

1977-01-01

The author has identified the following significant results. The supervised ECHO processor (which utilizes class statistics for object identification) successfully exploits the redundancy of states characteristic of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The nonsupervised ECHO processor (which identifies objects without the benefit of class statistics) successfully reduces the number of classifications required and the variability of the classification results.

Analysis of design attributes and crashes on the Oregon highway system : final report.

DOT National Transportation Integrated Search

2001-08-01

This report has investigated the statistical relationship between crash activity and roadway design attributes on the Oregon state : highway system. Crash models were estimated from highway segments distinguished by functional classification (freeway...

Voice classification and vocal tract of singers: a study of x-ray images and morphology.

PubMed

Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

2009-01-01

This investigation compares vocal tract dimensions and the classification of singer voices by examining an x-ray material assembled between 1959 and 1991 of students admitted to the solo singing education at the University of Music, Dresden, Germany. A total of 132 images were available to analysis. Different classifications' values of the lengths of the total vocal tract, the pharynx, and mouth cavities as well as of the relative position of the larynx, the height of the palatal arch, and the estimated vocal fold length were analyzed statistically, and some significant differences were found. The length of the pharynx cavity seemed particularly influential on the total vocal tract length, which varied systematically with classification. Also studied were the relationships between voice classification and the body height and weight and the body mass index. The data support the hypothesis that there are consistent morphological vocal tract differences between singers of different voice classifications.

3D texture analysis for classification of second harmonic generation images of human ovarian cancer

NASA Astrophysics Data System (ADS)

Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

2016-10-01

Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

Crop identification technology assessment for remote sensing (CITARS). Volume 6: Data processing at the laboratory for applications of remote sensing

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cary, T. K.; Davis, B. J.; Swain, P. H.

1975-01-01

The results of classifications and experiments for the crop identification technology assessment for remote sensing are summarized. Using two analysis procedures, 15 data sets were classified. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. Additionally, 20 data sets were classified using training statistics from another segment or date. The classification and proportion estimation results of the local and nonlocal classifications are reported. Data also describe several other experiments to provide additional understanding of the results of the crop identification technology assessment for remote sensing. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, spectral discriminability of corn, soybeans, and other, and analyses of aircraft multispectral data.

Proposition of a Classification of Adult Patients with Hemiparesis in Chronic Phase.

PubMed

Chantraine, Frédéric; Filipetti, Paul; Schreiber, Céline; Remacle, Angélique; Kolanowski, Elisabeth; Moissenet, Florent

2016-01-01

Patients who have developed hemiparesis as a result of a central nervous system lesion, often experience reduced walking capacity and worse gait quality. Although clinically, similar gait patterns have been observed, presently, no clinically driven classification has been validated to group these patients' gait abnormalities at the level of the hip, knee and ankle joints. This study has thus intended to put forward a new gait classification for adult patients with hemiparesis in chronic phase, and to validate its discriminatory capacity. Twenty-six patients with hemiparesis were included in this observational study. Following a clinical examination, a clinical gait analysis, complemented by a video analysis, was performed whereby participants were requested to walk spontaneously on a 10m walkway. A patient's classification was established from clinical examination data and video analysis. This classification was made up of three groups, including two sub-groups, defined with key abnormalities observed whilst walking. Statistical analysis was achieved on the basis of 25 parameters resulting from the clinical gait analysis in order to assess the discriminatory characteristic of the classification as displayed by the walking speed and kinematic parameters. Results revealed that the parameters related to the discriminant criteria of the proposed classification were all significantly different between groups and subgroups. More generally, nearly two thirds of the 25 parameters showed significant differences (p<0.05) between the groups and sub-groups. However, prior to being fully validated, this classification must still be tested on a larger number of patients, and the repeatability of inter-operator measures must be assessed. This classification enables patients to be grouped on the basis of key abnormalities observed whilst walking and has the advantage of being able to be used in clinical routines without necessitating complex apparatus. In the midterm, this classification may allow a decision-tree of therapies to be developed on the basis of the group in which the patient has been categorised.

Proposition of a Classification of Adult Patients with Hemiparesis in Chronic Phase

PubMed Central

Filipetti, Paul; Remacle, Angélique; Kolanowski, Elisabeth

2016-01-01

Background Patients who have developed hemiparesis as a result of a central nervous system lesion, often experience reduced walking capacity and worse gait quality. Although clinically, similar gait patterns have been observed, presently, no clinically driven classification has been validated to group these patients’ gait abnormalities at the level of the hip, knee and ankle joints. This study has thus intended to put forward a new gait classification for adult patients with hemiparesis in chronic phase, and to validate its discriminatory capacity. Methods and Findings Twenty-six patients with hemiparesis were included in this observational study. Following a clinical examination, a clinical gait analysis, complemented by a video analysis, was performed whereby participants were requested to walk spontaneously on a 10m walkway. A patient’s classification was established from clinical examination data and video analysis. This classification was made up of three groups, including two sub-groups, defined with key abnormalities observed whilst walking. Statistical analysis was achieved on the basis of 25 parameters resulting from the clinical gait analysis in order to assess the discriminatory characteristic of the classification as displayed by the walking speed and kinematic parameters. Results revealed that the parameters related to the discriminant criteria of the proposed classification were all significantly different between groups and subgroups. More generally, nearly two thirds of the 25 parameters showed significant differences (p<0.05) between the groups and sub-groups. However, prior to being fully validated, this classification must still be tested on a larger number of patients, and the repeatability of inter-operator measures must be assessed. Conclusions This classification enables patients to be grouped on the basis of key abnormalities observed whilst walking and has the advantage of being able to be used in clinical routines without necessitating complex apparatus. In the midterm, this classification may allow a decision-tree of therapies to be developed on the basis of the group in which the patient has been categorised. PMID:27271533

Predicting heart failure mortality in frail seniors: comparing the NYHA functional classification with the Resident Assessment Instrument (RAI) 2.0.

PubMed

Tjam, Erin Y; Heckman, George A; Smith, Stuart; Arai, Bruce; Hirdes, John; Poss, Jeff; McKelvie, Robert S

2012-02-23

Though the NYHA functional classification is recommended in clinical settings, concerns have been raised about its reliability particularly among older patients. The RAI 2.0 is a comprehensive assessment system specifically developed for frail seniors. We hypothesized that a prognostic model for heart failure (HF) developed from the RAI 2.0 would be superior to the NYHA classification. The purpose of this study was to determine whether a HF-specific prognostic model based on the RAI 2.0 is superior to the NYHA functional classification in predicting mortality in frail older HF patients. Secondary analysis of data from a prospective cohort study of a HF education program for care providers in long-term care and retirement homes. Univariate analyses identified RAI 2.0 variables predicting death at 6 months. These and the NYHA classification were used to develop logistic models. Two RAI 2.0 models were derived. The first includes six items: "weight gain of 5% or more of total body weight over 30 days", "leaving 25% or more food uneaten", "unable to lie flat", "unstable cognitive, ADL, moods, or behavioural patterns", "change in cognitive function" and "needing help to walk in room"; the C statistic was 0.866. The second includes the CHESS health instability scale and the item "requiring help walking in room"; the C statistic was 0.838. The C statistic for the NYHA scale was 0.686. These results suggest that data from the RAI 2.0, an instrument for comprehensive assessment of frail seniors, can better predict mortality than the NYHA classification. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

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.

Classification images for localization performance in ramp-spectrum noise.

PubMed

Abbey, Craig K; Samuelson, Frank W; Zeng, Rongping; Boone, John M; Eckstein, Miguel P; Myers, Kyle

2018-05-01

This study investigates forced localization of targets in simulated images with statistical properties similar to trans-axial sections of x-ray computed tomography (CT) volumes. A total of 24 imaging conditions are considered, comprising two target sizes, three levels of background variability, and four levels of frequency apodization. The goal of the study is to better understand how human observers perform forced-localization tasks in images with CT-like statistical properties. The transfer properties of CT systems are modeled by a shift-invariant transfer function in addition to apodization filters that modulate high spatial frequencies. The images contain noise that is the combination of a ramp-spectrum component, simulating the effect of acquisition noise in CT, and a power-law component, simulating the effect of normal anatomy in the background, which are modulated by the apodization filter as well. Observer performance is characterized using two psychophysical techniques: efficiency analysis and classification image analysis. Observer efficiency quantifies how much diagnostic information is being used by observers to perform a task, and classification images show how that information is being accessed in the form of a perceptual filter. Psychophysical studies from five subjects form the basis of the results. Observer efficiency ranges from 29% to 77% across the different conditions. The lowest efficiency is observed in conditions with uniform backgrounds, where significant effects of apodization are found. The classification images, estimated using smoothing windows, suggest that human observers use center-surround filters to perform the task, and these are subjected to a number of subsequent analyses. When implemented as a scanning linear filter, the classification images appear to capture most of the observer variability in efficiency (r 2 = 0.86). The frequency spectra of the classification images show that frequency weights generally appear bandpass in nature, with peak frequency and bandwidth that vary with statistical properties of the images. In these experiments, the classification images appear to capture important features of human-observer performance. Frequency apodization only appears to have a significant effect on performance in the absence of anatomical variability, where the observers appear to underweight low spatial frequencies that have relatively little noise. Frequency weights derived from the classification images generally have a bandpass structure, with adaptation to different conditions seen in the peak frequency and bandwidth. The classification image spectra show relatively modest changes in response to different levels of apodization, with some evidence that observers are attempting to rebalance the apodized spectrum presented to them. © 2018 American Association of Physicists in Medicine.

Bruxism: is it a new sign of the cardiovascular diseases?

PubMed

Atilgan, Z; Buyukkaya, R; Yaman, F; Tekbas, G; Atilgan, S; Gunay, A; Palanci, Y; Guven, S

2011-12-01

To determine the relationship between bruxism and cardiovascular diseases. 120 patients who referred to the Dentistry Faculty with the complaint of bruxism were selected. All patients gave informed consent for participation in the study. All of the patients were examined and bruxism was classified. And also these were examined by B-mode ultrasound to measure the Intima Media Thickness (IMT) at the far wall of the common carotid artery. A wide range of vascular risk factors including age, gender, body mass index, and previous history were surveyed. Spearman correlation analysis was performed to ascertain quantitative comparison, Mann-Whitney U and Kruskal-Wallis test were used for comparison of means There were 66 (55%) male and 54 (45%) female patients, with a female to male ratio of 1/1.2. The mean age was 35.6 +/- 1,25 years (range 18-65 years). In the analysis of bruxism classification and IMT there was a statistical significance between bruxism classification subgroup 1, 2, 3 and IMT. There was no statistical significance between bruxism classification Subgroup 4 and IMT due to the small number of the patients (n = 12). Stressful situations can cause both bruxism and cardiovascular disease such as coronary artery diseases, hypertension, arrhythmias, cardiomyopathy. The statistical analysis supported this hypothesis. However, we need to new studies with large number of samples to confirm this hypothesis. Clearly, future studies in this field will need to take into consideration the influence of the following variables: age, use of medication or drugs, smoking habits, and other sleep disorders.

Unsupervised classification of remote multispectral sensing data

NASA Technical Reports Server (NTRS)

Su, M. Y.

1972-01-01

The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

Significant Association of Urinary Toxic Metals and Autism-Related Symptoms—A Nonlinear Statistical Analysis with Cross Validation

PubMed Central

Adams, James; Kruger, Uwe; Geis, Elizabeth; Gehn, Eva; Fimbres, Valeria; Pollard, Elena; Mitchell, Jessica; Ingram, Julie; Hellmers, Robert; Quig, David; Hahn, Juergen

2017-01-01

Introduction A number of previous studies examined a possible association of toxic metals and autism, and over half of those studies suggest that toxic metal levels are different in individuals with Autism Spectrum Disorders (ASD). Additionally, several studies found that those levels correlate with the severity of ASD. Methods In order to further investigate these points, this paper performs the most detailed statistical analysis to date of a data set in this field. First morning urine samples were collected from 67 children and adults with ASD and 50 neurotypical controls of similar age and gender. The samples were analyzed to determine the levels of 10 urinary toxic metals (UTM). Autism-related symptoms were assessed with eleven behavioral measures. Statistical analysis was used to distinguish participants on the ASD spectrum and neurotypical participants based upon the UTM data alone. The analysis also included examining the association of autism severity with toxic metal excretion data using linear and nonlinear analysis. “Leave-one-out” cross-validation was used to ensure statistical independence of results. Results and Discussion Average excretion levels of several toxic metals (lead, tin, thallium, antimony) were significantly higher in the ASD group. However, ASD classification using univariate statistics proved difficult due to large variability, but nonlinear multivariate statistical analysis significantly improved ASD classification with Type I/II errors of 15% and 18%, respectively. These results clearly indicate that the urinary toxic metal excretion profiles of participants in the ASD group were significantly different from those of the neurotypical participants. Similarly, nonlinear methods determined a significantly stronger association between the behavioral measures and toxic metal excretion. The association was strongest for the Aberrant Behavior Checklist (including subscales on Irritability, Stereotypy, Hyperactivity, and Inappropriate Speech), but significant associations were found for UTM with all eleven autism-related assessments with cross-validation R2 values ranging from 0.12–0.48. PMID:28068407

Bioclimatic Classification of Northeast Asia for climate change response

NASA Astrophysics Data System (ADS)

Choi, Y.; Jeon, S. W.; Lim, C. H.

2016-12-01

As climate change has been getting worse, we should monitor the change of biodiversity, and distribution of species to handle the crisis and take advantage of climate change. The development of bioclimatic map which classifies land into homogenous zones by similar environment properties is the first step to establish a strategy. Statistically derived classifications of land provide useful spatial frameworks to support ecosystem research, monitoring and policy decisions. Many countries are trying to make this kind of map and actively utilize it to ecosystem conservation and management. However, the Northeast Asia including North Korea doesn't have detailed environmental information, and has not built environmental classification map. Therefore, this study presents a bioclimatic map of Northeast Asia based on statistical clustering of bioclimate data. Bioclim data ver1.4 which provided by WorldClim were considered for inclusion in a model. Eight of the most relevant climate variables were selected by correlation analysis, based on previous studies. Principal Components Analysis (PCA) was used to explain 86% of the variation into three independent dimensions, which were subsequently clustered using an ISODATA clustering. The bioclimatic zone of Northeast Asia could consist of 29, 35, and 50 zones. This bioclimatic map has a 30' resolution. To assess the accuracy, the correlation coefficient was calculated between the first principal component values of the classification variables and the vegetation index, Gross Primary Production (GPP). It shows about 0.5 Pearson correlation coefficient. This study constructed Northeast Asia bioclimatic map by statistical method with high resolution, but in order to better reflect the realities, the variety of climate variables should be considered. Also, further studies should do more quantitative and qualitative validation in various ways. Then, this could be used more effectively to support decision making on climate change adaptation.

An Analysis of Variance Framework for Matrix Sampling.

ERIC Educational Resources Information Center

Sirotnik, Kenneth

Significant cost savings can be achieved with the use of matrix sampling in estimating population parameters from psychometric data. The statistical design is intuitively simple, using the framework of the two-way classification analysis of variance technique. For example, the mean and variance are derived from the performance of a certain grade…

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

PubMed

Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

2018-05-16

There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

A graduated food addiction classification approach significantly differentiates obesity among people with type 2 diabetes.

PubMed

Raymond, Karren-Lee; Kannis-Dymand, Lee; Lovell, Geoff P

2016-10-01

This study examined a graduated severity level approach to food addiction classification against associations with World Health Organization obesity classifications (body mass index, kg/m 2 ) among 408 people with type 2 diabetes. A survey including the Yale Food Addiction Scale and several demographic questions demonstrated four distinct Yale Food Addiction Scale symptom severity groups (in line with Diagnostic and Statistical Manual of Mental Disorders (5th ed.) severity indicators): non-food addiction, mild food addiction, moderate food addiction and severe food addiction. Analysis of variance with post hoc tests demonstrated each severity classification group was significantly different in body mass index, with each grouping being associated with increased World Health Organization obesity classifications. These findings have implications for diagnosing food addiction and implementing treatment and prevention methodologies of obesity among people with type 2 diabetes.

The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

NASA Astrophysics Data System (ADS)

Theodorakou, Chrysoula; Farquharson, Michael J.

2009-08-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

Statistical analysis of textural features for improved classification of oral histopathological images.

PubMed

Muthu Rama Krishnan, M; Shah, Pratik; Chakraborty, Chandan; Ray, Ajoy K

2012-04-01

The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.

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2013-02-07

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49 CFR 1248.100 - Commodity classification designated.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 9 2011-10-01 2011-10-01 false Commodity classification designated. 1248.100... STATISTICS Commodity Code § 1248.100 Commodity classification designated. Commencing with reports for the..., reports of commodity statistics required to be made to the Board, shall be based on the commodity codes...

Automatic classification of animal vocalizations

NASA Astrophysics Data System (ADS)

Clemins, Patrick J.

2005-11-01

Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysis techniques in recent years. Some common tasks in bioacoustics are repertoire determination, call detection, individual identification, stress detection, and behavior correlation. Each research study, however, uses a wide variety of different measured variables, called features, and classification systems to accomplish these tasks. The well-established field of human speech processing has developed a number of different techniques to perform many of the aforementioned bioacoustics tasks. Melfrequency cepstral coefficients (MFCCs) and perceptual linear prediction (PLP) coefficients are two popular feature sets. The hidden Markov model (HMM), a statistical model similar to a finite autonoma machine, is the most commonly used supervised classification model and is capable of modeling both temporal and spectral variations. This research designs a framework that applies models from human speech processing for bioacoustic analysis tasks. The development of the generalized perceptual linear prediction (gPLP) feature extraction model is one of the more important novel contributions of the framework. Perceptual information from the species under study can be incorporated into the gPLP feature extraction model to represent the vocalizations as the animals might perceive them. By including this perceptual information and modifying parameters of the HMM classification system, this framework can be applied to a wide range of species. The effectiveness of the framework is shown by analyzing African elephant and beluga whale vocalizations. The features extracted from the African elephant data are used as input to a supervised classification system and compared to results from traditional statistical tests. The gPLP features extracted from the beluga whale data are used in an unsupervised classification system and the results are compared to labels assigned by experts. The development of a framework from which to build animal vocalization classifiers will provide bioacoustics researchers with a consistent platform to analyze and classify vocalizations. A common framework will also allow studies to compare results across species and institutions. In addition, the use of automated classification techniques can speed analysis and uncover behavioral correlations not readily apparent using traditional techniques.

Comparative study of wine tannin classification using Fourier transform mid-infrared spectrometry and sensory analysis.

PubMed

Fernández, Katherina; Labarca, Ximena; Bordeu, Edmundo; Guesalaga, Andrés; Agosin, Eduardo

2007-11-01

Wine tannins are fundamental to the determination of wine quality. However, the chemical and sensorial analysis of these compounds is not straightforward and a simple and rapid technique is necessary. We analyzed the mid-infrared spectra of white, red, and model wines spiked with known amounts of skin or seed tannins, collected using Fourier transform mid-infrared (FT-MIR) transmission spectroscopy (400-4000 cm(-1)). The spectral data were classified according to their tannin source, skin or seed, and tannin concentration by means of discriminant analysis (DA) and soft independent modeling of class analogy (SIMCA) to obtain a probabilistic classification. Wines were also classified sensorially by a trained panel and compared with FT-MIR. SIMCA models gave the most accurate classification (over 97%) and prediction (over 60%) among the wine samples. The prediction was increased (over 73%) using the leave-one-out cross-validation technique. Sensory classification of the wines was less accurate than that obtained with FT-MIR and SIMCA. Overall, these results show the potential of FT-MIR spectroscopy, in combination with adequate statistical tools, to discriminate wines with different tannin levels.

Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection

PubMed Central

Wong, Raymond

2013-01-01

Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC. PMID:24288684

Statistical Emulator for Expensive Classification Simulators

NASA Technical Reports Server (NTRS)

Ross, Jerret; Samareh, Jamshid A.

2016-01-01

Expensive simulators prevent any kind of meaningful analysis to be performed on the phenomena they model. To get around this problem the concept of using a statistical emulator as a surrogate representation of the simulator was introduced in the 1980's. Presently, simulators have become more and more complex and as a result running a single example on these simulators is very expensive and can take days to weeks or even months. Many new techniques have been introduced, termed criteria, which sequentially select the next best (most informative to the emulator) point that should be run on the simulator. These criteria methods allow for the creation of an emulator with only a small number of simulator runs. We follow and extend this framework to expensive classification simulators.

Analysis of framelets for breast cancer diagnosis.

PubMed

Thivya, K S; Sakthivel, P; Venkata Sai, P M

2016-01-01

Breast cancer is the second threatening tumor among the women. The effective way of reducing breast cancer is its early detection which helps to improve the diagnosing process. Digital mammography plays a significant role in mammogram screening at earlier stage of breast carcinoma. Even though, it is very difficult to find accurate abnormality in prevalent screening by radiologists. But the possibility of precise breast cancer screening is encouraged by predicting the accurate type of abnormality through Computer Aided Diagnosis (CAD) systems. The two most important indicators of breast malignancy are microcalcifications and masses. In this study, framelet transform, a multiresolutional analysis is investigated for the classification of the above mentioned two indicators. The statistical and co-occurrence features are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine is employed for classification with k-fold cross validation. This system achieves 94.82% and 100% accuracy in normal/abnormal classification (stage I) and benign/malignant classification (stage II) of mass classification system and 98.57% and 100% for microcalcification system when using the MIAS database.

Stability and bias of classification rates in biological applications of discriminant analysis

USGS Publications Warehouse

Williams, B.K.; Titus, K.; Hines, J.E.

1990-01-01

We assessed the sampling stability of classification rates in discriminant analysis by using a factorial design with factors for multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32,400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. Simulation results indicated strong bias in correct classification rates when group sample sizes were small and when overlap among groups was high. We also found that stability of the correct classification rates was influenced by these factors, indicating that the number of samples required for a given level of precision increases with the amount of overlap among groups. In a review of 60 published studies, we found that 57% of the articles presented results on classification rates, though few of them mentioned potential biases in their results. Wildlife researchers should choose the total number of samples per group to be at least 2 times the number of variables to be measured when overlap among groups is low. Substantially more samples are required as the overlap among groups increases

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78 FR 53148 - National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-28

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards Staff, Announces the... Administrator, Classifications and Public Health Data Standards Staff, NCHS, 3311 Toledo Road, Room 2337...

Pancreatic abnormalities detected by endoscopic ultrasound (EUS) in patients without clinical signs of pancreatic disease: any difference between standard and Rosemont classification scoring?

PubMed

Petrone, Maria Chiara; Terracciano, Fulvia; Perri, Francesco; Carrara, Silvia; Cavestro, Giulia Martina; Mariani, Alberto; Testoni, Pier Alberto; Arcidiacono, Paolo Giorgio

2014-01-01

The prevalence of nine EUS features of chronic pancreatitis (CP) according to the standard Wiersema classification has been investigated in 489 patients undergoing EUS for an indication not related to pancreatico-biliary disease. We showed that 82 subjects (16.8%) had at least one ductular or parenchymal abnormality. Among them, 18 (3.7% of study population) had ≥3 Wiersema criteria suggestive of CP. Recently, a new classification (Rosemont) of EUS findings consistent, suggestive or indeterminate for CP has been proposed. To stratify healthy subjects into different subgroups on the basis of EUS features of CP according to the Wiersema and Rosemont classifications and to evaluate the agreement in the diagnosis of CP with the two scoring systems. Weighted kappa statistics was computed to evaluate the strength of agreement between the two scoring systems. Univariate and multivariate analysis between any EUS abnormality and habits were performed. Eighty-two EUS videos were reviewed. Using the Wiersema classification, 18 subjects showed ≥3 EUS features suggestive of CP. The EUS diagnosis of CP in these 18 subjects was considered as consistent in only one patient, according to Rosemont classification. Weighted Kappa statistics was 0.34 showing that the strength of agreement was 'fair'. Alcohol use and smoking were identified as risk factors for having pancreatic abnormalities on EUS. The prevalence of EUS features consistent or suggestive of CP in healthy subjects according to the Rosemont classification is lower than that assessed by Wiersema criteria. In that regard the Rosemont classification seems to be more accurate in excluding clinically relevant CP. Overall agreement between the two classifications is fair. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Statistical Analysis of Categorical Time Series of Atmospheric Elementary Circulation Mechanisms - Dzerdzeevski Classification for the Northern Hemisphere

PubMed Central

Brenčič, Mihael

2016-01-01

Northern hemisphere elementary circulation mechanisms, defined with the Dzerdzeevski classification and published on a daily basis from 1899–2012, are analysed with statistical methods as continuous categorical time series. Classification consists of 41 elementary circulation mechanisms (ECM), which are assigned to calendar days. Empirical marginal probabilities of each ECM were determined. Seasonality and the periodicity effect were investigated with moving dispersion filters and randomisation procedure on the ECM categories as well as with the time analyses of the ECM mode. The time series were determined as being non-stationary with strong time-dependent trends. During the investigated period, periodicity interchanges with periods when no seasonality is present. In the time series structure, the strongest division is visible at the milestone of 1986, showing that the atmospheric circulation pattern reflected in the ECM has significantly changed. This change is result of the change in the frequency of ECM categories; before 1986, the appearance of ECM was more diverse, and afterwards fewer ECMs appear. The statistical approach applied to the categorical climatic time series opens up new potential insight into climate variability and change studies that have to be performed in the future. PMID:27116375

Statistical Analysis of Categorical Time Series of Atmospheric Elementary Circulation Mechanisms - Dzerdzeevski Classification for the Northern Hemisphere.

PubMed

Brenčič, Mihael

2016-01-01

Northern hemisphere elementary circulation mechanisms, defined with the Dzerdzeevski classification and published on a daily basis from 1899-2012, are analysed with statistical methods as continuous categorical time series. Classification consists of 41 elementary circulation mechanisms (ECM), which are assigned to calendar days. Empirical marginal probabilities of each ECM were determined. Seasonality and the periodicity effect were investigated with moving dispersion filters and randomisation procedure on the ECM categories as well as with the time analyses of the ECM mode. The time series were determined as being non-stationary with strong time-dependent trends. During the investigated period, periodicity interchanges with periods when no seasonality is present. In the time series structure, the strongest division is visible at the milestone of 1986, showing that the atmospheric circulation pattern reflected in the ECM has significantly changed. This change is result of the change in the frequency of ECM categories; before 1986, the appearance of ECM was more diverse, and afterwards fewer ECMs appear. The statistical approach applied to the categorical climatic time series opens up new potential insight into climate variability and change studies that have to be performed in the future.

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

PubMed Central

Subari, Norazian; Saleh, Junita Mohamad; Shakaff, Ali Yeon Md; Zakaria, Ammar

2012-01-01

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data. PMID:23202033

Contextual classification of multispectral image data: An unbiased estimator for the context distribution

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Swain, P. H. (Principal Investigator); Vardeman, S. B.

1981-01-01

A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

A Preliminary Statistical Investigation into the Impace of an N-Gram Analysis Approach Based on World Syntactic Categories Toward Text Author Classification

DTIC Science & Technology

2000-06-01

of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources...words: N-gram, Shakespeare , Middleton, Wardigo, Funeral Elegy, Author Classification Introduction Literary experts refer to the style of the ...signed W. S. at the time of the investigation; the jury was still out as to the identity of its author. It has been noted as of late

Effects of temporal variability in ground data collection on classification accuracy

USGS Publications Warehouse

Hoch, G.A.; Cully, J.F.

1999-01-01

This research tested whether the timing of ground data collection can significantly impact the accuracy of land cover classification. Ft. Riley Military Reservation, Kansas, USA was used to test this hypothesis. The U.S. Army's Land Condition Trend Analysis (LCTA) data annually collected at military bases was used to ground truth disturbance patterns. Ground data collected over an entire growing season and data collected one year after the imagery had a kappa statistic of 0.33. When using ground data from only within two weeks of image acquisition the kappa statistic improved to 0.55. Potential sources of this discrepancy are identified. These data demonstrate that there can be significant amounts of land cover change within a narrow time window on military reservations. To accurately conduct land cover classification at military reservations, ground data need to be collected in as narrow a window of time as possible and be closely synchronized with the date of the satellite imagery.

Differences and discriminatory power of water polo game-related statistics in men in international championships and their relationship with the phase of the competition.

PubMed

Escalante, Yolanda; Saavedra, Jose M; Tella, Victor; Mansilla, Mirella; García-Hermoso, Antonio; Domínguez, Ana M

2013-04-01

The aims of this study were (a) to compare water polo game-related statistics by context (winning and losing teams) and phase (preliminary, classification, and semifinal/bronze medal/gold medal), and (b) identify characteristics that discriminate performances for each phase. The game-related statistics of the 230 men's matches played in World Championships (2007, 2009, and 2011) and European Championships (2008 and 2010) were analyzed. Differences between contexts (winning or losing teams) in each phase (preliminary, classification, and semifinal/bronze medal/gold medal) were determined using the chi-squared statistic, also calculating the effect sizes of the differences. A discriminant analysis was then performed after the sample-splitting method according to context (winning and losing teams) in each of the 3 phases. It was found that the game-related statistics differentiate the winning from the losing teams in each phase of an international championship. The differentiating variables are both offensive and defensive, including action shots, sprints, goalkeeper-blocked shots, and goalkeeper-blocked action shots. However, the number of discriminatory variables decreases as the phase becomes more demanding and the teams become more equally matched. The discriminant analysis showed the game-related statistics to discriminate performance in all phases (preliminary, classificatory, and semifinal/bronze medal/gold medal phase) with high percentages (91, 90, and 73%, respectively). Again, the model selected both defensive and offensive variables.

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.

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.

Gold-standard for computer-assisted morphological sperm analysis.

PubMed

Chang, Violeta; Garcia, Alejandra; Hitschfeld, Nancy; Härtel, Steffen

2017-04-01

Published algorithms for classification of human sperm heads are based on relatively small image databases that are not open to the public, and thus no direct comparison is available for competing methods. We describe a gold-standard for morphological sperm analysis (SCIAN-MorphoSpermGS), a dataset of sperm head images with expert-classification labels in one of the following classes: normal, tapered, pyriform, small or amorphous. This gold-standard is for evaluating and comparing known techniques and future improvements to present approaches for classification of human sperm heads for semen analysis. Although this paper does not provide a computational tool for morphological sperm analysis, we present a set of experiments for comparing sperm head description and classification common techniques. This classification base-line is aimed to be used as a reference for future improvements to present approaches for human sperm head classification. The gold-standard provides a label for each sperm head, which is achieved by majority voting among experts. The classification base-line compares four supervised learning methods (1- Nearest Neighbor, naive Bayes, decision trees and Support Vector Machine (SVM)) and three shape-based descriptors (Hu moments, Zernike moments and Fourier descriptors), reporting the accuracy and the true positive rate for each experiment. We used Fleiss' Kappa Coefficient to evaluate the inter-expert agreement and Fisher's exact test for inter-expert variability and statistical significant differences between descriptors and learning techniques. Our results confirm the high degree of inter-expert variability in the morphological sperm analysis. Regarding the classification base line, we show that none of the standard descriptors or classification approaches is best suitable for tackling the problem of sperm head classification. We discovered that the correct classification rate was highly variable when trying to discriminate among non-normal sperm heads. By using the Fourier descriptor and SVM, we achieved the best mean correct classification: only 49%. We conclude that the SCIAN-MorphoSpermGS will provide a standard tool for evaluation of characterization and classification approaches for human sperm heads. Indeed, there is a clear need for a specific shape-based descriptor for human sperm heads and a specific classification approach to tackle the problem of high variability within subcategories of abnormal sperm cells. Copyright © 2017 Elsevier Ltd. All rights reserved.

A study of the utilization of ERTS-1 data from the Wabash River Basin

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Nine projects are defined, five ERTS data applications experiments and four supporting technology tasks. The most significant applications results were achieved in the soil association mapping, earth surface feature identification, and urban land use mapping efforts. Four soil association boundaries were accurately delineated from ERTS-1 imagery. A data bank has been developed to test surface feature classifications obtained from ERTS-1 data. Preliminary forest cover classifications indicated that the number of acres estimated tended to be greater than actually existed by 25%. Urban land use analysis of ERTS-1 data indicated highly accurate classification could be obtained for many urban catagories. The wooded residential category tended to be misclassified as woods or agricultural land. Further statistical analysis revealed that these classes could be separated using sample variance.

Pet fur color and texture classification

NASA Astrophysics Data System (ADS)

Yen, Jonathan; Mukherjee, Debarghar; Lim, SukHwan; Tretter, Daniel

2007-01-01

Object segmentation is important in image analysis for imaging tasks such as image rendering and image retrieval. Pet owners have been known to be quite vocal about how important it is to render their pets perfectly. We present here an algorithm for pet (mammal) fur color classification and an algorithm for pet (animal) fur texture classification. Per fur color classification can be applied as a necessary condition for identifying the regions in an image that may contain pets much like the skin tone classification for human flesh detection. As a result of the evolution, fur coloration of all mammals is caused by a natural organic pigment called Melanin and Melanin has only very limited color ranges. We have conducted a statistical analysis and concluded that mammal fur colors can be only in levels of gray or in two colors after the proper color quantization. This pet fur color classification algorithm has been applied for peteye detection. We also present here an algorithm for animal fur texture classification using the recently developed multi-resolution directional sub-band Contourlet transform. The experimental results are very promising as these transforms can identify regions of an image that may contain fur of mammals, scale of reptiles and feather of birds, etc. Combining the color and texture classification, one can have a set of strong classifiers for identifying possible animals in an image.

Landsat Thematic Mapper studies of land cover spatial variability related to hydrology

NASA Technical Reports Server (NTRS)

Wharton, S.; Ormsby, J.; Salomonson, V.; Mulligan, P.

1984-01-01

Past accomplishments involving remote sensing based land-cover analysis for hydrologic applications are reviewed. Ongoing research in exploiting the increased spatial, radiometric, and spectral capabilities afforded by the TM on Landsats 4 and 5 is considered. Specific studies to compare MSS and TM for urbanizing watersheds, wetlands, and floodplain mapping situations show that only a modest improvement in classification accuracy is achieved via statistical per pixel multispectral classifiers. The limitations of current approaches to multispectral classification are illustrated. The objectives, background, and progress in the development of an alternative analysis approach for defining inputs to urban hydrologic models using TM are discussed.

Variance estimates and confidence intervals for the Kappa measure of classification accuracy

Treesearch

M. A. Kalkhan; R. M. Reich; R. L. Czaplewski

1997-01-01

The Kappa statistic is frequently used to characterize the results of an accuracy assessment used to evaluate land use and land cover classifications obtained by remotely sensed data. This statistic allows comparisons of alternative sampling designs, classification algorithms, photo-interpreters, and so forth. In order to make these comparisons, it is...

TFM classification and staging of oral submucous fibrosis: A new proposal.

PubMed

Arakeri, Gururaj; Thomas, Deepak; Aljabab, Abdulsalam S; Hunasgi, Santosh; Rai, Kirthi Kumar; Hale, Beverley; Fonseca, Felipe Paiva; Gomez, Ricardo Santiago; Rahimi, Siavash; Merkx, Matthias A W; Brennan, Peter A

2018-04-01

We have evaluated the rationale of existing grading and staging schemes of oral submucous fibrosis (OSMF) based on how they are categorized. A novel classification and staging scheme is proposed. A total of 300 OSMF patients were evaluated for agreement between functional, clinical, and histopathological staging. Bilateral biopsies were assessed in 25 patients to evaluate for any differences in histopathological staging of OSMF in the same mouth. Extent of clinician agreement for categorized staging data was evaluated using Cohen's weighted kappa analysis. Cross-tabulation was performed on categorical grading data to understand the intercorrelation, and the unweighted kappa analysis was used to assess the bilateral grade agreement. Probabilities of less than 0.05 were considered significant. Data were analyzed using SPSS Statistics (version 25.0, IBM, USA). A low agreement was found between all the stages depicting the independent nature of trismus, clinical features, and histopathological components (K = 0.312, 0.167, 0.152) in OSMF. Following analysis, a three-component classification scheme (TFM classification) was developed that describes the severity of each independently, grouping them using a novel three-tier staging scheme as a guide to the treatment plan. The proposed classification and staging could be useful for effective communication, categorization, and for recording data and prognosis, and for guiding treatment plans. Furthermore, the classification considers OSMF malignant transformation in detail. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Workshop on Algorithms for Time-Series Analysis

NASA Astrophysics Data System (ADS)

Protopapas, Pavlos

2012-04-01

abstract-type="normal">SummaryThis Workshop covered the four major subjects listed below in two 90-minute sessions. Each talk or tutorial allowed questions, and concluded with a discussion. Classification: Automatic classification using machine-learning methods is becoming a standard in surveys that generate large datasets. Ashish Mahabal (Caltech) reviewed various methods, and presented examples of several applications. Time-Series Modelling: Suzanne Aigrain (Oxford University) discussed autoregressive models and multivariate approaches such as Gaussian Processes. Meta-classification/mixture of expert models: Karim Pichara (Pontificia Universidad Católica, Chile) described the substantial promise which machine-learning classification methods are now showing in automatic classification, and discussed how the various methods can be combined together. Event Detection: Pavlos Protopapas (Harvard) addressed methods of fast identification of events with low signal-to-noise ratios, enlarging on the characterization and statistical issues of low signal-to-noise ratios and rare events.

Optimal statistical damage detection and classification in an experimental wind turbine blade using minimum instrumentation

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2017-04-01

The increasing demand for carbon neutral energy in a challenging economic environment is a driving factor for erecting ever larger wind turbines in harsh environments using novel wind turbine blade (WTBs) designs characterized by high flexibilities and lower buckling capacities. To counteract resulting increasing of operation and maintenance costs, efficient structural health monitoring systems can be employed to prevent dramatic failures and to schedule maintenance actions according to the true structural state. This paper presents a novel methodology for classifying structural damages using vibrational responses from a single sensor. The method is based on statistical classification using Bayes' theorem and an advanced statistic, which allows controlling the performance by varying the number of samples which represent the current state. This is done for multivariate damage sensitive features defined as partial autocorrelation coefficients (PACCs) estimated from vibrational responses and principal component analysis scores from PACCs. Additionally, optimal DSFs are composed not only for damage classification but also for damage detection based on binary statistical hypothesis testing, where features selections are found with a fast forward procedure. The method is applied to laboratory experiments with a small scale WTB with wind-like excitation and non-destructive damage scenarios. The obtained results demonstrate the advantages of the proposed procedure and are promising for future applications of vibration-based structural health monitoring in WTBs.

A statistical evaluation of the effects of gender differences in assessment of acute inhalation toxicity

PubMed Central

Price, Charlotte; Stallard, Nigel; Creton, Stuart; Indans, Ian; Guest, Robert; Griffiths, David; Edwards, Philippa

2010-01-01

Acute inhalation toxicity of chemicals has conventionally been assessed by the median lethal concentration (LC50) test (organisation for economic co-operation and development (OECD) TG 403). Two new methods, the recently adopted acute toxic class method (ATC; OECD TG 436) and a proposed fixed concentration procedure (FCP), have recently been considered, but statistical evaluations of these methods did not investigate the influence of differential sensitivity between male and female rats on the outcomes. This paper presents an analysis of data from the assessment of acute inhalation toxicity for 56 substances. Statistically significant differences between the LC50 for males and females were found for 16 substances, with greater than 10-fold differences in the LC50 for two substances. The paper also reports a statistical evaluation of the three test methods in the presence of unanticipated gender differences. With TG 403, a gender difference leads to a slightly greater chance of under-classification. This is also the case for the ATC method, but more pronounced than for TG 403, with misclassification of nearly all substances from Globally Harmonised System (GHS) class 3 into class 4. As the FCP uses females only, if females are more sensitive, the classification is unchanged. If males are more sensitive, the procedure may lead to under-classification. Additional research on modification of the FCP is thus proposed. PMID:20488841

Comparing the MRI-based Goutallier Classification to an experimental quantitative MR spectroscopic fat measurement of the supraspinatus muscle.

PubMed

Gilbert, Fabian; Böhm, Dirk; Eden, Lars; Schmalzl, Jonas; Meffert, Rainer H; Köstler, Herbert; Weng, Andreas M; Ziegler, Dirk

2016-08-22

The Goutallier Classification is a semi quantitative classification system to determine the amount of fatty degeneration in rotator cuff muscles. Although initially proposed for axial computer tomography scans it is currently applied to magnet-resonance-imaging-scans. The role for its clinical use is controversial, as the reliability of the classification has been shown to be inconsistent. The purpose of this study was to compare the semi quantitative MRI-based Goutallier Classification applied by 5 different raters to experimental MR spectroscopic quantitative fat measurement in order to determine the correlation between this classification system and the true extent of fatty degeneration shown by spectroscopy. MRI-scans of 42 patients with rotator cuff tears were examined by 5 shoulder surgeons and were graduated according to the MRI-based Goutallier Classification proposed by Fuchs et al. Additionally the fat/water ratio was measured with MR spectroscopy using the experimental SPLASH technique. The semi quantitative grading according to the Goutallier Classification was statistically correlated with the quantitative measured fat/water ratio using Spearman's rank correlation. Statistical analysis of the data revealed only fair correlation of the Goutallier Classification system and the quantitative fat/water ratio with R = 0.35 (p < 0.05). By dichotomizing the scale the correlation was 0.72. The interobserver and intraobserver reliabilities were substantial with R = 0.62 and R = 0.74 (p < 0.01). The correlation between the semi quantitative MRI based Goutallier Classification system and MR spectroscopic fat measurement is weak. As an adequate estimation of fatty degeneration based on standard MRI may not be possible, quantitative methods need to be considered in order to increase diagnostic safety and thus provide patients with ideal care in regard to the amount of fatty degeneration. Spectroscopic MR measurement may increase the accuracy of the Goutallier classification and thus improve the prediction of clinical results after rotator cuff repair. However, these techniques are currently only available in an experimental setting.

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.

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

NASA Technical Reports Server (NTRS)

Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

2013-01-01

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas.

Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Vítková, Gabriela; Prokeš, Lubomír; Novotný, Karel; Pořízka, Pavel; Novotný, Jan; Všianský, Dalibor; Čelko, Ladislav; Kaiser, Jozef

2014-11-01

Focusing on historical aspect, during archeological excavation or restoration works of buildings or different structures built from bricks it is important to determine, preferably in-situ and in real-time, the locality of bricks origin. Fast classification of bricks on the base of Laser-Induced Breakdown Spectroscopy (LIBS) spectra is possible using multivariate statistical methods. Combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was applied in this case. LIBS was used to classify altogether the 29 brick samples from 7 different localities. Realizing comparative study using two different LIBS setups - stand-off and table-top it is shown that stand-off LIBS has a big potential for archeological in-field measurements.

ICD-11 and DSM-5 personality trait domains capture categorical personality disorders: Finding a common ground.

PubMed

Bach, Bo; Sellbom, Martin; Skjernov, Mathias; Simonsen, Erik

2018-05-01

The five personality disorder trait domains in the proposed International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition are comparable in terms of Negative Affectivity, Detachment, Antagonism/Dissociality and Disinhibition. However, the International Classification of Diseases, 11th edition model includes a separate domain of Anankastia, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model includes an additional domain of Psychoticism. This study examined associations of International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domains, simultaneously, with categorical personality disorders. Psychiatric outpatients ( N = 226) were administered the Structured Clinical Interview for DSM-IV Axis II Personality Disorders Interview and the Personality Inventory for DSM-5. International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domain scores were obtained using pertinent scoring algorithms for the Personality Inventory for DSM-5. Associations between categorical personality disorders and trait domains were examined using correlation and multiple regression analyses. Both the International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models showed relevant continuity with categorical personality disorders and captured a substantial amount of their information. As expected, the International Classification of Diseases, 11th edition model was superior in capturing obsessive-compulsive personality disorder, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model was superior in capturing schizotypal personality disorder. These preliminary findings suggest that little information is 'lost' in a transition to trait domain models and potentially adds to narrowing the gap between Diagnostic and Statistical Manual of Mental Disorders, 5th edition and the proposed International Classification of Diseases, 11th edition model. Accordingly, the International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models may be used to delineate one another as well as features of familiar categorical personality disorder types. A preliminary category-to-domain 'cross walk' is provided in the article.

49 CFR 1248.100 - Commodity classification designated.

Code of Federal Regulations, 2010 CFR

2010-10-01

... STATISTICS Commodity Code § 1248.100 Commodity classification designated. Commencing with reports for the..., reports of commodity statistics required to be made to the Board, shall be based on the commodity codes... Statistics, 1963, issued by the Bureau of the Budget, and on additional codes 411 through 462 shown in § 1248...

Acyl carrier protein structural classification and normal mode analysis

PubMed Central

Cantu, David C; Forrester, Michael J; Charov, Katherine; Reilly, Peter J

2012-01-01

All acyl carrier protein primary and tertiary structures were gathered into the ThYme database. They are classified into 16 families by amino acid sequence similarity, with members of the different families having sequences with statistically highly significant differences. These classifications are supported by tertiary structure superposition analysis. Tertiary structures from a number of families are very similar, suggesting that these families may come from a single distant ancestor. Normal vibrational mode analysis was conducted on experimentally determined freestanding structures, showing greater fluctuations at chain termini and loops than in most helices. Their modes overlap more so within families than between different families. The tertiary structures of three acyl carrier protein families that lacked any known structures were predicted as well. PMID:22374859

Prior-to-Secondary School Course Classification System: School Codes for the Exchange of Data (SCED). NFES 2011-801

ERIC Educational Resources Information Center

National Forum on Education Statistics, 2011

2011-01-01

In this handbook, "Prior-to-Secondary School Course Classification System: School Codes for the Exchange of Data" (SCED), the National Center for Education Statistics (NCES) and the National Forum on Education Statistics have extended the existing secondary course classification system with codes and descriptions for courses offered at…

Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance

NASA Astrophysics Data System (ADS)

Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y.; Drake, Steven K.; Gucek, Marjan; Suffredini, Anthony F.; Sacks, David B.; Yu, Yi-Kuo

2016-02-01

Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple `fingerprinting'; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification

PubMed Central

Rutzinger, Martin; Höfle, Bernhard; Hollaus, Markus; Pfeifer, Norbert

2008-01-01

Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (>20 echoes/m2) and additional classification variables from full-waveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple echoes from one shot, offer new possibilities in classifying the ALS point cloud. Currently FWF sensor information is hardly used for classification purposes. This contribution presents an object-based point cloud analysis (OBPA) approach, combining segmentation and classification of the 3D FWF ALS points designed to detect tall vegetation in urban environments. The definition tall vegetation includes trees and shrubs, but excludes grassland and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region growing procedure. All echoes sorted descending by their surface roughness are used as seed points. Segments are grown based on echo width homogeneity. Next, segment statistics (mean, standard deviation, and coefficient of variation) are calculated by aggregating echo features such as amplitude and surface roughness. For classification a rule base is derived automatically from a training area using a statistical classification tree. To demonstrate our method we present data of three sites with around 500,000 echoes each. The accuracy of the classified vegetation segments is evaluated for two independent validation sites. In a point-wise error assessment, where the classification is compared with manually classified 3D points, completeness and correctness better than 90% are reached for the validation sites. In comparison to many other algorithms the proposed 3D point classification works on the original measurements directly, i.e. the acquired points. Gridding of the data is not necessary, a process which is inherently coupled to loss of data and precision. The 3D properties provide especially a good separability of buildings and terrain points respectively, if they are occluded by vegetation. PMID:27873771

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.

Gap Shape Classification using Landscape Indices and Multivariate Statistics

PubMed Central

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-01-01

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

PubMed

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-11-30

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

NASA Astrophysics Data System (ADS)

Besic, Nikola; Ventura, Jordi Figueras i.; Grazioli, Jacopo; Gabella, Marco; Germann, Urs; Berne, Alexis

2016-09-01

Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Microscopic saw mark analysis: an empirical approach.

PubMed

Love, Jennifer C; Derrick, Sharon M; Wiersema, Jason M; Peters, Charles

2015-01-01

Microscopic saw mark analysis is a well published and generally accepted qualitative analytical method. However, little research has focused on identifying and mitigating potential sources of error associated with the method. The presented study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw mark analysis. The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62-17.82%. © 2014 American Academy of Forensic Sciences.

Classification Techniques for Multivariate Data Analysis.

DTIC Science & Technology

1980-03-28

analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

A computer analysis of ERTS data of the Lake Gregory area of South Australia with particular emphasis on its role in terrain classification for engineering. M.S. Thesis

NASA Technical Reports Server (NTRS)

Lodwick, G. D. (Principal Investigator)

1976-01-01

A digital computer and multivariate statistical techniques were used to analyze 4-band multispectral data. A representation of the original data for each of the four bands allows a certain degree of terrain interpretation; however, variations in appearance of sites within and between bands, without additional criteria for deciding which representation should be preferred, create difficulties for classification. Investigation of the video data groups produced by principal components analysis and cluster analysis techniques shows that effective correlations with classifications of terrain produced by conventional methods could be carried out. The analyses also highlighted underlying relationships between the various elements. The approach used allows large areas (185 cm by 185 cm) to be classified into fundamental units within a matter of hours and can be applied to those parts of the Earth where facilities for conventional studies are poor or lacking.

Landscape object-based analysis of wetland plant functional types: the effects of spatial scale, vegetation classes and classifier methods

NASA Astrophysics Data System (ADS)

Dronova, I.; Gong, P.; Wang, L.; Clinton, N.; Fu, W.; Qi, S.

2011-12-01

Remote sensing-based vegetation classifications representing plant function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new classification tools; however, few comparisons of different algorithms and spatial scales have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from 30-m Landsat TM scene at the peak of spring growing season. We targeted major PFTs (C3 grasses, C3 forbs and different types of C4 grasses and aquatic vegetation) that are both key players in system's biogeochemical cycles and critical providers of waterbird habitat. Classification results were compared among: a) several object segmentation scales (with average object sizes 900-9000 m2); b) several families of statistical classifiers (including Bayesian, Logistic, Neural Network, Decision Trees and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and more detailed 6-class set. We found that classification benefited from object-based approach which allowed including object shape, texture and context descriptors in classification. While a number of classifiers achieved high accuracy at the finest pixel-equivalent segmentation scale, the highest accuracies and best agreement among algorithms occurred at coarser object scales. No single classifier was consistently superior across all scales, although selected algorithms of Neural Network, Logistic and K-Nearest Neighbors families frequently provided the best discrimination of classes at different scales. The choice of vegetation categories also affected classification accuracy. The 6-class set allowed for higher individual class accuracies but lower overall accuracies than the 3-class set because individual classes differed in scales at which they were best discriminated from others. Main classification challenges included a) presence of C3 grasses in C4-grass areas, particularly following harvesting of C4 reeds and b) mixtures of emergent, floating and submerged aquatic plants at sub-object and sub-pixel scales. We conclude that OBIA with advanced statistical classifiers offers useful instruments for landscape vegetation analyses, and that spatial scale considerations are critical in mapping PFTs, while multi-scale comparisons can be used to guide class selection. Future work will further apply fuzzy classification and field-collected spectral data for PFT analysis and compare results with MODIS PFT products.

A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data

PubMed Central

Qadri, Salman; Khan, Dost Muhammad; Ahmad, Farooq; Qadri, Syed Furqan; Babar, Masroor Ellahi; Shahid, Muhammad; Ul-Rehman, Muzammil; Razzaq, Abdul; Shah Muhammad, Syed; Fahad, Muhammad; Ahmad, Sarfraz; Pervez, Muhammad Tariq; Naveed, Nasir; Aslam, Naeem; Jamil, Mutiullah; Rehmani, Ejaz Ahmad; Ahmad, Nazir; Akhtar Khan, Naeem

2016-01-01

The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively. PMID:27376088

Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease

NASA Astrophysics Data System (ADS)

Abdolmanafi, Atefeh; Prasad, Arpan Suravi; Duong, Luc; Dahdah, Nagib

2016-03-01

Intravascular imaging modalities, such as Optical Coherence Tomography (OCT) allow nowadays improving diagnosis, treatment, follow-up, and even prevention of coronary artery disease in the adult. OCT has been recently used in children following Kawasaki disease (KD), the most prevalent acquired coronary artery disease during childhood with devastating complications. The assessment of coronary artery layers with OCT and early detection of coronary sequelae secondary to KD is a promising tool for preventing myocardial infarction in this population. More importantly, OCT is promising for tissue quantification of the inner vessel wall, including neo intima luminal myofibroblast proliferation, calcification, and fibrous scar deposits. The goal of this study is to classify the coronary artery layers of OCT imaging obtained from a series of KD patients. Our approach is focused on developing a robust Random Forest classifier built on the idea of randomly selecting a subset of features at each node and based on second- and higher-order statistical texture analysis which estimates the gray-level spatial distribution of images by specifying the local features of each pixel and extracting the statistics from their distribution. The average classification accuracy for intima and media are 76.36% and 73.72% respectively. Random forest classifier with texture analysis promises for classification of coronary artery tissue.

Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

USGS Publications Warehouse

Mitchell, Michael; Wilson, R. Randy; Twedt, Daniel J.; Mini, Anne E.; James, J. Dale

2016-01-01

The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.

Classification of arterial and venous cerebral vasculature based on wavelet postprocessing of CT perfusion data.

PubMed

Havla, Lukas; Schneider, Moritz J; Thierfelder, Kolja M; Beyer, Sebastian E; Ertl-Wagner, Birgit; Reiser, Maximilian F; Sommer, Wieland H; Dietrich, Olaf

2016-02-01

The purpose of this study was to propose and evaluate a new wavelet-based technique for classification of arterial and venous vessels using time-resolved cerebral CT perfusion data sets. Fourteen consecutive patients (mean age 73 yr, range 17-97) with suspected stroke but no pathology in follow-up MRI were included. A CT perfusion scan with 32 dynamic phases was performed during intravenous bolus contrast-agent application. After rigid-body motion correction, a Paul wavelet (order 1) was used to calculate voxelwise the wavelet power spectrum (WPS) of each attenuation-time course. The angiographic intensity A was defined as the maximum of the WPS, located at the coordinates T (time axis) and W (scale/width axis) within the WPS. Using these three parameters (A, T, W) separately as well as combined by (1) Fisher's linear discriminant analysis (FLDA), (2) logistic regression (LogR) analysis, or (3) support vector machine (SVM) analysis, their potential to classify 18 different arterial and venous vessel segments per subject was evaluated. The best vessel classification was obtained using all three parameters A and T and W [area under the curve (AUC): 0.953 with FLDA and 0.957 with LogR or SVM]. In direct comparison, the wavelet-derived parameters provided performance at least equal to conventional attenuation-time-course parameters. The maximum AUC obtained from the proposed wavelet parameters was slightly (although not statistically significantly) higher than the maximum AUC (0.945) obtained from the conventional parameters. A new method to classify arterial and venous cerebral vessels with high statistical accuracy was introduced based on the time-domain wavelet transform of dynamic CT perfusion data in combination with linear or nonlinear multidimensional classification techniques.

Landsat real-time processing

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

Davis, E.L.

A novel method for performing real-time acquisition and processing Landsat/EROS data covers all aspects including radiometric and geometric corrections of multispectral scanner or return-beam vidicon inputs, image enhancement, statistical analysis, feature extraction, and classification. Radiometric transformations include bias/gain adjustment, noise suppression, calibration, scan angle compensation, and illumination compensation, including topography and atmospheric effects. Correction or compensation for geometric distortion includes sensor-related distortions, such as centering, skew, size, scan nonlinearity, radial symmetry, and tangential symmetry. Also included are object image-related distortions such as aspect angle (altitude), scale distortion (altitude), terrain relief, and earth curvature. Ephemeral corrections are also applied to compensatemore » for satellite forward movement, earth rotation, altitude variations, satellite vibration, and mirror scan velocity. Image enhancement includes high-pass, low-pass, and Laplacian mask filtering and data restoration for intermittent losses. Resource classification is provided by statistical analysis including histograms, correlational analysis, matrix manipulations, and determination of spectral responses. Feature extraction includes spatial frequency analysis, which is used in parallel discriminant functions in each array processor for rapid determination. The technique uses integrated parallel array processors that decimate the tasks concurrently under supervision of a control processor. The operator-machine interface is optimized for programming ease and graphics image windowing.« less

Contextual Classification of Point Cloud Data by Exploiting Individual 3d Neigbourhoods

NASA Astrophysics Data System (ADS)

Weinmann, M.; Schmidt, A.; Mallet, C.; Hinz, S.; Rottensteiner, F.; Jutzi, B.

2015-03-01

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

Preliminary results of the comparative study between EO-1/Hyperion and ALOS/PALSAR

NASA Astrophysics Data System (ADS)

Koizumi, E.; Furuta, R.; Yamamoto, A.

2011-12-01

[Introduction]Hyper-spectral remote sensing images have been used for land-cover classification due to their high spectral resolutions. Synthetic Aperture Radar (SAR) remote sensing data are also useful to probe surface condition because radar image reflects surface geometry, although there are not so many reports about the land-cover detection with combination use of both hyper-spectral data and SAR data. Among SAR sensors, L-band SAR is thought to be useful tool to find physical properties because its comparatively long wave length can through small objects on surface. We are comparing the result of land cover classification and/or physical values from hyper-spectral and L-band SAR data to find the relationship between these two quite different sensors and to confirm the possibility of the combined analysis of hyper-spectral and L-band SAR data, and in this presentation we will report the preliminary result of this study. There are only few sources of both hyper-spectral and L-band SAR data from the space in this time, however, several space organizations plan to launch new satellites on which hyper-spectral or L-band SAR equipments are mounted in next few years. So, the importance of the combined analysis will increase more than ever. [Target Area]We are performing and planning analyses on the following areas in this study. (a)South of Cairo, Nile river area, Egypt, for sand, sandstone, limestone, river, crops. (b)Mount Sakurajima, Japan, for igneous rock and other related geological property. [Methods and Results]EO-1 Hyperion data are analyzed in this study as hyper-spectral data. The Hyperion equipment has 242 channels but some of them include full noise or have no data. We selected channels for analysis by checking each channel, and select about 150 channels (depend on the area). Before analysis, the atmospheric correction of ATCOR-3 was applied for the selected channels. The corrected data were analyzed by unsupervised classification or principal component analysis (PCA). We also did the unsupervised classification with the several components from PCA. According to the analysis results, several classifications can be extracted for each category (vegetation, sand and rocks, and water). One of the interesting results is that there are a few classes for sand as those of other categories, and these classes seem to reflect artificial and natural surface changes that are some result of excavation or scratching. ALOS PALSAR data are analyzed as L-band SAR data. We selected the Dual Polarization data for each target area. The data were converted to backscattered images, and then calculated some image statistic values. The topographic information also calculates with SAR interferometry technique as reference. Comparing the Hyperion classification results with the result of the calculation of statistic values from PALSAR, there are some areas where relativities seem to be confirmed. To confirm the combined analysis between hyper-spectral and L-band SAR data to detect and classify the surface material, further studies are still required. We will continue to investigate more efficient analytic methods and to examine other functions like the adopted channels, the number of class in classification, the kind of statistic information, and so on, to refine the method.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

NASA Astrophysics Data System (ADS)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

Classification of passive auditory event-related potentials using discriminant analysis and self-organizing feature maps.

PubMed

Schönweiler, R; Wübbelt, P; Tolloczko, R; Rose, C; Ptok, M

2000-01-01

Discriminant analysis (DA) and self-organizing feature maps (SOFM) were used to classify passively evoked auditory event-related potentials (ERP) P(1), N(1), P(2) and N(2). Responses from 16 children with severe behavioral auditory perception deficits, 16 children with marked behavioral auditory perception deficits, and 14 controls were examined. Eighteen ERP amplitude parameters were selected for examination of statistical differences between the groups. Different DA methods and SOFM configurations were trained to the values. SOFM had better classification results than DA methods. Subsequently, measures on another 37 subjects that were unknown for the trained SOFM were used to test the reliability of the system. With 10-dimensional vectors, reliable classifications were obtained that matched behavioral auditory perception deficits in 96%, implying central auditory processing disorder (CAPD). The results also support the assumption that CAPD includes a 'non-peripheral' auditory processing deficit. Copyright 2000 S. Karger AG, Basel.

Metabolomic analysis applied to chemosystematics and evolution of megadiverse Brazilian Vernonieae (Asteraceae).

PubMed

Gallon, Marília Elias; Monge, Marcelo; Casoti, Rosana; Da Costa, Fernando Batista; Semir, João; Gobbo-Neto, Leonardo

2018-06-01

Vernonia sensu lato is the largest and most complex genus of the tribe Vernonieae (Asteraceae). The tribe is chemically characterized by the presence of sesquiterpene lactones and flavonoids. Over the years, several taxonomic classifications have been proposed for Vernonia s.l. and for the tribe; however, there has been no consensus among the researches. According to traditional classification, Vernonia s.l. comprises more than 1000 species divided into sections, subsections and series (sensu Bentham). In a more recent classification, these species have been segregated into other genera and some subtribes were proposed, while the genus Vernonia sensu stricto was restricted to 22 species distributed mainly in North America (sensu Robinson). In this study, species from the subtribes Vernoniinae, Lepidaploinae and Rolandrinae were analyzed by UHPLC-UV-HRMS followed by multivariate statistical analysis. Data mining was performed using unsupervised (HCA and PCA) and supervised methods (OPLS-DA). The HCA showed the segregation of the species into four main groups. Comparing the HCA with taxonomical classifications of Vernonieae, we observed that the groups of the dendogram, based on metabolic profiling, were in accordance with the generic classification proposed by Robinson and with previous phylogenetic studies. The species of the genera Stenocephalum, Stilpnopappus, Strophopappus and Rolandra (Group 1) were revealed to be more related to the species of the genus Vernonanthura (Group 2), while the genera Cyrtocymura, Chrysolaena and Echinocoryne (Group 3) were chemically more similar to the genera Lessingianthus and Lepidaploa (Group 4). These findings indicated that the subtribes Vernoniinae and Lepidaploinae are non-chemically homogeneous groups and highlighted the application of untargeted metabolomic tools for taxonomy and as indicators of species evolution. Discriminant compounds for the groups obtained by OPLS-DA were determined. Groups 1 and 2 were characterized by the presence of 3',4'-dimethoxyluteolin, glaucolide A and 8-tigloyloxyglaucolide A. The species of Groups 3 and 4 were characterized by the presence of putative acacetin 7-O-rutinoside and glaucolide B. Therefore, untargeted metabolomic approach combined with multivariate statistical analysis, as proposed herein, allowed the identification of potential chemotaxonomic markers, helping in the taxonomic classifications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Supervised classification of continental shelf sediment off western Donegal, Ireland

NASA Astrophysics Data System (ADS)

Monteys, X.; Craven, K.; McCarron, S. G.

2017-12-01

Managing human impacts on marine ecosystems requires natural regions to be identified and mapped over a range of hierarchically nested scales. In recent years (2000-present) the Irish National Seabed Survey (INSS) and Integrated Mapping for the Sustainable Development of Ireland's Marine Resources programme (INFOMAR) (Geological Survey Ireland and Marine Institute collaborations) has provided unprecedented quantities of high quality data on Ireland's offshore territories. The increasing availability of large, detailed digital representations of these environments requires the application of objective and quantitative analyses. This study presents results of a new approach for sea floor sediment mapping based on an integrated analysis of INFOMAR multibeam bathymetric data (including the derivatives of slope and relative position), backscatter data (including derivatives of angular response analysis) and sediment groundtruthing over the continental shelf, west of Donegal. It applies a Geographic-Object-Based Image Analysis software package to provide a supervised classification of the surface sediment. This approach can provide a statistically robust, high resolution classification of the seafloor. Initial results display a differentiation of sediment classes and a reduction in artefacts from previously applied methodologies. These results indicate a methodology that could be used during physical habitat mapping and classification of marine environments.

Classification of Malaysia aromatic rice using multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-05-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Classifying breast cancer surgery: a novel, complexity-based system for oncological, oncoplastic and reconstructive procedures, and proof of principle by analysis of 1225 operations in 1166 patients.

PubMed

Hoffmann, Jürgen; Wallwiener, Diethelm

2009-04-08

One of the basic prerequisites for generating evidence-based data is the availability of classification systems. Attempts to date to classify breast cancer operations have focussed on specific problems, e.g. the avoidance of secondary corrective surgery for surgical defects, rather than taking a generic approach. Starting from an existing, simpler empirical scheme based on the complexity of breast surgical procedures, which was used in-house primarily in operative report-writing, a novel classification of ablative and breast-conserving procedures initially needed to be developed and elaborated systematically. To obtain proof of principle, a prospectively planned analysis of patient records for all major breast cancer-related operations performed at our breast centre in 2005 and 2006 was conducted using the new classification. Data were analysed using basic descriptive statistics such as frequency tables. A novel two-type, six-tier classification system comprising 12 main categories, 13 subcategories and 39 sub-subcategories of oncological, oncoplastic and reconstructive breast cancer-related surgery was successfully developed. Our system permitted unequivocal classification, without exception, of all 1225 procedures performed in 1166 breast cancer patients in 2005 and 2006. Breast cancer-related surgical procedures can be generically classified according to their surgical complexity. Analysis of all major procedures performed at our breast centre during the study period provides proof of principle for this novel classification system. We envisage various applications for this classification, including uses in randomised clinical trials, guideline development, specialist surgical training, continuing professional development as well as quality of care and public health research.

Analysis of the 1996 Wisconsin forest statistics by habitat type.

Treesearch

John Kotar; Joseph A. Kovach; Gary Brand

1999-01-01

The fifth inventory of Wisconsin's forests is presented from the perspective of habitat type as a classification tool. Habitat type classifies forests based on the species composition of the understory plant community. Various forest attributes are summarized by habitat type and management implications are discussed.

A stylistic classification of Russian-language texts based on the random walk model

NASA Astrophysics Data System (ADS)

Kramarenko, A. A.; Nekrasov, K. A.; Filimonov, V. V.; Zhivoderov, A. A.; Amieva, A. A.

2017-09-01

A formal approach to text analysis is suggested that is based on the random walk model. The frequencies and reciprocal positions of the vowel letters are matched up by a process of quasi-particle migration. Statistically significant difference in the migration parameters for the texts of different functional styles is found. Thus, a possibility of classification of texts using the suggested method is demonstrated. Five groups of the texts are singled out that can be distinguished from one another by the parameters of the quasi-particle migration process.

A tutorial on the use of ROC analysis for computer-aided diagnostic systems.

PubMed

Scheipers, Ulrich; Perrey, Christian; Siebers, Stefan; Hansen, Christian; Ermert, Helmut

2005-07-01

The application of the receiver operating characteristic (ROC) curve for computer-aided diagnostic systems is reviewed. A statistical framework is presented and different methods of evaluating the classification performance of computer-aided diagnostic systems, and, in particular, systems for ultrasonic tissue characterization, are derived. Most classifiers that are used today are dependent on a separation threshold, which can be chosen freely in many cases. The separation threshold separates the range of output values of the classification system into different target groups, thus conducting the actual classification process. In the first part of this paper, threshold specific performance measures, e.g., sensitivity and specificity, are presented. In the second part, a threshold-independent performance measure, the area under the ROC curve, is reviewed. Only the use of separation threshold-independent performance measures provides classification results that are overall representative for computer-aided diagnostic systems. The following text was motivated by the lack of a complete and definite discussion of the underlying subject in available textbooks, references and publications. Most manuscripts published so far address the theme of performance evaluation using ROC analysis in a manner too general to be practical for everyday use in the development of computer-aided diagnostic systems. Nowadays, the user of computer-aided diagnostic systems typically handles huge amounts of numerical data, not always distributed normally. Many assumptions made in more or less theoretical works on ROC analysis are no longer valid for real-life data. The paper aims at closing the gap between theoretical works and real-life data. The review provides the interested scientist with information needed to conduct ROC analysis and to integrate algorithms performing ROC analysis into classification systems while understanding the basic principles of classification.

Parametric Time-Frequency Analysis and Its Applications in Music Classification

NASA Astrophysics Data System (ADS)

Shen, Ying; Li, Xiaoli; Ma, Ngok-Wah; Krishnan, Sridhar

2010-12-01

Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data

NASA Astrophysics Data System (ADS)

Churches, Christopher E.; Wampler, Peter J.; Sun, Wanxiao; Smith, Andrew J.

2014-08-01

This study uses 2010-2011 Landsat Thematic Mapper (TM) imagery to estimate total forested area in Haiti. The thematic map was generated using radiometric normalization of digital numbers by a modified normalization method utilizing pseudo-invariant polygons (PIPs), followed by supervised classification of the mosaicked image using the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System. Classification results were compared to other sources of land-cover data produced for similar years, with an emphasis on the statistics presented by the FAO. Three global land cover datasets (GLC2000, Globcover, 2009, and MODIS MCD12Q1), and a national-scale dataset (a land cover analysis by Haitian National Centre for Geospatial Information (CNIGS)) were reclassified and compared. According to our classification, approximately 32.3% of Haiti's total land area was tree covered in 2010-2011. This result was confirmed using an error-adjusted area estimator, which predicted a tree covered area of 32.4%. Standardization to the FAO's forest cover class definition reduces the amount of tree cover of our supervised classification to 29.4%. This result was greater than the reported FAO value of 4% and the value for the recoded GLC2000 dataset of 7.0%, but is comparable to values for three other recoded datasets: MCD12Q1 (21.1%), Globcover (2009) (26.9%), and CNIGS (19.5%). We propose that at coarse resolutions, the segmented and patchy nature of Haiti's forests resulted in a systematic underestimation of the extent of forest cover. It appears the best explanation for the significant difference between our results, FAO statistics, and compared datasets is the accuracy of the data sources and the resolution of the imagery used for land cover analyses. Analysis of recoded global datasets and results from this study suggest a strong linear relationship (R2 = 0.996 for tree cover) between spatial resolution and land cover estimates.

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

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

Application of statistical classification methods for predicting the acceptability of well-water quality

NASA Astrophysics Data System (ADS)

Cameron, Enrico; Pilla, Giorgio; Stella, Fabio A.

2018-06-01

The application of statistical classification methods is investigated—in comparison also to spatial interpolation methods—for predicting the acceptability of well-water quality in a situation where an effective quantitative model of the hydrogeological system under consideration cannot be developed. In the example area in northern Italy, in particular, the aquifer is locally affected by saline water and the concentration of chloride is the main indicator of both saltwater occurrence and groundwater quality. The goal is to predict if the chloride concentration in a water well will exceed the allowable concentration so that the water is unfit for the intended use. A statistical classification algorithm achieved the best predictive performances and the results of the study show that statistical classification methods provide further tools for dealing with groundwater quality problems concerning hydrogeological systems that are too difficult to describe analytically or to simulate effectively.

Chemical modeling of groundwater in the Banat Plain, southwestern Romania, with elevated As content and co-occurring species by combining diagrams and unsupervised multivariate statistical approaches.

PubMed

Butaciu, Sinziana; Senila, Marin; Sarbu, Costel; Ponta, Michaela; Tanaselia, Claudiu; Cadar, Oana; Roman, Marius; Radu, Emil; Sima, Mihaela; Frentiu, Tiberiu

2017-04-01

The study proposes a combined model based on diagrams (Gibbs, Piper, Stuyfzand Hydrogeochemical Classification System) and unsupervised statistical approaches (Cluster Analysis, Principal Component Analysis, Fuzzy Principal Component Analysis, Fuzzy Hierarchical Cross-Clustering) to describe natural enrichment of inorganic arsenic and co-occurring species in groundwater in the Banat Plain, southwestern Romania. Speciation of inorganic As (arsenite, arsenate), ion concentrations (Na + , K + , Ca 2+ , Mg 2+ , HCO 3 - , Cl - , F - , SO 4 2- , PO 4 3- , NO 3 - ), pH, redox potential, conductivity and total dissolved substances were performed. Classical diagrams provided the hydrochemical characterization, while statistical approaches were helpful to establish (i) the mechanism of naturally occurring of As and F - species and the anthropogenic one for NO 3 - , SO 4 2- , PO 4 3- and K + and (ii) classification of groundwater based on content of arsenic species. The HCO 3 - type of local groundwater and alkaline pH (8.31-8.49) were found to be responsible for the enrichment of arsenic species and occurrence of F - but by different paths. The PO 4 3- -AsO 4 3- ion exchange, water-rock interaction (silicates hydrolysis and desorption from clay) were associated to arsenate enrichment in the oxidizing aquifer. Fuzzy Hierarchical Cross-Clustering was the strongest tool for the rapid simultaneous classification of groundwaters as a function of arsenic content and hydrogeochemical characteristics. The approach indicated the Na + -F - -pH cluster as marker for groundwater with naturally elevated As and highlighted which parameters need to be monitored. A chemical conceptual model illustrating the natural and anthropogenic paths and enrichment of As and co-occurring species in the local groundwater supported by mineralogical analysis of rocks was established. Copyright © 2016 Elsevier Ltd. All rights reserved.

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

USGS Publications Warehouse

Rosenfield, George H.; Fitzpatrick-Lins, Katherine

1984-01-01

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

Statistical strategy for anisotropic adventitia modelling in IVUS.

PubMed

Gil, Debora; Hernández, Aura; Rodriguez, Oriol; Mauri, Josepa; Radeva, Petia

2006-06-01

Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders.

Assessment of a Learning Strategy among Spine Surgeons.

PubMed

Gotfryd, Alberto Ofenhejm; Corredor, Jose Alfredo; Teixeira, William Jacobsen; Martins, Delio Eulálio; Milano, Jeronimo; Iutaka, Alexandre Sadao

2017-02-01

Pilot test, observational study. To evaluate objectively the knowledge transfer provided by theoretical and practical activities during AOSpine courses for spine surgeons. During two AOSpine principles courses, 62 participants underwent precourse assessment, which consisted of questions about their professional experience, preferences regarding adolescent idiopathic scoliosis (AIS) classification, and classifying the curves by means of the Lenke classification of two AIS clinical cases. Two learning strategies were used during the course. A postcourse questionnaire was applied to reclassify the same deformity cases. Differences in the correct answers of clinical cases between pre- and postcourse were analyzed, revealing the number of participants whose accuracy in classification improved after the course. Analysis showed a decrease in the number of participants with wrong answers in both cases after the course. In the first case, statistically significant differences were observed in both curve pattern (83.3%, p   =  0.005) and lumbar spine modifier (46.6%, p   =  0.049). No statistically significant improvement was seen in the sagittal thoracic modifier (33.3%, p   =  0.309). In the second case, statistical improvement was obtained in curve pattern (27.4%, p   =  0.018). No statistically significant improvement was seen regarding lumbar spine modifier (9.8%, p   =  0.121) and sagittal thoracic modifier (12.9%, p   =  0.081). This pilot test showed objectively that learning strategies used during AOSpine courses improved the participants' knowledge. Teaching strategies must be continually improved to ensure an optimal level of knowledge transfer.

Assessment of a Learning Strategy among Spine Surgeons

PubMed Central

Gotfryd, Alberto Ofenhejm; Teixeira, William Jacobsen; Martins, Delio Eulálio; Milano, Jeronimo; Iutaka, Alexandre Sadao

2017-01-01

Study Design Pilot test, observational study. Objective To evaluate objectively the knowledge transfer provided by theoretical and practical activities during AOSpine courses for spine surgeons. Methods During two AOSpine principles courses, 62 participants underwent precourse assessment, which consisted of questions about their professional experience, preferences regarding adolescent idiopathic scoliosis (AIS) classification, and classifying the curves by means of the Lenke classification of two AIS clinical cases. Two learning strategies were used during the course. A postcourse questionnaire was applied to reclassify the same deformity cases. Differences in the correct answers of clinical cases between pre- and postcourse were analyzed, revealing the number of participants whose accuracy in classification improved after the course. Results Analysis showed a decrease in the number of participants with wrong answers in both cases after the course. In the first case, statistically significant differences were observed in both curve pattern (83.3%, p  =  0.005) and lumbar spine modifier (46.6%, p  =  0.049). No statistically significant improvement was seen in the sagittal thoracic modifier (33.3%, p  =  0.309). In the second case, statistical improvement was obtained in curve pattern (27.4%, p  =  0.018). No statistically significant improvement was seen regarding lumbar spine modifier (9.8%, p  =  0.121) and sagittal thoracic modifier (12.9%, p  =  0.081). Conclusion This pilot test showed objectively that learning strategies used during AOSpine courses improved the participants' knowledge. Teaching strategies must be continually improved to ensure an optimal level of knowledge transfer. PMID:28451507

Using complex networks for text classification: Discriminating informative and imaginative documents

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-01-01

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Galaxy Zoo: Infrared and Optical Morphology

NASA Astrophysics Data System (ADS)

Carla Shanahan, Jesse; Lintott, Chris; Zoo, Galaxy

2018-01-01

We present the detailed, visual morphologies of approximately 60,000 galaxies observed by the UKIRT Infrared Deep Sky Survey and then classified by participants in the Galaxy Zoo project. Our sample is composed entirely of nearby objects with redshifts of z ≤ 0.3, which enables us to robustly analyze their morphological characteristics including smoothness, bulge properties, spiral structure, and evidence of bars or rings. The determination of these features is made via a consensus-based analysis of the Galaxy Zoo project data in which inconsistent and outlying classifications are statistically down-weighted. We then compare these classifications of infrared morphology to the objects’ optical classifications in the Galaxy Zoo 2 release (Willett et al. 2013). It is already known that morphology is an effective tool for uncovering a galaxy’s dynamical past, and previous studies have shown significant correlations with physical characteristics such as stellar mass distribution and star formation history. We show that majority of the sample has agreement or expected differences between the optical and infrared classifications, but also present a preliminary analysis of a subsample of objects with striking discrepancies.

Classification of edible oils by employing 31P and 1H NMR spectroscopy in combination with multivariate statistical analysis. A proposal for the detection of seed oil adulteration in virgin olive oils.

PubMed

Vigli, Georgia; Philippidis, Angelos; Spyros, Apostolos; Dais, Photis

2003-09-10

A combination of (1)H NMR and (31)P NMR spectroscopy and multivariate statistical analysis was used to classify 192 samples from 13 types of vegetable oils, namely, hazelnut, sunflower, corn, soybean, sesame, walnut, rapeseed, almond, palm, groundnut, safflower, coconut, and virgin olive oils from various regions of Greece. 1,2-Diglycerides, 1,3-diglycerides, the ratio of 1,2-diglycerides to total diglycerides, acidity, iodine value, and fatty acid composition determined upon analysis of the respective (1)H NMR and (31)P NMR spectra were selected as variables to establish a classification/prediction model by employing discriminant analysis. This model, obtained from the training set of 128 samples, resulted in a significant discrimination among the different classes of oils, whereas 100% of correct validated assignments for 64 samples were obtained. Different artificial mixtures of olive-hazelnut, olive-corn, olive-sunflower, and olive-soybean oils were prepared and analyzed by (1)H NMR and (31)P NMR spectroscopy. Subsequent discriminant analysis of the data allowed detection of adulteration as low as 5% w/w, provided that fresh virgin olive oil samples were used, as reflected by their high 1,2-diglycerides to total diglycerides ratio (D > or = 0.90).

Routes to failure: analysis of 41 civil aviation accidents from the Republic of China using the human factors analysis and classification system.

PubMed

Li, Wen-Chin; Harris, Don; Yu, Chung-San

2008-03-01

The human factors analysis and classification system (HFACS) is based upon Reason's organizational model of human error. HFACS was developed as an analytical framework for the investigation of the role of human error in aviation accidents, however, there is little empirical work formally describing the relationship between the components in the model. This research analyses 41 civil aviation accidents occurring to aircraft registered in the Republic of China (ROC) between 1999 and 2006 using the HFACS framework. The results show statistically significant relationships between errors at the operational level and organizational inadequacies at both the immediately adjacent level (preconditions for unsafe acts) and higher levels in the organization (unsafe supervision and organizational influences). The pattern of the 'routes to failure' observed in the data from this analysis of civil aircraft accidents show great similarities to that observed in the analysis of military accidents. This research lends further support to Reason's model that suggests that active failures are promoted by latent conditions in the organization. Statistical relationships linking fallible decisions in upper management levels were found to directly affect supervisory practices, thereby creating the psychological preconditions for unsafe acts and hence indirectly impairing the performance of pilots, ultimately leading to accidents.

Statistical Methods for Passive Vehicle Classification in Urban Traffic Surveillance and Control

DOT National Transportation Integrated Search

1980-01-01

A statistical approach to passive vehicle classification using the phase-shift signature from electromagnetic presence-type vehicle detectors is developed with digitized samples of the analog phase-shift signature, the problem of classifying vehicle ...

Quality classification of Spanish olive oils by untargeted gas chromatography coupled to hybrid quadrupole-time of flight mass spectrometry with atmospheric pressure chemical ionization and metabolomics-based statistical approach.

PubMed

Sales, C; Cervera, M I; Gil, R; Portolés, T; Pitarch, E; Beltran, J

2017-02-01

The novel atmospheric pressure chemical ionization (APCI) source has been used in combination with gas chromatography (GC) coupled to hybrid quadrupole time-of-flight (QTOF) mass spectrometry (MS) for determination of volatile components of olive oil, enhancing its potential for classification of olive oil samples according to their quality using a metabolomics-based approach. The full-spectrum acquisition has allowed the detection of volatile organic compounds (VOCs) in olive oil samples, including Extra Virgin, Virgin and Lampante qualities. A dynamic headspace extraction with cartridge solvent elution was applied. The metabolomics strategy consisted of three different steps: a full mass spectral alignment of GC-MS data using MzMine 2.0, a multivariate analysis using Ez-Info and the creation of the statistical model with combinations of responses for molecular fragments. The model was finally validated using blind samples, obtaining an accuracy in oil classification of 70%, taking the official established method, "PANEL TEST", as reference. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural network approach in multichannel auditory event-related potential analysis.

PubMed

Wu, F Y; Slater, J D; Ramsay, R E

1994-04-01

Even though there are presently no clearly defined criteria for the assessment of P300 event-related potential (ERP) abnormality, it is strongly indicated through statistical analysis that such criteria exist for classifying control subjects and patients with diseases resulting in neuropsychological impairment such as multiple sclerosis (MS). We have demonstrated the feasibility of artificial neural network (ANN) methods in classifying ERP waveforms measured at a single channel (Cz) from control subjects and MS patients. In this paper, we report the results of multichannel ERP analysis and a modified network analysis methodology to enhance automation of the classification rule extraction process. The proposed methodology significantly reduces the work of statistical analysis. It also helps to standardize the criteria of P300 ERP assessment and facilitate the computer-aided analysis on neuropsychological functions.

7 CFR 601.1 - Functions assigned.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) National Resources Inventory (NRI) that is a statistically-based survey designed and implemented using... both the provisions of the Food Security Act and Section 404 of the Clean Water Act. (ii) Soil surveys.... Soil surveys are based on scientific analysis and classification of the soils and are used to determine...

7 CFR 601.1 - Functions assigned.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) National Resources Inventory (NRI) that is a statistically-based survey designed and implemented using... both the provisions of the Food Security Act and Section 404 of the Clean Water Act. (ii) Soil surveys.... Soil surveys are based on scientific analysis and classification of the soils and are used to determine...

7 CFR 601.1 - Functions assigned.

Code of Federal Regulations, 2012 CFR

2012-01-01

...) National Resources Inventory (NRI) that is a statistically-based survey designed and implemented using... both the provisions of the Food Security Act and Section 404 of the Clean Water Act. (ii) Soil surveys.... Soil surveys are based on scientific analysis and classification of the soils and are used to determine...

7 CFR 601.1 - Functions assigned.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) National Resources Inventory (NRI) that is a statistically-based survey designed and implemented using... both the provisions of the Food Security Act and Section 404 of the Clean Water Act. (ii) Soil surveys.... Soil surveys are based on scientific analysis and classification of the soils and are used to determine...

7 CFR 601.1 - Functions assigned.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) National Resources Inventory (NRI) that is a statistically-based survey designed and implemented using... both the provisions of the Food Security Act and Section 404 of the Clean Water Act. (ii) Soil surveys.... Soil surveys are based on scientific analysis and classification of the soils and are used to determine...

Collected Notes on the Workshop for Pattern Discovery in Large Databases

NASA Technical Reports Server (NTRS)

Buntine, Wray (Editor); Delalto, Martha (Editor)

1991-01-01

These collected notes are a record of material presented at the Workshop. The core data analysis is addressed that have traditionally required statistical or pattern recognition techniques. Some of the core tasks include classification, discrimination, clustering, supervised and unsupervised learning, discovery and diagnosis, i.e., general pattern discovery.

Artificial Neural Networks in Policy Research: A Current Assessment.

ERIC Educational Resources Information Center

Woelfel, Joseph

1993-01-01

Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

Face recognition using an enhanced independent component analysis approach.

PubMed

Kwak, Keun-Chang; Pedrycz, Witold

2007-03-01

This paper is concerned with an enhanced independent component analysis (ICA) and its application to face recognition. Typically, face representations obtained by ICA involve unsupervised learning and high-order statistics. In this paper, we develop an enhancement of the generic ICA by augmenting this method by the Fisher linear discriminant analysis (LDA); hence, its abbreviation, FICA. The FICA is systematically developed and presented along with its underlying architecture. A comparative analysis explores four distance metrics, as well as classification with support vector machines (SVMs). We demonstrate that the FICA approach leads to the formation of well-separated classes in low-dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression. The comprehensive experiments are completed for the facial-recognition technology (FERET) face database; a comparative analysis demonstrates that FICA comes with improved classification rates when compared with some other conventional approaches such as eigenface, fisherface, and the ICA itself.

Sampling surface and subsurface particle-size distributions in wadable gravel-and cobble-bed streams for analyses in sediment transport, hydraulics, and streambed monitoring

Treesearch

Kristin Bunte; Steven R. Abt

2001-01-01

This document provides guidance for sampling surface and subsurface sediment from wadable gravel-and cobble-bed streams. After a short introduction to streams types and classifications in gravel-bed rivers, the document explains the field and laboratory measurement of particle sizes and the statistical analysis of particle-size distributions. Analysis of particle...

ADP of multispectral scanner data for land use mapping

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1971-01-01

The advantages and disadvantages of various remote sensing instrumentation and analysis techniques are reviewed. The use of multispectral scanner data and the automatic data processing techniques are considered. A computer-aided analysis system for remote sensor data is described with emphasis on the image display, statistics processor, wavelength band selection, classification processor, and results display. Advanced techniques in using spectral and temporal data are also considered.

A land classification protocol for pollinator ecology research: An urbanization case study.

PubMed

Samuelson, Ash E; Leadbeater, Ellouise

2018-06-01

Land-use change is one of the most important drivers of widespread declines in pollinator populations. Comprehensive quantitative methods for land classification are critical to understanding these effects, but co-option of existing human-focussed land classifications is often inappropriate for pollinator research. Here, we present a flexible GIS-based land classification protocol for pollinator research using a bottom-up approach driven by reference to pollinator ecology, with urbanization as a case study. Our multistep method involves manually generating land cover maps at multiple biologically relevant radii surrounding study sites using GIS, with a focus on identifying land cover types that have a specific relevance to pollinators. This is followed by a three-step refinement process using statistical tools: (i) definition of land-use categories, (ii) principal components analysis on the categories, and (iii) cluster analysis to generate a categorical land-use variable for use in subsequent analysis. Model selection is then used to determine the appropriate spatial scale for analysis. We demonstrate an application of our protocol using a case study of 38 sites across a gradient of urbanization in South-East England. In our case study, the land classification generated a categorical land-use variable at each of four radii based on the clustering of sites with different degrees of urbanization, open land, and flower-rich habitat. Studies of land-use effects on pollinators have historically employed a wide array of land classification techniques from descriptive and qualitative to complex and quantitative. We suggest that land-use studies in pollinator ecology should broadly adopt GIS-based multistep land classification techniques to enable robust analysis and aid comparative research. Our protocol offers a customizable approach that combines specific relevance to pollinator research with the potential for application to a wide range of ecological questions, including agroecological studies of pest control.

Application of classification trees for the qualitative differentiation of focal liver lesions suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging.

PubMed

Schelhorn, J; Benndorf, M; Dietzel, M; Burmeister, H P; Kaiser, W A; Baltzer, P A T

2012-09-01

To evaluate the diagnostic accuracy of qualitative descriptors alone and in combination for the classification of focal liver lesions (FLLs) suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging. Consecutive patients with clinically suspected liver metastases were eligible for this retrospective investigation. 50 patients met the inclusion criteria. All underwent Gd-EOB-DTPA-enhanced liver MRI (T2w, chemical shift T1w, dynamic T1w). Primary liver malignancies or treated lesions were excluded. All investigations were read by two blinded observers (O1, O2). Both independently identified the presence of lesions and evaluated predefined qualitative lesion descriptors (signal intensities, enhancement pattern and morphology). A reference standard was determined under consideration of all clinical and follow-up information. Statistical analysis besides contingency tables (chi square, kappa statistics) included descriptor combinations using classification trees (CHAID methodology) as well as ROC analysis. In 38 patients, 120 FLLs (52 benign, 68 malignant) were present. 115 (48 benign, 67 malignant) were identified by the observers. The enhancement pattern, relative SI upon T2w and late enhanced T1w images contributed significantly to the differentiation of FLLs. The overall classification accuracy was 91.3 % (O1) and 88.7 % (O2), kappa = 0.902. The combination of qualitative lesion descriptors proposed in this work revealed high diagnostic accuracy and interobserver agreement in the differentiation of focal liver lesions suspicious for metastases using Gd-EOB-DTPA-enhanced liver MRI. © Georg Thieme Verlag KG Stuttgart · New York.

Fatty acid methyl ester analysis to identify sources of soil in surface water.

PubMed

Banowetz, Gary M; Whittaker, Gerald W; Dierksen, Karen P; Azevedo, Mark D; Kennedy, Ann C; Griffith, Stephen M; Steiner, Jeffrey J

2006-01-01

Efforts to improve land-use practices to prevent contamination of surface waters with soil are limited by an inability to identify the primary sources of soil present in these waters. We evaluated the utility of fatty acid methyl ester (FAME) profiles of dry reference soils for multivariate statistical classification of soils collected from surface waters adjacent to agricultural production fields and a wooded riparian zone. Trials that compared approaches to concentrate soil from surface water showed that aluminum sulfate precipitation provided comparable yields to that obtained by vacuum filtration and was more suitable for handling large numbers of samples. Fatty acid methyl ester profiles were developed from reference soils collected from contrasting land uses in different seasons to determine whether specific fatty acids would consistently serve as variables in multivariate statistical analyses to permit reliable classification of soils. We used a Bayesian method and an independent iterative process to select appropriate fatty acids and found that variable selection was strongly impacted by the season during which soil was collected. The apparent seasonal variation in the occurrence of marker fatty acids in FAME profiles from reference soils prevented preparation of a standardized set of variables. Nevertheless, accurate classification of soil in surface water was achieved utilizing fatty acid variables identified in seasonally matched reference soils. Correlation analysis of entire chromatograms and subsequent discriminant analyses utilizing a restricted number of fatty acid variables showed that FAME profiles of soils exposed to the aquatic environment still had utility for classification at least 1 wk after submersion.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

The 7th lung cancer TNM classification and staging system: Review of the changes and implications.

PubMed

Mirsadraee, Saeed; Oswal, Dilip; Alizadeh, Yalda; Caulo, Andrea; van Beek, Edwin

2012-04-28

Lung cancer is the most common cause of death from cancer in males, accounting for more than 1.4 million deaths in 2008. It is a growing concern in China, Asia and Africa as well. Accurate staging of the disease is an important part of the management as it provides estimation of patient's prognosis and identifies treatment sterategies. It also helps to build a database for future staging projects. A major revision of lung cancer staging has been announced with effect from January 2010. The new classification is based on a larger surgical and non-surgical cohort of patients, and thus more accurate in terms of outcome prediction compared to the previous classification. There are several original papers regarding this new classification which give comprehensive description of the methodology, the changes in the staging and the statistical analysis. This overview is a simplified description of the changes in the new classification and their potential impact on patients' treatment and prognosis.

[Variability in nursing workload within Swiss Diagnosis Related Groups].

PubMed

Baumberger, Dieter; Bürgin, Reto; Bartholomeyczik, Sabine

2014-04-01

Nursing care inputs represent one of the major cost components in the Swiss Diagnosis Related Group (DRG) structure. High and low nursing workloads in individual cases are supposed to balance out via the DRG group. Research results indicating possible problems in this area cannot be reliably extrapolated to SwissDRG. An analysis of nursing workload figures with DRG indicators was carried out in order to decide whether there is a need to develop SwissDRG classification criteria that are specific to nursing care. The case groups were determined with SwissDRG 0.1, and nursing workload with LEP Nursing 2. Robust statistical methods were used. The evaluation of classification accuracy was carried out with R2 as the measurement of variance reduction and the coefficient of homogeneity (CH). To ensure reliable conclusions, statistical tests with bootstrapping methods were performed. The sample included 213 groups with a total of 73930 cases from ten hospitals. The DRG classification was seen to have limited explanatory power for variability in nursing workload inputs, both for all cases (R2 = 0.16) and for inliers (R2 = 0.32). Nursing workload homogeneity was statistically significant unsatisfactory (CH < 0.67) in 123 groups, including 24 groups in which it was significant defective (CH < 0.60). Therefore, there is a high risk of high and low nursing workloads not balancing out in these groups, and, as a result, of financial resources being wrongly allocated. The development of nursing-care-specific SwissDRG classification criteria for improved homogeneity and variance reduction is therefore indicated.

Which sociodemographic factors are important on smoking behaviour of high school students? The contribution of classification and regression tree methodology in a broad epidemiological survey.

PubMed

Ozge, C; Toros, F; Bayramkaya, E; Camdeviren, H; Sasmaz, T

2006-08-01

The purpose of this study is to evaluate the most important sociodemographic factors on smoking status of high school students using a broad randomised epidemiological survey. Using in-class, self administered questionnaire about their sociodemographic variables and smoking behaviour, a representative sample of total 3304 students of preparatory, 9th, 10th, and 11th grades, from 22 randomly selected schools of Mersin, were evaluated and discriminative factors have been determined using appropriate statistics. In addition to binary logistic regression analysis, the study evaluated combined effects of these factors using classification and regression tree methodology, as a new statistical method. The data showed that 38% of the students reported lifetime smoking and 16.9% of them reported current smoking with a male predominancy and increasing prevalence by age. Second hand smoking was reported at a 74.3% frequency with father predominance (56.6%). The significantly important factors that affect current smoking in these age groups were increased by household size, late birth rank, certain school types, low academic performance, increased second hand smoking, and stress (especially reported as separation from a close friend or because of violence at home). Classification and regression tree methodology showed the importance of some neglected sociodemographic factors with a good classification capacity. It was concluded that, as closely related with sociocultural factors, smoking was a common problem in this young population, generating important academic and social burden in youth life and with increasing data about this behaviour and using new statistical methods, effective coping strategies could be composed.

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

Optical diagnosis of cervical cancer by higher order spectra and boosting

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Barman, Ritwik; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this contribution, we report the application of higher order statistical moments using decision tree and ensemble based learning methodology for the development of diagnostic algorithms for optical diagnosis of cancer. The classification results were compared to those obtained with an independent feature extractors like linear discriminant analysis (LDA). The performance and efficacy of these methodology using higher order statistics as a classifier using boosting has higher specificity and sensitivity while being much faster as compared to other time-frequency domain based methods.

Using Context Variety and Students' Discussions in Recognizing Statistical Situations

ERIC Educational Resources Information Center

Silva, José Luis Ángel Rodríguez; Aguilar, Mario Sánchez

2016-01-01

We present a proposal for helping students to cope with statistical word problems related to the classification of different cases of confidence intervals. The proposal promotes an environment where students can explicitly discuss the reasons underlying their classification of cases.

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites.

PubMed

Ielpo, Pierina; Leardi, Riccardo; Pappagallo, Giuseppe; Uricchio, Vito Felice

2017-06-01

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.

Grouping patients for masseter muscle genotype-phenotype studies.

PubMed

Moawad, Hadwah Abdelmatloub; Sinanan, Andrea C M; Lewis, Mark P; Hunt, Nigel P

2012-03-01

To use various facial classifications, including either/both vertical and horizontal facial criteria, to assess their effects on the interpretation of masseter muscle (MM) gene expression. Fresh MM biopsies were obtained from 29 patients (age, 16-36 years) with various facial phenotypes. Based on clinical and cephalometric analysis, patients were grouped using three different classifications: (1) basic vertical, (2) basic horizontal, and (3) combined vertical and horizontal. Gene expression levels of the myosin heavy chain genes MYH1, MYH2, MYH3, MYH6, MYH7, and MYH8 were recorded using quantitative reverse transcriptase polymerase chain reaction (RT-PCR) and were related to the various classifications. The significance level for statistical analysis was set at P ≤ .05. Using classification 1, none of the MYH genes were found to be significantly different between long face (LF) patients and the average vertical group. Using classification 2, MYH3, MYH6, and MYH7 genes were found to be significantly upregulated in retrognathic patients compared with prognathic and average horizontal groups. Using classification 3, only the MYH7 gene was found to be significantly upregulated in retrognathic LF compared with prognathic LF, prognathic average vertical faces, and average vertical and horizontal groups. The use of basic vertical or basic horizontal facial classifications may not be sufficient for genetics-based studies of facial phenotypes. Prognathic and retrognathic facial phenotypes have different MM gene expressions; therefore, it is not recommended to combine them into one single group, even though they may have a similar vertical facial phenotype.

Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

NASA Astrophysics Data System (ADS)

Liu, Haijian; Wu, Changshan

2018-06-01

Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

Hyperspectral Biofilm Classification Analysis for Carrying Capacity of Migratory Birds in the South Bay Salt Ponds

NASA Technical Reports Server (NTRS)

Hsu, Wei-Chen; Kuss, Amber Jean; Ketron, Tyler; Nguyen, Andrew; Remar, Alex Covello; Newcomer, Michelle; Fleming, Erich; Debout, Leslie; Debout, Brad; Detweiler, Angela;

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

14 CFR Sec. 19-4 - Service classes.

Code of Federal Regulations, 2010 CFR

2010-01-01

... a composite of first class, coach, and mixed passenger/cargo service. The following classifications... integral part of services performed pursuant to published flight schedules. The following classifications... Classifications Sec. 19-4 Service classes. The statistical classifications are designed to reflect the operating...

14 CFR Sec. 19-4 - Service classes.

Code of Federal Regulations, 2011 CFR

2011-01-01

... a composite of first class, coach, and mixed passenger/cargo service. The following classifications... integral part of services performed pursuant to published flight schedules. The following classifications... Classifications Sec. 19-4 Service classes. The statistical classifications are designed to reflect the operating...

Food Safety by Using Machine Learning for Automatic Classification of Seeds of the South-American Incanut Plant

NASA Astrophysics Data System (ADS)

Lemanzyk, Thomas; Anding, Katharina; Linss, Gerhard; Rodriguez Hernández, Jorge; Theska, René

2015-02-01

The following paper deals with the classification of seeds and seed components of the South-American Incanut plant and the modification of a machine to handle this task. Initially the state of the art is being illustrated. The research was executed in Germany and with a relevant part in Peru and Ecuador. Theoretical considerations for the solution of an automatically analysis of the Incanut seeds were specified. The optimization of the analyzing software and the separation unit of the mechanical hardware are carried out with recognition results. In a final step the practical application of the analysis of the Incanut seeds is held on a trial basis and rated on the bases of statistic values.

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

PubMed

Li, Yunhai; Lee, Kee Khoon; Walsh, Sean; Smith, Caroline; Hadingham, Sophie; Sorefan, Karim; Cawley, Gavin; Bevan, Michael W

2006-03-01

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Characterization and classification of oral tissues using excitation and emission matrix: a statistical modeling approach

NASA Astrophysics Data System (ADS)

Kanniyappan, Udayakumar; Gnanatheepaminstein, Einstein; Prakasarao, Aruna; Dornadula, Koteeswaran; Singaravelu, Ganesan

2017-02-01

Cancer is one of the most common human threats around the world and diagnosis based on optical spectroscopy especially fluorescence technique has been established as the standard approach among scientist to explore the biochemical and morphological changes in tissues. In this regard, the present work aims to extract spectral signatures of the various fluorophores present in oral tissues using parallel factor analysis (PARAFAC). Subsequently, the statistical analysis also to be performed to show its diagnostic potential in distinguishing malignant, premalignant from normal oral tissues. Hence, the present study may lead to the possible and/or alternative tool for oral cancer diagnosis.

The limb movement analysis of rehabilitation exercises using wearable inertial sensors.

PubMed

Bingquan Huang; Giggins, Oonagh; Kechadi, Tahar; Caulfield, Brian

2016-08-01

Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.

Evaluation of air quality zone classification methods based on ambient air concentration exposure.

PubMed

Freeman, Brian; McBean, Ed; Gharabaghi, Bahram; Thé, Jesse

2017-05-01

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO 2 and 8-hr O 3 within a zone that meets the compliance requirements of each method. The first method, the "3 Strike" method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method. A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.

Statistical Analysis of Atmospheric Forecast Model Accuracy - A Focus on Multiple Atmospheric Variables and Location-Based Analysis

DTIC Science & Technology

2014-04-01

WRF ) model is a numerical weather prediction system designed for operational forecasting and atmospheric research. This report examined WRF model... WRF , weather research and forecasting, atmospheric effects 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR 18. NUMBER OF...and Forecasting ( WRF ) model. The authors would also like to thank Ms. Sherry Larson, STS Systems Integration, LLC, ARL Technical Publishing Branch

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

PubMed Central

Cai, Suxian; Yang, Shanshan; Zheng, Fang; Lu, Meng; Wu, Yunfeng; Krishnan, Sridhar

2013-01-01

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis. PMID:23573175

Audio-guided audiovisual data segmentation, indexing, and retrieval

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1998-12-01

While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data, based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e., speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based upon morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes, such as applause, explosions, bird sounds, etc. This fine-level classification and indexing step is based upon time- frequency analysis of audio signals and the use of the hidden Markov model as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90 percent for the coarse-level classification, and higher than 85 percent for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M.; Dean, M. E.; Knowlton, D. J.; Latty, R. S.

1982-01-01

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

Empirical Testing of an Algorithm for Defining Somatization in Children

PubMed Central

Eisman, Howard D.; Fogel, Joshua; Lazarovich, Regina; Pustilnik, Inna

2007-01-01

Introduction A previous article proposed an algorithm for defining somatization in children by classifying them into three categories: well, medically ill, and somatizer; the authors suggested further empirical validation of the algorithm (Postilnik et al., 2006). We use the Child Behavior Checklist (CBCL) to provide this empirical validation. Method Parents of children seen in pediatric clinics completed the CBCL (n=126). The physicians of these children completed specially-designed questionnaires. The sample comprised of 62 boys and 64 girls (age range 2 to 15 years). Classification categories included: well (n=53), medically ill (n=55), and somatizer (n=18). Analysis of variance (ANOVA) was used for statistical comparisons. Discriminant function analysis was conducted with the CBCL subscales. Results There were significant differences between the classification categories for the somatic complaints (p=<0.001), social problems (p=0.004), thought problems (p=0.01), attention problems (0.006), and internalizing (p=0.003) subscales and also total (p=0.001), and total-t (p=0.001) scales of the CBCL. Discriminant function analysis showed that 78% of somatizers and 66% of well were accurately classified, while only 35% of medically ill were accurately classified. Conclusion The somatization classification algorithm proposed by Postilnik et al. (2006) shows promise for classification of children and adolescents with somatic symptoms. PMID:18421368

Optimal linear and nonlinear feature extraction based on the minimization of the increased risk of misclassification. [Bayes theorem - statistical analysis/data processing

NASA Technical Reports Server (NTRS)

Defigueiredo, R. J. P.

1974-01-01

General classes of nonlinear and linear transformations were investigated for the reduction of the dimensionality of the classification (feature) space so that, for a prescribed dimension m of this space, the increase of the misclassification risk is minimized.

PUNCHED CARD SYSTEM NEEDN'T BE COMPLEX TO GIVE COMPLETE CONTROL.

ERIC Educational Resources Information Center

BEMIS, HAZEL T.

AT WORCESTER JUNIOR COLLEGE, MASSACHUSETTS, USE OF A MANUALLY OPERATED PUNCHED CARD SYSTEM HAS RESULTED IN (1) SIMPLIFIED REGISTRATION PROCEDURES, (2) QUICK ANALYSIS OF CONFLICTS AND PROBLEMS IN CLASS SCHEDULING, (3) READY ACCESS TO STATISTICAL INFORMATION, (4) DIRECTORY INFORMATION IN A WIDE RANGE OF CLASSIFICATIONS, (5) EASY VERIFICATION OF…

Estimating global distribution of boreal, temperate, and tropical tree plant functional types using clustering techniques

NASA Astrophysics Data System (ADS)

Wang, Audrey; Price, David T.

2007-03-01

A simple integrated algorithm was developed to relate global climatology to distributions of tree plant functional types (PFT). Multivariate cluster analysis was performed to analyze the statistical homogeneity of the climate space occupied by individual tree PFTs. Forested regions identified from the satellite-based GLC2000 classification were separated into tropical, temperate, and boreal sub-PFTs for use in the Canadian Terrestrial Ecosystem Model (CTEM). Global data sets of monthly minimum temperature, growing degree days, an index of climatic moisture, and estimated PFT cover fractions were then used as variables in the cluster analysis. The statistical results for individual PFT clusters were found consistent with other global-scale classifications of dominant vegetation. As an improvement of the quantification of the climatic limitations on PFT distributions, the results also demonstrated overlapping of PFT cluster boundaries that reflected vegetation transitions, for example, between tropical and temperate biomes. The resulting global database should provide a better basis for simulating the interaction of climate change and terrestrial ecosystem dynamics using global vegetation models.

Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

NASA Astrophysics Data System (ADS)

Melville, Bethany; Lucieer, Arko; Aryal, Jagannath

2018-04-01

This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed; a Landsat ETM+ scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k = 50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. Analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be used to identify optimal datasets for vegetation community mapping.

Unsupervised Wishart Classfication of Wetlands in Newfoundland, Canada Using Polsar Data Based on Fisher Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Mohammadimanesh, F.; Salehi, B.; Mahdianpari, M.; Homayouni, S.

2016-06-01

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.

PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements.

PubMed

Mi, Huaiyu; Huang, Xiaosong; Muruganujan, Anushya; Tang, Haiming; Mills, Caitlin; Kang, Diane; Thomas, Paul D

2017-01-04

The PANTHER database (Protein ANalysis THrough Evolutionary Relationships, http://pantherdb.org) contains comprehensive information on the evolution and function of protein-coding genes from 104 completely sequenced genomes. PANTHER software tools allow users to classify new protein sequences, and to analyze gene lists obtained from large-scale genomics experiments. In the past year, major improvements include a large expansion of classification information available in PANTHER, as well as significant enhancements to the analysis tools. Protein subfamily functional classifications have more than doubled due to progress of the Gene Ontology Phylogenetic Annotation Project. For human genes (as well as a few other organisms), PANTHER now also supports enrichment analysis using pathway classifications from the Reactome resource. The gene list enrichment tools include a new 'hierarchical view' of results, enabling users to leverage the structure of the classifications/ontologies; the tools also allow users to upload genetic variant data directly, rather than requiring prior conversion to a gene list. The updated coding single-nucleotide polymorphisms (SNP) scoring tool uses an improved algorithm. The hidden Markov model (HMM) search tools now use HMMER3, dramatically reducing search times and improving accuracy of E-value statistics. Finally, the PANTHER Tree-Attribute Viewer has been implemented in JavaScript, with new views for exploring protein sequence evolution. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping

PubMed Central

Sun, Min; Wong, David; Kronenfeld, Barry

2016-01-01

Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification. PMID:28286426

Bruxism and dental implant failures: a multilevel mixed effects parametric survival analysis approach.

PubMed

Chrcanovic, B R; Kisch, J; Albrektsson, T; Wennerberg, A

2016-11-01

Recent studies have suggested that the insertion of dental implants in patients being diagnosed with bruxism negatively affected the implant failure rates. The aim of the present study was to investigate the association between the bruxism and the risk of dental implant failure. This retrospective study is based on 2670 patients who received 10 096 implants at one specialist clinic. Implant- and patient-related data were collected. Descriptive statistics were used to describe the patients and implants. Multilevel mixed effects parametric survival analysis was used to test the association between bruxism and risk of implant failure adjusting for several potential confounders. Criteria from a recent international consensus (Lobbezoo et al., J Oral Rehabil, 40, 2013, 2) and from the International Classification of Sleep Disorders (International classification of sleep disorders, revised: diagnostic and coding manual, American Academy of Sleep Medicine, Chicago, 2014) were used to define and diagnose the condition. The number of implants with information available for all variables totalled 3549, placed in 994 patients, with 179 implants reported as failures. The implant failure rates were 13·0% (24/185) for bruxers and 4·6% (155/3364) for non-bruxers (P < 0·001). The statistical model showed that bruxism was a statistically significantly risk factor to implant failure (HR 3·396; 95% CI 1·314, 8·777; P = 0·012), as well as implant length, implant diameter, implant surface, bone quantity D in relation to quantity A, bone quality 4 in relation to quality 1 (Lekholm and Zarb classification), smoking and the intake of proton pump inhibitors. It is suggested that the bruxism may be associated with an increased risk of dental implant failure. © 2016 John Wiley & Sons Ltd.

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Multi-classification of cell deformation based on object alignment and run length statistic.

PubMed

Li, Heng; Liu, Zhiwen; An, Xing; Shi, Yonggang

2014-01-01

Cellular morphology is widely applied in digital pathology and is essential for improving our understanding of the basic physiological processes of organisms. One of the main issues of application is to develop efficient methods for cell deformation measurement. We propose an innovative indirect approach to analyze dynamic cell morphology in image sequences. The proposed approach considers both the cellular shape change and cytoplasm variation, and takes each frame in the image sequence into account. The cell deformation is measured by the minimum energy function of object alignment, which is invariant to object pose. Then an indirect analysis strategy is employed to overcome the limitation of gradual deformation by run length statistic. We demonstrate the power of the proposed approach with one application: multi-classification of cell deformation. Experimental results show that the proposed method is sensitive to the morphology variation and performs better than standard shape representation methods.

Distinguishing Man from Molecules: The Distinctiveness of Medical Concepts at Different Levels of Description

PubMed Central

Cole, William G.; Michael, Patricia; Blois, Marsden S.

1987-01-01

A computer program was created to use information about the statistical distribution of words in journal abstracts to make probabilistic judgments about the level of description (e.g. molecular, cell, organ) of medical text. Statistical analysis of 7,409 journal abstracts taken from three medical journals representing distinct levels of description revealed that many medical words seem to be highly specific to one or another level of description. For example, the word adrenoreceptors occurred only in the American Journal of Physiology, never in Journal of Biological Chemistry or in Journal of American Medical Association. Such highly specific words occured so frequently that the automatic classification program was able to classify correctly 45 out of 45 test abstracts, with 100% confidence. These findings are interpreted in terms of both a theory of the structure of medical knowledge and the pragmatics of automatic classification.

Contextual classification of multispectral image data: Approximate algorithm

NASA Technical Reports Server (NTRS)

Tilton, J. C. (Principal Investigator)

1980-01-01

An approximation to a classification algorithm incorporating spatial context information in a general, statistical manner is presented which is computationally less intensive. Classifications that are nearly as accurate are produced.

Classification of speech dysfluencies using LPC based parameterization techniques.

PubMed

Hariharan, M; Chee, Lim Sin; Ai, Ooi Chia; Yaacob, Sazali

2012-06-01

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.

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.

Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.

PubMed

Korczowski, L; Congedo, M; Jutten, C

2015-08-01

The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.

Analysis of the Tanana River Basin using LANDSAT data

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Ambrosia, V. G.; Carson-Henry, C.

1981-01-01

Digital image classification techniques were used to classify land cover/resource information in the Tanana River Basin of Alaska. Portions of four scenes of LANDSAT digital data were analyzed using computer systems at Ames Research Center in an unsupervised approach to derive cluster statistics. The spectral classes were identified using the IDIMS display and color infrared photography. Classification errors were corrected using stratification procedures. The classification scheme resulted in the following eleven categories; sedimented/shallow water, clear/deep water, coniferous forest, mixed forest, deciduous forest, shrub and grass, bog, alpine tundra, barrens, snow and ice, and cultural features. Color coded maps and acreage summaries of the major land cover categories were generated for selected USGS quadrangles (1:250,000) which lie within the drainage basin. The project was completed within six months.

Making COD statistics useful for public health at local level in the city of Cape Town: utilidad para la salud pública a nivel local en Ciudad del Cabo.

PubMed Central

Bradshaw, Debbie; Groenewald, Pamela; Bourne, David E.; Mahomed, Hassan; Nojilana, Beatrice; Daniels, Johan; Nixon, Jo

2006-01-01

OBJECTIVE: To review the quality of the coding of the cause of death (COD) statistics and assess the mortality information needs of the City of Cape Town. METHODS: Using an action research approach, a study was set up to investigate the quality of COD information, the accuracy of COD coding and consistency of coding practices in the larger health subdistricts. Mortality information needs and the best way of presenting the statistics to assist health managers were explored. FINDINGS: Useful information was contained in 75% of death certificates, but nearly 60% had only a single cause certified; 55% of forms were coded accurately. Disagreement was mainly because routine coders coded the immediate instead of the underlying COD. An abridged classification of COD, based on causes of public health importance, prevalent causes and selected combinations of diseases was implemented with training on underlying cause. Analysis of the 2001 data identified the leading causes of death and premature mortality and illustrated striking differences in the disease burden and profile between health subdistricts. CONCLUSION: Action research is particularly useful for improving information systems and revealed the need to standardize the coding practice to identify underlying cause. The specificity of the full ICD classification is beyond the level of detail on the death certificates currently available. An abridged classification for coding provides a practical tool appropriate for local level public health surveillance. Attention to the presentation of COD statistics is important to enable the data to inform decision-makers. PMID:16583080

Making COD statistics useful for public health at local level in the city of Cape Town: utilidad para la salud pública a nivel local en Ciudad del Cabo.

PubMed

Bradshaw, Debbie; Groenewald, Pamela; Bourne, David E; Mahomed, Hassan; Nojilana, Beatrice; Daniels, Johan; Nixon, Jo

2006-03-01

To review the quality of the coding of the cause of death (COD) statistics and assess the mortality information needs of the City of Cape Town. Using an action research approach, a study was set up to investigate the quality of COD information, the accuracy of COD coding and consistency of coding practices in the larger health subdistricts. Mortality information needs and the best way of presenting the statistics to assist health managers were explored. Useful information was contained in 75% of death certificates, but nearly 60% had only a single cause certified; 55% of forms were coded accurately. Disagreement was mainly because routine coders coded the immediate instead of the underlying COD. An abridged classification of COD, based on causes of public health importance, prevalent causes and selected combinations of diseases was implemented with training on underlying cause. Analysis of the 2001 data identified the leading causes of death and premature mortality and illustrated striking differences in the disease burden and profile between health subdistricts. Action research is particularly useful for improving information systems and revealed the need to standardize the coding practice to identify underlying cause. The specificity of the full ICD classification is beyond the level of detail on the death certificates currently available. An abridged classification for coding provides a practical tool appropriate for local level public health surveillance. Attention to the presentation of COD statistics is important to enable the data to inform decision-makers.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Estimating Classification Consistency and Accuracy for Cognitive Diagnostic Assessment

ERIC Educational Resources Information Center

Cui, Ying; Gierl, Mark J.; Chang, Hua-Hua

2012-01-01

This article introduces procedures for the computation and asymptotic statistical inference for classification consistency and accuracy indices specifically designed for cognitive diagnostic assessments. The new classification indices can be used as important indicators of the reliability and validity of classification results produced by…

77 FR 59989 - Labor Surplus Area Classification Under Executive Orders

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-01

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification Under... Bureau of Labor Statistics are used in making these classifications. The average unemployment rate for all states includes data for the Commonwealth of Puerto Rico. The basic LSA classification criteria...

Articular cartilage degeneration classification by means of high-frequency ultrasound.

PubMed

Männicke, N; Schöne, M; Oelze, M; Raum, K

2014-10-01

To date only single ultrasound parameters were regarded in statistical analyses to characterize osteoarthritic changes in articular cartilage and the potential benefit of using parameter combinations for characterization remains unclear. Therefore, the aim of this work was to utilize feature selection and classification of a Mankin subset score (i.e., cartilage surface and cell sub-scores) using ultrasound-based parameter pairs and investigate both classification accuracy and the sensitivity towards different degeneration stages. 40 punch biopsies of human cartilage were previously scanned ex vivo with a 40-MHz transducer. Ultrasound-based surface parameters, as well as backscatter and envelope statistics parameters were available. Logistic regression was performed with each unique US parameter pair as predictor and different degeneration stages as response variables. The best ultrasound-based parameter pair for each Mankin subset score value was assessed by highest classification accuracy and utilized in receiver operating characteristics (ROC) analysis. The classifications discriminating between early degenerations yielded area under the ROC curve (AUC) values of 0.94-0.99 (mean ± SD: 0.97 ± 0.03). In contrast, classifications among higher Mankin subset scores resulted in lower AUC values: 0.75-0.91 (mean ± SD: 0.84 ± 0.08). Variable sensitivities of the different ultrasound features were observed with respect to different degeneration stages. Our results strongly suggest that combinations of high-frequency ultrasound-based parameters exhibit potential to characterize different, particularly very early, degeneration stages of hyaline cartilage. Variable sensitivities towards different degeneration stages suggest that a concurrent estimation of multiple ultrasound-based parameters is diagnostically valuable. In-vivo application of the present findings is conceivable in both minimally invasive arthroscopic ultrasound and high-frequency transcutaneous ultrasound. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Neural net applied to anthropological material: a methodical study on the human nasal skeleton.

PubMed

Prescher, Andreas; Meyers, Anne; Gerf von Keyserlingk, Diedrich

2005-07-01

A new information processing method, an artificial neural net, was applied to characterise the variability of anthropological features of the human nasal skeleton. The aim was to find different types of nasal skeletons. A neural net with 15*15 nodes was trained by 17 standard anthropological parameters taken from 184 skulls of the Aachen collection. The trained neural net delivers its classification in a two-dimensional map. Different types of noses were locally separated within the map. Rare and frequent types may be distinguished after one passage of the complete collection through the net. Statistical descriptive analysis, hierarchical cluster analysis, and discriminant analysis were applied to the same data set. These parallel applications allowed comparison of the new approach to the more traditional ones. In general the classification by the neural net is in correspondence with cluster analysis and discriminant analysis. However, it goes beyond these classifications because of the possibility of differentiating the types in multi-dimensional dependencies. Furthermore, places in the map are kept blank for intermediate forms, which may be theoretically expected, but were not included in the training set. In conclusion, the application of a neural network is a suitable method for investigating large collections of biological material. The gained classification may be helpful in anatomy and anthropology as well as in forensic medicine. It may be used to characterise the peculiarity of a whole set as well as to find particular cases within the set.

Investigating the limitations of tree species classification using the Combined Cluster and Discriminant Analysis method for low density ALS data from a dense forest region in Aggtelek (Hungary)

NASA Astrophysics Data System (ADS)

Koma, Zsófia; Deák, Márton; Kovács, József; Székely, Balázs; Kelemen, Kristóf; Standovár, Tibor

2016-04-01

Airborne Laser Scanning (ALS) is a widely used technology for forestry classification applications. However, single tree detection and species classification from low density ALS point cloud is limited in a dense forest region. In this study we investigate the division of a forest into homogenous groups at stand level. The study area is located in the Aggtelek karst region (Northeast Hungary) with a complex relief topography. The ALS dataset contained only 4 discrete echoes (at 2-4 pt/m2 density) from the study area during leaf-on season. Ground-truth measurements about canopy closure and proportion of tree species cover are available for every 70 meter in 500 square meter circular plots. In the first step, ALS data were processed and geometrical and intensity based features were calculated into a 5×5 meter raster based grid. The derived features contained: basic statistics of relative height, canopy RMS, echo ratio, openness, pulse penetration ratio, basic statistics of radiometric feature. In the second step the data were investigated using Combined Cluster and Discriminant Analysis (CCDA, Kovács et al., 2014). The CCDA method first determines a basic grouping for the multiple circle shaped sampling locations using hierarchical clustering and then for the arising grouping possibilities a core cycle is executed comparing the goodness of the investigated groupings with random ones. Out of these comparisons difference values arise, yielding information about the optimal grouping out of the investigated ones. If sub-groups are then further investigated, one might even find homogeneous groups. We found that low density ALS data classification into homogeneous groups are highly dependent on canopy closure, and the proportion of the dominant tree species. The presented results show high potential using CCDA for determination of homogenous separable groups in LiDAR based tree species classification. Aggtelek Karst/Slovakian Karst Caves" (HUSK/1101/221/0180, Aggtelek NP), data evaluation: 'Multipurpose assessment serving forest biodiversity conservation in the Carpathian region of Hungary', Swiss-Hungarian Cooperation Programme (SH/4/13 Project). BS contributed as an Alexander von Humboldt Research Fellow. J. Kovács, S. Kovács, N. Magyar, P. Tanos, I. G. Hatvani, and A. Anda (2014), Classification into homogeneous groups using combined cluster and discriminant analysis, Environmental Modelling & Software, 57, 52-59.

75 FR 56549 - National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-16

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards Staff, Announces the... Public Health Data Standards Staff, NCHS, 3311 Toledo Road, Room 2337, Hyattsville, Maryland 20782, e...

Applying Descriptive Statistics to Teaching the Regional Classification of Climate.

ERIC Educational Resources Information Center

Lindquist, Peter S.; Hammel, Daniel J.

1998-01-01

Describes an exercise for college and high school students that relates descriptive statistics to the regional climatic classification. The exercise introduces students to simple calculations of central tendency and dispersion, the construction and interpretation of scatterplots, and the definition of climatic regions. Forces students to engage…

Accurate discrimination of Alzheimer's disease from other dementia and/or normal subjects using SPECT specific volume analysis

NASA Astrophysics Data System (ADS)

Iyatomi, Hitoshi; Hashimoto, Jun; Yoshii, Fumuhito; Kazama, Toshiki; Kawada, Shuichi; Imai, Yutaka

2014-03-01

Discrimination between Alzheimer's disease and other dementia is clinically significant, however it is often difficult. In this study, we developed classification models among Alzheimer's disease (AD), other dementia (OD) and/or normal subjects (NC) using patient factors and indices obtained by brain perfusion SPECT. SPECT is commonly used to assess cerebral blood flow (CBF) and allows the evaluation of the severity of hypoperfusion by introducing statistical parametric mapping (SPM). We investigated a total of 150 cases (50 cases each for AD, OD, and NC) from Tokai University Hospital, Japan. In each case, we obtained a total of 127 candidate parameters from: (A) 2 patient factors (age and sex), (B) 12 CBF parameters and 113 SPM parameters including (C) 3 from specific volume analysis (SVA), and (D) 110 from voxel-based analysis stereotactic extraction estimation (vbSEE). We built linear classifiers with a statistical stepwise feature selection and evaluated the performance with the leave-one-out cross validation strategy. Our classifiers achieved very high classification performances with reasonable number of selected parameters. In the most significant discrimination in clinical, namely those of AD from OD, our classifier achieved both sensitivity (SE) and specificity (SP) of 96%. In a similar way, our classifiers achieved a SE of 90% and a SP of 98% in AD from NC, as well as a SE of 88% and a SP of 86% in AD from OD and NC cases. Introducing SPM indices such as SVA and vbSEE, classification performances improved around 7-15%. We confirmed that these SPM factors are quite important for diagnosing Alzheimer's disease.

78 FR 63248 - Labor Surplus Area Classification under Executive Orders 12073 and 10582

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification under... Statistics unemployment estimates to make these classifications. The average unemployment rate for all states includes data for the Commonwealth of Puerto Rico. The basic LSA classification criteria include a ``floor...

Quantifying the Availability of Tidewater Glacial Ice as Habitat for Harbor Seals in a Tidewater Glacial Fjord in Alaska Using Object-Based Image Analysis of Airborne Visible Imagery

NASA Astrophysics Data System (ADS)

Prakash, A.; Haselwimmer, C. E.; Gens, R.; Womble, J. N.; Ver Hoef, J.

2013-12-01

Tidewater glaciers are prominent landscape features that play a significant role in landscape and ecosystem processes along the southeastern and southcentral coasts of Alaska. Tidewater glaciers calve large icebergs that serve as an important substrate for harbor seals (Phoca vitulina richardii) for resting, pupping, nursing young, molting, and avoiding predators. Many of the tidewater glaciers in Alaska are retreating, which may influence harbor seal populations. Our objectives are to investigate the relationship between ice conditions and harbor seal distributions, which are poorly understood, in John's Hopkins Inlet, Glacier Bay National Park, Alaska, using a combination of airborne remote sensing and statistical modeling techniques. We present an overview of some results from Object-Based Image Analysis (OBIA) for classification of a time series of very high spatial resolution (4 cm pixels) airborne imagery acquired over John's Hopkins Inlet during the harbor seal pupping season in June and during the molting season in August from 2007 - 2012. Using OBIA we have developed a workflow to automate processing of the large volumes (~1250 images/survey) of airborne visible imagery for 1) classification of ice products (e.g. percent ice cover, percent brash ice, percent ice bergs) at a range of scales, and 2) quantitative determination of ice morphological properties such as iceberg size, roundness, and texture that are not found in traditional per-pixel classification approaches. These ice classifications and morphological variables are then used in statistical models to assess relationships with harbor seal abundance and distribution. Ultimately, understanding these relationships may provide novel perspectives on the spatial and temporal variation of harbor seals in tidewater glacial fjords.

Which sociodemographic factors are important on smoking behaviour of high school students? The contribution of classification and regression tree methodology in a broad epidemiological survey

PubMed Central

Özge, C; Toros, F; Bayramkaya, E; Çamdeviren, H; Şaşmaz, T

2006-01-01

Background The purpose of this study is to evaluate the most important sociodemographic factors on smoking status of high school students using a broad randomised epidemiological survey. Methods Using in‐class, self administered questionnaire about their sociodemographic variables and smoking behaviour, a representative sample of total 3304 students of preparatory, 9th, 10th, and 11th grades, from 22 randomly selected schools of Mersin, were evaluated and discriminative factors have been determined using appropriate statistics. In addition to binary logistic regression analysis, the study evaluated combined effects of these factors using classification and regression tree methodology, as a new statistical method. Results The data showed that 38% of the students reported lifetime smoking and 16.9% of them reported current smoking with a male predominancy and increasing prevalence by age. Second hand smoking was reported at a 74.3% frequency with father predominance (56.6%). The significantly important factors that affect current smoking in these age groups were increased by household size, late birth rank, certain school types, low academic performance, increased second hand smoking, and stress (especially reported as separation from a close friend or because of violence at home). Classification and regression tree methodology showed the importance of some neglected sociodemographic factors with a good classification capacity. Conclusions It was concluded that, as closely related with sociocultural factors, smoking was a common problem in this young population, generating important academic and social burden in youth life and with increasing data about this behaviour and using new statistical methods, effective coping strategies could be composed. PMID:16891446

Automated connectionist-geostatistical classification as an approach to identify sea ice and land ice types, properties and provinces

NASA Astrophysics Data System (ADS)

Goetz-Weiss, L. R.; Herzfeld, U. C.; Trantow, T.; Hunke, E. C.; Maslanik, J. A.; Crocker, R. I.

2016-12-01

An important problem in model-data comparison is the identification of parameters that can be extracted from observational data as well as used in numerical models, which are typically based on idealized physical processes. Here, we present a suite of approaches to characterization and classification of sea ice and land ice types, properties and provinces based on several types of remote-sensing data. Applications will be given to not only illustrate the approach, but employ it in model evaluation and understanding of physical processes. (1) In a geostatistical characterization, spatial sea-ice properties in the Chukchi and Beaufort Sea and in Elsoon Lagoon are derived from analysis of RADARSAT and ERS-2 SAR data. (2) The analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification, which facilitates classification of different sea-ice types. (3) Characteristic sea-ice parameters, as resultant from the classification, can then be applied in model evaluation, as demonstrated for the ridging scheme of the Los Alamos sea ice model, CICE, using high-resolution altimeter and image data collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE). The characteristic parameters chosen in this application are directly related to deformation processes, which also underly the ridging scheme. (4) The method that is capable of the most complex classification tasks is the connectionist-geostatistical classification method. This approach has been developed to identify currently up to 18 different crevasse types in order to map progression of the surge through the complex Bering-Bagley Glacier System, Alaska, in 2011-2014. The analysis utilizes airborne altimeter data and video image data and satellite image data. Results of the crevasse classification are compare to fracture modeling and found to match.

Brain fingerprinting classification concealed information test detects US Navy military medical information with P300

PubMed Central

Farwell, Lawrence A.; Richardson, Drew C.; Richardson, Graham M.; Furedy, John J.

2014-01-01

A classification concealed information test (CIT) used the “brain fingerprinting” method of applying P300 event-related potential (ERP) in detecting information that is (1) acquired in real life and (2) unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to three types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified). We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP?) vs. comparison CIT (Does a probe produce a larger ERP than an irrelevant?) using P300 plus the late negative component (LNP; together, P300-MERMER). Comparison CIT produced a significantly higher error rate (20%) and lower statistical confidences: mean 67%; information-absent mean was 28.9%, less than chance (50%). We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational affiliation. PMID:25565941

Statistical approach for selection of biologically informative genes.

PubMed

Das, Samarendra; Rai, Anil; Mishra, D C; Rai, Shesh N

2018-05-20

Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes from high dimensional expression data for breeding and system biology studies. Published by Elsevier B.V.

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.

76 FR 53699 - Labor Surplus Area Classification Under Executive Orders 12073 and 10582

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-29

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification Under... estimates provided to ETA by the Bureau of Labor Statistics are used in making these classifications. The... classification criteria include a ``floor unemployment rate'' (6.0%) and a ``ceiling unemployment rate'' (10.0...

Report to the Higher Education Policy Commission. West Virginia Higher Education Facilities Information System Statewide Institution Report.

ERIC Educational Resources Information Center

West Virginia Higher Education Policy Commission, 2004

2004-01-01

The West Virginia Higher Education Facilities Information System was formed as a method for instituting statewide standardization of space use and classification; to serve as a vehicle for statewide data acquisition; and to provide statistical data that contributes to detailed institutional planning analysis. The result thus far is the production…

Utilization of Skylab (EREP) system for appraising changes in continental migratory bird habitat

USGS Publications Warehouse

Work, E.A.; Gilmer, D.S.

1975-01-01

The author has identified the following significant results. Surface water statistics using data obtained by supporting aircraft were generated. Signature extraction and refinement preliminary to wetland and associated upland vegetation recognition were accomplished, using a selected portion of the aircraft data. Final classification mapping and analysis of surface water trends will be accomplished.

Statistical Analysis of the Association Between Subject Headings and Their Corresponding Class Notations in Science and Technology Monographs.

ERIC Educational Resources Information Center

Khosh-khui, Abolghasem

This study investigates the degree of relationship between scientific and technical subject headings and their corresponding class notations in the Dewey Decimal (DDC) and Library of Congress Classification (LCC) systems. The degree of association between a subject heading and its corresponding class of notation or notations is measured by…

Sorting: Groups and Graphs. Used Numbers. Grades 2-3.

ERIC Educational Resources Information Center

Russell, Susan Jo; Corwin, Rebecca B.

A unit of study that introduces sorting and classification as a way of organizing data is presented. Suitable for students in grades 2 and 3, it provides a foundation for further work in statistics and data analysis. The investigations may extend from one to five class sessions and are grouped into three parts: "Introduction to Sorting"; "Sorting…

Are Student Evaluations of Teaching Effectiveness Valid for Measuring Student Learning Outcomes in Business Related Classes? A Neural Network and Bayesian Analyses

ERIC Educational Resources Information Center

Galbraith, Craig S.; Merrill, Gregory B.; Kline, Doug M.

2012-01-01

In this study we investigate the underlying relational structure between student evaluations of teaching effectiveness (SETEs) and achievement of student learning outcomes in 116 business related courses. Utilizing traditional statistical techniques, a neural network analysis and a Bayesian data reduction and classification algorithm, we find…

Factors Influencing Student Prerequisite Preparation for and Subsequent Performance in College Chemistry Two: A Statistical Investigation

ERIC Educational Resources Information Center

Easter, David C.

2010-01-01

For students entering Chemistry Two following a Chemistry One course, an assessment exam was given and the results were evaluated in combination with other variables to develop a predictive model that forecasts student achievement in the course. Variables considered in the analysis included student major, GPA, classification (student standing:…

Load Weight Classification of The Quayside Container Crane Based On K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Bingqian; Hu, Xiong; Tang, Gang; Wang, Yide

2017-07-01

The precise knowledge of the load weight of each operation of the quayside container crane is important for accurately assessing the service life of the crane. The load weight is directly related to the vibration intensity. Through the study on the vibration of the hoist motor of the crane in radial and axial directions, we can classify the load using K-means clustering algorithm and quantitative statistical analysis. Vibration in radial direction is significantly and positively correlated with that in axial direction by correlation analysis, which means that we can use the data only in one of the directions to carry out the study improving then the efficiency without degrading the accuracy of load classification. The proposed method can well represent the real-time working condition of the crane.

Reliability analysis of the AOSpine thoracolumbar spine injury classification system by a worldwide group of naïve spinal surgeons.

PubMed

Kepler, Christopher K; Vaccaro, Alexander R; Koerner, John D; Dvorak, Marcel F; Kandziora, Frank; Rajasekaran, Shanmuganathan; Aarabi, Bizhan; Vialle, Luiz R; Fehlings, Michael G; Schroeder, Gregory D; Reinhold, Maximilian; Schnake, Klaus John; Bellabarba, Carlo; Cumhur Öner, F

2016-04-01

The aims of this study were (1) to demonstrate the AOSpine thoracolumbar spine injury classification system can be reliably applied by an international group of surgeons and (2) to delineate those injury types which are difficult for spine surgeons to classify reliably. A previously described classification system of thoracolumbar injuries which consists of a morphologic classification of the fracture, a grading system for the neurologic status and relevant patient-specific modifiers was applied to 25 cases by 100 spinal surgeons from across the world twice independently, in grading sessions 1 month apart. The results were analyzed for classification reliability using the Kappa coefficient (κ). The overall Kappa coefficient for all cases was 0.56, which represents moderate reliability. Kappa values describing interobserver agreement were 0.80 for type A injuries, 0.68 for type B injuries and 0.72 for type C injuries, all representing substantial reliability. The lowest level of agreement for specific subtypes was for fracture subtype A4 (Kappa = 0.19). Intraobserver analysis demonstrated overall average Kappa statistic for subtype grading of 0.68 also representing substantial reproducibility. In a worldwide sample of spinal surgeons without previous exposure to the recently described AOSpine Thoracolumbar Spine Injury Classification System, we demonstrated moderate interobserver and substantial intraobserver reliability. These results suggest that most spine surgeons can reliably apply this system to spine trauma patients as or more reliably than previously described systems.

Reliable classification of children’s fractures according to the comprehensive classification of long bone fractures by Müller

PubMed Central

2013-01-01

Background and purpose Guidelines for fracture treatment and evaluation require a valid classification. Classifications especially designed for children are available, but they might lead to reduced accuracy, considering the relative infrequency of childhood fractures in a general orthopedic department. We tested the reliability and accuracy of the Müller classification when used for long bone fractures in children. Methods We included all long bone fractures in children aged < 16 years who were treated in 2008 at the surgical ward of Stavanger University Hospital. 20 surgeons recorded 232 fractures. Datasets were generated for intra- and inter-rater analysis, as well as a reference dataset for accuracy calculations. We present proportion of agreement (PA) and kappa (K) statistics. Results For intra-rater analysis, overall agreement (κ) was 0.75 (95% CI: 0.68–0.81) and PA was 79%. For inter-rater assessment, K was 0.71 (95% CI: 0.61–0.80) and PA was 77%. Accuracy was estimated: κ = 0.72 (95% CI: 0.64–0.79) and PA = 76%. Interpretation The Müller classification (slightly adjusted for pediatric fractures) showed substantial to excellent accuracy among general orthopedic surgeons when applied to long bone fractures in children. However, separate knowledge about the child-specific fracture pattern, the maturity of the bone, and the degree of displacement must be considered when the treatment and the prognosis of the fractures are evaluated. PMID:23245225

Statistical inference for remote sensing-based estimates of net deforestation

Treesearch

Ronald E. McRoberts; Brian F. Walters

2012-01-01

Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because...

Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control.

PubMed

Olives, Casey; Valadez, Joseph J; Brooker, Simon J; Pagano, Marcello

2012-01-01

Originally a binary classifier, Lot Quality Assurance Sampling (LQAS) has proven to be a useful tool for classification of the prevalence of Schistosoma mansoni into multiple categories (≤10%, >10 and <50%, ≥50%), and semi-curtailed sampling has been shown to effectively reduce the number of observations needed to reach a decision. To date the statistical underpinnings for Multiple Category-LQAS (MC-LQAS) have not received full treatment. We explore the analytical properties of MC-LQAS, and validate its use for the classification of S. mansoni prevalence in multiple settings in East Africa. We outline MC-LQAS design principles and formulae for operating characteristic curves. In addition, we derive the average sample number for MC-LQAS when utilizing semi-curtailed sampling and introduce curtailed sampling in this setting. We also assess the performance of MC-LQAS designs with maximum sample sizes of n=15 and n=25 via a weighted kappa-statistic using S. mansoni data collected in 388 schools from four studies in East Africa. Overall performance of MC-LQAS classification was high (kappa-statistic of 0.87). In three of the studies, the kappa-statistic for a design with n=15 was greater than 0.75. In the fourth study, where these designs performed poorly (kappa-statistic less than 0.50), the majority of observations fell in regions where potential error is known to be high. Employment of semi-curtailed and curtailed sampling further reduced the sample size by as many as 0.5 and 3.5 observations per school, respectively, without increasing classification error. This work provides the needed analytics to understand the properties of MC-LQAS for assessing the prevalance of S. mansoni and shows that in most settings a sample size of 15 children provides a reliable classification of schools.

Meteorological regimes for the classification of aerospace air quality predictions for NASA-Kennedy Space Center

NASA Technical Reports Server (NTRS)

Stephens, J. B.; Sloan, J. C.

1976-01-01

A method is described for developing a statistical air quality assessment for the launch of an aerospace vehicle from the Kennedy Space Center in terms of existing climatological data sets. The procedure can be refined as developing meteorological conditions are identified for use with the NASA-Marshall Space Flight Center Rocket Exhaust Effluent Diffusion (REED) description. Classical climatological regimes for the long range analysis can be narrowed as the synoptic and mesoscale structure is identified. Only broad synoptic regimes are identified at this stage of analysis. As the statistical data matrix is developed, synoptic regimes will be refined in terms of the resulting eigenvectors as applicable to aerospace air quality predictions.

A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

PubMed

de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

2018-07-01

The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis. Published by Elsevier Ltd.

The evaluation of alternate methodologies for land cover classification in an urbanizing area

NASA Technical Reports Server (NTRS)

Smekofski, R. M.

1981-01-01

The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions.

PubMed

Acosta-Mesa, Héctor-Gabriel; Rechy-Ramírez, Fernando; Mezura-Montes, Efrén; Cruz-Ramírez, Nicandro; Hernández Jiménez, Rodolfo

2014-06-01

In this work, we present a novel application of time series discretization using evolutionary programming for the classification of precancerous cervical lesions. The approach optimizes the number of intervals in which the length and amplitude of the time series should be compressed, preserving the important information for classification purposes. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. This discretization approach is evaluated using a time series data based on temporal patterns observed during a classical test used in cervical cancer detection; the classification accuracy reached by our method is compared with the well-known times series discretization algorithm SAX and the dimensionality reduction method PCA. Statistical analysis of the classification accuracy shows that the discrete representation is as efficient as the complete raw representation for the present application, reducing the dimensionality of the time series length by 97%. This representation is also very competitive in terms of classification accuracy when compared with similar approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

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

Young, M; Craft, D

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore » clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.« less

Analysis of data on nutrients and organic compounds in ground water in the upper Snake River basin, Idaho and western Wyoming, 1980-91

USGS Publications Warehouse

Rupert, Michael G.

1994-01-01

Nutrient and organic compound data from the U.S. Geological Survey and the U.S. Environmental Protection Agency STORET data bases provided information for development of a preliminary conceptual model of spatial and temporal ground-water quality in the upper Snake River Basin. Nitrite plus nitrate (as nitrogen; hereafter referred to as nitrate) concentrations exceeded the Federal drinking-water regulation of 10 milligrams per liter in three areas in Idaho" the Idaho National Engineering Laboratory, the area north of Pocatello (Fort Hall area), and the area surrounding Burley. Water from many wells in the Twin Falls area also contained elevated (greater than two milligrams per liter) nitrate concentrations. Water from domestic wells contained the highest median nitrate concentrations; water from industrial and public supply wells contained the lowest. Nitrate concentrations decreased with increasing well depth, increasing depth to water (unsaturated thickness), and increasing depth below water table (saturated thickness). Kjeldahl nitrogen concentrations decreased with increasing well depth and depth below water table. The relation between kjeldahl nitrogen concentrations and depth to water was poor. Nitrate and total phosphorus concentrations in water from wells were correlated among three hydrogeomorphic regions in the upper Snake River Basin, Concentrations of nitrate were statistically higher in the eastern Snake River Plain and local aquifers than in the tributary valleys. There was no statistical difference in total phosphorus concentrations among the three hydrogeomorphic regions. Nitrate and total phosphorus concentrations were correlated with land-use classifications developed using the Geographic Information Retrieval and Analysis System. Concentrations of nitrate were statistically higher in area of agricultural land than in areas of rangeland. There was no statistical difference in concentrations between rangeland and urban land and between urban land and agricultural land. There was no statistical difference in total phosphorus concentrations among any of the land-use classifications. Nitrate and total phosphorus concentrations also were correlated with land-use classifications developed by the Idaho Department of Water Resources for the Idaho part of the upper Snake River Basin. Nitrate concentrations were statistically higher in areas of irrigated agriculture than in areas of dryland agriculture and rangeland. There was no statistical difference in total phosphorus concentrations among any of the Idaho Department of Water Resources land-use classifications. Data were sufficient to assess long-term trends of nitrate concentrations in water from only eight wells: four wells north of Burley and four wells northwest of Pocatello. The trend in nitrate concentrations in water from all wells in upward. The following organic compounds were detected in ground water in the upper Snake River Basin: cyanazine, 2,4-D DDT, dacthal, diazinon, dichloropropane, dieldrin, malathion, and metribuzin. Of 211 wells sampled for organic compounds, water from 17 contained detectable concentrations.

Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China

NASA Astrophysics Data System (ADS)

Wu, Chong; Liu, Liping; Wei, Ming; Xi, Baozhu; Yu, Minghui

2018-03-01

A modified hydrometeor classification algorithm (HCA) is developed in this study for Chinese polarimetric radars. This algorithm is based on the U.S. operational HCA. Meanwhile, the methodology of statistics-based optimization is proposed including calibration checking, datasets selection, membership functions modification, computation thresholds modification, and effect verification. Zhuhai radar, the first operational polarimetric radar in South China, applies these procedures. The systematic bias of calibration is corrected, the reliability of radar measurements deteriorates when the signal-to-noise ratio is low, and correlation coefficient within the melting layer is usually lower than that of the U.S. WSR-88D radar. Through modification based on statistical analysis of polarimetric variables, the localized HCA especially for Zhuhai is obtained, and it performs well over a one-month test through comparison with sounding and surface observations. The algorithm is then utilized for analysis of a squall line process on 11 May 2014 and is found to provide reasonable details with respect to horizontal and vertical structures, and the HCA results—especially in the mixed rain-hail region—can reflect the life cycle of the squall line. In addition, the kinematic and microphysical processes of cloud evolution and the differences between radar-detected hail and surface observations are also analyzed. The results of this study provide evidence for the improvement of this HCA developed specifically for China.

Artillery/mortar type classification based on detected acoustic transients

NASA Astrophysics Data System (ADS)

Morcos, Amir; Grasing, David; Desai, Sachi

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Artillery/mortar round type classification to increase system situational awareness

NASA Astrophysics Data System (ADS)

Desai, Sachi; Grasing, David; Morcos, Amir; Hohil, Myron

2008-04-01

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

A Pruning Neural Network Model in Credit Classification Analysis

PubMed Central

Tang, Yajiao; Ji, Junkai; Dai, Hongwei; Yu, Yang; Todo, Yuki

2018-01-01

Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN) and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs) to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency. PMID:29606961

New Casemix Classification as an Alternative Method for Budget Allocation in Thai Oral Healthcare Service: A Pilot Study

PubMed Central

Wisaijohn, Thunthita; Pimkhaokham, Atiphan; Lapying, Phenkhae; Itthichaisri, Chumpot; Pannarunothai, Supasit; Igarashi, Isao; Kawabuchi, Koichi

2010-01-01

This study aimed to develop a new casemix classification system as an alternative method for the budget allocation of oral healthcare service (OHCS). Initially, the International Statistical of Diseases and Related Health Problem, 10th revision, Thai Modification (ICD-10-TM) related to OHCS was used for developing the software “Grouper”. This model was designed to allow the translation of dental procedures into eight-digit codes. Multiple regression analysis was used to analyze the relationship between the factors used for developing the model and the resource consumption. Furthermore, the coefficient of variance, reduction in variance, and relative weight (RW) were applied to test the validity. The results demonstrated that 1,624 OHCS classifications, according to the diagnoses and the procedures performed, showed high homogeneity within groups and heterogeneity between groups. Moreover, the RW of the OHCS could be used to predict and control the production costs. In conclusion, this new OHCS casemix classification has a potential use in a global decision making. PMID:20936134

New casemix classification as an alternative method for budget allocation in thai oral healthcare service: a pilot study.

PubMed

Wisaijohn, Thunthita; Pimkhaokham, Atiphan; Lapying, Phenkhae; Itthichaisri, Chumpot; Pannarunothai, Supasit; Igarashi, Isao; Kawabuchi, Koichi

2010-01-01

This study aimed to develop a new casemix classification system as an alternative method for the budget allocation of oral healthcare service (OHCS). Initially, the International Statistical of Diseases and Related Health Problem, 10th revision, Thai Modification (ICD-10-TM) related to OHCS was used for developing the software "Grouper". This model was designed to allow the translation of dental procedures into eight-digit codes. Multiple regression analysis was used to analyze the relationship between the factors used for developing the model and the resource consumption. Furthermore, the coefficient of variance, reduction in variance, and relative weight (RW) were applied to test the validity. The results demonstrated that 1,624 OHCS classifications, according to the diagnoses and the procedures performed, showed high homogeneity within groups and heterogeneity between groups. Moreover, the RW of the OHCS could be used to predict and control the production costs. In conclusion, this new OHCS casemix classification has a potential use in a global decision making.

Parallel and Scalable Clustering and Classification for Big Data in Geosciences

NASA Astrophysics Data System (ADS)

Riedel, M.

2015-12-01

Machine learning, data mining, and statistical computing are common techniques to perform analysis in earth sciences. This contribution will focus on two concrete and widely used data analytics methods suitable to analyse 'big data' in the context of geoscience use cases: clustering and classification. From the broad class of available clustering methods we focus on the density-based spatial clustering of appliactions with noise (DBSCAN) algorithm that enables the identification of outliers or interesting anomalies. A new open source parallel and scalable DBSCAN implementation will be discussed in the light of a scientific use case that detects water mixing events in the Koljoefjords. The second technique we cover is classification, with a focus set on the support vector machines algorithm (SVMs), as one of the best out-of-the-box classification algorithm. A parallel and scalable SVM implementation will be discussed in the light of a scientific use case in the field of remote sensing with 52 different classes of land cover types.

Statistical and Ontological Analysis of Adverse Events Associated with Monovalent and Combination Vaccines against Hepatitis A and B Diseases

PubMed Central

Xie, Jiangan; Zhao, Lili; Zhou, Shangbo; He, Yongqun

2016-01-01

Vaccinations often induce various adverse events (AEs), and sometimes serious AEs (SAEs). While many vaccines are used in combination, the effects of vaccine-vaccine interactions (VVIs) on vaccine AEs are rarely studied. In this study, AE profiles induced by hepatitis A vaccine (Havrix), hepatitis B vaccine (Engerix-B), and hepatitis A and B combination vaccine (Twinrix) were studied using the VAERS data. From May 2001 to January 2015, VAERS recorded 941, 3,885, and 1,624 AE case reports where patients aged at least 18 years old were vaccinated with only Havrix, Engerix-B, and Twinrix, respectively. Using these data, our statistical analysis identified 46, 69, and 82 AEs significantly associated with Havrix, Engerix-B, and Twinrix, respectively. Based on the Ontology of Adverse Events (OAE) hierarchical classification, these AEs were enriched in the AEs related to behavioral and neurological conditions, immune system, and investigation results. Twenty-nine AEs were classified as SAEs and mainly related to immune conditions. Using a logistic regression model accompanied with MCMC sampling, 13 AEs (e.g., hepatosplenomegaly) were identified to result from VVI synergistic effects. Classifications of these 13 AEs using OAE and MedDRA hierarchies confirmed the advantages of the OAE-based method over MedDRA in AE term hierarchical analysis. PMID:27694888

Should Age at Diagnosis Be Included as an Additional Variable in the Risk of Recurrence Classification System in Patients with Differentiated Thyroid Cancer.

PubMed

Pitoia, Fabián; Jerkovich, Fernando; Smulever, Anabella; Brenta, Gabriela; Bueno, Fernanda; Cross, Graciela

2017-07-01

To evaluate the influence of age at diagnosis on the frequency of structural incomplete response (SIR) according to the modified risk of recurrence (RR) staging system from the American Thyroid Association guidelines. We performed a retrospective analysis of 268 patients with differentiated thyroid cancer (DTC) followed up for at least 3 years after initial treatment (total thyroidectomy and remnant ablation). The median follow-up in the whole cohort was 74.3 months (range: 36.1-317.9) and the median age at diagnosis was 45.9 years (range: 18-87). The association between age at diagnosis and the initial and final response to treatment was assessed with analysis of variance (ANOVA). Patients were also divided into several groups considering age younger and older than 40, 50, and 60 years. Age at diagnosis was not associated with either an initial or final statistically significant different SIR to treatment ( p = 0.14 and p = 0.58, respectively). Additionally, we did not find any statistically significant differences when the percentages of SIR considering the classification of RR were compared between different groups of patients by using several age cutoffs. When patients are correctly risk stratified, it seems that age at diagnosis is not involved in the frequency of having a SIR at the initial evaluation or at the final follow-up, so it should not be included as an additional variable to be considered in the RR classifications.

Physicochemical properties of honey from Marche, Central Italy: classification of unifloral and multifloral honeys by multivariate analysis.

PubMed

Truzzi, Cristina; Illuminati, Silvia; Annibaldia, Anna; Finale, Carolina; Rossetti, Monica; Scarponi, Giuseppe

2014-11-01

The purpose of this study was the physicochemical characterization and classification of Italian honey from Marche Region with a chemometric approach. A total of 135 honeys of different botanical origins [acacia (Robinia pseudoacacia L.), chestnut (Castanea sativa), coriander (Coriandrum sativum L.), lime (Tilia spp.), sunflower (Helianthus annuus L.), Metcalfa honeydew and multifloral honey] were considered. The average results of electrical conductivity (0.14-1.45 mS cm(-1)), pH (3.89-5.42), free acidity (10.9-39.0 meq(NaOH) kg(-1)), lactones (2.4-4.5 meq(NaOH) kg(-1)), total acidity (14.5-40.9 meq(NaOH) kg(-1)), proline (229-665 mg kg(-1)) and 5-(hydroxy-methyl)-2-furaldehyde (0.6-3.9 mg kg(-1)) content show wide variability among the analysed honey types, with statistically significant differences between the different honey types. Pattern recognition methods such as principal component analysis and discriminant analysis were performed in order to find a relationship between variables and types of honey and to classify honey on the basis of its physicochemical properties. The variables of electrical conductivity, acidity (free, lactones), pH and proline content exhibited higher discriminant power and provided enough information for the classification and distinction of unifloral honey types, but not for the classification of multifloral honey (100% and 85% of samples correctly classified, respectively).

A randomized trial in a massive online open course shows people don't know what a statistically significant relationship looks like, but they can learn.

PubMed

Fisher, Aaron; Anderson, G Brooke; Peng, Roger; Leek, Jeff

2014-01-01

Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely publicized controversy, P-values remain the most commonly used measure to statistically justify relationships identified between variables. Here we measure the ability to detect statistically significant relationships from scatterplots in a randomized trial of 2,039 students in a statistics massive open online course (MOOC). Each subject was shown a random set of scatterplots and asked to visually determine if the underlying relationships were statistically significant at the P < 0.05 level. Subjects correctly classified only 47.4% (95% CI [45.1%-49.7%]) of statistically significant relationships, and 74.6% (95% CI [72.5%-76.6%]) of non-significant relationships. Adding visual aids such as a best fit line or scatterplot smooth increased the probability a relationship was called significant, regardless of whether the relationship was actually significant. Classification of statistically significant relationships improved on repeat attempts of the survey, although classification of non-significant relationships did not. Our results suggest: (1) that evidence-based data analysis can be used to identify weaknesses in theoretical procedures in the hands of average users, (2) data analysts can be trained to improve detection of statistically significant results with practice, but (3) data analysts have incorrect intuition about what statistically significant relationships look like, particularly for small effects. We have built a web tool for people to compare scatterplots with their corresponding p-values which is available here: http://glimmer.rstudio.com/afisher/EDA/.

A randomized trial in a massive online open course shows people don’t know what a statistically significant relationship looks like, but they can learn

PubMed Central

Fisher, Aaron; Anderson, G. Brooke; Peng, Roger

2014-01-01

Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely publicized controversy, P-values remain the most commonly used measure to statistically justify relationships identified between variables. Here we measure the ability to detect statistically significant relationships from scatterplots in a randomized trial of 2,039 students in a statistics massive open online course (MOOC). Each subject was shown a random set of scatterplots and asked to visually determine if the underlying relationships were statistically significant at the P < 0.05 level. Subjects correctly classified only 47.4% (95% CI [45.1%–49.7%]) of statistically significant relationships, and 74.6% (95% CI [72.5%–76.6%]) of non-significant relationships. Adding visual aids such as a best fit line or scatterplot smooth increased the probability a relationship was called significant, regardless of whether the relationship was actually significant. Classification of statistically significant relationships improved on repeat attempts of the survey, although classification of non-significant relationships did not. Our results suggest: (1) that evidence-based data analysis can be used to identify weaknesses in theoretical procedures in the hands of average users, (2) data analysts can be trained to improve detection of statistically significant results with practice, but (3) data analysts have incorrect intuition about what statistically significant relationships look like, particularly for small effects. We have built a web tool for people to compare scatterplots with their corresponding p-values which is available here: http://glimmer.rstudio.com/afisher/EDA/. PMID:25337457

Evaluation criteria for software classification inventories, accuracies, and maps

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1976-01-01

Statistical criteria are presented for modifying the contingency table used to evaluate tabular classification results obtained from remote sensing and ground truth maps. This classification technique contains information on the spatial complexity of the test site, on the relative location of classification errors, on agreement of the classification maps with ground truth maps, and reduces back to the original information normally found in a contingency table.

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.

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Physique and Performance of Young Wheelchair Basketball Players in Relation with Classification

PubMed Central

Zancanaro, Carlo

2015-01-01

The relationships among physical characteristics, performance, and functional ability classification of younger wheelchair basketball players have been barely investigated to date. The purpose of this work was to assess anthropometry, body composition, and performance in sport-specific field tests in a national sample of Italian younger wheelchair basketball players as well as to evaluate the association of these variables with the players’ functional ability classification and game-related statistics. Several anthropometric measurements were obtained for 52 out of 91 eligible players nationwide. Performance was assessed in seven sport-specific field tests (5m sprint, 20m sprint with ball, suicide, maximal pass, pass for accuracy, spot shot and lay-ups) and game-related statistics (free-throw points scored per match, two- and three-point field-goals scored per match, and their sum). Association between variables, and predictivity was assessed by correlation and regression analysis, respectively. Players were grouped into four Classes of increasing functional ability (A-D). One-way ANOVA with Bonferroni’s correction for multiple comparisons was used to assess differences between Classes. Sitting height and functional ability Class especially correlated with performance outcomes, but wheelchair basketball experience and skinfolds did not. Game-related statistics and sport-specific field-test scores all showed significant correlation with each other. Upper arm circumference and/or maximal pass and lay-ups test scores were able to explain 42 to 59% of variance in game-related statistics (P<0.001). A clear difference in performance was only found for functional ability Class A and D. Conclusion: In younger wheelchair basketball players, sitting height positively contributes to performance. The maximal pass and lay-ups test should be carefully considered in younger wheelchair basketball training plans. Functional ability Class reflects to a limited extent the actual differences in performance. PMID:26606681

An objective and parsimonious approach for classifying natural flow regimes at a continental scale

NASA Astrophysics Data System (ADS)

Archfield, S. A.; Kennen, J.; Carlisle, D.; Wolock, D.

2013-12-01

Hydroecological stream classification--the process of grouping streams by similar hydrologic responses and, thereby, similar aquatic habitat--has been widely accepted and is often one of the first steps towards developing ecological flow targets. Despite its importance, the last national classification of streamgauges was completed about 20 years ago. A new classification of 1,534 streamgauges in the contiguous United States is presented using a novel and parsimonious approach to understand similarity in ecological streamflow response. This new classification approach uses seven fundamental daily streamflow statistics (FDSS) rather than winnowing down an uncorrelated subset from 200 or more ecologically relevant streamflow statistics (ERSS) commonly used in hydroecological classification studies. The results of this investigation demonstrate that the distributions of 33 tested ERSS are consistently different among the classes derived from the seven FDSS. It is further shown that classification based solely on the 33 ERSS generally does a poorer job in grouping similar streamgauges than the classification based on the seven FDSS. This new classification approach has the additional advantages of overcoming some of the subjectivity associated with the selection of the classification variables and provides a set of robust continental-scale classes of US streamgauges.

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

Gissi, Andrea; Dipartimento di Farmacia – Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Via E. Orabona 4, 70125 Bari; Lombardo, Anna

The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R{sup 2}) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemicalmore » Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R{sup 2}>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R{sup 2}{sub ext}=0.59), followed by the EPISuite Meylan model (R{sup 2}{sub ext}=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R{sup 2}=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R{sup 2}=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach. - Highlights: • REACH encourages the use of in silico methods in the assessment of chemicals safety. • The performances of nine BCF models were evaluated on a benchmark database of 851 chemicals. • We compared the models on the basis of both regression and classification performance. • Statistics on chemicals out of the training set and/or within the applicability domain were compiled. • The results show that QSAR models are useful as weight-of-evidence in support to other methods.« less

Classification of pulmonary airway disease based on mucosal color analysis

NASA Astrophysics Data System (ADS)

Suter, Melissa; Reinhardt, Joseph M.; Riker, David; Ferguson, John Scott; McLennan, Geoffrey

2005-04-01

Airway mucosal color changes occur in response to the development of bronchial diseases including lung cancer, cystic fibrosis, chronic bronchitis, emphysema and asthma. These associated changes are often visualized using standard macro-optical bronchoscopy techniques. A limitation to this form of assessment is that the subtle changes that indicate early stages in disease development may often be missed as a result of this highly subjective assessment, especially in inexperienced bronchoscopists. Tri-chromatic CCD chip bronchoscopes allow for digital color analysis of the pulmonary airway mucosa. This form of analysis may facilitate a greater understanding of airway disease response. A 2-step image classification approach is employed: the first step is to distinguish between healthy and diseased bronchoscope images and the second is to classify the detected abnormal images into 1 of 4 possible disease categories. A database of airway mucosal color constructed from healthy human volunteers is used as a standard against which statistical comparisons are made from mucosa with known apparent airway abnormalities. This approach demonstrates great promise as an effective detection and diagnosis tool to highlight potentially abnormal airway mucosa identifying a region possibly suited to further analysis via airway forceps biopsy, or newly developed micro-optical biopsy strategies. Following the identification of abnormal airway images a neural network is used to distinguish between the different disease classes. We have shown that classification of potentially diseased airway mucosa is possible through comparative color analysis of digital bronchoscope images. The combination of the two strategies appears to increase the classification accuracy in addition to greatly decreasing the computational time.

Spatio-temporal dependencies between hospital beds, physicians and health expenditure using visual variables and data classification in statistical table

NASA Astrophysics Data System (ADS)

Medyńska-Gulij, Beata; Cybulski, Paweł

2016-06-01

This paper analyses the use of table visual variables of statistical data of hospital beds as an important tool for revealing spatio-temporal dependencies. It is argued that some of conclusions from the data about public health and public expenditure on health have a spatio-temporal reference. Different from previous studies, this article adopts combination of cartographic pragmatics and spatial visualization with previous conclusions made in public health literature. While the significant conclusions about health care and economic factors has been highlighted in research papers, this article is the first to apply visual analysis to statistical table together with maps which is called previsualisation.

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

Utilization of Skylab EREP system for appraising changes in continental migratory bird habitat. [using multispectral band scanner

NASA Technical Reports Server (NTRS)

Gilmer, D. S. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Surface water statistics using data obtained by supporting aircraft were generated. Signature extraction and refinement preliminary to wetland and associated upland vegetation recognition were accomplished, using a selected portion of the aircraft data. Final classification mapping and analysis of surface water trends will be accomplished.

Multiband tissue classification for ultrasonic transmission tomography using spectral profile detection

NASA Astrophysics Data System (ADS)

Jeong, Jeong-Won; Kim, Tae-Seong; Shin, Dae-Chul; Do, Synho; Marmarelis, Vasilis Z.

2004-04-01

Recently it was shown that soft tissue can be differentiated with spectral unmixing and detection methods that utilize multi-band information obtained from a High-Resolution Ultrasonic Transmission Tomography (HUTT) system. In this study, we focus on tissue differentiation using the spectral target detection method based on Constrained Energy Minimization (CEM). We have developed a new tissue differentiation method called "CEM filter bank". Statistical inference on the output of each CEM filter of a filter bank is used to make a decision based on the maximum statistical significance rather than the magnitude of each CEM filter output. We validate this method through 3-D inter/intra-phantom soft tissue classification where target profiles obtained from an arbitrary single slice are used for differentiation in multiple tomographic slices. Also spectral coherence between target and object profiles of an identical tissue at different slices and phantoms is evaluated by conventional cross-correlation analysis. The performance of the proposed classifier is assessed using Receiver Operating Characteristic (ROC) analysis. Finally we apply our method to classify tiny structures inside a beef kidney such as Styrofoam balls (~1mm), chicken tissue (~5mm), and vessel-duct structures.

The impact of catchment source group classification on the accuracy of sediment fingerprinting outputs.

PubMed

Pulley, Simon; Foster, Ian; Collins, Adrian L

2017-06-01

The objective classification of sediment source groups is at present an under-investigated aspect of source tracing studies, which has the potential to statistically improve discrimination between sediment sources and reduce uncertainty. This paper investigates this potential using three different source group classification schemes. The first classification scheme was simple surface and subsurface groupings (Scheme 1). The tracer signatures were then used in a two-step cluster analysis to identify the sediment source groupings naturally defined by the tracer signatures (Scheme 2). The cluster source groups were then modified by splitting each one into a surface and subsurface component to suit catchment management goals (Scheme 3). The schemes were tested using artificial mixtures of sediment source samples. Controlled corruptions were made to some of the mixtures to mimic the potential causes of tracer non-conservatism present when using tracers in natural fluvial environments. It was determined how accurately the known proportions of sediment sources in the mixtures were identified after unmixing modelling using the three classification schemes. The cluster analysis derived source groups (2) significantly increased tracer variability ratios (inter-/intra-source group variability) (up to 2122%, median 194%) compared to the surface and subsurface groupings (1). As a result, the composition of the artificial mixtures was identified an average of 9.8% more accurately on the 0-100% contribution scale. It was found that the cluster groups could be reclassified into a surface and subsurface component (3) with no significant increase in composite uncertainty (a 0.1% increase over Scheme 2). The far smaller effects of simulated tracer non-conservatism for the cluster analysis based schemes (2 and 3) was primarily attributed to the increased inter-group variability producing a far larger sediment source signal that the non-conservatism noise (1). Modified cluster analysis based classification methods have the potential to reduce composite uncertainty significantly in future source tracing studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes

PubMed Central

Marko, Nicholas F.; Weil, Robert J.

2012-01-01

Introduction Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. Methods We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. Results Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. Conclusions Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. PMID:23118863

Signal Detection Techniques for Diagnostic Monitoring of Space Shuttle Main Engine Turbomachinery

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Jong, Jen-Yi

1986-01-01

An investigation to develop, implement, and evaluate signal analysis techniques for the detection and classification of incipient mechanical failures in turbomachinery is reviewed. A brief description of the Space Shuttle Main Engine (SSME) test/measurement program is presented. Signal analysis techniques available to describe dynamic measurement characteristics are reviewed. Time domain and spectral methods are described, and statistical classification in terms of moments is discussed. Several of these waveform analysis techniques have been implemented on a computer and applied to dynamc signals. A laboratory evaluation of the methods with respect to signal detection capability is described. A unique coherence function (the hyper-coherence) was developed through the course of this investigation, which appears promising as a diagnostic tool. This technique and several other non-linear methods of signal analysis are presented and illustrated by application. Software for application of these techniques has been installed on the signal processing system at the NASA/MSFC Systems Dynamics Laboratory.

DARHT Multi-intelligence Seismic and Acoustic Data Analysis

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

Stevens, Garrison Nicole; Van Buren, Kendra Lu; Hemez, Francois M.

The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, andmore » finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.« less

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

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

Vatsavai, Raju; Bhaduri, Budhendra L

2011-01-01

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, itmore » is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.« less

Classification of the European Union member states according to the relative level of sustainable development.

PubMed

Anna, Bluszcz

Nowadays methods of measurement and assessment of the level of sustained development at the international, national and regional level are a current research problem, which requires multi-dimensional analysis. The relative assessment of the sustainability level of the European Union member states and the comparative analysis of the position of Poland relative to other countries was the aim of the conducted studies in the article. EU member states were treated as objects in the multi-dimensional space. Dimensions of space were specified by ten diagnostic variables describing the sustainability level of UE countries in three dimensions, i.e., social, economic and environmental. Because the compiled statistical data were expressed in different units of measure, taxonomic methods were used for building an aggregated measure to assess the level of sustainable development of EU member states, which through normalisation of variables enabled the comparative analysis between countries. Methodology of studies consisted of eight stages, which included, among others: defining data matrices, calculating the variability coefficient for all variables, which variability coefficient was under 10 %, division of variables into stimulants and destimulants, selection of the method of variable normalisation, developing matrices of normalised data, selection of the formula and calculating the aggregated indicator of the relative level of sustainable development of the EU countries, calculating partial development indicators for three studies dimensions: social, economic and environmental and the classification of the EU countries according to the relative level of sustainable development. Statistical date were collected based on the Polish Central Statistical Office publication.

Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition

NASA Astrophysics Data System (ADS)

Colone, L.; Hovgaard, M. K.; Glavind, L.; Brincker, R.

2018-07-01

A method for mass change detection on wind turbine blades using natural frequencies is presented. The approach is based on two statistical tests. The first test decides if there is a significant mass change and the second test is a statistical group classification based on Linear Discriminant Analysis. The frequencies are identified by means of Operational Modal Analysis using natural excitation. Based on the assumption of Gaussianity of the frequencies, a multi-class statistical model is developed by combining finite element model sensitivities in 10 classes of change location on the blade, the smallest area being 1/5 of the span. The method is experimentally validated for a full scale wind turbine blade in a test setup and loaded by natural wind. Mass change from natural causes was imitated with sand bags and the algorithm was observed to perform well with an experimental detection rate of 1, localization rate of 0.88 and mass estimation rate of 0.72.

The Power of Neuroimaging Biomarkers for Screening Frontotemporal Dementia

PubMed Central

McMillan, Corey T.; Avants, Brian B.; Cook, Philip; Ungar, Lyle; Trojanowski, John Q.; Grossman, Murray

2014-01-01

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer’s disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N=93) with cerebrospinal fluid, gray matter (GM) MRI, and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes (mSS) required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% SENSITIVE; 89% SPECIFIC) and required a lower minimum sample size (N=26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach employing Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials. PMID:24687814

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification.

PubMed

Reboiro-Jato, Miguel; Arrais, Joel P; Oliveira, José Luis; Fdez-Riverola, Florentino

2014-01-30

The diagnosis and prognosis of several diseases can be shortened through the use of different large-scale genome experiments. In this context, microarrays can generate expression data for a huge set of genes. However, to obtain solid statistical evidence from the resulting data, it is necessary to train and to validate many classification techniques in order to find the best discriminative method. This is a time-consuming process that normally depends on intricate statistical tools. geneCommittee is a web-based interactive tool for routinely evaluating the discriminative classification power of custom hypothesis in the form of biologically relevant gene sets. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. Provided with a straightforward and intuitive interface, geneCommittee is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research. geneCommittee allows the enrichment of microarrays raw data with gene functional annotations, producing integrated datasets that simplify the construction of better discriminative hypothesis, and allows the creation of a set of complementary classifiers. The trained committees can then be used for clinical research and diagnosis. Full documentation including common use cases and guided analysis workflows is freely available at http://sing.ei.uvigo.es/GC/.

Modeling and replicating statistical topology and evidence for CMB nonhomogeneity

PubMed Central

Agami, Sarit

2017-01-01

Under the banner of “big data,” the detection and classification of structure in extremely large, high-dimensional, data sets are two of the central statistical challenges of our times. Among the most intriguing new approaches to this challenge is “TDA,” or “topological data analysis,” one of the primary aims of which is providing nonmetric, but topologically informative, preanalyses of data which make later, more quantitative, analyses feasible. While TDA rests on strong mathematical foundations from topology, in applications, it has faced challenges due to difficulties in handling issues of statistical reliability and robustness, often leading to an inability to make scientific claims with verifiable levels of statistical confidence. We propose a methodology for the parametric representation, estimation, and replication of persistence diagrams, the main diagnostic tool of TDA. The power of the methodology lies in the fact that even if only one persistence diagram is available for analysis—the typical case for big data applications—the replications permit conventional statistical hypothesis testing. The methodology is conceptually simple and computationally practical, and provides a broadly effective statistical framework for persistence diagram TDA analysis. We demonstrate the basic ideas on a toy example, and the power of the parametric approach to TDA modeling in an analysis of cosmic microwave background (CMB) nonhomogeneity. PMID:29078301

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Multispectral scanner system parameter study and analysis software system description, volume 2

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Mobasseri, B. G.; Wiersma, D. J.; Wiswell, E. R.; Mcgillem, C. D.; Anuta, P. E.

1978-01-01

The author has identified the following significant results. The integration of the available methods provided the analyst with the unified scanner analysis package (USAP), the flexibility and versatility of which was superior to many previous integrated techniques. The USAP consisted of three main subsystems; (1) a spatial path, (2) a spectral path, and (3) a set of analytic classification accuracy estimators which evaluated the system performance. The spatial path consisted of satellite and/or aircraft data, data correlation analyzer, scanner IFOV, and random noise model. The output of the spatial path was fed into the analytic classification and accuracy predictor. The spectral path consisted of laboratory and/or field spectral data, EXOSYS data retrieval, optimum spectral function calculation, data transformation, and statistics calculation. The output of the spectral path was fended into the stratified posterior performance estimator.

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

Effects of eye artifact removal methods on single trial P300 detection, a comparative study.

PubMed

Ghaderi, Foad; Kim, Su Kyoung; Kirchner, Elsa Andrea

2014-01-15

Electroencephalographic signals are commonly contaminated by eye artifacts, even if recorded under controlled conditions. The objective of this work was to quantitatively compare standard artifact removal methods (regression, filtered regression, Infomax, and second order blind identification (SOBI)) and two artifact identification approaches for independent component analysis (ICA) methods, i.e. ADJUST and correlation. To this end, eye artifacts were removed and the cleaned datasets were used for single trial classification of P300 (a type of event related potentials elicited using the oddball paradigm). Statistical analysis of the results confirms that the combination of Infomax and ADJUST provides a relatively better performance (0.6% improvement on average of all subject) while the combination of SOBI and correlation performs the worst. Low-pass filtering the data at lower cutoffs (here 4 Hz) can also improve the classification accuracy. Without requiring any artifact reference channel, the combination of Infomax and ADJUST improves the classification performance more than the other methods for both examined filtering cutoffs, i.e., 4 Hz and 25 Hz. Copyright © 2013 Elsevier B.V. All rights reserved.

Geomorphology Classification of Shandong Province Based on Digital Elevation Model in the 1 Arc-second Format of Shuttle Radar Topography Mission Data

NASA Astrophysics Data System (ADS)

Fu, Jundong; Zhang, Guangcheng; Wang, Lei; Xia, Nuan

2018-01-01

Based on gigital elevation model in the 1 arc-second format of shuttle radar topography mission data, using the window analysis and mean change point analysis of geographic information system (GIS) technology, programmed with python modules this, automatically extracted and calculated geomorphic elements of Shandong province. The best access to quantitatively study area relief amplitude of statistical area. According to Chinese landscape classification standard, the landscape type in Shandong province was divided into 8 types: low altitude plain, medium altitude plain, low altitude platform, medium altitude platform, low altitude hills, medium altitude hills, low relief mountain, medium relief mountain and the percentages of Shandong province’s total area are as follows: 12.72%, 0.01%, 36.38%, 0.24%, 17.26%, 15.64%, 11.1%, 6.65%. The results of landforms are basically the same as the overall terrain of Shandong Province, Shandong province’s total area, and the study can quantitatively and scientifically provide reference for the classification of landforms in Shandong province.

Automated Analysis of Planktic Foraminifers Part III: Neural Network Classification

NASA Astrophysics Data System (ADS)

Schiebel, R.; Bollmann, J.; Quinn, P.; Vela, M.; Schmidt, D. N.; Thierstein, H. R.

2003-04-01

The abundance and assemblage composition of microplankton, together with the chemical and stable isotopic composition of their shells, are among the most successful methods in paleoceanography and paleoclimatology. However, the manual collection of statistically significant numbers of unbiased, reproducible data is time consuming. Consequently, automated microfossil analysis and species recognition has been a long-standing goal in micropaleontology. We have developed a Windows based software package COGNIS for the segmentation, preprocessing, and classification of automatically acquired microfossil images (see Part II, Bollmann et al., this volume), using operator designed neural network structures. With a five-layered convolutional neural network we obtain an average recognition rate of 75 % (max. 88 %) for 6 taxa (N. dutertrei, N. pachyderma dextral, N. pachyderma sinistral, G. inflata, G. menardii/tumida, O. universa), represented by 50 images each for 20 classes (separation of spiral and umbilical views, and of sinistral and dextral forms). Our investigation indicates that neural networks hold great potential for the automated classification of planktic foraminifers and offer new perspectives in micropaleontology, paleoceanography, and paleoclimatology (see Part I, Schmidt et al., this volume).

Enhancing image classification models with multi-modal biomarkers

NASA Astrophysics Data System (ADS)

Caban, Jesus J.; Liao, David; Yao, Jianhua; Mollura, Daniel J.; Gochuico, Bernadette; Yoo, Terry

2011-03-01

Currently, most computer-aided diagnosis (CAD) systems rely on image analysis and statistical models to diagnose, quantify, and monitor the progression of a particular disease. In general, CAD systems have proven to be effective at providing quantitative measurements and assisting physicians during the decision-making process. As the need for more flexible and effective CADs continues to grow, questions about how to enhance their accuracy have surged. In this paper, we show how statistical image models can be augmented with multi-modal physiological values to create more robust, stable, and accurate CAD systems. In particular, this paper demonstrates how highly correlated blood and EKG features can be treated as biomarkers and used to enhance image classification models designed to automatically score subjects with pulmonary fibrosis. In our results, a 3-5% improvement was observed when comparing the accuracy of CADs that use multi-modal biomarkers with those that only used image features. Our results show that lab values such as Erythrocyte Sedimentation Rate and Fibrinogen, as well as EKG measurements such as QRS and I:40, are statistically significant and can provide valuable insights about the severity of the pulmonary fibrosis disease.

Unbiased metabolite profiling by liquid chromatography-quadrupole time-of-flight mass spectrometry and multivariate data analysis for herbal authentication: classification of seven Lonicera species flower buds.

PubMed

Gao, Wen; Yang, Hua; Qi, Lian-Wen; Liu, E-Hu; Ren, Mei-Ting; Yan, Yu-Ting; Chen, Jun; Li, Ping

2012-07-06

Plant-based medicines become increasingly popular over the world. Authentication of herbal raw materials is important to ensure their safety and efficacy. Some herbs belonging to closely related species but differing in medicinal properties are difficult to be identified because of similar morphological and microscopic characteristics. Chromatographic fingerprinting is an alternative method to distinguish them. Existing approaches do not allow a comprehensive analysis for herbal authentication. We have now developed a strategy consisting of (1) full metabolic profiling of herbal medicines by rapid resolution liquid chromatography (RRLC) combined with quadrupole time-of-flight mass spectrometry (QTOF MS), (2) global analysis of non-targeted compounds by molecular feature extraction algorithm, (3) multivariate statistical analysis for classification and prediction, and (4) marker compounds characterization. This approach has provided a fast and unbiased comparative multivariate analysis of the metabolite composition of 33-batch samples covering seven Lonicera species. Individual metabolic profiles are performed at the level of molecular fragments without prior structural assignment. In the entire set, the obtained classifier for seven Lonicera species flower buds showed good prediction performance and a total of 82 statistically different components were rapidly obtained by the strategy. The elemental compositions of discriminative metabolites were characterized by the accurate mass measurement of the pseudomolecular ions and their chemical types were assigned by the MS/MS spectra. The high-resolution, comprehensive and unbiased strategy for metabolite data analysis presented here is powerful and opens the new direction of authentication in herbal analysis. Copyright © 2012 Elsevier B.V. All rights reserved.

How Can Dolphins Recognize Fish According to Their Echoes? A Statistical Analysis of Fish Echoes

PubMed Central

Yovel, Yossi; Au, Whitlow W. L.

2010-01-01

Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders). In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification. PMID:21124908

How can dolphins recognize fish according to their echoes? A statistical analysis of fish echoes.

PubMed

Yovel, Yossi; Au, Whitlow W L

2010-11-19

Echo-based object classification is a fundamental task of animals that use a biosonar system. Dolphins and porpoises should be able to rely on echoes to discriminate a predator from a prey or to select a desired prey from an undesired object. Many studies have shown that dolphins and porpoises can discriminate between objects according to their echoes. All of these studies however, used unnatural objects that can be easily characterized in human terminologies (e.g., metallic spheres, disks, cylinders). In this work, we collected real fish echoes from many angles of acquisition using a sonar system that mimics the emission properties of dolphins and porpoises. We then tested two alternative statistical approaches in classifying these echoes. Our results suggest that fish species can be classified according to echoes returning from porpoise- and dolphin-like signals. These results suggest how dolphins and porpoises can classify fish based on their echoes and provide some insight as to which features might enable the classification.

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

USGS Publications Warehouse

Chastain, R.A.; Struckhoff, M.A.; He, H.S.; Larsen, D.R.

2008-01-01

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood.

PubMed

Sikirzhytskaya, Aliaksandra; Sikirzhytski, Vitali; Lednev, Igor K

2014-01-01

Body fluids are a common and important type of forensic evidence. In particular, the identification of menstrual blood stains is often a key step during the investigation of rape cases. Here, we report on the application of near-infrared Raman microspectroscopy for differentiating menstrual blood from peripheral blood. We observed that the menstrual and peripheral blood samples have similar but distinct Raman spectra. Advanced statistical analysis of the multiple Raman spectra that were automatically (Raman mapping) acquired from the 40 dried blood stains (20 donors for each group) allowed us to build classification model with maximum (100%) sensitivity and specificity. We also demonstrated that despite certain common constituents, menstrual blood can be readily distinguished from vaginal fluid. All of the classification models were verified using cross-validation methods. The proposed method overcomes the problems associated with currently used biochemical methods, which are destructive, time consuming and expensive. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Classification of complementary and alternative medical practices: Family physicians' ratings of effectiveness.

PubMed

Fries, Christopher J

2008-11-01

ABSTRACTOBJECTIVETo develop a classification of complementary and alternative medicine (CAM) practices widely available in Canada based on physicians' effectiveness ratings of the therapies.DESIGNA self-administered postal questionnaire asking family physicians to rate their "belief in the degree of therapeutic effectiveness" of 15 CAM therapies.SETTINGProvince of Alberta.PARTICIPANTSA total of 875 family physicians.MAIN OUTCOME MEASURESDescriptive statistics of physicians' awareness of and effectiveness ratings for each of the therapies; factor analysis was applied to the ratings of the 15 therapies in order to explore whether or not the data support the proposed classification of CAM practices into categories of accepted and rejected.RESULTSPhysicians believed that acupuncture, massage therapy, chiropractic care, relaxation therapy, biofeedback, and spiritual or religious healing were effective when used in conjunction with biomedicine to treat chronic or psychosomatic indications. Physicians attributed little effectiveness to homeopathy or naturopathy, Feldenkrais or Alexander technique, Rolfing, herbal medicine, traditional Chinese medicine, and reflexology. The factor analysis revealed an underlying dimensionality to physicians' effectiveness ratings of the CAM therapies that supports the classification of these practices as either accepted or rejected.CONCLUSIONThis study provides Canadian family physicians with information concerning which CAM therapies are generally accepted by their peers as effective and which are not.

a Single-Exposure Dual-Energy Computed Radiography Technique for Improved Nodule Detection and Classification in Chest Imaging

NASA Astrophysics Data System (ADS)

Zink, Frank Edward

The detection and classification of pulmonary nodules is of great interest in chest radiography. Nodules are often indicative of primary cancer, and their detection is particularly important in asymptomatic patients. The ability to classify nodules as calcified or non-calcified is important because calcification is a positive indicator that the nodule is benign. Dual-energy methods offer the potential to improve both the detection and classification of nodules by allowing the formation of material-selective images. Tissue-selective images can improve detection by virtue of the elimination of obscuring rib structure. Bone -selective images are essentially calcium images, allowing classification of the nodule. A dual-energy technique is introduced which uses a computed radiography system to acquire dual-energy chest radiographs in a single-exposure. All aspects of the dual-energy technique are described, with particular emphasis on scatter-correction, beam-hardening correction, and noise-reduction algorithms. The adaptive noise-reduction algorithm employed improves material-selective signal-to-noise ratio by up to a factor of seven with minimal sacrifice in selectivity. A clinical comparison study is described, undertaken to compare the dual-energy technique to conventional chest radiography for the tasks of nodule detection and classification. Observer performance data were collected using the Free Response Observer Characteristic (FROC) method and the bi-normal Alternative FROC (AFROC) performance model. Results of the comparison study, analyzed using two common multiple observer statistical models, showed that the dual-energy technique was superior to conventional chest radiography for detection of nodules at a statistically significant level (p < .05). Discussion of the comparison study emphasizes the unique combination of data collection and analysis techniques employed, as well as the limitations of comparison techniques in the larger context of technology assessment.

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Machine Learning Methods for Production Cases Analysis

NASA Astrophysics Data System (ADS)

Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

2018-03-01

Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

Applications of satellite image processing to the analysis of Amazonian cultural ecology

NASA Technical Reports Server (NTRS)

Behrens, Clifford A.

1991-01-01

This paper examines the application of satellite image processing towards identifying and comparing resource exploitation among indigenous Amazonian peoples. The use of statistical and heuristic procedures for developing land cover/land use classifications from Thematic Mapper satellite imagery will be discussed along with actual results from studies of relatively small (100 - 200 people) settlements. Preliminary research indicates that analysis of satellite imagery holds great potential for measuring agricultural intensification, comparing rates of tropical deforestation, and detecting changes in resource utilization patterns over time.

Comparative Analysis of Document level Text Classification Algorithms using R

NASA Astrophysics Data System (ADS)

Syamala, Maganti; Nalini, N. J., Dr; Maguluri, Lakshamanaphaneendra; Ragupathy, R., Dr.

2017-08-01

From the past few decades there has been tremendous volumes of data available in Internet either in structured or unstructured form. Also, there is an exponential growth of information on Internet, so there is an emergent need of text classifiers. Text mining is an interdisciplinary field which draws attention on information retrieval, data mining, machine learning, statistics and computational linguistics. And to handle this situation, a wide range of supervised learning algorithms has been introduced. Among all these K-Nearest Neighbor(KNN) is efficient and simplest classifier in text classification family. But KNN suffers from imbalanced class distribution and noisy term features. So, to cope up with this challenge we use document based centroid dimensionality reduction(CentroidDR) using R Programming. By combining these two text classification techniques, KNN and Centroid classifiers, we propose a scalable and effective flat classifier, called MCenKNN which works well substantially better than CenKNN.

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.

Statistical Analysis of Q-matrix Based Diagnostic Classification Models

PubMed Central

Chen, Yunxiao; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2014-01-01

Diagnostic classification models have recently gained prominence in educational assessment, psychiatric evaluation, and many other disciplines. Central to the model specification is the so-called Q-matrix that provides a qualitative specification of the item-attribute relationship. In this paper, we develop theories on the identifiability for the Q-matrix under the DINA and the DINO models. We further propose an estimation procedure for the Q-matrix through the regularized maximum likelihood. The applicability of this procedure is not limited to the DINA or the DINO model and it can be applied to essentially all Q-matrix based diagnostic classification models. Simulation studies are conducted to illustrate its performance. Furthermore, two case studies are presented. The first case is a data set on fraction subtraction (educational application) and the second case is a subsample of the National Epidemiological Survey on Alcohol and Related Conditions concerning the social anxiety disorder (psychiatric application). PMID:26294801

Modality-Driven Classification and Visualization of Ensemble Variance

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

Bensema, Kevin; Gosink, Luke; Obermaier, Harald

Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no informationmore » about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.« less

Validity and reliability of the Paprosky acetabular defect classification.

PubMed

Yu, Raymond; Hofstaetter, Jochen G; Sullivan, Thomas; Costi, Kerry; Howie, Donald W; Solomon, Lucian B

2013-07-01

The Paprosky acetabular defect classification is widely used but has not been appropriately validated. Reliability of the Paprosky system has not been evaluated in combination with standardized techniques of measurement and scoring. This study evaluated the reliability, teachability, and validity of the Paprosky acetabular defect classification. Preoperative radiographs from a random sample of 83 patients undergoing 85 acetabular revisions were classified by four observers, and their classifications were compared with quantitative intraoperative measurements. Teachability of the classification scheme was tested by dividing the four observers into two groups. The observers in Group 1 underwent three teaching sessions; those in Group 2 underwent one session and the influence of teaching on the accuracy of their classifications was ascertained. Radiographic evaluation showed statistically significant relationships with intraoperative measurements of anterior, medial, and superior acetabular defect sizes. Interobserver reliability improved substantially after teaching and did not improve without it. The weighted kappa coefficient went from 0.56 at Occasion 1 to 0.79 after three teaching sessions in Group 1 observers, and from 0.49 to 0.65 after one teaching session in Group 2 observers. The Paprosky system is valid and shows good reliability when combined with standardized definitions of radiographic landmarks and a structured analysis. Level II, diagnostic study. See the Guidelines for Authors for a complete description of levels of evidence.

Classifying GRB 170817A/GW170817 in a Fermi duration-hardness plane

NASA Astrophysics Data System (ADS)

Horváth, I.; Tóth, B. G.; Hakkila, J.; Tóth, L. V.; Balázs, L. G.; Rácz, I. I.; Pintér, S.; Bagoly, Z.

2018-03-01

GRB 170817A, associated with the LIGO-Virgo GW170817 neutron-star merger event, lacks the short duration and hard spectrum of a Short gamma-ray burst (GRB) expected from long-standing classification models. Correctly identifying the class to which this burst belongs requires comparison with other GRBs detected by the Fermi GBM. The aim of our analysis is to classify Fermi GRBs and to test whether or not GRB 170817A belongs—as suggested—to the Short GRB class. The Fermi GBM catalog provides a large database with many measured variables that can be used to explore gamma-ray burst classification. We use statistical techniques to look for clustering in a sample of 1298 gamma-ray bursts described by duration and spectral hardness. Classification of the detected bursts shows that GRB 170817A most likely belongs to the Intermediate, rather than the Short GRB class. We discuss this result in light of theoretical neutron-star merger models and existing GRB classification schemes. It appears that GRB classification schemes may not yet be linked to appropriate theoretical models, and that theoretical models may not yet adequately account for known GRB class properties. We conclude that GRB 170817A may not fit into a simple phenomenological classification scheme.

[Construction and application of special analysis database of geoherbs based on 3S technology].

PubMed

Guo, Lan-ping; Huang, Lu-qi; Lv, Dong-mei; Shao, Ai-juan; Wang, Jian

2007-09-01

In this paper,the structures, data sources, data codes of "the spacial analysis database of geoherbs" based 3S technology are introduced, and the essential functions of the database, such as data management, remote sensing, spacial interpolation, spacial statistics, spacial analysis and developing are described. At last, two examples for database usage are given, the one is classification and calculating of NDVI index of remote sensing image in geoherbal area of Atractylodes lancea, the other one is adaptation analysis of A. lancea. These indicate that "the spacial analysis database of geoherbs" has bright prospect in spacial analysis of geoherbs.

Analysis tools for discovering strong parity violation at hadron colliders

NASA Astrophysics Data System (ADS)

Backović, Mihailo; Ralston, John P.

2011-07-01

Several arguments suggest parity violation may be observable in high energy strong interactions. We introduce new analysis tools to describe the azimuthal dependence of multiparticle distributions, or “azimuthal flow.” Analysis uses the representations of the orthogonal group O(2) and dihedral groups DN necessary to define parity completely in two dimensions. Classification finds that collective angles used in event-by-event statistics represent inequivalent tensor observables that cannot generally be represented by a single “reaction plane.” Many new parity-violating observables exist that have never been measured, while many parity-conserving observables formerly lumped together are now distinguished. We use the concept of “event-shape sorting” to suggest separating right- and left-handed events, and we discuss the effects of transverse and longitudinal spin. The analysis tools are statistically robust, and can be applied equally to low or high multiplicity events at the Tevatron, RHIC or RHIC Spin, and the LHC.

[Perinatal mortality research in Brazil: review of methodology and results].

PubMed

Fonseca, Sandra Costa; Coutinho, Evandro da Silva Freire

2004-01-01

The perinatal mortality rate remains a public health problem, demanding epidemiological studies to describe its magnitude and time trends, identify risk factors, and define adequate interventions. There are still methodological controversies, resulting in heterogeneous studies and possible biases. In Brazil, there has been a growing scientific output on this theme, mainly in the South and Southeast of the country. Twenty-four articles from 1996 to 2003 were reviewed, focusing on definitions and classifications, data sources, study designs, measurement of variables, statistical analysis, and results. The review showed an increasing utilization of data bases (mainly SINASC and SIM), few studies on stillbirth, the incorporation of classification schemes, and disagreement concerning risk factors.

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.

An Australian casemix classification for palliative care: lessons and policy implications of a national study.

PubMed

Eagar, Kathy; Gordon, Robert; Green, Janette; Smith, Michael

2004-04-01

To provide a nontechnical discussion of the development of a palliative care casemix classification and some policy implications of its implementation. 3866 palliative care patients who, in a three month period, had 4596 episodes of care provided by 58 palliative care services in Australia and New Zealand. A detailed clinical and service utilization profile was collected on each patient with staff time and other resources measured on a daily basis. A statistical summary of the clinical variables was compiled as the first stage of the analysis. Palliative care phase was found to be a good predictor of resource use, with patients fairly evenly distributed across the five categories. Clients treated in an inpatient setting had poorer function and higher symptom severity scores than those treated in an ambulatory setting, a result that is not surprising in this Australian setting. Implementation of the resultant AN-SNAP classification has been proceeding since 1998 in some Australian jurisdictions. The development and implementation of a classification such as AN-SNAP provides the possibility of having a consistent approach to collecting palliative care data in Australia as well as a growing body of experience on how to progressively improve the classification over time.

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

PubMed

Madsen, Kristoffer H; Krohne, Laerke G; Cai, Xin-Lu; Wang, Yi; Chan, Raymond C K

2018-03-15

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

An objective and parsimonious approach for classifying natural flow regimes at a continental scale

USGS Publications Warehouse

Archfield, Stacey A.; Kennen, Jonathan G.; Carlisle, Daren M.; Wolock, David M.

2014-01-01

Hydro-ecological stream classification-the process of grouping streams by similar hydrologic responses and, by extension, similar aquatic habitat-has been widely accepted and is considered by some to be one of the first steps towards developing ecological flow targets. A new classification of 1543 streamgauges in the contiguous USA is presented by use of a novel and parsimonious approach to understand similarity in ecological streamflow response. This novel classification approach uses seven fundamental daily streamflow statistics (FDSS) rather than winnowing down an uncorrelated subset from 200 or more ecologically relevant streamflow statistics (ERSS) commonly used in hydro-ecological classification studies. The results of this investigation demonstrate that the distributions of 33 tested ERSS are consistently different among the classification groups derived from the seven FDSS. It is further shown that classification based solely on the 33 ERSS generally does a poorer job in grouping similar streamgauges than the classification based on the seven FDSS. This new classification approach has the additional advantages of overcoming some of the subjectivity associated with the selection of the classification variables and provides a set of robust continental-scale classes of US streamgauges. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Multiple Category-Lot Quality Assurance Sampling: A New Classification System with Application to Schistosomiasis Control

PubMed Central

Olives, Casey; Valadez, Joseph J.; Brooker, Simon J.; Pagano, Marcello

2012-01-01

Background Originally a binary classifier, Lot Quality Assurance Sampling (LQAS) has proven to be a useful tool for classification of the prevalence of Schistosoma mansoni into multiple categories (≤10%, >10 and <50%, ≥50%), and semi-curtailed sampling has been shown to effectively reduce the number of observations needed to reach a decision. To date the statistical underpinnings for Multiple Category-LQAS (MC-LQAS) have not received full treatment. We explore the analytical properties of MC-LQAS, and validate its use for the classification of S. mansoni prevalence in multiple settings in East Africa. Methodology We outline MC-LQAS design principles and formulae for operating characteristic curves. In addition, we derive the average sample number for MC-LQAS when utilizing semi-curtailed sampling and introduce curtailed sampling in this setting. We also assess the performance of MC-LQAS designs with maximum sample sizes of n = 15 and n = 25 via a weighted kappa-statistic using S. mansoni data collected in 388 schools from four studies in East Africa. Principle Findings Overall performance of MC-LQAS classification was high (kappa-statistic of 0.87). In three of the studies, the kappa-statistic for a design with n = 15 was greater than 0.75. In the fourth study, where these designs performed poorly (kappa-statistic less than 0.50), the majority of observations fell in regions where potential error is known to be high. Employment of semi-curtailed and curtailed sampling further reduced the sample size by as many as 0.5 and 3.5 observations per school, respectively, without increasing classification error. Conclusion/Significance This work provides the needed analytics to understand the properties of MC-LQAS for assessing the prevalance of S. mansoni and shows that in most settings a sample size of 15 children provides a reliable classification of schools. PMID:22970333

Comparison of Danish dichotomous and BI-RADS classifications of mammographic density.

PubMed

Hodge, Rebecca; Hellmann, Sophie Sell; von Euler-Chelpin, My; Vejborg, Ilse; Andersen, Zorana Jovanovic

2014-06-01

In the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before. To compare the Danish dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system. The study sample consisted of 120 mammograms taken in Copenhagen in 1991-2001, which tested false positive, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic. Of the 120 women, 32 (26.7%) were classified as having fatty and 88 (73.3%) as mixed/dense mammographic density, according to Danish dichotomous classification. According to BI-RADS density classification, 12 (10.0%) women were classified as having predominantly fatty (BI-RADS code 1), 46 (38.3%) as having scattered fibroglandular (BI-RADS code 2), 57 (47.5%) as having heterogeneously dense (BI-RADS 3), and five (4.2%) as having extremely dense (BI-RADS code 4) mammographic density. The inter-rater variability assessed by weighted kappa statistic showed a substantial agreement (0.75). The dichotomous mammographic density classification system utilized in early years of Copenhagen's mammographic screening program (1991-2001) agreed well with the BI-RADS density classification system.

Women's health and women's work in health services: what statistics tell us.

PubMed

Hedman, B; Herner, E

1988-01-01

This article draws together statistical information in several broad areas that relate to women's health, women's reproductive activities and women's occupations in Sweden. The statistical analysis reflects the major changes that have occurred in Swedish society and that have had a major impact on the health and well-being, as well as on the social participation rate, of women. Much of the data is drawn from a recent special effort at Statistic Sweden aimed at influencing the classification, collection and presentation of statistical data in all fields in such a way that family, working, education, health and other conditions of women can be more readily and equitably compared with those of men. In addition, social changes have seen the shifting of the responsibility of health care from the unpaid duties of women in the home to health care institutions, where female employees predominate. These trends are also discussed.

Global dengue death before and after the new World Health Organization 2009 case classification: A systematic review and meta-regression analysis.

PubMed

Low, Gary Kim-Kuan; Ogston, Simon A; Yong, Mun-Hin; Gan, Seng-Chiew; Chee, Hui-Yee

2018-06-01

Since the introduction of 2009 WHO dengue case classification, no literature was found regarding its effect on dengue death. This study was to evaluate the effect of 2009 WHO dengue case classification towards dengue case fatality rate. Various databases were used to search relevant articles since 1995. Studies included were cohort and cross-sectional studies, all patients with dengue infection and must report the number of death or case fatality rate. The Joanna Briggs Institute appraisal checklist was used to evaluate the risk of bias of the full-texts. The studies were grouped according to the classification adopted: WHO 1997 and WHO 2009. Meta-regression was employed using a logistic transformation (log-odds) of the case fatality rate. The result of the meta-regression was the adjusted case fatality rate and odds ratio on the explanatory variables. A total of 77 studies were included in the meta-regression analysis. The case fatality rate for all studies combined was 1.14% with 95% confidence interval (CI) of 0.82-1.58%. The combined (unadjusted) case fatality rate for 69 studies which adopted WHO 1997 dengue case classification was 1.09% with 95% CI of 0.77-1.55%; and for eight studies with WHO 2009 was 1.62% with 95% CI of 0.64-4.02%. The unadjusted and adjusted odds ratio of case fatality using WHO 2009 dengue case classification was 1.49 (95% CI: 0.52, 4.24) and 0.83 (95% CI: 0.26, 2.63) respectively, compared to WHO 1997 dengue case classification. There was an apparent increase in trend of case fatality rate from the year 1992-2016. Neither was statistically significant. The WHO 2009 dengue case classification might have no effect towards the case fatality rate although the adjusted results indicated a lower case fatality rate. Future studies are required for an update in the meta-regression analysis to confirm the findings. Copyright © 2018 Elsevier B.V. All rights reserved.

Decision Process to Identify Lessons for Transition to a Distributed (or Blended) Learning Instructional Format

DTIC Science & Technology

2009-09-01

instructional format. Using a mixed- method coding and analysis approach, the sample of POIs were categorized, coded, statistically analyzed, and a... Method SECURITY CLASSIFICATION OF 19. LIMITATION OF 20. NUMBER 21. RESPONSIBLE PERSON 16. REPORT Unclassified 17. ABSTRACT...transition to a distributed (or blended) learning format. Procedure: A mixed- methods approach, combining qualitative coding procedures with basic

Integration of Advanced Statistical Analysis Tools and Geophysical Modeling

DTIC Science & Technology

2010-12-01

Carin Duke University Douglas Oldenburg University of British Columbia Stephen Billings, Leonard Pasion Laurens Beran Sky Research...means and covariances estimated for each class [5]. For this study, dipole polarizabilities were fit with a Pasion -Oldenburg parameterization of 8 −1...model for unexploded ordnance classification with EMI data,” IEEE Geosci. Remote Sensing Letters, vol. 4, pp. 629–633, 2007. [4] L. R. Pasion

Multispecies, Integrative GWAS for Focal Segmental Glomerulosclerosis

DTIC Science & Technology

2017-09-01

is a frequent cause of end-stage renal disease (ESRD. We investigated the genetic basis of FSGS and recruited a heterogeneous population of...understanding the complex genetic mechanisms of FSGS. 15. SUBJECT TERMS FSGS, MCD, GWAS, CNV  16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT uu...disease (MCD). Using a variety of statistical and genetic approaches, including genome wide association analysis and rare copy number variations (CNVs

An assessment of training needs for the lumber manufacturing industry in the eastern United States

Treesearch

Joseph Denig; Scott Page; Yuhua Su; Karen Martinson

2008-01-01

A training needs assessment of the primary forest products industry was conducted for 33 eastern states. his publication presents in detail the statistical analysis of the study. Of the 2,570 lumber manufacturing companies, consisting of firms with more than six employees for the U.S. Department of Labor Standard Industrial Classification Code 2421, the response rate...

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

PubMed

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

Comparing Facial 3D Analysis With DNA Testing to Determine Zygosities of Twins.

PubMed

Vuollo, Ville; Sidlauskas, Mantas; Sidlauskas, Antanas; Harila, Virpi; Salomskiene, Loreta; Zhurov, Alexei; Holmström, Lasse; Pirttiniemi, Pertti; Heikkinen, Tuomo

2015-06-01

The aim of this study was to compare facial 3D analysis to DNA testing in twin zygosity determinations. Facial 3D images of 106 pairs of young adult Lithuanian twins were taken with a stereophotogrammetric device (3dMD, Atlanta, Georgia) and zygosity was determined according to similarity of facial form. Statistical pattern recognition methodology was used for classification. The results showed that in 75% to 90% of the cases, zygosity determinations were similar to DNA-based results. There were 81 different classification scenarios, including 3 groups, 3 features, 3 different scaling methods, and 3 threshold levels. It appeared that coincidence with 0.5 mm tolerance is the most suitable feature for classification. Also, leaving out scaling improves results in most cases. Scaling was expected to equalize the magnitude of differences and therefore lead to better recognition performance. Still, better classification features and a more effective scaling method or classification in different facial areas could further improve the results. In most of the cases, male pair zygosity recognition was at a higher level compared with females. Erroneously classified twin pairs appear to be obvious outliers in the sample. In particular, faces of young dizygotic (DZ) twins may be so similar that it is very hard to define a feature that would help classify the pair as DZ. Correspondingly, monozygotic (MZ) twins may have faces with quite different shapes. Such anomalous twin pairs are interesting exceptions, but they form a considerable portion in both zygosity groups.

A multiple-point spatially weighted k-NN method for object-based classification

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

2016-10-01

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Overweight and Obesity Prevalence Among School-Aged Nunavik Inuit Children According to Three Body Mass Index Classification Systems.

PubMed

Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel

2015-07-01

Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Should Age at Diagnosis Be Included as an Additional Variable in the Risk of Recurrence Classification System in Patients with Differentiated Thyroid Cancer

PubMed Central

Pitoia, Fabián; Jerkovich, Fernando; Smulever, Anabella; Brenta, Gabriela; Bueno, Fernanda; Cross, Graciela

2017-01-01

Objective To evaluate the influence of age at diagnosis on the frequency of structural incomplete response (SIR) according to the modified risk of recurrence (RR) staging system from the American Thyroid Association guidelines. Patients and Methods We performed a retrospective analysis of 268 patients with differentiated thyroid cancer (DTC) followed up for at least 3 years after initial treatment (total thyroidectomy and remnant ablation). The median follow-up in the whole cohort was 74.3 months (range: 36.1-317.9) and the median age at diagnosis was 45.9 years (range: 18-87). The association between age at diagnosis and the initial and final response to treatment was assessed with analysis of variance (ANOVA). Patients were also divided into several groups considering age younger and older than 40, 50, and 60 years. Results Age at diagnosis was not associated with either an initial or final statistically significant different SIR to treatment (p = 0.14 and p = 0.58, respectively). Additionally, we did not find any statistically significant differences when the percentages of SIR considering the classification of RR were compared between different groups of patients by using several age cutoffs. Conclusions When patients are correctly risk stratified, it seems that age at diagnosis is not involved in the frequency of having a SIR at the initial evaluation or at the final follow-up, so it should not be included as an additional variable to be considered in the RR classifications. PMID:28785543

Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

PubMed

Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

2012-12-05

Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (P<0.05). Correlation analysis between feature importance and the statistical significance of metrics was investigated, and the results revealed a strong positive correlation between them. Overall, the current study demonstrated that major depressive disorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

Classification of Forefoot Plantar Pressure Distribution in Persons with Diabetes: A Novel Perspective for the Mechanical Management of Diabetic Foot?

PubMed Central

Deschamps, Kevin; Matricali, Giovanni Arnoldo; Roosen, Philip; Desloovere, Kaat; Bruyninckx, Herman; Spaepen, Pieter; Nobels, Frank; Tits, Jos; Flour, Mieke; Staes, Filip

2013-01-01

Background The aim of this study was to identify groups of subjects with similar patterns of forefoot loading and verify if specific groups of patients with diabetes could be isolated from non-diabetics. Methodology/Principal Findings Ninety-seven patients with diabetes and 33 control participants between 45 and 70 years were prospectively recruited in two Belgian Diabetic Foot Clinics. Barefoot plantar pressure measurements were recorded and subsequently analysed using a semi-automatic total mapping technique. Kmeans cluster analysis was applied on relative regional impulses of six forefoot segments in order to pursue a classification for the control group separately, the diabetic group separately and both groups together. Cluster analysis led to identification of three distinct groups when considering only the control group. For the diabetic group, and the computation considering both groups together, four distinct groups were isolated. Compared to the cluster analysis of the control group an additional forefoot loading pattern was identified. This group comprised diabetic feet only. The relevance of the reported clusters was supported by ANOVA statistics indicating significant differences between different regions of interest and different clusters. Conclusion/s Significance There seems to emerge a new era in diabetic foot medicine which embraces the classification of diabetic patients according to their biomechanical profile. Classification of the plantar pressure distribution has the potential to provide a means to determine mechanical interventions for the prevention and/or treatment of the diabetic foot. PMID:24278219

Computerized Classification Testing with the Rasch Model

ERIC Educational Resources Information Center

Eggen, Theo J. H. M.

2011-01-01

If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

Review and classification of variability analysis techniques with clinical applications.

PubMed

Bravi, Andrea; Longtin, André; Seely, Andrew J E

2011-10-10

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.

Review and classification of variability analysis techniques with clinical applications

PubMed Central

2011-01-01

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis. PMID:21985357

Applications of remote sensing, volume 1

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1977-01-01

The author has identified the following significant results. ECHO successfully exploits the redundancy of states characteristics of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The information required to produce ECHO classifications are cell size, cell homogeneity, cell-to-field annexation parameters, input data, and a class conditional marginal density statistics deck.

Linear and Order Statistics Combiners for Pattern Classification

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep; Lau, Sonie (Technical Monitor)

2001-01-01

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

Sex determination by three-dimensional geometric morphometrics of craniofacial form.

PubMed

Chovalopoulou, Maria-Eleni; Valakos, Efstratios D; Manolis, Sotiris K

The purpose of the present study is to define which regions of the cranium, the upper-face, the orbits and the nasal are the most sexually dimorphic, by using three-dimensional geometric morphometric methods, and investigate the effectiveness of this method in determining sex from the shape of these regions. The study sample consisted of 176 crania of known sex (94 males, 82 females) belonging to individuals who lived in Greece during the 20(th) century. The three-dimensional co-ordinates of 31 ecto-cranial landmarks were digitized using a MicroScribe 3DX contact digitizer. Goodall's F-test was performed in order to compare statistical differences in shape between males and females. Generalized Procrustes Analysis (GPA) was used to obtain size and shape variables for statistical analysis. Shape, Size and Form analyses were carried out by logistic regression and discriminant function analysis. The results indicate that there are shape differences between the sexes in the upper-face and the orbits. The highest shape classification rate was obtained from the upper-face region. The centroid size of the caraniofacial and the orbital regions was smaller in females than males. Moreover, it was found that size is significant for sexual dimorphism in the upper-face region. As anticipated, the classification accuracy improves when both size and shape are combined. The findings presented here constitute a firm basis upon which further research can be conducted.

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

NASA Technical Reports Server (NTRS)

Kim, H.; Swain, P. H.

1991-01-01

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Data preprocessing methods of FT-NIR spectral data for the classification cooking oil

NASA Astrophysics Data System (ADS)

Ruah, Mas Ezatul Nadia Mohd; Rasaruddin, Nor Fazila; Fong, Sim Siong; Jaafar, Mohd Zuli

2014-12-01

This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm-1-14000cm-1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.

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.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

PubMed

Wang, Gang; Wang, Yalin

2017-02-15

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Classification of the medicinal plants of the genus Atractylodes using high-performance liquid chromatography with diode array and tandem mass spectrometry detection combined with multivariate statistical analysis.

PubMed

Cho, Hyun-Deok; Kim, Unyong; Suh, Joon Hyuk; Eom, Han Young; Kim, Junghyun; Lee, Seul Gi; Choi, Yong Seok; Han, Sang Beom

2016-04-01

Analytical methods using high-performance liquid chromatography with diode array and tandem mass spectrometry detection were developed for the discrimination of the rhizomes of four Atractylodes medicinal plants: A. japonica, A. macrocephala, A. chinensis, and A. lancea. A quantitative study was performed, selecting five bioactive components, including atractylenolide I, II, III, eudesma-4(14),7(11)-dien-8-one and atractylodin, on twenty-six Atractylodes samples of various origins. Sample extraction was optimized to sonication with 80% methanol for 40 min at room temperature. High-performance liquid chromatography with diode array detection was established using a C18 column with a water/acetonitrile gradient system at a flow rate of 1.0 mL/min, and the detection wavelength was set at 236 nm. Liquid chromatography with tandem mass spectrometry was applied to certify the reliability of the quantitative results. The developed methods were validated by ensuring specificity, linearity, limit of quantification, accuracy, precision, recovery, robustness, and stability. Results showed that cangzhu contained higher amounts of atractylenolide I and atractylodin than baizhu, and especially atractylodin contents showed the greatest variation between baizhu and cangzhu. Multivariate statistical analysis, such as principal component analysis and hierarchical cluster analysis, were also employed for further classification of the Atractylodes plants. The established method was suitable for quality control of the Atractylodes plants. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A comprehensive simulation study on classification of RNA-Seq data.

PubMed

Zararsız, Gökmen; Goksuluk, Dincer; Korkmaz, Selcuk; Eldem, Vahap; Zararsiz, Gozde Erturk; Duru, Izzet Parug; Ozturk, Ahmet

2017-01-01

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

New FIGO and Swedish intrapartum cardiotocography classification systems incorporated in the fetal ECG ST analysis (STAN) interpretation algorithm: agreements and discrepancies in cardiotocography classification and evaluation of significant ST events.

PubMed

Olofsson, Per; Norén, Håkan; Carlsson, Ann

2018-02-01

The updated intrapartum cardiotocography (CTG) classification system by FIGO in 2015 (FIGO2015) and the FIGO2015-approached classification by the Swedish Society of Obstetricians and Gynecologist in 2017 (SSOG2017) are not harmonized with the fetal ECG ST analysis (STAN) algorithm from 2007 (STAN2007). The study aimed to reveal homogeneity and agreement between the systems in classifying CTG and ST events, and relate them to maternal and perinatal outcomes. Among CTG traces with ST events, 100 traces originally classified as normal, 100 as suspicious and 100 as pathological were randomly selected from a STAN database and classified by two experts in consensus. Homogeneity and agreement statistics between the CTG classifications were performed. Maternal and perinatal outcomes were evaluated in cases with clinically hidden ST data (n = 151). A two-tailed p < 0.05 was regarded as significant. For CTG classes, the heterogeneity was significant between the old and new systems, and agreements were moderate to strong (proportion of agreement, kappa index 0.70-0.86). Between the new classifications, heterogeneity was significant and agreements strong (0.90, 0.92). For significant ST events, heterogeneities were significant and agreements moderate to almost perfect (STAN2007 vs. FIGO2015 0.86, 0.72; STAN2007 vs. SSOG2017 0.92, 0.84; FIGO2015 vs. SSOG2017 0.94, 0.87). Significant ST events occurred more often combined with STAN2007 than with FIGO2015 classification, but not with SSOG2017; correct identification of adverse outcomes was not significantly different between the systems. There are discrepancies in the classification of CTG patterns and significant ST events between the old and new systems. The clinical relevance of the findings remains to be shown. © 2017 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

A comparative analysis of swarm intelligence techniques for feature selection in cancer classification.

PubMed

Gunavathi, Chellamuthu; Premalatha, Kandasamy

2014-01-01

Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.

Speaker gender identification based on majority vote classifiers

NASA Astrophysics Data System (ADS)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2017-03-01

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

PubMed

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S S

2017-06-08

Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

[ATD index in Perthes disease].

PubMed

Grzegorzewski, Andrzej; Synder, Marek; Szymczak, Wiesław; Kowalewski, Maciej; Kozłowski, Piotr

2003-01-01

Authors present an estimation of articulo-trochanteric-distance (ATD) and ATD index in patients with Perthes disease and if there is any correlation between ATD and ATD index and age at the onset, gender, type of treatment, Herring and Stulberg classification. The study population consisted of 242 patients (35 female and 207 male) who had reached skeletal maturity at last follow up. The mean age at the onset of symptoms was 7 years and 4 months. All patients were treated by containment methods (bed rest and traction in abduction, brace, Petri cast, varus osteotomy, Salter osteotomy and shelf operation). ATD was estimated according to the Edgren methods and ATD index was calculated as relation ATD on Perthes site to ATD in normal joint. The late results were classified according to the Stulberg classification. Statistical analysis did not revealed any correlation between the age at the onset, gender and ATD index and ATD during last follow up. Both parameters decreased with poor results according to the Stulberg classifications. ATD index and ATD were statistically significant less after surgical treatment than after non-operative treatment. The same relations were seen between patients with leg length discrepancy (LLD) and without LLD. Patients in Herring group A had statistically significant bigger both parameters than patients in group B, C and patients in Herring group B than C. Articulo-trochanteric-distance and ATD index decreased during follow up and ATD decreased also in normal joint. In our opinion ATD index is a more reliable radiological parameter than ATD. ATD index decreases with bigger necrosis of the femoral head and poor result according to the Stulberg classification. This parameter is an evidence of the dysfunction proximal femoral growth plate in patients with LLD. The most decreased ATD index was observed after surgical treatment. There was no correlation between the age at the onset, gender and ATD index at last follow up.

Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

PubMed

Abbasian Ardakani, Ali; Gharbali, Akbar; Mohammadi, Afshin

2015-01-01

The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.

Innovative use of self-organising maps (SOMs) in model validation.

NASA Astrophysics Data System (ADS)

Jolly, Ben; McDonald, Adrian; Coggins, Jack

2016-04-01

We present an innovative combination of techniques for validation of numerical weather prediction (NWP) output against both observations and reanalyses using two classification schemes, demonstrated by a validation of the operational NWP 'AMPS' (the Antarctic Mesoscale Prediction System). Historically, model validation techniques have centred on case studies or statistics at various time scales (yearly/seasonal/monthly). Within the past decade the latter technique has been expanded by the addition of classification schemes in place of time scales, allowing more precise analysis. Classifications are typically generated for either the model or the observations, then used to create composites for both which are compared. Our method creates and trains a single self-organising map (SOM) on both the model output and observations, which is then used to classify both datasets using the same class definitions. In addition to the standard statistics on class composites, we compare the classifications themselves between the model and the observations. To add further context to the area studied, we use the same techniques to compare the SOM classifications with regimes developed for another study to great effect. The AMPS validation study compares model output against surface observations from SNOWWEB and existing University of Wisconsin-Madison Antarctic Automatic Weather Stations (AWS) during two months over the austral summer of 2014-15. Twelve SOM classes were defined in a '4 x 3' pattern, trained on both model output and observations of 2 m wind components, then used to classify both training datasets. Simple statistics (correlation, bias and normalised root-mean-square-difference) computed for SOM class composites showed that AMPS performed well during extreme weather events, but less well during lighter winds and poorly during the more changeable conditions between either extreme. Comparison of the classification time-series showed that, while correlations were lower during lighter wind periods, AMPS actually forecast the existence of those periods well suggesting that the correlations may be unfairly low. Further investigation showed poor temporal alignment during more changeable conditions, highlighting problems AMPS has around the exact timing of events. There was also a tendency for AMPS to over-predict certain wind flow patterns at the expense of others. In order to gain a larger scale perspective, we compared our mesoscale SOM classification time-series with synoptic scale regimes developed by another study using ERA-Interim reanalysis output and k-means clustering. There was good alignment between the regimes and the observations classifications (observations/regimes), highlighting the effect of synoptic scale forcing on the area. However, comparing the alignment between observations/regimes and AMPS/regimes showed that AMPS may have problems accurately resolving the strength and location of cyclones in the Ross Sea to the north of the target area.

Identification of Enterococcus, Streptococcus, and Staphylococcus by Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

PubMed Central

Bourne, Roger; Himmelreich, Uwe; Sharma, Ansuiya; Mountford, Carolyn; Sorrell, Tania

2001-01-01

A new fingerprinting technique with the potential for rapid identification of bacteria was developed by combining proton magnetic resonance spectroscopy (1H MRS) with multivariate statistical analysis. This resulted in an objective identification strategy for common clinical isolates belonging to the bacterial species Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus agalactiae, and the Streptococcus milleri group. Duplicate cultures of 104 different isolates were examined one or more times using 1H MRS. A total of 312 cultures were examined. An optimized classifier was developed using a bootstrapping process and a seven-group linear discriminant analysis to provide objective classification of the spectra. Identification of isolates was based on consistent high-probability classification of spectra from duplicate cultures and achieved 92% agreement with conventional methods of identification. Fewer than 1% of isolates were identified incorrectly. Identification of the remaining 7% of isolates was defined as indeterminate. PMID:11474013

The acoustic repertoire and behavioural context of the vocalisations of a nocturnal dasyurid, the eastern quoll (Dasyurus viverrinus)

PubMed Central

Dorph, Annalie

2017-01-01

Defining an acoustic repertoire is essential to understanding vocal signalling and communicative interactions within a species. Currently, quantitative and statistical definition is lacking for the vocalisations of many dasyurids, an important group of small to medium-sized marsupials from Australasia that includes the eastern quoll (Dasyurus viverrinus), a species of conservation concern. Beyond generating a better understanding of this species' social interactions, determining an acoustic repertoire will further improve detection rates and inference of vocalisations gathered by automated bioacoustic recorders. Hence, this study investigated eastern quoll vocalisations using objective signal processing techniques to quantitatively analyse spectrograms recorded from 15 different individuals. Recordings were collected in conjunction with observations of the behaviours associated with each vocalisation to develop an acoustic-based behavioural repertoire for the species. Analysis of recordings produced a putative classification of five vocalisation types: Bark, Growl, Hiss, Cp-cp, and Chuck. These were most frequently observed during agonistic encounters between conspecifics, most likely as a graded sequence from Hisses occurring in a warning context through to Growls and finally Barks being given prior to, or during, physical confrontations between individuals. Quantitative and statistical methods were used to objectively establish the accuracy of these five putative call types. A multinomial logistic regression indicated a 97.27% correlation with the perceptual classification, demonstrating support for the five different vocalisation types. This putative classification was further supported by hierarchical cluster analysis and silhouette information that determined the optimal number of clusters to be five. Minor disparity between the objective and perceptual classifications was potentially the result of gradation between vocalisations, or subtle differences present within vocalisations not discernible to the human ear. The implication of these different vocalisations and their given context is discussed in relation to the ecology of the species and the potential application of passive acoustic monitoring techniques. PMID:28686679

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

PubMed

Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

2015-06-01

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

PubMed

Sen Sarma, Moushumi; Arcoleo, David; Khetani, Radhika S; Chee, Brant; Ling, Xu; He, Xin; Jiang, Jing; Mei, Qiaozhu; Zhai, ChengXiang; Schatz, Bruce

2011-07-01

With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments can be analyzed to extract concepts that are computational equivalents of a classification such as Gene Ontology, yielding discriminating concepts that differentiate gene mentions from other mentions. The functions of individual genes can be summarized from sentences in biological literature, to produce results resembling a model organism database entry that is automatically computed. Statistical frequency analysis based on literature phrase extraction generates offline semantic indexes to support these gene function services. The website with BeeSpace Navigator is free and open to all; there is no login requirement at www.beespace.illinois.edu for version 4. Materials from the 2010 BeeSpace Software Training Workshop are available at www.beespace.illinois.edu/bstwmaterials.php.

Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Clark, Kal; Czarnek, Nicholas M.; Shamsesfandabadi, Parisa; Peters, Katherine B.; Saha, Ashirbani

2017-03-01

Recent studies showed that genomic analysis of lower grade gliomas can be very effective for stratification of patients into groups with different prognosis and proposed specific genomic classifications. In this study, we explore the association of one of those genomic classifications with imaging parameters to determine whether imaging could serve a similar role to genomics in cancer patient treatment. Specifically, we analyzed imaging and genomics data for 110 patients from 5 institutions from The Cancer Genome Atlas and The Cancer Imaging Archive datasets. The analyzed imaging data contained preoperative FLAIR sequence for each patient. The images were analyzed using the in-house algorithms which quantify 2D and 3D aspects of the tumor shape. Genomic data consisted of a cluster of clusters classification proposed in a very recent and leading publication in the field of lower grade glioma genomics. Our statistical analysis showed that there is a strong association between the tumor cluster-of-clusters subtype and two imaging features: bounding ellipsoid volume ratio and angular standard deviation. This result shows high promise for the potential use of imaging as a surrogate measure for genomics in the decision process regarding treatment of lower grade glioma patients.

Analysis and classification of optical tomographic images of rheumatoid fingers with ANOVA and discriminate analysis

NASA Astrophysics Data System (ADS)

Montejo, Ludguier D.; Kim, Hyun K.; Häme, Yrjö; Jia, Jingfei; Montejo, Julio D.; Netz, Uwe J.; Blaschke, Sabine; Zwaka, Paul; Müeller, Gerhard A.; Beuthan, Jürgen; Hielscher, Andreas H.

2011-03-01

We present a study on the effectiveness of computer-aided diagnosis (CAD) of rheumatoid arthritis (RA) from frequency-domain diffuse optical tomographic (FDOT) images. FDOT is used to obtain the distribution of tissue optical properties. Subsequently, the non-parametric Kruskal-Wallis ANOVA test is employed to verify statistically significant differences between the optical parameters of patients affected by RA and healthy volunteers. Furthermore, quadratic discriminate analysis (QDA) of the absorption (μa) and scattering (μa or μ's) distributions is used to classify subjects as affected or not affected by RA. We evaluate the classification efficiency by determining the sensitivity (Se), specificity (Sp), and the Youden index (Y). We find that combining features extracted from μa and μa or μ's images allows for more accurate classification than when μa or μa or μ's features are considered individually on their own. Combining μa and μa or μ's features yields values of up to Y = 0.75 (Se = 0.84 and Sp = 0.91). The best results when μa or μ's features are considered individually are Y = 0.65 (Se = 0.85 and Sp = 0.80) and Y = 0.70 (Se = 0.80 and Sp = 0.90), respectively.

Determining the saliency of feature measurements obtained from images of sedimentary organic matter for use in its classification

NASA Astrophysics Data System (ADS)

Weller, Andrew F.; Harris, Anthony J.; Ware, J. Andrew; Jarvis, Paul S.

2006-11-01

The classification of sedimentary organic matter (OM) images can be improved by determining the saliency of image analysis (IA) features measured from them. Knowing the saliency of IA feature measurements means that only the most significant discriminating features need be used in the classification process. This is an important consideration for classification techniques such as artificial neural networks (ANNs), where too many features can lead to the 'curse of dimensionality'. The classification scheme adopted in this work is a hybrid of morphologically and texturally descriptive features from previous manual classification schemes. Some of these descriptive features are assigned to IA features, along with several others built into the IA software (Halcon) to ensure that a valid cross-section is available. After an image is captured and segmented, a total of 194 features are measured for each particle. To reduce this number to a more manageable magnitude, the SPSS AnswerTree Exhaustive CHAID (χ 2 automatic interaction detector) classification tree algorithm is used to establish each measurement's saliency as a classification discriminator. In the case of continuous data as used here, the F-test is used as opposed to the published algorithm. The F-test checks various statistical hypotheses about the variance of groups of IA feature measurements obtained from the particles to be classified. The aim is to reduce the number of features required to perform the classification without reducing its accuracy. In the best-case scenario, 194 inputs are reduced to 8, with a subsequent multi-layer back-propagation ANN recognition rate of 98.65%. This paper demonstrates the ability of the algorithm to reduce noise, help overcome the curse of dimensionality, and facilitate an understanding of the saliency of IA features as discriminators for sedimentary OM classification.

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

PubMed Central

Mukherjee, Rashmi; Manohar, Dhiraj Dhane; Das, Dev Kumar; Achar, Arun; Mitra, Analava; Chakraborty, Chandan

2014-01-01

The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793). PMID:25114925

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

NASA Astrophysics Data System (ADS)

Ranaie, Mehrdad; Soffianian, Alireza; Pourmanafi, Saeid; Mirghaffari, Noorollah; Tarkesh, Mostafa

2018-03-01

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

NASA Astrophysics Data System (ADS)

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

2014-05-01

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

The Rural Inpatient Mortality Study: Does Urban-Rural County Classification Predict Hospital Mortality in California?

PubMed

Linnen, Daniel T; Kornak, John; Stephens, Caroline

2018-03-28

Evidence suggests an association between rurality and decreased life expectancy. To determine whether rural hospitals have higher hospital mortality, given that very sick patients may be transferred to regional hospitals. In this ecologic study, we combined Medicare hospital mortality ratings (N = 1267) with US census data, critical access hospital classification, and National Center for Health Statistics urban-rural county classifications. Ratings included mortality for coronary artery bypass grafting, stroke, chronic obstructive pulmonary disease, heart attack, heart failure, and pneumonia across 277 California hospitals between July 2011 and June 2014. We used generalized estimating equations to evaluate the association of urban-rural county classifications on mortality ratings. Unfavorable Medicare hospital mortality rating "worse than the national rate" compared with "better" or "same." Compared with large central "metro" (metropolitan) counties, hospitals in medium-sized metro counties had 6.4 times the odds of rating "worse than the national rate" for hospital mortality (95% confidence interval = 2.8-14.8, p < 0.001). For hospitals in small metro counties, the odds of having such a rating were 3.7 times greater (95% confidence interval = 0.7-23.4, p = 0.12), although not statistically significant. Few ratings were provided for rural counties, and analysis of rural counties was underpowered. Hospitals in medium-sized metro counties are associated with unfavorable Medicare mortality ratings, but current methods to assign mortality ratings may hinder fair comparisons. Patient transfers from rural locations to regional medical centers may contribute to these results, a potential factor that future research should examine.

UNDERSTANDING THE INTERNATIONAL CONSENSUS FOR ACUTE PANCREATITIS: CLASSIFICATION OF ATLANTA 2012

PubMed Central

de SOUZA, Gleim Dias; SOUZA, Luciana Rodrigues Queiroz; CUENCA, Ronaldo Máfia; JERÔNIMO, Bárbara Stephane de Medeiros; de SOUZA, Guilherme Medeiros; VILELA, Vinícius Martins

2016-01-01

ABSTRACT Introduction: Contrast computed tomography and magnetic resonance imaging are widely used due to its image quality and ability to study pancreatic and peripancreatic morphology. The understanding of the various subtypes of the disease and identification of possible complications requires a familiarity with the terminology, which allows effective communication between the different members of the multidisciplinary team. Aim: Demonstrate the terminology and parameters to identify the different classifications and findings of the disease based on the international consensus for acute pancreatitis ( Atlanta Classification 2012). Methods: Search and analysis of articles in the "CAPES Portal de Periódicos with headings "acute pancreatitis" and "Atlanta Review". Results: Were selected 23 articles containing radiological descriptions, management or statistical data related to pathology. Additional statistical data were obtained from Datasus and Population Census 2010. The radiological diagnostic criterion adopted was the Radiology American College system. The "acute pancreatitis - 2012 Rating: Review Atlanta classification and definitions for international consensus" tries to eliminate inconsistency and divergence from the determination of uniformity to the radiological findings, especially the terminology related to fluid collections. More broadly as "pancreatic abscess" and "phlegmon" went into disuse and the evolution of the collection of patient fluids can be described as "acute peripancreatic collections", "acute necrotic collections", "pseudocyst" and "necrosis pancreatic walled or isolated". Conclusion: Computed tomography and magnetic resonance represent the best techniques with sequential images available for diagnosis. Standardization of the terminology is critical and should improve the management of patients with multiple professionals care, risk stratification and adequate treatment. PMID:27759788

Ice Water Classification Using Statistical Distribution Based Conditional Random Fields in RADARSAT-2 Dual Polarization Imagery

NASA Astrophysics Data System (ADS)

Zhang, Y.; Li, F.; Zhang, S.; Hao, W.; Zhu, T.; Yuan, L.; Xiao, F.

2017-09-01

In this paper, Statistical Distribution based Conditional Random Fields (STA-CRF) algorithm is exploited for improving marginal ice-water classification. Pixel level ice concentration is presented as the comparison of methods based on CRF. Furthermore, in order to explore the effective statistical distribution model to be integrated into STA-CRF, five statistical distribution models are investigated. The STA-CRF methods are tested on 2 scenes around Prydz Bay and Adélie Depression, where contain a variety of ice types during melt season. Experimental results indicate that the proposed method can resolve sea ice edge well in Marginal Ice Zone (MIZ) and show a robust distinction of ice and water.

Earth science research

NASA Technical Reports Server (NTRS)

Botkin, Daniel B.

1987-01-01

The analysis of ground-truth data from the boreal forest plots in the Superior National Forest, Minnesota, was completed. Development of statistical methods was completed for dimension analysis (equations to estimate the biomass of trees from measurements of diameter and height). The dimension-analysis equations were applied to the data obtained from ground-truth plots, to estimate the biomass. Classification and analyses of remote sensing images of the Superior National Forest were done as a test of the technique to determine forest biomass and ecological state by remote sensing. Data was archived on diskette and tape and transferred to UCSB to be used in subsequent research.

Intraoperative optical biopsy for brain tumors using spectro-lifetime properties of intrinsic fluorophores

NASA Astrophysics Data System (ADS)

Vasefi, Fartash; Kittle, David S.; Nie, Zhaojun; Falcone, Christina; Patil, Chirag G.; Chu, Ray M.; Mamelak, Adam N.; Black, Keith L.; Butte, Pramod V.

2016-04-01

We have developed and tested a system for real-time intra-operative optical identification and classification of brain tissues using time-resolved fluorescence spectroscopy (TRFS). A supervised learning algorithm using linear discriminant analysis (LDA) employing selected intrinsic fluorescence decay temporal points in 6 spectral bands was employed to maximize statistical significance difference between training groups. The linear discriminant analysis on in vivo human tissues obtained by TRFS measurements (N = 35) were validated by histopathologic analysis and neuronavigation correlation to pre-operative MRI images. These results demonstrate that TRFS can differentiate between normal cortex, white matter and glioma.

Incremental Validity of Multidimensional Proficiency Scores from Diagnostic Classification Models: An Illustration for Elementary School Mathematics

ERIC Educational Resources Information Center

Kunina-Habenicht, Olga; Rupp, André A.; Wilhelm, Oliver

2017-01-01

Diagnostic classification models (DCMs) hold great potential for applications in summative and formative assessment by providing discrete multivariate proficiency scores that yield statistically driven classifications of students. Using data from a newly developed diagnostic arithmetic assessment that was administered to 2032 fourth-grade students…

75 FR 34319 - User Fees for 2010 Crop Cotton Classification Services to Growers

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-17

...-AC99 User Fees for 2010 Crop Cotton Classification Services to Growers AGENCY: Agricultural Marketing... fees for cotton producers for 2010 crop cotton classification services under the Cotton Statistics and Estimates Act at the same level as in 2009. These fees are also authorized under the Cotton Standards Act of...

77 FR 33289 - User Fees for 2012 Crop Cotton Classification Services to Growers

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-06

... User Fees for 2012 Crop Cotton Classification Services to Growers AGENCY: Agricultural Marketing... fees for cotton producers for 2012 crop cotton classification services under the Cotton Statistics and Estimates Act and the Cotton Standards Act of 1923 at $2.20 per bale--the same level as in 2011. This fee...

76 FR 25533 - User Fees for 2011 Crop Cotton Classification Services to Growers

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-05

...-AD11 User Fees for 2011 Crop Cotton Classification Services to Growers AGENCY: Agricultural Marketing... fees for cotton producers for 2011 crop cotton classification services under the Cotton Statistics and Estimates Act at the same level as in 2010. These fees are also authorized under the Cotton Standards Act of...

Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF).

PubMed

Gissi, Andrea; Lombardo, Anna; Roncaglioni, Alessandra; Gadaleta, Domenico; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio; Benfenati, Emilio

2015-02-01

The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R(2)) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100L/kg for Chemical Safety Assessment; 500L/kg for Classification and Labeling; 2000 and 5000L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R(2)>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot-Gobas models. As classifiers, ACD and logP-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R(2)ext=0.59), followed by the EPISuite Meylan model (R(2)ext=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R(2)=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R(2)=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach. Copyright © 2014 Elsevier Inc. All rights reserved.

A Categorization of Dynamic Analyzers

NASA Technical Reports Server (NTRS)

Lujan, Michelle R.

1997-01-01

Program analysis techniques and tools are essential to the development process because of the support they provide in detecting errors and deficiencies at different phases of development. The types of information rendered through analysis includes the following: statistical measurements of code, type checks, dataflow analysis, consistency checks, test data,verification of code, and debugging information. Analyzers can be broken into two major categories: dynamic and static. Static analyzers examine programs with respect to syntax errors and structural properties., This includes gathering statistical information on program content, such as the number of lines of executable code, source lines. and cyclomatic complexity. In addition, static analyzers provide the ability to check for the consistency of programs with respect to variables. Dynamic analyzers in contrast are dependent on input and the execution of a program providing the ability to find errors that cannot be detected through the use of static analysis alone. Dynamic analysis provides information on the behavior of a program rather than on the syntax. Both types of analysis detect errors in a program, but dynamic analyzers accomplish this through run-time behavior. This paper focuses on the following broad classification of dynamic analyzers: 1) Metrics; 2) Models; and 3) Monitors. Metrics are those analyzers that provide measurement. The next category, models, captures those analyzers that present the state of the program to the user at specified points in time. The last category, monitors, checks specified code based on some criteria. The paper discusses each classification and the techniques that are included under them. In addition, the role of each technique in the software life cycle is discussed. Familiarization with the tools that measure, model and monitor programs provides a framework for understanding the program's dynamic behavior from different, perspectives through analysis of the input/output data.

A Classification and Analysis of National Contract Management Journal Articles from 1966 Through 1989

DTIC Science & Technology

1991-06-01

THEORETICAL, NORMATIVE, EMPIRICAL, INDUCTIVE [24] "New Approaches for Quantifying Risk and Determining Sharing Arrangements," Raymond S. Lieber, pp...the negotiation process can be enhanced by quantifying risk using statistical methods. The article discusses two approaches which allow the...in Incentive Contracting," Melvin W. Lifson, pp. 59-80. The purpose to this article is to suggest a general approach for defining and quantifying

An Analysis of Healthcare Provider Productivity in Selected Clinical Services at Moncrief Army Community Hospital

DTIC Science & Technology

1990-08-13

MARKINGS ./ A n’ ., ___ ___,_ _"_ __ ___,_ __’,,___, 2a. SECURITY CLASSIFICATION AUTHORITY"" DISTRIBUTION/AVAILABILITfe OF REPORT . A . UNCLASSIFIED...of thirteen clinical services in a medium sized Army Community Hospital to provider based productivity standards found in the Joint Healthcare...statistically significant margins. The study concludes that the low productivity is due to a lack of guidance and emphasis in the productivity arena

Comparative analysis of pharmaceuticals versus industrial chemicals acute aquatic toxicity classification according to the United Nations classification system for chemicals. Assessment of the (Q)SAR predictability of pharmaceuticals acute aquatic toxicity and their predominant acute toxic mode-of-action.

PubMed

Sanderson, Hans; Thomsen, Marianne

2009-06-01

Pharmaceuticals have been reported to be ubiquitously present in surface waters prompting concerns of effects of these bioactive substances. Meanwhile, there is a general scarcity of publicly available ecotoxicological data concerning pharmaceuticals. The aim of this paper was to compile a comprehensive database based on OECD's standardized measured ecotoxicological data and to evaluate if there is generally cause of greater concern with regards to pharmaceutical aquatic toxicological profiles relative to industrial chemicals. Comparisons were based upon aquatic ecotoxicity classification under the United Nations Global Harmonized System for classification and labeling of chemicals (GHS). Moreover, we statistically explored whether the predominant mode-of-action (MOA) for pharmaceuticals is narcosis. We found 275 pharmaceuticals with 569 acute aquatic effect data; 23 pharmaceuticals had chronic data. Pharmaceuticals were found to be more frequent than industrial chemicals in GHS category III. Acute toxicity was predictable (>92%) using a generic (Q)SAR ((Quantitative) Structure Activity Relationship) suggesting a narcotic MOA. Analysis of model prediction error suggests that 68% of the pharmaceuticals have a non-specific MOA. Additionally, the acute-to-chronic ratio (ACR) for 70% of the analyzed pharmaceuticals was below 25 further suggesting a non-specific MOA. Sub-lethal receptor-mediated effects may however have a more specific MOA.

Interobserver agreement in analysis of cardiotocograms recorded during trial of labor after cesarean.

PubMed

Caning, M M; Thisted, D L A; Amer-Wählin, I; Laier, G H; Krebs, L

2018-05-17

To examine interobserver agreement in intrapartum cardiotocography (CTG) classification in women undergoing trial of labor after a cesarean section (TOLAC) at term with or without complete uterine rupture. Nineteen blinded and independent Danish obstetricians assessed CTG tracings from 47 women (174 individual pages) with a complete uterine rupture during TOLAC and 37 women (133 individual pages) with no uterine rupture during TOLAC. Individual pages with CTG tracings lasting at least 20 min were evaluated by three different assessors and counted as an individual case. The tracings were analyzed according to the modified version of the Federation of Gynaecology and Obstetrics (FIGO) guidelines elaborated for the use of STAN (ST-analysis). Occurrence of defined abnormalities was recorded and the tracings were classified as normal, suspicious, pathological, or preterminal. The interobserver agreement was evaluated using Fleiss' kappa. Agreement on classification of a preterminal CTG was almost perfect. The interobserver agreement on normal, suspicious or pathological CTG was moderate to substantial. Regarding the presence of severe variable decelerations, the agreement was moderate. No statistical difference was found in the interobserver agreement between classification of tracings from women undergoing TOLAC with and without complete uterine rupture. The interobserver agreement on classification of CTG tracings from high-risk deliveries during TOLAC is best for assessment of a preterminal CTG and the poorest for the identification of severe variable decelerations.

A cautionary note on the use of the Analysis of Covariance (ANCOVA) in classification designs with and without within-subject factors

PubMed Central

Schneider, Bruce A.; Avivi-Reich, Meital; Mozuraitis, Mindaugas

2015-01-01

A number of statistical textbooks recommend using an analysis of covariance (ANCOVA) to control for the effects of extraneous factors that might influence the dependent measure of interest. However, it is not generally recognized that serious problems of interpretation can arise when the design contains comparisons of participants sampled from different populations (classification designs). Designs that include a comparison of younger and older adults, or a comparison of musicians and non-musicians are examples of classification designs. In such cases, estimates of differences among groups can be contaminated by differences in the covariate population means across groups. A second problem of interpretation will arise if the experimenter fails to center the covariate measures (subtracting the mean covariate score from each covariate score) whenever the design contains within-subject factors. Unless the covariate measures on the participants are centered, estimates of within-subject factors are distorted, and significant increases in Type I error rates, and/or losses in power can occur when evaluating the effects of within-subject factors. This paper: (1) alerts potential users of ANCOVA of the need to center the covariate measures when the design contains within-subject factors, and (2) indicates how they can avoid biases when one cannot assume that the expected value of the covariate measure is the same for all of the groups in a classification design. PMID:25954230

Cesarean Section Rate Analysis in University Hospital Tuzla - According to Robson's Classification.

PubMed

Fatusic, Jasenko; Hudic, Igor; Fatusic, Zlatan; Zildzic-Moralic, Aida; Zivkovic, Milorad

2016-06-01

For last decades, there has public concern about increasing Cesarean Section (CS) rates, and it is an issue of international public health concern. According to World Health Organisation (WHO) there is no justification to have more than 10-15% CS births. WHO proposes the Robson ten-group classification, as a global standard for assessing, monitoring and comparing cesarean section rates. The aim of this study was to investigate Cesarean section rate at University Hospital Tuzla, Bosnia and Herzegovina. Cross sectional study was conducted for one-year period, 2015. Statistical analysis and graph-table presentation was performed using Excel 2010 and Microsoft Office programs. Out of 3,672 births, a total of 936 births were performed by CS. Percentage of the total number of CS to the total birth number was 25,47%. According to Robson classification, the largest was group 5 with relative contribution of 29,80%. On second and third place were group 1 and 2 with relative contribution of 26,06% and 15,78% respectively. Groups 1, 2, 5 made account of realtive contribution of 71,65%. All other groups had entirely relative contribution of 28,35%. Robson 10-group classification provides easy way in collecting information about CS rate. It is important that efforts to reduce the overall CS rate should focus on reducing the primary CS. Data from our study confirm this attitude.

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.

Analysis of miRNA expression profile based on SVM algorithm

NASA Astrophysics Data System (ADS)

Ting-ting, Dai; Chang-ji, Shan; Yan-shou, Dong; Yi-duo, Bian

2018-05-01

Based on mirna expression spectrum data set, a new data mining algorithm - tSVM - KNN (t statistic with support vector machine - k nearest neighbor) is proposed. the idea of the algorithm is: firstly, the feature selection of the data set is carried out by the unified measurement method; Secondly, SVM - KNN algorithm, which combines support vector machine (SVM) and k - nearest neighbor (k - nearest neighbor) is used as classifier. Simulation results show that SVM - KNN algorithm has better classification ability than SVM and KNN alone. Tsvm - KNN algorithm only needs 5 mirnas to obtain 96.08 % classification accuracy in terms of the number of mirna " tags" and recognition accuracy. compared with similar algorithms, tsvm - KNN algorithm has obvious advantages.

High-Reproducibility and High-Accuracy Method for Automated Topic Classification

NASA Astrophysics Data System (ADS)

Lancichinetti, Andrea; Sirer, M. Irmak; Wang, Jane X.; Acuna, Daniel; Körding, Konrad; Amaral, Luís A. Nunes

2015-01-01

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

Crack classification in concrete beams using AE parameters

NASA Astrophysics Data System (ADS)

Bahari, N. A. A. S.; Shahidan, S.; Abdullah, S. R.; Ali, N.; Zuki, S. S. Mohd; Ibrahim, M. H. W.; Rahim, M. A.

2017-11-01

The acoustic emission (AE) technique is an effective tool for the evaluation of crack growth. The aim of this study is to evaluate crack classification in reinforced concrete beams using statistical analysis. AE has been applied for the early monitoring of reinforced concrete structures using AE parameters such as average frequency, rise time, amplitude counts and duration. This experimental study focuses on the utilisation of this method in evaluating reinforced concrete beams. Beam specimens measuring 150 mm × 250 mm × 1200 mm were tested using a three-point load flexural test using Universal Testing Machines (UTM) together with an AE monitoring system. The results indicated that RA value can be used to determine the relationship between tensile crack and shear movement in reinforced concrete beams.

Statistical text classifier to detect specific type of medical incidents.

PubMed

Wong, Zoie Shui-Yee; Akiyama, Masanori

2013-01-01

WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.

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).

Chicago Classification Criteria of Esophageal Motility Disorders Defined in High Resolution Esophageal Pressure Topography (EPT)†

PubMed Central

Bredenoord, Albert J; Fox, Mark; Kahrilas, Peter J; Pandolfino, John E; Schwizer, Werner; Smout, AJPM; Conklin, Jeffrey L; Cook, Ian J; Gyawali, Prakash; Hebbard, Geoffrey; Holloway, Richard H; Ke, Meiyun; Keller, Jutta; Mittal, Ravinder K; Peters, Jeff; Richter, Joel; Roman, Sabine; Rommel, Nathalie; Sifrim, Daniel; Tutuian, Radu; Valdovinos, Miguel; Vela, Marcelo F; Zerbib, Frank

2011-01-01

Background The Chicago Classification of esophageal motility was developed to facilitate the interpretation of clinical high resolution esophageal pressure topography (EPT) studies, concurrent with the widespread adoption of this technology into clinical practice. The Chicago Classification has been, and will continue to be, an evolutionary process, molded first by published evidence pertinent to the clinical interpretation of high resolution manometry (HRM) studies and secondarily by group experience when suitable evidence is lacking. Methods This publication summarizes the state of our knowledge as of the most recent meeting of the International High Resolution Manometry Working Group in Ascona, Switzerland in April 2011. The prior iteration of the Chicago Classification was updated through a process of literature analysis and discussion. Key Results The major changes in this document from the prior iteration are largely attributable to research studies published since the prior iteration, in many cases research conducted in response to prior deliberations of the International High Resolution Manometry Working Group. The classification now includes criteria for subtyping achalasia, EGJ outflow obstruction, motility disorders not observed in normal subjects (Distal esophageal spasm, Hypercontractile esophagus, and Absent peristalsis), and statistically defined peristaltic abnormalities (Weak peristalsis, Frequent failed peristalsis, Rapid contractions with normal latency, and Hypertensive peristalsis). Conclusions & Inferences The Chicago Classification is an algorithmic scheme for diagnosis of esophageal motility disorders from clinical EPT studies. Moving forward, we anticipate continuing this process with increased emphasis placed on natural history studies and outcome data based on the classification. PMID:22248109

Chicago classification criteria of esophageal motility disorders defined in high resolution esophageal pressure topography.

PubMed

Bredenoord, A J; Fox, M; Kahrilas, P J; Pandolfino, J E; Schwizer, W; Smout, A J P M

2012-03-01

The Chicago Classification of esophageal motility was developed to facilitate the interpretation of clinical high resolution esophageal pressure topography (EPT) studies, concurrent with the widespread adoption of this technology into clinical practice. The Chicago Classification has been an evolutionary process, molded first by published evidence pertinent to the clinical interpretation of high resolution manometry (HRM) studies and secondarily by group experience when suitable evidence is lacking. This publication summarizes the state of our knowledge as of the most recent meeting of the International High Resolution Manometry Working Group in Ascona, Switzerland in April 2011. The prior iteration of the Chicago Classification was updated through a process of literature analysis and discussion. The major changes in this document from the prior iteration are largely attributable to research studies published since the prior iteration, in many cases research conducted in response to prior deliberations of the International High Resolution Manometry Working Group. The classification now includes criteria for subtyping achalasia, EGJ outflow obstruction, motility disorders not observed in normal subjects (Distal esophageal spasm, Hypercontractile esophagus, and Absent peristalsis), and statistically defined peristaltic abnormalities (Weak peristalsis, Frequent failed peristalsis, Rapid contractions with normal latency, and Hypertensive peristalsis). The Chicago Classification is an algorithmic scheme for diagnosis of esophageal motility disorders from clinical EPT studies. Moving forward, we anticipate continuing this process with increased emphasis placed on natural history studies and outcome data based on the classification. © 2012 Blackwell Publishing Ltd.

The information extraction of Gannan citrus orchard based on the GF-1 remote sensing image

NASA Astrophysics Data System (ADS)

Wang, S.; Chen, Y. L.

2017-02-01

The production of Gannan oranges is the largest in China, which occupied an important part in the world. The extraction of citrus orchard quickly and effectively has important significance for fruit pathogen defense, fruit production and industrial planning. The traditional spectra extraction method of citrus orchard based on pixel has a lower classification accuracy, difficult to avoid the “pepper phenomenon”. In the influence of noise, the phenomenon that different spectrums of objects have the same spectrum is graveness. Taking Xunwu County citrus fruit planting area of Ganzhou as the research object, aiming at the disadvantage of the lower accuracy of the traditional method based on image element classification method, a decision tree classification method based on object-oriented rule set is proposed. Firstly, multi-scale segmentation is performed on the GF-1 remote sensing image data of the study area. Subsequently the sample objects are selected for statistical analysis of spectral features and geometric features. Finally, combined with the concept of decision tree classification, a variety of empirical values of single band threshold, NDVI, band combination and object geometry characteristics are used hierarchically to execute the information extraction of the research area, and multi-scale segmentation and hierarchical decision tree classification is implemented. The classification results are verified with the confusion matrix, and the overall Kappa index is 87.91%.

Object-based locust habitat mapping using high-resolution multispectral satellite data in the southern Aral Sea basin

NASA Astrophysics Data System (ADS)

Navratil, Peter; Wilps, Hans

2013-01-01

Three different object-based image classification techniques are applied to high-resolution satellite data for the mapping of the habitats of Asian migratory locust (Locusta migratoria migratoria) in the southern Aral Sea basin, Uzbekistan. A set of panchromatic and multispectral Système Pour l'Observation de la Terre-5 satellite images was spectrally enhanced by normalized difference vegetation index and tasseled cap transformation and segmented into image objects, which were then classified by three different classification approaches: a rule-based hierarchical fuzzy threshold (HFT) classification method was compared to a supervised nearest neighbor classifier and classification tree analysis by the quick, unbiased, efficient statistical trees algorithm. Special emphasis was laid on the discrimination of locust feeding and breeding habitats due to the significance of this discrimination for practical locust control. Field data on vegetation and land cover, collected at the time of satellite image acquisition, was used to evaluate classification accuracy. The results show that a robust HFT classifier outperformed the two automated procedures by 13% overall accuracy. The classification method allowed a reliable discrimination of locust feeding and breeding habitats, which is of significant importance for the application of the resulting data for an economically and environmentally sound control of locust pests because exact spatial knowledge on the habitat types allows a more effective surveying and use of pesticides.

Blazar flaring patterns (B-FlaP) classifying blazar candidate of uncertain type in the third Fermi-LAT catalogue by artificial neural networks

NASA Astrophysics Data System (ADS)

Chiaro, G.; Salvetti, D.; La Mura, G.; Giroletti, M.; Thompson, D. J.; Bastieri, D.

2016-11-01

The Fermi-Large Area Telescope (LAT) is currently the most important facility for investigating the GeV γ-ray sky. With Fermi-LAT, more than three thousand γ-ray sources have been discovered so far. 1144 (˜40 per cent) of the sources are active galaxies of the blazar class, and 573 (˜20 per cent) are listed as blazar candidate of uncertain type (BCU), or sources without a conclusive classification. We use the empirical cumulative distribution functions and the artificial neural networks for a fast method of screening and classification for BCUs based on data collected at γ-ray energies only, when rigorous multiwavelength analysis is not available. Based on our method, we classify 342 BCUs as BL Lacs and 154 as flat-spectrum radio quasars, while 77 objects remain uncertain. Moreover, radio analysis and direct observations in ground-based optical observatories are used as counterparts to the statistical classifications to validate the method. This approach is of interest because of the increasing number of unclassified sources in Fermi catalogues and because blazars and in particular their subclass high synchrotron peak objects are the main targets of atmospheric Cherenkov telescopes.

Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

NASA Astrophysics Data System (ADS)

Iabchoon, Sanwit; Wongsai, Sangdao; Chankon, Kanoksuk

2017-10-01

Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.

Random whole metagenomic sequencing for forensic discrimination of soils.

PubMed

Khodakova, Anastasia S; Smith, Renee J; Burgoyne, Leigh; Abarno, Damien; Linacre, Adrian

2014-01-01

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.

Tsallis statistics and neurodegenerative disorders

NASA Astrophysics Data System (ADS)

Iliopoulos, Aggelos C.; Tsolaki, Magdalini; Aifantis, Elias C.

2016-08-01

In this paper, we perform statistical analysis of time series deriving from four neurodegenerative disorders, namely epilepsy, amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD). The time series are concerned with electroencephalograms (EEGs) of healthy and epileptic states, as well as gait dynamics (in particular stride intervals) of the ALS, PD and HDs. We study data concerning one subject for each neurodegenerative disorder and one healthy control. The analysis is based on Tsallis non-extensive statistical mechanics and in particular on the estimation of Tsallis q-triplet, namely {qstat, qsen, qrel}. The deviation of Tsallis q-triplet from unity indicates non-Gaussian statistics and long-range dependencies for all time series considered. In addition, the results reveal the efficiency of Tsallis statistics in capturing differences in brain dynamics between healthy and epileptic states, as well as differences between ALS, PD, HDs from healthy control subjects. The results indicate that estimations of Tsallis q-indices could be used as possible biomarkers, along with others, for improving classification and prediction of epileptic seizures, as well as for studying the gait complex dynamics of various diseases providing new insights into severity, medications and fall risk, improving therapeutic interventions.

Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

NASA Astrophysics Data System (ADS)

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

2018-01-01

The growing use of optimization for geographic object-based image analysis and the possibility to derive a wide range of information about the image in textual form makes machine learning (data mining) a versatile tool for information extraction from multiple data sources. This paper presents application of data mining for land-cover classification by fusing SPOT-6, RADARSAT-2, and derived dataset. First, the images and other derived indices (normalized difference vegetation index, normalized difference water index, and soil adjusted vegetation index) were combined and subjected to segmentation process with optimal segmentation parameters obtained using combination of spatial and Taguchi statistical optimization. The image objects, which carry all the attributes of the input datasets, were extracted and related to the target land-cover classes through data mining algorithms (decision tree) for classification. To evaluate the performance, the result was compared with two nonparametric classifiers: support vector machine (SVM) and random forest (RF). Furthermore, the decision tree classification result was evaluated against six unoptimized trials segmented using arbitrary parameter combinations. The result shows that the optimized process produces better land-use land-cover classification with overall classification accuracy of 91.79%, 87.25%, and 88.69% for SVM and RF, respectively, while the results of the six unoptimized classifications yield overall accuracy between 84.44% and 88.08%. Higher accuracy of the optimized data mining classification approach compared to the unoptimized results indicates that the optimization process has significant impact on the classification quality.

Model-based approach to the detection and classification of mines in sidescan sonar.

PubMed

Reed, Scott; Petillot, Yvan; Bell, Judith

2004-01-10

This paper presents a model-based approach to mine detection and classification by use of sidescan sonar. Advances in autonomous underwater vehicle technology have increased the interest in automatic target recognition systems in an effort to automate a process that is currently carried out by a human operator. Current automated systems generally require training and thus produce poor results when the test data set is different from the training set. This has led to research into unsupervised systems, which are able to cope with the large variability in conditions and terrains seen in sidescan imagery. The system presented in this paper first detects possible minelike objects using a Markov random field model, which operates well on noisy images, such as sidescan, and allows a priori information to be included through the use of priors. The highlight and shadow regions of the object are then extracted with a cooperating statistical snake, which assumes these regions are statistically separate from the background. Finally, a classification decision is made using Dempster-Shafer theory, where the extracted features are compared with synthetic realizations generated with a sidescan sonar simulator model. Results for the entire process are shown on real sidescan sonar data. Similarities between the sidescan sonar and synthetic aperture radar (SAR) imaging processes ensure that the approach outlined here could be made applied to SAR image analysis.

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

2017-03-01

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.