Management Overview of System Technical Support Plan for the FIREFINDER System Support Center.

DTIC Science & Technology

1980-08-06

34.. /b , UNCLASSIFIED SECU ITY CLASSIFICATION OF THIS PAGE (When Data Entered) READ INSTRUCTIONSREPORT DOCUMENTATION PAGE BEFORE COMPLETING FORM I. REPORT...Evaluation Agency USASA U.S. Army Security Agency UTM Universal Transverse Mercator V Volts V&V Verification and Validation VCSA Vice-Chief of Staff, Army VDD... classification , configuration audits, and so forth. INSTR 5010.27 Management of Automatic Data System 9 November 1971 Development Establishes uniform

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Automated Classification of Selected Data Elements from Free-text Diagnostic Reports for Clinical Research.

PubMed

Löpprich, Martin; Krauss, Felix; Ganzinger, Matthias; Senghas, Karsten; Riezler, Stefan; Knaup, Petra

2016-08-05

In the Multiple Myeloma clinical registry at Heidelberg University Hospital, most data are extracted from discharge letters. Our aim was to analyze if it is possible to make the manual documentation process more efficient by using methods of natural language processing for multiclass classification of free-text diagnostic reports to automatically document the diagnosis and state of disease of myeloma patients. The first objective was to create a corpus consisting of free-text diagnosis paragraphs of patients with multiple myeloma from German diagnostic reports, and its manual annotation of relevant data elements by documentation specialists. The second objective was to construct and evaluate a framework using different NLP methods to enable automatic multiclass classification of relevant data elements from free-text diagnostic reports. The main diagnoses paragraph was extracted from the clinical report of one third randomly selected patients of the multiple myeloma research database from Heidelberg University Hospital (in total 737 selected patients). An EDC system was setup and two data entry specialists performed independently a manual documentation of at least nine specific data elements for multiple myeloma characterization. Both data entries were compared and assessed by a third specialist and an annotated text corpus was created. A framework was constructed, consisting of a self-developed package to split multiple diagnosis sequences into several subsequences, four different preprocessing steps to normalize the input data and two classifiers: a maximum entropy classifier (MEC) and a support vector machine (SVM). In total 15 different pipelines were examined and assessed by a ten-fold cross-validation, reiterated 100 times. For quality indication the average error rate and the average F1-score were conducted. For significance testing the approximate randomization test was used. The created annotated corpus consists of 737 different diagnoses paragraphs with a total number of 865 coded diagnosis. The dataset is publicly available in the supplementary online files for training and testing of further NLP methods. Both classifiers showed low average error rates (MEC: 1.05; SVM: 0.84) and high F1-scores (MEC: 0.89; SVM: 0.92). However the results varied widely depending on the classified data element. Preprocessing methods increased this effect and had significant impact on the classification, both positive and negative. The automatic diagnosis splitter increased the average error rate significantly, even if the F1-score decreased only slightly. The low average error rates and high average F1-scores of each pipeline demonstrate the suitability of the investigated NPL methods. However, it was also shown that there is no best practice for an automatic classification of data elements from free-text diagnostic reports.

Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature

PubMed Central

2011-01-01

Background The selection of relevant articles for curation, and linking those articles to experimental techniques confirming the findings became one of the primary subjects of the recent BioCreative III contest. The contest’s Protein-Protein Interaction (PPI) task consisted of two sub-tasks: Article Classification Task (ACT) and Interaction Method Task (IMT). ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles. Results We proposed and compared several classification-based methods for both tasks, employing rich contextual features as well as features extracted from external knowledge sources. For IMT, a new method that classifies pair-wise relations between every text phrase and candidate interaction method obtained promising results with an F1 score of 64.49%, as tested on the task’s development dataset. We also explored ways to combine this new approach and more conventional, multi-label document classification methods. For ACT, our classifiers exploited automatically detected named entities and other linguistic information. The evaluation results on the BioCreative III PPI test datasets showed that our systems were very competitive: one of our IMT methods yielded the best performance among all participants, as measured by F1 score, Matthew’s Correlation Coefficient and AUC iP/R; whereas for ACT, our best classifier was ranked second as measured by AUC iP/R, and also competitive according to other metrics. Conclusions Our novel approach that converts the multi-class, multi-label classification problem to a binary classification problem showed much promise in IMT. Nevertheless, on the test dataset the best performance was achieved by taking the union of the output of this method and that of a multi-class, multi-label document classifier, which indicates that the two types of systems complement each other in terms of recall. For ACT, our system exploited a rich set of features and also obtained encouraging results. We examined the features with respect to their contributions to the classification results, and concluded that contextual words surrounding named entities, as well as the MeSH headings associated with the documents were among the main contributors to the performance. PMID:22151769

Text Classification for Organizational Researchers

PubMed Central

Kobayashi, Vladimer B.; Mol, Stefan T.; Berkers, Hannah A.; Kismihók, Gábor; Den Hartog, Deanne N.

2017-01-01

Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output. PMID:29881249

Lidar-based individual tree species classification using convolutional neural network

NASA Astrophysics Data System (ADS)

Mizoguchi, Tomohiro; Ishii, Akira; Nakamura, Hiroyuki; Inoue, Tsuyoshi; Takamatsu, Hisashi

2017-06-01

Terrestrial lidar is commonly used for detailed documentation in the field of forest inventory investigation. Recent improvements of point cloud processing techniques enabled efficient and precise computation of an individual tree shape parameters, such as breast-height diameter, height, and volume. However, tree species are manually specified by skilled workers to date. Previous works for automatic tree species classification mainly focused on aerial or satellite images, and few works have been reported for classification techniques using ground-based sensor data. Several candidate sensors can be considered for classification, such as RGB or multi/hyper spectral cameras. Above all candidates, we use terrestrial lidar because it can obtain high resolution point cloud in the dark forest. We selected bark texture for the classification criteria, since they clearly represent unique characteristics of each tree and do not change their appearance under seasonable variation and aged deterioration. In this paper, we propose a new method for automatic individual tree species classification based on terrestrial lidar using Convolutional Neural Network (CNN). The key component is the creation step of a depth image which well describe the characteristics of each species from a point cloud. We focus on Japanese cedar and cypress which cover the large part of domestic forest. Our experimental results demonstrate the effectiveness of our proposed method.

A framework for biomedical figure segmentation towards image-based document retrieval

PubMed Central

2013-01-01

The figures included in many of the biomedical publications play an important role in understanding the biological experiments and facts described within. Recent studies have shown that it is possible to integrate the information that is extracted from figures in classical document classification and retrieval tasks in order to improve their accuracy. One important observation about the figures included in biomedical publications is that they are often composed of multiple subfigures or panels, each describing different methodologies or results. The use of these multimodal figures is a common practice in bioscience, as experimental results are graphically validated via multiple methodologies or procedures. Thus, for a better use of multimodal figures in document classification or retrieval tasks, as well as for providing the evidence source for derived assertions, it is important to automatically segment multimodal figures into subfigures and panels. This is a challenging task, however, as different panels can contain similar objects (i.e., barcharts and linecharts) with multiple layouts. Also, certain types of biomedical figures are text-heavy (e.g., DNA sequences and protein sequences images) and they differ from traditional images. As a result, classical image segmentation techniques based on low-level image features, such as edges or color, are not directly applicable to robustly partition multimodal figures into single modal panels. In this paper, we describe a robust solution for automatically identifying and segmenting unimodal panels from a multimodal figure. Our framework starts by robustly harvesting figure-caption pairs from biomedical articles. We base our approach on the observation that the document layout can be used to identify encoded figures and figure boundaries within PDF files. Taking into consideration the document layout allows us to correctly extract figures from the PDF document and associate their corresponding caption. We combine pixel-level representations of the extracted images with information gathered from their corresponding captions to estimate the number of panels in the figure. Thus, our approach simultaneously identifies the number of panels and the layout of figures. In order to evaluate the approach described here, we applied our system on documents containing protein-protein interactions (PPIs) and compared the results against a gold standard that was annotated by biologists. Experimental results showed that our automatic figure segmentation approach surpasses pure caption-based and image-based approaches, achieving a 96.64% accuracy. To allow for efficient retrieval of information, as well as to provide the basis for integration into document classification and retrieval systems among other, we further developed a web-based interface that lets users easily retrieve panels containing the terms specified in the user queries. PMID:24565394

Text mining and natural language processing approaches for automatic categorization of lay requests to web-based expert forums.

PubMed

Himmel, Wolfgang; Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-07-22

Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called "ask the doctor" services. To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website "Rund ums Baby" ("Everything about Babies") into one or more of 38 categories belonging to two dimensions ("subject matter" and "expectations"). After creating start and synonym lists, we calculated the average Cramer's V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. According to the manual classification of 988 documents, 102 (10%) documents fell into the category "in vitro fertilization (IVF)," 81 (8%) into the category "ovulation," 79 (8%) into "cycle," and 57 (6%) into "semen analysis." These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as "general information" and 351 (36%) as a wish for "treatment recommendations." The generation of indicator variables based on the chi-square analysis and Cramer's V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, "words") also included variables from other categories, most often with a negative sign. For example, absence of words predictive for "menstruation" was a strong indicator for the category "pregnancy test." Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback.

Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed.

PubMed

Eisinger, Daniel; Tsatsaronis, George; Bundschus, Markus; Wieneke, Ulrich; Schroeder, Michael

2013-04-15

Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Patent documents are another important information source, though they are considerably less accessible. One option to expand patent search beyond pure keywords is the inclusion of classification information: Since every patent is assigned at least one class code, it should be possible for these assignments to be automatically used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. This report describes our comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms.Our analysis shows a strong structural similarity of the hierarchies, but significant differences of terms and annotations. The low number of IPC class assignments and the lack of occurrences of class labels in patent texts imply that current patent search is severely limited. To overcome these limits, we evaluate a method for the automated assignment of additional classes to patent documents, and we propose a system for guided patent search based on the use of class co-occurrence information and external resources.

Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed

PubMed Central

2013-01-01

Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Patent documents are another important information source, though they are considerably less accessible. One option to expand patent search beyond pure keywords is the inclusion of classification information: Since every patent is assigned at least one class code, it should be possible for these assignments to be automatically used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. This report describes our comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows a strong structural similarity of the hierarchies, but significant differences of terms and annotations. The low number of IPC class assignments and the lack of occurrences of class labels in patent texts imply that current patent search is severely limited. To overcome these limits, we evaluate a method for the automated assignment of additional classes to patent documents, and we propose a system for guided patent search based on the use of class co-occurrence information and external resources. PMID:23734562

Automatic Adviser on Mobile Objects Status Identification and Classification

NASA Astrophysics Data System (ADS)

Shabelnikov, A. N.; Liabakh, N. N.; Gibner, Ya M.; Saryan, A. S.

2018-05-01

A mobile object status identification task is defined within the image discrimination theory. It is proposed to classify objects into three classes: object operation status; its maintenance is required and object should be removed from the production process. Two methods were developed to construct the separating boundaries between the designated classes: a) using statistical information on the research objects executed movement, b) basing on regulatory documents and expert commentary. Automatic Adviser operation simulation and the operation results analysis complex were synthesized. Research results are commented using a specific example of cuts rolling from the hump yard. The work was supported by Russian Fundamental Research Fund, project No. 17-20-01040.

Old document image segmentation using the autocorrelation function and multiresolution analysis

NASA Astrophysics Data System (ADS)

Mehri, Maroua; Gomez-Krämer, Petra; Héroux, Pierre; Mullot, Rémy

2013-01-01

Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges in information retrieval in digital libraries and document layout analysis. Therefore, in order to control the quality of historical document image digitization and to meet the need of a characterization of their content using intermediate level metadata (between image and document structure), we propose a fast automatic layout segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The method proposed in this article has the advantage that it is performed without any hypothesis on the document structure, either about the document model (physical structure), or the typographical parameters (logical structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly, we detail our proposal to characterize the content of old documents by extracting the autocorrelation features in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure, which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85% of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of homogeneous blocks and their topology.

Text Mining and Natural Language Processing Approaches for Automatic Categorization of Lay Requests to Web-Based Expert Forums

PubMed Central

Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-01-01

Background Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called “ask the doctor” services. Objective To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. Methods We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website “Rund ums Baby” (“Everything about Babies”) into one or more of 38 categories belonging to two dimensions (“subject matter” and “expectations”). After creating start and synonym lists, we calculated the average Cramer’s V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. Results According to the manual classification of 988 documents, 102 (10%) documents fell into the category “in vitro fertilization (IVF),” 81 (8%) into the category “ovulation,” 79 (8%) into “cycle,” and 57 (6%) into “semen analysis.” These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as “general information” and 351 (36%) as a wish for “treatment recommendations.” The generation of indicator variables based on the chi-square analysis and Cramer’s V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, “words”) also included variables from other categories, most often with a negative sign. For example, absence of words predictive for “menstruation” was a strong indicator for the category “pregnancy test.” Conclusions Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback. PMID:19632978

Document cards: a top trumps visualization for documents.

PubMed

Strobelt, Hendrik; Oelke, Daniela; Rohrdantz, Christian; Stoffel, Andreas; Keim, Daniel A; Deussen, Oliver

2009-01-01

Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images.

An artificial intelligence approach to classify and analyse EEG traces.

PubMed

Castellaro, C; Favaro, G; Castellaro, A; Casagrande, A; Castellaro, S; Puthenparampil, D V; Salimbeni, C Fattorello

2002-06-01

We present a fully automatic system for the classification and analysis of adult electroencephalograms (EEGs). The system is based on an artificial neural network which classifies the single epochs of trace, and on an Expert System (ES) which studies the time and space correlation among the outputs of the neural network; compiling a final report. On the last 2000 EEGs representing different kinds of alterations according to clinical occurrences, the system was able to produce 80% good or very good final comments and 18% sufficient comments, which represent the documents delivered to the patient. In the remaining 2% the automatic comment needed some modifications prior to be presented to the patient. No clinical false-negative classifications did arise, i.e. no altered traces were classified as 'normal' by the neural network. The analysis method we describe is based on the interpretation of objective measures performed on the trace. It can improve the quality and reliability of the EEG exam and appears useful for the EEG medical reports although it cannot totally substitute the medical doctor who should now read the automatic EEG analysis in light of the patient's history and age.

Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Bassier, M.; Vergauwen, M.; Van Genechten, B.

2017-08-01

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

A vectorial semantics approach to personality assessment.

PubMed

Neuman, Yair; Cohen, Yochai

2014-04-23

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.

A Vectorial Semantics Approach to Personality Assessment

NASA Astrophysics Data System (ADS)

Neuman, Yair; Cohen, Yochai

2014-04-01

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.

A Vectorial Semantics Approach to Personality Assessment

PubMed Central

Neuman, Yair; Cohen, Yochai

2014-01-01

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy. PMID:24755833

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Bryant, W. F.

1976-01-01

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

"What is relevant in a text document?": An interpretable machine learning approach

PubMed Central

Arras, Leila; Horn, Franziska; Montavon, Grégoire; Müller, Klaus-Robert

2017-01-01

Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text’s category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications. PMID:28800619

Document image improvement for OCR as a classification problem

NASA Astrophysics Data System (ADS)

Summers, Kristen M.

2003-01-01

In support of the goal of automatically selecting methods of enhancing an image to improve the accuracy of OCR on that image, we consider the problem of determining whether to apply each of a set of methods as a supervised classification problem for machine learning. We characterize each image according to a combination of two sets of measures: a set that are intended to reflect the degree of particular types of noise present in documents in a single font of Roman or similar script and a more general set based on connected component statistics. We consider several potential methods of image improvement, each of which constitutes its own 2-class classification problem, according to whether transforming the image with this method improves the accuracy of OCR. In our experiments, the results varied for the different image transformation methods, but the system made the correct choice in 77% of the cases in which the decision affected the OCR score (in the range [0,1]) by at least .01, and it made the correct choice 64% of the time overall.

Automatic recognition of conceptualization zones in scientific articles and two life science applications.

PubMed

Liakata, Maria; Saha, Shyamasree; Dobnik, Simon; Batchelor, Colin; Rebholz-Schuhmann, Dietrich

2012-04-01

Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work, we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of 11 categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within an article and their automatic recognition can greatly facilitate biomedical information extraction by characterizing the different types of facts, hypotheses and evidence available in a scientific publication. We have trained and compared machine learning classifiers (support vector machines and conditional random fields) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognize CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with 'Experiment', 'Background' and 'Model' being the categories with the highest F1-scores (76%, 62% and 53%, respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. A web-based tool for the automatic annotation of articles with CoreSCs and corresponding documentation is available online at http://www.sapientaproject.com/software http://www.sapientaproject.com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated articles, which served as our training data. liakata@ebi.ac.uk Supplementary data are available at Bioinformatics online.

Supervised Gamma Process Poisson Factorization

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

Anderson, Dylan Zachary

This thesis develops the supervised gamma process Poisson factorization (S- GPPF) framework, a novel supervised topic model for joint modeling of count matrices and document labels. S-GPPF is fully generative and nonparametric: document labels and count matrices are modeled under a uni ed probabilistic framework and the number of latent topics is controlled automatically via a gamma process prior. The framework provides for multi-class classification of documents using a generative max-margin classifier. Several recent data augmentation techniques are leveraged to provide for exact inference using a Gibbs sampling scheme. The first portion of this thesis reviews supervised topic modeling andmore » several key mathematical devices used in the formulation of S-GPPF. The thesis then introduces the S-GPPF generative model and derives the conditional posterior distributions of the latent variables for posterior inference via Gibbs sampling. The S-GPPF is shown to exhibit state-of-the-art performance for joint topic modeling and document classification on a dataset of conference abstracts, beating out competing supervised topic models. The unique properties of S-GPPF along with its competitive performance make it a novel contribution to supervised topic modeling.« less

Use of an automatic procedure for determination of classes of land use in the Teste Araras area of the peripheral Paulist depression

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Lombardo, M. A.; Valeriano, D. D.

1981-01-01

An evaluation of the multispectral image analyzer (system Image 1-100), using automatic classification, is presented. The region studied is situated. The automatic was carried out using the maximum likelihood (MAXVER) classification system. The following classes were established: urban area, bare soil, sugar cane, citrus culture (oranges), pastures, and reforestation. The classification matrix of the test sites indicate that the percentage of correct classification varied between 63% and 100%.

Natural language processing of spoken diet records (SDRs).

PubMed

Lacson, Ronilda; Long, William

2006-01-01

Dietary assessment is a fundamental aspect of nutritional evaluation that is essential for management of obesity as well as for assessing dietary impact on chronic diseases. Various methods have been used for dietary assessment including written records, 24-hour recalls, and food frequency questionnaires. The use of mobile phones to provide real-time dietary records provides potential advantages for accessibility, ease of use and automated documentation. However, understanding even a perfect transcript of spoken dietary records (SDRs) is challenging for people. This work presents a first step towards automatic analysis of SDRs. Our approach consists of four steps - identification of food items, identification of food quantifiers, classification of food quantifiers and temporal annotation. Our method enables automatic extraction of dietary information from SDRs, which in turn allows automated mapping to a Diet History Questionnaire dietary database. Our model has an accuracy of 90%. This work demonstrates the feasibility of automatically processing SDRs.

PDF text classification to leverage information extraction from publication reports.

PubMed

Bui, Duy Duc An; Del Fiol, Guilherme; Jonnalagadda, Siddhartha

2016-06-01

Data extraction from original study reports is a time-consuming, error-prone process in systematic review development. Information extraction (IE) systems have the potential to assist humans in the extraction task, however majority of IE systems were not designed to work on Portable Document Format (PDF) document, an important and common extraction source for systematic review. In a PDF document, narrative content is often mixed with publication metadata or semi-structured text, which add challenges to the underlining natural language processing algorithm. Our goal is to categorize PDF texts for strategic use by IE systems. We used an open-source tool to extract raw texts from a PDF document and developed a text classification algorithm that follows a multi-pass sieve framework to automatically classify PDF text snippets (for brevity, texts) into TITLE, ABSTRACT, BODYTEXT, SEMISTRUCTURE, and METADATA categories. To validate the algorithm, we developed a gold standard of PDF reports that were included in the development of previous systematic reviews by the Cochrane Collaboration. In a two-step procedure, we evaluated (1) classification performance, and compared it with machine learning classifier, and (2) the effects of the algorithm on an IE system that extracts clinical outcome mentions. The multi-pass sieve algorithm achieved an accuracy of 92.6%, which was 9.7% (p<0.001) higher than the best performing machine learning classifier that used a logistic regression algorithm. F-measure improvements were observed in the classification of TITLE (+15.6%), ABSTRACT (+54.2%), BODYTEXT (+3.7%), SEMISTRUCTURE (+34%), and MEDADATA (+14.2%). In addition, use of the algorithm to filter semi-structured texts and publication metadata improved performance of the outcome extraction system (F-measure +4.1%, p=0.002). It also reduced of number of sentences to be processed by 44.9% (p<0.001), which corresponds to a processing time reduction of 50% (p=0.005). The rule-based multi-pass sieve framework can be used effectively in categorizing texts extracted from PDF documents. Text classification is an important prerequisite step to leverage information extraction from PDF documents. Copyright © 2016 Elsevier Inc. All rights reserved.

Automatic Classification of Medical Text: The Influence of Publication Form1

PubMed Central

Cole, William G.; Michael, Patricia A.; Stewart, James G.; Blois, Marsden S.

1988-01-01

Previous research has shown that within the domain of medical journal abstracts the statistical distribution of words is neither random nor uniform, but is highly characteristic. Many words are used mainly or solely by one medical specialty or when writing about one particular level of description. Due to this regularity of usage, automatic classification within journal abstracts has proved quite successful. The present research asks two further questions. It investigates whether this statistical regularity and automatic classification success can also be achieved in medical textbook chapters. It then goes on to see whether the statistical distribution found in textbooks is sufficiently similar to that found in abstracts to permit accurate classification of abstracts based solely on previous knowledge of textbooks. 14 textbook chapters and 45 MEDLINE abstracts were submitted to an automatic classification program that had been trained only on chapters drawn from a standard textbook series. Statistical analysis of the properties of abstracts vs. chapters revealed important differences in word use. Automatic classification performance was good for chapters, but poor for abstracts.

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

NASA Technical Reports Server (NTRS)

Kocurek, Michael J.

2005-01-01

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

Using clustering and a modified classification algorithm for automatic text summarization

NASA Astrophysics Data System (ADS)

Aries, Abdelkrime; Oufaida, Houda; Nouali, Omar

2013-01-01

In this paper we describe a modified classification method destined for extractive summarization purpose. The classification in this method doesn't need a learning corpus; it uses the input text to do that. First, we cluster the document sentences to exploit the diversity of topics, then we use a learning algorithm (here we used Naive Bayes) on each cluster considering it as a class. After obtaining the classification model, we calculate the score of a sentence in each class, using a scoring model derived from classification algorithm. These scores are used, then, to reorder the sentences and extract the first ones as the output summary. We conducted some experiments using a corpus of scientific papers, and we have compared our results to another summarization system called UNIS.1 Also, we experiment the impact of clustering threshold tuning, on the resulted summary, as well as the impact of adding more features to the classifier. We found that this method is interesting, and gives good performance, and the addition of new features (which is simple using this method) can improve summary's accuracy.

An automatic agricultural zone classification procedure for crop inventory satellite images

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Kux, H. J.; Velasco, F. R. D.; Deoliveira, M. O. B.

1982-01-01

A classification procedure for assessing crop areal proportion in multispectral scanner image is discussed. The procedure is into four parts: labeling; classification; proportion estimation; and evaluation. The procedure also has the following characteristics: multitemporal classification; the need for a minimum field information; and verification capability between automatic classification and analyst labeling. The processing steps and the main algorithms involved are discussed. An outlook on the future of this technology is also presented.

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

PubMed Central

Papageorgiou, Eirini; Nieuwenhuys, Angela; Desloovere, Kaat

2017-01-01

Background This study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions. Hypotheses Two main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification. Findings This study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability. PMID:28570616

Breaking the Cost Barrier in Automatic Classification.

ERIC Educational Resources Information Center

Doyle, L. B.

A low-cost automatic classification method is reported that uses computer time in proportion to NlogN, where N is the number of information items and the base is a parameter, some barriers besides cost are treated briefly in the opening section, including types of intellectual resistance to the idea of doing classification by content-word…

The impact of OCR accuracy on automated cancer classification of pathology reports.

PubMed

Zuccon, Guido; Nguyen, Anthony N; Bergheim, Anton; Wickman, Sandra; Grayson, Narelle

2012-01-01

To evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology reports. Scanned images of pathology reports were converted to electronic free-text using a commercial OCR system. A state-of-the-art cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR reports. The employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type, etc. The automatic cancer classification system used in this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned images through OCR software. However, issues emerge when considering the extraction of cancer notification items.

Activity classification using realistic data from wearable sensors.

PubMed

Pärkkä, Juha; Ermes, Miikka; Korpipää, Panu; Mäntyjärvi, Jani; Peltola, Johannes; Korhonen, Ilkka

2006-01-01

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

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.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

TEXTINFO: a tool for automatic determination of patient clinical profiles using text analysis.

PubMed Central

Borst, F.; Lyman, M.; Nhàn, N. T.; Tick, L. J.; Sager, N.; Scherrer, J. R.

1991-01-01

The clinical data contained in narrative patient documents is made available via grammatical and semantic processing. Retrievals from the resulting relational database tables are matched against a set of clinical descriptors to obtain clinical profiles of the patients in terms of the descriptors present in the documents. Discharge summaries of 57 Dept. of Digestive Surgery patients were processed in this manner. Factor analysis and discriminant analysis procedures were then applied, showing the profiles to be useful for diagnosis definitions (by establishing relations between diagnoses and clinical findings), for diagnosis assessment (by viewing the match between a definition and observed events recorded in a patient text), and potentially for outcome evaluation based on the classification abilities of clinical signs. PMID:1807679

Classifying seismic waveforms from scratch: a case study in the alpine environment

NASA Astrophysics Data System (ADS)

Hammer, C.; Ohrnberger, M.; Fäh, D.

2013-01-01

Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.

Automated compound classification using a chemical ontology.

PubMed

Bobach, Claudia; Böhme, Timo; Laube, Ulf; Püschel, Anett; Weber, Lutz

2012-12-29

Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated.

Automated compound classification using a chemical ontology

PubMed Central

2012-01-01

Background Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. Results In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. Conclusions A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated. PMID:23273256

EDMUS, a European database for multiple sclerosis.

PubMed

Confavreux, C; Compston, D A; Hommes, O R; McDonald, W I; Thompson, A J

1992-08-01

EDMUS is a minimal descriptive record developed for research purposes to document clinical and laboratory data in patients with multiple sclerosis (MS). It has been designed by a committee of the European Concerted Action for MS, organised under the auspices of the Commission of the European Communities. The software is user-friendly and fast, with a minimal set of obligatory data. Priority has been given to analytical data and the system is capable of automatically generating data, such as diagnosis classification, using appropriate algorithms. This procedure saves time, ensures a uniform approach to individual cases and allows automatic updating of the classification whenever additional information becomes available. It is also compatible with future developments and requirements since new algorithms can be entered in the programme when necessary. This system is flexible and may be adapted to the users needs. It is run on Apple and IBM-PC personal microcomputers. Great care has been taken to preserve confidentiality of the data. It is anticipated that this "common" language will enable the collection of appropriate cases for specific purposes, including population-based studies of MS and will be particularly useful in projects where the collaboration of several centres is needed to recruit a critical number of patients.

A new patent-based approach for technology mapping in the pharmaceutical domain.

PubMed

Russo, Davide; Montecchi, Tiziano; Carrara, Paolo

2013-09-01

The key factor in decision-making is the quality of information collected and processed in the problem analysis. In most cases, patents represent a very important source of information. The main problem is how to extract such information from the huge corpus of documents with a high recall and precision, and in a short time. This article demonstrates a patent search and classification method, called Knowledge Organizing Module, which consists of creating, almost automatically, a pool of patents based on polysemy expansion and homonymy disambiguation. Since the pool is done, an automatic patent technology landscaping is provided for fixing the state of the art of our product, and exploring competing alternative treatments and/or possible technological opportunities. An exemplary case study is provided, it deals with a patent analysis in the field of verruca treatments.

Computerized training management system

DOEpatents

Rice, H.B.; McNair, R.C.; White, K.; Maugeri, T.

1998-08-04

A Computerized Training Management System (CTMS) is disclosed for providing a procedurally defined process that is employed to develop accreditable performance based training programs for job classifications that are sensitive to documented regulations and technical information. CTMS is a database that links information needed to maintain a five-phase approach to training-analysis, design, development, implementation, and evaluation independent of training program design. CTMS is designed using R-Base{trademark}, an-SQL compliant software platform. Information is logically entered and linked in CTMS. Each task is linked directly to a performance objective, which, in turn, is linked directly to a learning objective; then, each enabling objective is linked to its respective test items. In addition, tasks, performance objectives, enabling objectives, and test items are linked to their associated reference documents. CTMS keeps all information up to date since it automatically sorts, files and links all data; CTMS includes key word and reference document searches. 18 figs.

Computerized training management system

DOEpatents

Rice, Harold B.; McNair, Robert C.; White, Kenneth; Maugeri, Terry

1998-08-04

A Computerized Training Management System (CTMS) for providing a procedurally defined process that is employed to develop accreditable performance based training programs for job classifications that are sensitive to documented regulations and technical information. CTMS is a database that links information needed to maintain a five-phase approach to training-analysis, design, development, implementation, and evaluation independent of training program design. CTMS is designed using R-Base.RTM., an-SQL compliant software platform. Information is logically entered and linked in CTMS. Each task is linked directly to a performance objective, which, in turn, is linked directly to a learning objective; then, each enabling objective is linked to its respective test items. In addition, tasks, performance objectives, enabling objectives, and test items are linked to their associated reference documents. CTMS keeps all information up to date since it automatically sorts, files and links all data; CTMS includes key word and reference document searches.

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Automatic detection of sleep macrostructure based on a sensorized T-shirt.

PubMed

Bianchi, Anna M; Mendez, Martin O

2010-01-01

In the present work we apply a fully automatic procedure to the analysis of signal coming from a sensorized T-shit, worn during the night, for sleep evaluation. The goodness and reliability of the signals recorded trough the T-shirt was previously tested, while the employed algorithms for feature extraction and sleep classification were previously developed on standard ECG recordings and the obtained classification was compared to the standard clinical practice based on polysomnography (PSG). In the present work we combined T-shirt recordings and automatic classification and could obtain reliable sleep profiles, i.e. the sleep classification in WAKE, REM (rapid eye movement) and NREM stages, based on heart rate variability (HRV), respiration and movement signals.

Metric learning for automatic sleep stage classification.

PubMed

Phan, Huy; Do, Quan; Do, The-Luan; Vu, Duc-Lung

2013-01-01

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step.

Automating document classification for the Immune Epitope Database

PubMed Central

Wang, Peng; Morgan, Alexander A; Zhang, Qing; Sette, Alessandro; Peters, Bjoern

2007-01-01

Background The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results We here report our experience in automating this process using Naïve Bayes classifiers trained on 20,910 abstracts classified by domain experts. Improvements on the basic classifier performance were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e.g. identify peptides sequences. We have implemented the classifier into the curation process determining if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be made, in which case the abstracts are manually classified. Testing this classification scheme on an independent dataset, we achieve 95% sensitivity and specificity in the 51.1% of abstracts that were automatically classified. Conclusion By implementing text classification, we have sped up the reference selection process without sacrificing sensitivity or specificity of the human expert classification. This study provides both practical recommendations for users of text classification tools, as well as a large dataset which can serve as a benchmark for tool developers. PMID:17655769

Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

NASA Technical Reports Server (NTRS)

Coggeshall, M. E.; Hoffer, R. M.

1973-01-01

Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

PubMed Central

Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

2016-01-01

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter.

PubMed

Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Scotch, Matthew; Smith, Karen; Malone, Dan; Gonzalez, Graciela

2016-03-01

Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall(®), oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall(®): 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

PubMed

Zhang, Junming; Wu, Yan

2018-03-28

Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

PubMed

Kropf, Stefan; Krücken, Peter; Mueller, Wolf; Denecke, Kerstin

2017-05-18

Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse. Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML. Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries. Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.

Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?

PubMed

Boudissa, M; Orfeuvre, B; Chabanas, M; Tonetti, J

2017-09-01

The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users. Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents. Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi 2 test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility. Correct classification rates were significantly higher in the "segmentation" group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21-35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84). Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training. III: prospective case-control study of a diagnostic procedure. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Use of LANDSAT data for automatic classification and area estimation of sugarcane plantation in Sao Paulo state, Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Mendonca, F. J.

1980-01-01

Ten segments of the size 20 x 10 km were aerially photographed and used as training areas for automatic classifications. The study areas was covered by four LANDSAT paths: 235, 236, 237, and 238. The percentages of overall correct classification for these paths range from 79.56 percent for path 238 to 95.59 percent for path 237.

An automatic aerosol classification for earlinet: application and results

NASA Astrophysics Data System (ADS)

Papagiannopoulos, Nikolaos; Mona, Lucia; Amiridis, Vassilis; Binietoglou, Ioannis; D'Amico, Giuseppe; Guma-Claramunt, P.; Schwarz, Anja; Alados-Arboledas, Lucas; Amodeo, Aldo; Apituley, Arnoud; Baars, Holger; Bortoli, Daniele; Comeron, Adolfo; Guerrero-Rascado, Juan Luis; Kokkalis, Panos; Nicolae, Doina; Papayannis, Alex; Pappalardo, Gelsomina; Wandinger, Ulla; Wiegner, Matthias

2018-04-01

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

Documentation of pharmaceutical care: Validation of an intervention oriented classification system.

PubMed

Maes, Karen A; Studer, Helene; Berger, Jérôme; Hersberger, Kurt E; Lampert, Markus L

2017-12-01

During the dispensing process, pharmacists may come across technical and clinical issues requiring a pharmaceutical intervention (PI). An intervention-oriented classification system is a helpful tool to document these PIs in a structured manner. Therefore, we developed the PharmDISC classification system (Pharmacists' Documentation of Interventions in Seamless Care). The aim of this study was to evaluate the PharmDISC system in the daily practice environment (in terms of interrater reliability, appropriateness, interpretability, acceptability, feasibility, and validity); to assess its user satisfaction, the descriptive manual, and the online training; and to explore first implementation aspects. Twenty-one pharmacists from different community pharmacies each classified 30 prescriptions requiring a PI with the PharmDISC system on 5 selected days within 5 weeks. Interrater reliability was determined using model PIs and Fleiss's kappa coefficients (κ) were calculated. User satisfaction was assessed by questionnaire with a 4-point Likert scale. The main outcome measures were interrater reliability (κ); appropriateness, interpretability, validity (ratio of completely classified PIs/all PIs); feasibility, and acceptability (user satisfaction and suggestions). The PharmDISC system reached an average substantial agreement (κ = 0.66). Of documented 519 PIs, 430 (82.9%) were completely classified. Most users found the system comprehensive (median user agreement 3 [2/3.25 quartiles]) and practical (3[2.75/3]). The PharmDISC system raised the awareness regarding drug-related problems for most users (n = 16). To facilitate its implementation, an electronic version that automatically connects to the prescription together with a task manager for PIs needing follow-up was suggested. Barriers could be time expenditure and lack of understanding the benefits. Substantial interrater reliability and acceptable user satisfaction indicate that the PharmDISC system is a valid system to document PIs in daily community pharmacy practice. © 2017 John Wiley & Sons, Ltd.

An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy

PubMed Central

Ramanujam, Nedunchelian; Kaliappan, Manivannan

2016-01-01

Nowadays, automatic multidocument text summarization systems can successfully retrieve the summary sentences from the input documents. But, it has many limitations such as inaccurate extraction to essential sentences, low coverage, poor coherence among the sentences, and redundancy. This paper introduces a new concept of timestamp approach with Naïve Bayesian Classification approach for multidocument text summarization. The timestamp provides the summary an ordered look, which achieves the coherent looking summary. It extracts the more relevant information from the multiple documents. Here, scoring strategy is also used to calculate the score for the words to obtain the word frequency. The higher linguistic quality is estimated in terms of readability and comprehensibility. In order to show the efficiency of the proposed method, this paper presents the comparison between the proposed methods with the existing MEAD algorithm. The timestamp procedure is also applied on the MEAD algorithm and the results are examined with the proposed method. The results show that the proposed method results in lesser time than the existing MEAD algorithm to execute the summarization process. Moreover, the proposed method results in better precision, recall, and F-score than the existing clustering with lexical chaining approach. PMID:27034971

10 CFR 1045.38 - Automatic declassification prohibition.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Automatic declassification prohibition. (a) Documents containing RD and FRD remain classified until a... Energy Act, no date or event for automatic declassification ever applies to RD and FRD documents, even if such documents also contain NSI. (c) E.O. 12958 acknowledges that RD and FRD are exempt from all...

10 CFR 1045.38 - Automatic declassification prohibition.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Automatic declassification prohibition. (a) Documents containing RD and FRD remain classified until a... Energy Act, no date or event for automatic declassification ever applies to RD and FRD documents, even if such documents also contain NSI. (c) E.O. 12958 acknowledges that RD and FRD are exempt from all...

10 CFR 1045.38 - Automatic declassification prohibition.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Automatic declassification prohibition. (a) Documents containing RD and FRD remain classified until a... Energy Act, no date or event for automatic declassification ever applies to RD and FRD documents, even if such documents also contain NSI. (c) E.O. 12958 acknowledges that RD and FRD are exempt from all...

10 CFR 1045.38 - Automatic declassification prohibition.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Automatic declassification prohibition. (a) Documents containing RD and FRD remain classified until a... Energy Act, no date or event for automatic declassification ever applies to RD and FRD documents, even if such documents also contain NSI. (c) E.O. 12958 acknowledges that RD and FRD are exempt from all...

10 CFR 1045.38 - Automatic declassification prohibition.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Automatic declassification prohibition. (a) Documents containing RD and FRD remain classified until a... Energy Act, no date or event for automatic declassification ever applies to RD and FRD documents, even if such documents also contain NSI. (c) E.O. 12958 acknowledges that RD and FRD are exempt from all...

Open Dataset for the Automatic Recognition of Sedentary Behaviors.

PubMed

Possos, William; Cruz, Robinson; Cerón, Jesús D; López, Diego M; Sierra-Torres, Carlos H

2017-01-01

Sedentarism is associated with the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Therefore, the identification of specific sedentary behaviors (TV viewing, sitting at work, driving, relaxing, etc.) is especially relevant for planning personalized prevention programs. To build and evaluate a public a dataset for the automatic recognition (classification) of sedentary behaviors. The dataset included data from 30 subjects, who performed 23 sedentary behaviors while wearing a commercial wearable on the wrist, a smartphone on the hip and another in the thigh. Bluetooth Low Energy (BLE) beacons were used in order to improve the automatic classification of different sedentary behaviors. The study also compared six well know data mining classification techniques in order to identify the more precise method of solving the classification problem of the 23 defined behaviors. A better classification accuracy was obtained using the Random Forest algorithm and when data were collected from the phone on the hip. Furthermore, the use of beacons as a reference for obtaining the symbolic location of the individual improved the precision of the classification.

Automatic 3d Building Model Generations with Airborne LiDAR Data

NASA Astrophysics Data System (ADS)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

Research on Automatic Classification, Indexing and Extracting. Annual Progress Report.

ERIC Educational Resources Information Center

Baker, F.T.; And Others

In order to contribute to the success of several studies for automatic classification, indexing and extracting currently in progress, as well as to further the theoretical and practical understanding of textual item distributions, the development of a frequency program capable of supplying these types of information was undertaken. The program…

Automatic interpretation of ERTS data for forest management

NASA Technical Reports Server (NTRS)

Kirvida, L.; Johnson, G. R.

1973-01-01

Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wild life management, forest inventory and forest condition monitoring. Automatic procedures based on both multi-spectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74% was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 99% was obtained.

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

NASA Technical Reports Server (NTRS)

Mausel, P. W.; Todd, W. J.; Baumgardner, M. F.

1976-01-01

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas.

Automatic classification of atypical lymphoid B cells using digital blood image processing.

PubMed

Alférez, S; Merino, A; Mujica, L E; Ruiz, M; Bigorra, L; Rodellar, J

2014-08-01

There are automated systems for digital peripheral blood (PB) cell analysis, but they operate most effectively in nonpathological blood samples. The objective of this work was to design a methodology to improve the automatic classification of abnormal lymphoid cells. We analyzed 340 digital images of individual lymphoid cells from PB films obtained in the CellaVision DM96:150 chronic lymphocytic leukemia (CLL) cells, 100 hairy cell leukemia (HCL) cells, and 90 normal lymphocytes (N). We implemented the Watershed Transformation to segment the nucleus, the cytoplasm, and the peripheral cell region. We extracted 44 features and then the clustering Fuzzy C-Means (FCM) was applied in two steps for the lymphocyte classification. The images were automatically clustered in three groups, one of them with 98% of the HCL cells. The set of the remaining cells was clustered again using FCM and texture features. The two new groups contained 83.3% of the N cells and 71.3% of the CLL cells, respectively. The approach has been able to automatically classify with high precision three types of lymphoid cells. The addition of more descriptors and other classification techniques will allow extending the classification to other classes of atypical lymphoid cells. © 2013 John Wiley & Sons Ltd.

Using binary classification to prioritize and curate articles for the Comparative Toxicogenomics Database.

PubMed

Vishnyakova, Dina; Pasche, Emilie; Ruch, Patrick

2012-01-01

We report on the original integration of an automatic text categorization pipeline, so-called ToxiCat (Toxicogenomic Categorizer), that we developed to perform biomedical documents classification and prioritization in order to speed up the curation of the Comparative Toxicogenomics Database (CTD). The task can be basically described as a binary classification task, where a scoring function is used to rank a selected set of articles. Then components of a question-answering system are used to extract CTD-specific annotations from the ranked list of articles. The ranking function is generated using a Support Vector Machine, which combines three main modules: an information retrieval engine for MEDLINE (EAGLi), a gene normalization service (NormaGene) developed for a previous BioCreative campaign and finally, a set of answering components and entity recognizer for diseases and chemicals. The main components of the pipeline are publicly available both as web application and web services. The specific integration performed for the BioCreative competition is available via a web user interface at http://pingu.unige.ch:8080/Toxicat.

Automatic grade classification of Barretts Esophagus through feature enhancement

NASA Astrophysics Data System (ADS)

Ghatwary, Noha; Ahmed, Amr; Ye, Xujiong; Jalab, Hamid

2017-03-01

Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi-classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.

Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

PubMed Central

2012-01-01

Background While progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps. Results We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features. Conclusions We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be possible to replace this with other texture identifiers, and we plan to explore this in future work. PMID:22321695

Automatic photointerpretation for plant species and stress identification (ERTS-A1)

NASA Technical Reports Server (NTRS)

Swanlund, G. D. (Principal Investigator); Kirvida, L.; Johnson, G. R.

1973-01-01

The author has identified the following significant results. Automatic stratification of forested land from ERTS-1 data provides a valuable tool for resource management. The results are useful for wood product yield estimates, recreation and wildlife management, forest inventory, and forest condition monitoring. Automatic procedures based on both multispectral and spatial features are evaluated. With five classes, training and testing on the same samples, classification accuracy of 74 percent was achieved using the MSS multispectral features. When adding texture computed from 8 x 8 arrays, classification accuracy of 90 percent was obtained.

Automatic liver volume segmentation and fibrosis classification

NASA Astrophysics Data System (ADS)

Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

2018-02-01

In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

Towards an Automatic Classification System for Supporting the Development of Critical Reflective Skills in L2 Learning

ERIC Educational Resources Information Center

Cheng, Gary

2017-01-01

This study aimed to develop an automatic classification system, namely ACTIVE, for generating immediate and individualised feedback on students' reflective entries about their second language (L2) learning experiences. It also aimed to explore students' attitudes towards using the system to support the development of their reflective skills in L2…

10 CFR 1016.32 - Classification and preparation of documents.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 4 2013-01-01 2013-01-01 false Classification and preparation of documents. 1016.32... of Information § 1016.32 Classification and preparation of documents. (a) Classification. Restricted... he is not positive is not within that definition and CG-UF-3 does not provide positive classification...

10 CFR 1016.32 - Classification and preparation of documents.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 4 2014-01-01 2014-01-01 false Classification and preparation of documents. 1016.32... of Information § 1016.32 Classification and preparation of documents. (a) Classification. Restricted... he is not positive is not within that definition and CG-UF-3 does not provide positive classification...

10 CFR 1016.32 - Classification and preparation of documents.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 4 2012-01-01 2012-01-01 false Classification and preparation of documents. 1016.32... of Information § 1016.32 Classification and preparation of documents. (a) Classification. Restricted... he is not positive is not within that definition and CG-UF-3 does not provide positive classification...

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Culto: AN Ontology-Based Annotation Tool for Data Curation in Cultural Heritage

NASA Astrophysics Data System (ADS)

Garozzo, R.; Murabito, F.; Santagati, C.; Pino, C.; Spampinato, C.

2017-08-01

This paper proposes CulTO, a software tool relying on a computational ontology for Cultural Heritage domain modelling, with a specific focus on religious historical buildings, for supporting cultural heritage experts in their investigations. It is specifically thought to support annotation, automatic indexing, classification and curation of photographic data and text documents of historical buildings. CULTO also serves as a useful tool for Historical Building Information Modeling (H-BIM) by enabling semantic 3D data modeling and further enrichment with non-geometrical information of historical buildings through the inclusion of new concepts about historical documents, images, decay or deformation evidence as well as decorative elements into BIM platforms. CulTO is the result of a joint research effort between the Laboratory of Surveying and Architectural Photogrammetry "Luigi Andreozzi" and the PeRCeiVe Lab (Pattern Recognition and Computer Vision Lab) of the University of Catania,

Fitness Tracker for Weight Lifting Style Workouts

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

Wihl, B. M.

This document proposes an early, high level design for a fitness tracking system which can automatically log weight lifting style workouts. The system will provide an easy to use interface both physically through the use of several wireless wristband style motion trackers worn on the limbs, and graphically through a smartphone application. Exercise classification will be accomplished by calibration of the user’s specific motions. The system will accurately track a user’s workout, miscounting no more than one repetition in every 20, have sufficient battery life to last several hours, work with existing smartphones and have a cost similar to thosemore » of current fitness tracking devices. This document presents the mission background, current state-of-theart, stakeholders and their expectations, the proposed system’s context and concepts, implementation concepts, system requirements, first sublevel function decomposition, possible risks for the system, and a reflection on the design process.« less

A Method for Extracting Important Segments from Documents Using Support Vector Machines

NASA Astrophysics Data System (ADS)

Suzuki, Daisuke; Utsumi, Akira

In this paper we propose an extraction-based method for automatic summarization. The proposed method consists of two processes: important segment extraction and sentence compaction. The process of important segment extraction classifies each segment in a document as important or not by Support Vector Machines (SVMs). The process of sentence compaction then determines grammatically appropriate portions of a sentence for a summary according to its dependency structure and the classification result by SVMs. To test the performance of our method, we conducted an evaluation experiment using the Text Summarization Challenge (TSC-1) corpus of human-prepared summaries. The result was that our method achieved better performance than a segment-extraction-only method and the Lead method, especially for sentences only a part of which was included in human summaries. Further analysis of the experimental results suggests that a hybrid method that integrates sentence extraction with segment extraction may generate better summaries.

Comparison of Document Index Graph Using TextRank and HITS Weighting Method in Automatic Text Summarization

NASA Astrophysics Data System (ADS)

Hadyan, Fadhlil; Shaufiah; Arif Bijaksana, Moch.

2017-01-01

Automatic summarization is a system that can help someone to take the core information of a long text instantly. The system can help by summarizing text automatically. there’s Already many summarization systems that have been developed at this time but there are still many problems in those system. In this final task proposed summarization method using document index graph. This method utilizes the PageRank and HITS formula used to assess the web page, adapted to make an assessment of words in the sentences in a text document. The expected outcome of this final task is a system that can do summarization of a single document, by utilizing document index graph with TextRank and HITS to improve the quality of the summary results automatically.

[The maintenance of automatic analysers and associated documentation].

PubMed

Adjidé, V; Fournier, P; Vassault, A

2010-12-01

The maintenance of automatic analysers and associated documentation taking part in the requirements of the ISO 15189 Standard and the French regulation as well have to be defined in the laboratory policy. The management of the periodic maintenance and documentation shall be implemented and fulfilled. The organisation of corrective maintenance has to be managed to avoid interruption of the task of the laboratory. The different recommendations concern the identification of materials including automatic analysers, the environmental conditions to take into account, the documentation provided by the manufacturer and documents prepared by the laboratory including procedures for maintenance.

Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images.

PubMed

Mane, Vijay Mahadeo; Jadhav, D V

2017-05-24

Diabetic retinopathy (DR) is the most common diabetic eye disease. Doctors are using various test methods to detect DR. But, the availability of test methods and requirements of domain experts pose a new challenge in the automatic detection of DR. In order to fulfill this objective, a variety of algorithms has been developed in the literature. In this paper, we propose a system consisting of a novel sparking process and a holoentropy-based decision tree for automatic classification of DR images to further improve the effectiveness. The sparking process algorithm is developed for automatic segmentation of blood vessels through the estimation of optimal threshold. The holoentropy enabled decision tree is newly developed for automatic classification of retinal images into normal or abnormal using hybrid features which preserve the disease-level patterns even more than the signal level of the feature. The effectiveness of the proposed system is analyzed using standard fundus image databases DIARETDB0 and DIARETDB1 for sensitivity, specificity and accuracy. The proposed system yields sensitivity, specificity and accuracy values of 96.72%, 97.01% and 96.45%, respectively. The experimental result reveals that the proposed technique outperforms the existing algorithms.

An efficient scheme for automatic web pages categorization using the support vector machine

NASA Astrophysics Data System (ADS)

Bhalla, Vinod Kumar; Kumar, Neeraj

2016-07-01

In the past few years, with an evolution of the Internet and related technologies, the number of the Internet users grows exponentially. These users demand access to relevant web pages from the Internet within fraction of seconds. To achieve this goal, there is a requirement of an efficient categorization of web page contents. Manual categorization of these billions of web pages to achieve high accuracy is a challenging task. Most of the existing techniques reported in the literature are semi-automatic. Using these techniques, higher level of accuracy cannot be achieved. To achieve these goals, this paper proposes an automatic web pages categorization into the domain category. The proposed scheme is based on the identification of specific and relevant features of the web pages. In the proposed scheme, first extraction and evaluation of features are done followed by filtering the feature set for categorization of domain web pages. A feature extraction tool based on the HTML document object model of the web page is developed in the proposed scheme. Feature extraction and weight assignment are based on the collection of domain-specific keyword list developed by considering various domain pages. Moreover, the keyword list is reduced on the basis of ids of keywords in keyword list. Also, stemming of keywords and tag text is done to achieve a higher accuracy. An extensive feature set is generated to develop a robust classification technique. The proposed scheme was evaluated using a machine learning method in combination with feature extraction and statistical analysis using support vector machine kernel as the classification tool. The results obtained confirm the effectiveness of the proposed scheme in terms of its accuracy in different categories of web pages.

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

10 CFR 1016.32 - Classification and preparation of documents.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Classification and preparation of documents. 1016.32 Section 1016.32 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) SAFEGUARDING OF RESTRICTED DATA Control of Information § 1016.32 Classification and preparation of documents. (a) Classification. Restricted...

10 CFR 1016.32 - Classification and preparation of documents.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 4 2011-01-01 2011-01-01 false Classification and preparation of documents. 1016.32 Section 1016.32 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) SAFEGUARDING OF RESTRICTED DATA Control of Information § 1016.32 Classification and preparation of documents. (a) Classification. Restricted...

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

PubMed Central

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Automatic Classification of Time-variable X-Ray Sources

NASA Astrophysics Data System (ADS)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation

PubMed Central

Zhang, Fan; Zhang, Xinhong

2011-01-01

Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves. PMID:22163744

INFORMATION STORAGE AND RETRIEVAL, REPORTS ON EVALUATION PROCEDURES AND RESULTS 1965-1967.

ERIC Educational Resources Information Center

SALTON, GERALD

A DETAILED ANALYSIS OF THE RETRIEVAL EVALUATION RESULTS OBTAINED WITH THE AUTOMATIC SMART DOCUMENT RETRIEVAL SYSTEM FOR DOCUMENT COLLECTIONS IN THE FIELDS OF AERODYNAMICS, COMPUTER SCIENCE, AND DOCUMENTATION IS GIVEN IN THIS REPORT. THE VARIOUS COMPONENTS OF FULLY AUTOMATIC DOCUMENT RETRIEVAL SYSTEMS ARE DISCUSSED IN DETAIL, INCLUDING THE FORMS OF…

An examination of the potential applications of automatic classification techniques to Georgia management problems

NASA Technical Reports Server (NTRS)

Rado, B. Q.

1975-01-01

Automatic classification techniques are described in relation to future information and natural resource planning systems with emphasis on application to Georgia resource management problems. The concept, design, and purpose of Georgia's statewide Resource AS Assessment Program is reviewed along with participation in a workshop at the Earth Resources Laboratory. Potential areas of application discussed include: agriculture, forestry, water resources, environmental planning, and geology.

Automated retinal vessel type classification in color fundus images

NASA Astrophysics Data System (ADS)

Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

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

PubMed

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-05-18

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

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

PubMed

Zare, Marzieh; Rezvani, Zahra; Benasich, April A

2016-07-01

This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

SYRIAC: The systematic review information automated collection system a data warehouse for facilitating automated biomedical text classification.

PubMed

Yang, Jianji J; Cohen, Aaron M; Cohen, Aaron; McDonagh, Marian S

2008-11-06

Automatic document classification can be valuable in increasing the efficiency in updating systematic reviews (SR). In order for the machine learning process to work well, it is critical to create and maintain high-quality training datasets consisting of expert SR inclusion/exclusion decisions. This task can be laborious, especially when the number of topics is large and source data format is inconsistent.To approach this problem, we build an automated system to streamline the required steps, from initial notification of update in source annotation files to loading the data warehouse, along with a web interface to monitor the status of each topic. In our current collection of 26 SR topics, we were able to standardize almost all of the relevance judgments and recovered PMIDs for over 80% of all articles. Of those PMIDs, over 99% were correct in a manual random sample study. Our system performs an essential function in creating training and evaluation data sets for SR text mining research.

SYRIAC: The SYstematic Review Information Automated Collection System A Data Warehouse for Facilitating Automated Biomedical Text Classification

PubMed Central

Yang, Jianji J.; Cohen, Aaron M.; McDonagh, Marian S.

2008-01-01

Automatic document classification can be valuable in increasing the efficiency in updating systematic reviews (SR). In order for the machine learning process to work well, it is critical to create and maintain high-quality training datasets consisting of expert SR inclusion/exclusion decisions. This task can be laborious, especially when the number of topics is large and source data format is inconsistent. To approach this problem, we build an automated system to streamline the required steps, from initial notification of update in source annotation files to loading the data warehouse, along with a web interface to monitor the status of each topic. In our current collection of 26 SR topics, we were able to standardize almost all of the relevance judgments and recovered PMIDs for over 80% of all articles. Of those PMIDs, over 99% were correct in a manual random sample study. Our system performs an essential function in creating training and evaluation datasets for SR text mining research. PMID:18999194

A study and evaluation of image analysis techniques applied to remotely sensed data

NASA Technical Reports Server (NTRS)

Atkinson, R. J.; Dasarathy, B. V.; Lybanon, M.; Ramapriyan, H. K.

1976-01-01

An analysis of phenomena causing nonlinearities in the transformation from Landsat multispectral scanner coordinates to ground coordinates is presented. Experimental results comparing rms errors at ground control points indicated a slight improvement when a nonlinear (8-parameter) transformation was used instead of an affine (6-parameter) transformation. Using a preliminary ground truth map of a test site in Alabama covering the Mobile Bay area and six Landsat images of the same scene, several classification methods were assessed. A methodology was developed for automatic change detection using classification/cluster maps. A coding scheme was employed for generation of change depiction maps indicating specific types of changes. Inter- and intraseasonal data of the Mobile Bay test area were compared to illustrate the method. A beginning was made in the study of data compression by applying a Karhunen-Loeve transform technique to a small section of the test data set. The second part of the report provides a formal documentation of the several programs developed for the analysis and assessments presented.

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2010 CFR

2010-01-01

... classification decisions. (c) Markings required on face of documents. (1) For derivative classification of... to a document must be placed in a conspicuous fashion in letters at the top and bottom of the outside... on the face of the document: Reproduction or Further Dissemination Requires Approval of If any...

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2011 CFR

2011-01-01

... classification decisions. (c) Markings required on face of documents. (1) For derivative classification of... to a document must be placed in a conspicuous fashion in letters at the top and bottom of the outside... on the face of the document: Reproduction or Further Dissemination Requires Approval of If any...

Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques.

PubMed

Caixinha, Miguel; Santos, Mário; Santos, Jaime

2016-04-01

To demonstrate the feasibility of a new methodology for cataract hardness characterization and automatic classification using ultrasound techniques, different cataract degrees were induced in 210 porcine lenses. A 25-MHz ultrasound transducer was used to obtain acoustical parameters (velocity and attenuation) and backscattering signals. B-Scan and parametric Nakagami images were constructed. Ninety-seven parameters were extracted and subjected to a Principal Component Analysis. Bayes, K-Nearest-Neighbours, Fisher Linear Discriminant and Support Vector Machine (SVM) classifiers were used to automatically classify the different cataract severities. Statistically significant increases with cataract formation were found for velocity, attenuation, mean brightness intensity of the B-Scan images and mean Nakagami m parameter (p < 0.01). The four classifiers showed a good performance for healthy versus cataractous lenses (F-measure ≥ 92.68%), while for initial versus severe cataracts the SVM classifier showed the higher performance (90.62%). The results showed that ultrasound techniques can be used for non-invasive cataract hardness characterization and automatic classification. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Classification of cloud fields based on textural characteristics

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1987-01-01

The present study reexamines the applicability of texture-based features for automatic cloud classification using very high spatial resolution (57 m) Landsat multispectral scanner digital data. It is concluded that cloud classification can be accomplished using only a single visible channel.

Automatic comparison of striation marks and automatic classification of shoe prints

NASA Astrophysics Data System (ADS)

Geradts, Zeno J.; Keijzer, Jan; Keereweer, Isaac

1995-09-01

A database for toolmarks (named TRAX) and a database for footwear outsole designs (named REBEZO) have been developed on a PC. The databases are filled with video-images and administrative data about the toolmarks and the footwear designs. An algorithm for the automatic comparison of the digitized striation patterns has been developed for TRAX. The algorithm appears to work well for deep and complete striation marks and will be implemented in TRAX. For REBEZO some efforts have been made to the automatic classification of outsole patterns. The algorithm first segments the shoeprofile. Fourier-features are selected for the separate elements and are classified with a neural network. In future developments information on invariant moments of the shape and rotation angle will be included in the neural network.

Ensemble methods with simple features for document zone classification

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing

2012-01-01

Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Hoynck, Michael

2005-01-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Towards automatic music transcription: note extraction based on independent subspace analysis

NASA Astrophysics Data System (ADS)

Wellhausen, Jens; Höynck, Michael

2004-12-01

Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness.

Research on Generating Method of Embedded Software Test Document Based on Dynamic Model

NASA Astrophysics Data System (ADS)

Qu, MingCheng; Wu, XiangHu; Tao, YongChao; Liu, Ying

2018-03-01

This paper provides a dynamic model-based test document generation method for embedded software that provides automatic generation of two documents: test requirements specification documentation and configuration item test documentation. This method enables dynamic test requirements to be implemented in dynamic models, enabling dynamic test demand tracking to be easily generated; able to automatically generate standardized, standardized test requirements and test documentation, improved document-related content inconsistency and lack of integrity And other issues, improve the efficiency.

Comprehensible knowledge model creation for cancer treatment decision making.

PubMed

Afzal, Muhammad; Hussain, Maqbool; Ali Khan, Wajahat; Ali, Taqdir; Lee, Sungyoung; Huh, Eui-Nam; Farooq Ahmad, Hafiz; Jamshed, Arif; Iqbal, Hassan; Irfan, Muhammad; Abbas Hydari, Manzar

2017-03-01

A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

PubMed

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness. This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

Automatic crack detection and classification method for subway tunnel safety monitoring.

PubMed

Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

2014-10-16

Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.

Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images

PubMed Central

Phan, Thanh Vân; Seoud, Lama; Chakor, Hadi; Cheriet, Farida

2016-01-01

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality. PMID:27190636

Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

2014-01-01

Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification. PMID:25325337

Using machine learning classifiers to assist healthcare-related decisions: classification of electronic patient records.

PubMed

Pollettini, Juliana T; Panico, Sylvia R G; Daneluzzi, Julio C; Tinós, Renato; Baranauskas, José A; Macedo, Alessandra A

2012-12-01

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Automatic classification of time-variable X-ray sources

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

Lo, Kitty K.; Farrell, Sean; Murphy, Tara

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, andmore » other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.« less

An automatic taxonomy of galaxy morphology using unsupervised machine learning

NASA Astrophysics Data System (ADS)

Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

2018-01-01

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

6 CFR 7.28 - Automatic declassification.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Classification Appeals Panel (ISCAP) for approval. (d) Declassification guides that narrowly and precisely define... years after the date of its original classification with the exception of specific information exempt...

Automatic breast density classification using a convolutional neural network architecture search procedure

NASA Astrophysics Data System (ADS)

Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

2015-03-01

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

PubMed

Mumtaz, Wajid; Saad, Mohamad Naufal B Mohamad; Kamel, Nidal; Ali, Syed Saad Azhar; Malik, Aamir Saeed

2018-01-01

The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used. The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95. The SL features could be utilized as objective markers to screen the AUD patients and healthy controls. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users

PubMed Central

Shatkay, Hagit; Pan, Fengxia; Rzhetsky, Andrey; Wilbur, W. John

2008-01-01

Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no ‘average biologist’ client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks. Results: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three individual annotators independently tagged each sentence. We then trained and tested machine learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice. Contact: shatkay@cs.queensu.ca PMID:18718948

Methodology for classification of geographical features with remote sensing images: Application to tidal flats

NASA Astrophysics Data System (ADS)

Revollo Sarmiento, G. N.; Cipolletti, M. P.; Perillo, M. M.; Delrieux, C. A.; Perillo, Gerardo M. E.

2016-03-01

Tidal flats generally exhibit ponds of diverse size, shape, orientation and origin. Studying the genesis, evolution, stability and erosive mechanisms of these geographic features is critical to understand the dynamics of coastal wetlands. However, monitoring these locations through direct access is hard and expensive, not always feasible, and environmentally damaging. Processing remote sensing images is a natural alternative for the extraction of qualitative and quantitative data due to their non-invasive nature. In this work, a robust methodology for automatic classification of ponds and tidal creeks in tidal flats using Google Earth images is proposed. The applicability of our method is tested in nine zones with different morphological settings. Each zone is processed by a segmentation stage, where ponds and tidal creeks are identified. Next, each geographical feature is measured and a set of shape descriptors is calculated. This dataset, together with a-priori classification of each geographical feature, is used to define a regression model, which allows an extensive automatic classification of large volumes of data discriminating ponds and tidal creeks against other various geographical features. In all cases, we identified and automatically classified different geographic features with an average accuracy over 90% (89.7% in the worst case, and 99.4% in the best case). These results show the feasibility of using freely available Google Earth imagery for the automatic identification and classification of complex geographical features. Also, the presented methodology may be easily applied in other wetlands of the world and perhaps employing other remote sensing imagery.

Classification of hepatocellular carcinoma stages from free-text clinical and radiology reports

PubMed Central

Yim, Wen-wai; Kwan, Sharon W; Johnson, Guy; Yetisgen, Meliha

2017-01-01

Cancer stage information is important for clinical research. However, they are not always explicitly noted in electronic medical records. In this paper, we present our work on automatic classification of hepatocellular carcinoma (HCC) stages from free-text clinical and radiology notes. To accomplish this, we defined 11 stage parameters used in the three HCC staging systems, American Joint Committee on Cancer (AJCC), Barcelona Clinic Liver Cancer (BCLC), and Cancer of the Liver Italian Program (CLIP). After aggregating stage parameters to the patient-level, the final stage classifications were achieved using an expert-created decision logic. Each stage parameter relevant for staging was extracted using several classification methods, e.g. sentence classification and automatic information structuring, to identify and normalize text as cancer stage parameter values. Stage parameter extraction for the test set performed at 0.81 F1. Cancer stage prediction for AJCC, BCLC, and CLIP stage classifications were 0.55, 0.50, and 0.43 F1.

Automatic morphological classification of galaxy images

PubMed Central

Shamir, Lior

2009-01-01

We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbor rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with accuracy of ~90% compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects. PMID:20161594

Preliminary clinical evaluation of semi-automated nailfold capillaroscopy in the assessment of patients with Raynaud's phenomenon.

PubMed

Murray, Andrea K; Feng, Kaiyan; Moore, Tonia L; Allen, Phillip D; Taylor, Christopher J; Herrick, Ariane L

2011-08-01

  Nailfold capillaroscopy is well established in screening patients with Raynaud's phenomenon for underlying SSc-spectrum disorders, by identifying abnormal capillaries. Our aim was to compare semi-automatic feature measurement from newly developed software with manual measurements, and determine the degree to which semi-automated data allows disease group classification.   Images from 46 healthy controls, 21 patients with PRP and 49 with SSc were preprocessed, and semi-automated measurements of intercapillary distance and capillary width, tortuosity, and derangement were performed. These were compared with manual measurements. Features were used to classify images into the three subject groups.   Comparison of automatic and manual measures for distance, width, tortuosity, and derangement had correlations of r=0.583, 0.624, 0.495 (p<0.001), and 0.195 (p=0.040). For automatic measures, correlations were found between width and intercapillary distance, r=0.374, and width and tortuosity, r=0.573 (p<0.001). Significant differences between subject groups were found for all features (p<0.002). Overall, 75% of images correctly matched clinical classification using semi-automated features, compared with 71% for manual measurements.   Semi-automatic and manual measurements of distance, width, and tortuosity showed moderate (but statistically significant) correlations. Correlation for derangement was weaker. Semi-automatic measurements are faster than manual measurements. Semi-automatic parameters identify differences between groups, and are as good as manual measurements for between-group classification. © 2011 John Wiley & Sons Ltd.

Automatic classification of blank substrate defects

NASA Astrophysics Data System (ADS)

Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

2014-10-01

Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask Technology Center (MPMask). The Calibre ADC tool was qualified on production mask blanks against the manual classification. The classification accuracy of ADC is greater than 95% for critical defects with an overall accuracy of 90%. The sensitivity to weak defect signals and locating the defect in the images is a challenge we are resolving. The performance of the tool has been demonstrated on multiple mask types and is ready for deployment in full volume mask manufacturing production flow. Implementation of Calibre ADC is estimated to reduce the misclassification of critical defects by 60-80%.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

Automatic detection of confusion in elderly users of a web-based health instruction video.

PubMed

Postma-Nilsenová, Marie; Postma, Eric; Tates, Kiek

2015-06-01

Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare delivery applications on the Internet. Twenty-four elderly participants (70-90 years old) were recorded while watching Web-based health instruction videos involving easy and complex medical terminology. Relevant fragments of the participants' facial expressions were rated by 40 medical students for perceived level of confusion and analyzed with automatic software for facial movement recognition. A computer classification of the automatically detected facial features performed more accurately and with a higher sensitivity than the human observers (automatic detection and classification, 64% accuracy, 0.64 sensitivity; human observers, 41% accuracy, 0.43 sensitivity). A drill-down analysis of cues to confusion indicated the importance of the eye and eyebrow region. Confusion caused by misunderstanding of medical terminology is signaled by facial cues that can be automatically detected with currently available facial expression detection technology. The findings are relevant for the development of Web-based services for healthcare consumers.

49 CFR 1177.1 - Definitions and classifications of documents.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 8 2011-10-01 2011-10-01 false Definitions and classifications of documents. 1177.1 Section 1177.1 Transportation Other Regulations Relating to Transportation (Continued) SURFACE... Definitions and classifications of documents. (a) A “primary document” is a mortgage (excluding those under...

Crescent Evaluation : appendix D : crescent computer system components evaluation report

DOT National Transportation Integrated Search

1994-02-01

In 1990, Lockheed Integrated Systems Company (LISC) was awarded a contract, under the Crescent Demonstration Project, to demonstrate the integration of Weigh In Motion (WIM), Automatic Vehicle Classification (AVC) and Automatic Vehicle Identification...

Vietnamese Document Representation and Classification

NASA Astrophysics Data System (ADS)

Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

[Automatic Sleep Stage Classification Based on an Improved K-means Clustering Algorithm].

PubMed

Xiao, Shuyuan; Wang, Bei; Zhang, Jian; Zhang, Qunfeng; Zou, Junzhong

2016-10-01

Sleep stage scoring is a hotspot in the field of medicine and neuroscience.Visual inspection of sleep is laborious and the results may be subjective to different clinicians.Automatic sleep stage classification algorithm can be used to reduce the manual workload.However,there are still limitations when it encounters complicated and changeable clinical cases.The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data.In the proposed improved K-means clustering algorithm,points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm.Meanwhile,the cluster centers were updated according to the‘Three-Sigma Rule’during the iteration to abate the influence of the outliers.The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure(CPAP)treatment.The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%.With the analysis of morphological diversity of sleep data,it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.

Second Language Writing Classification System Based on Word-Alignment Distribution

ERIC Educational Resources Information Center

Kotani, Katsunori; Yoshimi, Takehiko

2010-01-01

The present paper introduces an automatic classification system for assisting second language (L2) writing evaluation. This system, which classifies sentences written by L2 learners as either native speaker-like or learner-like sentences, is constructed by machine learning algorithms using word-alignment distributions as classification features…

Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery

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

Warner, Timothy; Steinmaus, Karen L.

2005-02-01

New high resolution single spectral band imagery offers the capability to conduct image classifications based on spatial patterns in imagery. A classification algorithm based on autocorrelation patterns was developed to automatically extract orchards and vineyards from satellite imagery. The algorithm was tested on IKONOS imagery over Granger, WA, which resulted in a classification accuracy of 95%.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

EUCLID: automatic classification of proteins in functional classes by their database annotations.

PubMed

Tamames, J; Ouzounis, C; Casari, G; Sander, C; Valencia, A

1998-01-01

A tool is described for the automatic classification of sequences in functional classes using their database annotations. The Euclid system is based on a simple learning procedure from examples provided by human experts. Euclid is freely available for academics at http://www.gredos.cnb.uam.es/EUCLID, with the corresponding dictionaries for the generation of three, eight and 14 functional classes. E-mail: valencia@cnb.uam.es The results of the EUCLID classification of different genomes are available at http://www.sander.ebi.ac. uk/genequiz/. A detailed description of the different applications mentioned in the text is available at http://www.gredos.cnb.uam. es/EUCLID/Full_Paper

Crop identification technology assessment for remote sensing (CITARS). Volume 10: Interpretation of results

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Feiveson, A. H.; Hall, F. G.; Bauer, M. E.; Davis, B. J.; Malila, W. A.; Rice, D. P.

1975-01-01

The CITARS was an experiment designed to quantitatively evaluate crop identification performance for corn and soybeans in various environments using a well-defined set of automatic data processing (ADP) techniques. Each technique was applied to data acquired to recognize and estimate proportions of corn and soybeans. The CITARS documentation summarizes, interprets, and discusses the crop identification performances obtained using (1) different ADP procedures; (2) a linear versus a quadratic classifier; (3) prior probability information derived from historic data; (4) local versus nonlocal recognition training statistics and the associated use of preprocessing; (5) multitemporal data; (6) classification bias and mixed pixels in proportion estimation; and (7) data with differnt site characteristics, including crop, soil, atmospheric effects, and stages of crop maturity.

Affordable, automatic quantitative fall risk assessment based on clinical balance scales and Kinect data.

PubMed

Colagiorgio, P; Romano, F; Sardi, F; Moraschini, M; Sozzi, A; Bejor, M; Ricevuti, G; Buizza, A; Ramat, S

2014-01-01

The problem of a correct fall risk assessment is becoming more and more critical with the ageing of the population. In spite of the available approaches allowing a quantitative analysis of the human movement control system's performance, the clinical assessment and diagnostic approach to fall risk assessment still relies mostly on non-quantitative exams, such as clinical scales. This work documents our current effort to develop a novel method to assess balance control abilities through a system implementing an automatic evaluation of exercises drawn from balance assessment scales. Our aim is to overcome the classical limits characterizing these scales i.e. limited granularity and inter-/intra-examiner reliability, to obtain objective scores and more detailed information allowing to predict fall risk. We used Microsoft Kinect to record subjects' movements while performing challenging exercises drawn from clinical balance scales. We then computed a set of parameters quantifying the execution of the exercises and fed them to a supervised classifier to perform a classification based on the clinical score. We obtained a good accuracy (~82%) and especially a high sensitivity (~83%).

Classification of independent components of EEG into multiple artifact classes.

PubMed

Frølich, Laura; Andersen, Tobias S; Mørup, Morten

2015-01-01

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them. Copyright © 2014 Society for Psychophysiological Research.

Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

PubMed

Xu, Kele; Feng, Dawei; Mi, Haibo

2017-11-23

The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

NASA Astrophysics Data System (ADS)

Wu, Jie; Besnehard, Quentin; Marchessoux, Cédric

2011-03-01

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

Automatic indexing of scanned documents: a layout-based approach

NASA Astrophysics Data System (ADS)

Esser, Daniel; Schuster, Daniel; Muthmann, Klemens; Berger, Michael; Schill, Alexander

2012-01-01

Archiving official written documents such as invoices, reminders and account statements in business and private area gets more and more important. Creating appropriate index entries for document archives like sender's name, creation date or document number is a tedious manual work. We present a novel approach to handle automatic indexing of documents based on generic positional extraction of index terms. For this purpose we apply the knowledge of document templates stored in a common full text search index to find index positions that were successfully extracted in the past.

Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs

NASA Astrophysics Data System (ADS)

Mendonça, Ana Maria; Remeseiro, Beatriz; Dashtbozorg, Behdad; Campilho, Aurélio

2017-03-01

The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients' condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error.

Electronic Derivative Classifier/Reviewing Official

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

Harris, Joshua C; McDuffie, Gregory P; Light, Ken L

2017-02-17

The electronic Derivative Classifier, Reviewing Official (eDC/RO) is a web based document management and routing system that reduces security risks and increases workflow efficiencies. The system automates the upload, notification review request, and document status tracking of documents for classification review on a secure server. It supports a variety of document formats (i.e., pdf, doc, docx, xls, xlsx, xlsm, ppt, pptx, vsd, vsdx and txt), and allows for the dynamic placement of classification markings such as the classification level, category and caveats on the document, in addition to a document footer and digital signature.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels.

PubMed

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Semiotic indexing of digital resources

DOEpatents

Parker, Charles T; Garrity, George M

2014-12-02

A method of classifying a plurality of documents. The method includes steps of providing a first set of classification terms and a second set of classification terms, the second set of classification terms being different from the first set of classification terms; generating a first frequency array of a number of occurrences of each term from the first set of classification terms in each document; generating a second frequency array of a number of occurrences of each term from the second set of classification terms in each document; generating a first similarity matrix from the first frequency array; generating a second similarity matrix from the second frequency array; determining an entrywise combination of the first similarity matrix and the second similarity matrix; and clustering the plurality of documents based on the result of the entrywise combination.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

Adaptive video-based vehicle classification technique for monitoring traffic : [executive summary].

DOT National Transportation Integrated Search

2015-08-01

Federal Highway Administration (FHWA) recommends axle-based classification standards to map : passenger vehicles, single unit trucks, and multi-unit trucks, at Automatic Traffic Recorder (ATR) stations : statewide. Many state Departments of Transport...

Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

PubMed

Igual, Laura; Soliva, Joan Carles; Escalera, Sergio; Gimeno, Roger; Vilarroya, Oscar; Radeva, Petia

2012-12-01

We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. Copyright © 2012 Elsevier Ltd. All rights reserved.

Morphological feature extraction for the classification of digital images of cancerous tissues.

PubMed

Thiran, J P; Macq, B

1996-10-01

This paper presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of Geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size, and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.

Automatic analysis and classification of surface electromyography.

PubMed

Abou-Chadi, F E; Nashar, A; Saad, M

2001-01-01

In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.

Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

PubMed Central

Seeberg, Trine M.; Tjønnås, Johannes; Haugnes, Pål; Sandbakk, Øyvind

2017-01-01

The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs) that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers. PMID:29283421

The Crescent Project : an evaluation of an element of the HELP Program : executive summary

DOT National Transportation Integrated Search

1994-02-01

The HELP/Crescent Project on the West Coast evaluated the applicability of four technologies for screening transponder-equipped vehicles. The technologies included automatic vehicle identification, weigh-in-motion, automatic vehicle classification, a...

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

An automatic graph-based approach for artery/vein classification in retinal images.

PubMed

Dashtbozorg, Behdad; Mendonça, Ana Maria; Campilho, Aurélio

2014-03-01

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

Research on Remote Sensing Geological Information Extraction Based on Object Oriented Classification

NASA Astrophysics Data System (ADS)

Gao, Hui

2018-04-01

The northern Tibet belongs to the Sub cold arid climate zone in the plateau. It is rarely visited by people. The geological working conditions are very poor. However, the stratum exposures are good and human interference is very small. Therefore, the research on the automatic classification and extraction of remote sensing geological information has typical significance and good application prospect. Based on the object-oriented classification in Northern Tibet, using the Worldview2 high-resolution remote sensing data, combined with the tectonic information and image enhancement, the lithological spectral features, shape features, spatial locations and topological relations of various geological information are excavated. By setting the threshold, based on the hierarchical classification, eight kinds of geological information were classified and extracted. Compared with the existing geological maps, the accuracy analysis shows that the overall accuracy reached 87.8561 %, indicating that the classification-oriented method is effective and feasible for this study area and provides a new idea for the automatic extraction of remote sensing geological information.

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…

Chemical-induced disease relation extraction with various linguistic features.

PubMed

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

2016-01-01

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

Adaptive sleep-wake discrimination for wearable devices.

PubMed

Karlen, Walter; Floreano, Dario

2011-04-01

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human-rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

Documenting Community Engagement Practices and Outcomes: Insights from Recipients of the 2010 Carnegie Community Engagement Classification

ERIC Educational Resources Information Center

Noel, Jana; Earwicker, David P.

2015-01-01

This study was performed to document the strategies and methods used by successful applicants for the 2010 Carnegie Community Engagement Classification and to document the cultural shifts connected with the application process and receipt of the Classification. Four major findings emerged: (1) Applicants benefited from a team approach; (2)…

Automatic system for computer program documentation

NASA Technical Reports Server (NTRS)

Simmons, D. B.; Elliott, R. W.; Arseven, S.; Colunga, D.

1972-01-01

Work done on a project to design an automatic system for computer program documentation aids was made to determine what existing programs could be used effectively to document computer programs. Results of the study are included in the form of an extensive bibliography and working papers on appropriate operating systems, text editors, program editors, data structures, standards, decision tables, flowchart systems, and proprietary documentation aids. The preliminary design for an automated documentation system is also included. An actual program has been documented in detail to demonstrate the types of output that can be produced by the proposed system.

Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

PubMed

Hsu, Wei-Yen

2013-12-01

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

Accelerometry-based classification of human activities using Markov modeling.

PubMed

Mannini, Andrea; Sabatini, Angelo Maria

2011-01-01

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.

Automated measurement of retinal vascular tortuosity.

PubMed Central

Hart, W. E.; Goldbaum, M.; Côté, B.; Kube, P.; Nelson, M. R.

1997-01-01

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity. Images Figure 1 PMID:9357668

Web information retrieval for health professionals.

PubMed

Ting, S L; See-To, Eric W K; Tse, Y K

2013-06-01

This paper presents a Web Information Retrieval System (WebIRS), which is designed to assist the healthcare professionals to obtain up-to-date medical knowledge and information via the World Wide Web (WWW). The system leverages the document classification and text summarization techniques to deliver the highly correlated medical information to the physicians. The system architecture of the proposed WebIRS is first discussed, and then a case study on an application of the proposed system in a Hong Kong medical organization is presented to illustrate the adoption process and a questionnaire is administrated to collect feedback on the operation and performance of WebIRS in comparison with conventional information retrieval in the WWW. A prototype system has been constructed and implemented on a trial basis in a medical organization. It has proven to be of benefit to healthcare professionals through its automatic functions in classification and summarizing the medical information that the physicians needed and interested. The results of the case study show that with the use of the proposed WebIRS, significant reduction of searching time and effort, with retrieval of highly relevant materials can be attained.

A Feature Selection Method Based on Fisher's Discriminant Ratio for Text Sentiment Classification

NASA Astrophysics Data System (ADS)

Wang, Suge; Li, Deyu; Wei, Yingjie; Li, Hongxia

With the rapid growth of e-commerce, product reviews on the Web have become an important information source for customers' decision making when they intend to buy some product. As the reviews are often too many for customers to go through, how to automatically classify them into different sentiment orientation categories (i.e. positive/negative) has become a research problem. In this paper, based on Fisher's discriminant ratio, an effective feature selection method is proposed for product review text sentiment classification. In order to validate the validity of the proposed method, we compared it with other methods respectively based on information gain and mutual information while support vector machine is adopted as the classifier. In this paper, 6 subexperiments are conducted by combining different feature selection methods with 2 kinds of candidate feature sets. Under 1006 review documents of cars, the experimental results indicate that the Fisher's discriminant ratio based on word frequency estimation has the best performance with F value 83.3% while the candidate features are the words which appear in both positive and negative texts.

Search Analytics: Automated Learning, Analysis, and Search with Open Source

NASA Astrophysics Data System (ADS)

Hundman, K.; Mattmann, C. A.; Hyon, J.; Ramirez, P.

2016-12-01

The sheer volume of unstructured scientific data makes comprehensive human analysis impossible, resulting in missed opportunities to identify relationships, trends, gaps, and outliers. As the open source community continues to grow, tools like Apache Tika, Apache Solr, Stanford's DeepDive, and Data-Driven Documents (D3) can help address this challenge. With a focus on journal publications and conference abstracts often in the form of PDF and Microsoft Office documents, we've initiated an exploratory NASA Advanced Concepts project aiming to use the aforementioned open source text analytics tools to build a data-driven justification for the HyspIRI Decadal Survey mission. We call this capability Search Analytics, and it fuses and augments these open source tools to enable the automatic discovery and extraction of salient information. In the case of HyspIRI, a hyperspectral infrared imager mission, key findings resulted from the extractions and visualizations of relationships from thousands of unstructured scientific documents. The relationships include links between satellites (e.g. Landsat 8), domain-specific measurements (e.g. spectral coverage) and subjects (e.g. invasive species). Using the above open source tools, Search Analytics mined and characterized a corpus of information that would be infeasible for a human to process. More broadly, Search Analytics offers insights into various scientific and commercial applications enabled through missions and instrumentation with specific technical capabilities. For example, the following phrases were extracted in close proximity within a publication: "In this study, hyperspectral images…with high spatial resolution (1 m) were analyzed to detect cutleaf teasel in two areas. …Classification of cutleaf teasel reached a users accuracy of 82 to 84%." Without reading a single paper we can use Search Analytics to automatically identify that a 1 m spatial resolution provides a cutleaf teasel detection users accuracy of 82-84%, which could have tangible, direct downstream implications for crop protection. Automatically assimilating this information expedites and supplements human analysis, and, ultimately, Search Analytics and its foundation of open source tools will result in more efficient scientific investment and research.

Query Expansion for Noisy Legal Documents

DTIC Science & Technology

2008-11-01

9] G. Salton (ed). The SMART retrieval system experiments in automatic document processing. 1971. [10] H. Schutze and J . Pedersen. A cooccurrence...Language Modeling and Information Retrieval. http://www.lemurproject.org. [2] J . Baron, D. Lewis, and D. Oard. TREC 2006 legal track overview. In...Retrieval, 1993. [8] J . Rocchio. Relevance feedback in information retrieval. In The SMART retrieval system experiments in automatic document processing, 1971

2D image classification for 3D anatomy localization: employing deep convolutional neural networks

NASA Astrophysics Data System (ADS)

de Vos, Bob D.; Wolterink, Jelmer M.; de Jong, Pim A.; Viergever, Max A.; Išgum, Ivana

2016-03-01

Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs -- heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.

Development of a Cancer Care Summary Through the Electronic Health Record.

PubMed

Carr, Laurie L; Zelarney, Pearlanne; Meadows, Sarah; Kern, Jeffrey A; Long, M Bronwyn; Kern, Elizabeth

2016-02-01

Our objective was to improve communication concerning lung cancer patients by developing and distributing a Cancer Care Summary that would provide clinically useful information about the patient's diagnosis and care to providers in diverse settings. We designed structured, electronic forms for the electronic health record (EHR), detailing tumor staging, classification, and treatment. To ensure completeness and accuracy of the information, we implemented a data quality cycle, composed of reports that are reviewed by oncology clinicians. The data from the EHR forms are extracted into a structured query language database system on a daily basis, from which the Summaries are derived. We conducted focus groups regarding the utility, format, and content of the Summary. Cancer Care Summaries are automatically generated 4 months after a patient's date of diagnosis, then every 6 months for those receiving treatment, and on an as-needed basis for urgent care or hospital admission. The product of our improvement project is the Cancer Care Summary. To date, 102 individual patient Summaries have been generated. These documents are automatically entered into the National Jewish Health (NJH) EHR, attached to correspondence to primary care providers, available to patients as electronic documents on the NJH patient portal, and faxed to emergency departments and admitting physicians on patient evaluation. We developed a sustainable tool to improve cancer care communication. The Cancer Care Summary integrates information from the EHR in a timely manner and distributes the information through multiple avenues. Copyright © 2016 by American Society of Clinical Oncology.

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

PubMed Central

Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2013-01-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts. PMID:24236224

Automatic Classification of Aerial Imagery for Urban Hydrological Applications

NASA Astrophysics Data System (ADS)

Paul, A.; Yang, C.; Breitkopf, U.; Liu, Y.; Wang, Z.; Rottensteiner, F.; Wallner, M.; Verworn, A.; Heipke, C.

2018-04-01

In this paper we investigate the potential of automatic supervised classification for urban hydrological applications. In particular, we contribute to runoff simulations using hydrodynamic urban drainage models. In order to assess whether the capacity of the sewers is sufficient to avoid surcharge within certain return periods, precipitation is transformed into runoff. The transformation of precipitation into runoff requires knowledge about the proportion of drainage-effective areas and their spatial distribution in the catchment area. Common simulation methods use the coefficient of imperviousness as an important parameter to estimate the overland flow, which subsequently contributes to the pipe flow. The coefficient of imperviousness is the percentage of area covered by impervious surfaces such as roofs or road surfaces. It is still common practice to assign the coefficient of imperviousness for each particular land parcel manually by visual interpretation of aerial images. Based on classification results of these imagery we contribute to an objective automatic determination of the coefficient of imperviousness. In this context we compare two classification techniques: Random Forests (RF) and Conditional Random Fields (CRF). Experimental results performed on an urban test area show good results and confirm that the automated derivation of the coefficient of imperviousness, apart from being more objective and, thus, reproducible, delivers more accurate results than the interactive estimation. We achieve an overall accuracy of about 85 % for both classifiers. The root mean square error of the differences of the coefficient of imperviousness compared to the reference is 4.4 % for the CRF-based classification, and 3.8 % for the RF-based classification.

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

NASA Astrophysics Data System (ADS)

Perry, Daniel; Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2012-03-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts.

New York State Thruway Authority automatic vehicle classification (AVC) : research report.

DOT National Transportation Integrated Search

2008-03-31

In December 2007, the N.Y.S. Thruway Authority (Thruway) concluded a Federal : funded research effort to study technology and develop a design for retrofitting : devices required in implementing a fully automated vehicle classification system i...

A method for automatically abstracting visual documents

NASA Technical Reports Server (NTRS)

Rorvig, Mark E.

1994-01-01

Visual documents--motion sequences on film, videotape, and digital recording--constitute a major source of information for the Space Agency, as well as all other government and private sector entities. This article describes a method for automatically selecting key frames from visual documents. These frames may in turn be used to represent the total image sequence of visual documents in visual libraries, hypermedia systems, and training algorithm reduces 51 minutes of video sequences to 134 frames; a reduction of information in the range of 700:1.

Portable automatic text classification for adverse drug reaction detection via multi-corpus training.

PubMed

Sarker, Abeed; Gonzalez, Graciela

2015-02-01

Automatic detection of adverse drug reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media-where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing (NLP) approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training

PubMed Central

Gonzalez, Graciela

2014-01-01

Objective Automatic detection of Adverse Drug Reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media — where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. Methods One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Results Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Conclusions Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi-corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g., social media data), and may reduce the time and costs associated with the annotation of data in the future. PMID:25451103

Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance.

PubMed

Liljeqvist, Henning T G; Muscatello, David; Sara, Grant; Dinh, Michael; Lawrence, Glenda L

2014-09-23

Syndromic surveillance in emergency departments (EDs) may be used to deliver early warnings of increases in disease activity, to provide situational awareness during events of public health significance, to supplement other information on trends in acute disease and injury, and to support the development and monitoring of prevention or response strategies. Changes in mental health related ED presentations may be relevant to these goals, provided they can be identified accurately and efficiently. This study aimed to measure the accuracy of using diagnostic codes in electronic ED presentation records to identify mental health-related visits. We selected a random sample of 500 records from a total of 1,815,588 ED electronic presentation records from 59 NSW public hospitals during 2010. ED diagnoses were recorded using any of ICD-9, ICD-10 or SNOMED CT classifications. Three clinicians, blinded to the automatically generated syndromic grouping and each other's classification, reviewed the triage notes and classified each of the 500 visits as mental health-related or not. A "mental health problem presentation" for the purposes of this study was defined as any ED presentation where either a mental disorder or a mental health problem was the reason for the ED visit. The combined clinicians' assessment of the records was used as reference standard to measure the sensitivity, specificity, and positive and negative predictive values of the automatic classification of coded emergency department diagnoses. Agreement between the reference standard and the automated coded classification was estimated using the Kappa statistic. Agreement between clinician's classification and automated coded classification was substantial (Kappa = 0.73. 95% CI: 0.58 - 0.87). The automatic syndromic grouping of coded ED diagnoses for mental health-related visits was found to be moderately sensitive (68% 95% CI: 46%-84%) and highly specific at 99% (95% CI: 98%-99.7%) when compared with the reference standard in identifying mental health related ED visits. Positive predictive value was 81% (95% CI: 0.57 - 0.94) and negative predictive value was 98% (95% CI: 0.97-0.99). Mental health presentations identified using diagnoses coded with various classifications in electronic ED presentation records offers sufficient accuracy for application in near real-time syndromic surveillance.

Linguistically informed digital fingerprints for text

NASA Astrophysics Data System (ADS)

Uzuner, Özlem

2006-02-01

Digital fingerprinting, watermarking, and tracking technologies have gained importance in the recent years in response to growing problems such as digital copyright infringement. While fingerprints and watermarks can be generated in many different ways, use of natural language processing for these purposes has so far been limited. Measuring similarity of literary works for automatic copyright infringement detection requires identifying and comparing creative expression of content in documents. In this paper, we present a linguistic approach to automatically fingerprinting novels based on their expression of content. We use natural language processing techniques to generate "expression fingerprints". These fingerprints consist of both syntactic and semantic elements of language, i.e., syntactic and semantic elements of expression. Our experiments indicate that syntactic and semantic elements of expression enable accurate identification of novels and their paraphrases, providing a significant improvement over techniques used in text classification literature for automatic copy recognition. We show that these elements of expression can be used to fingerprint, label, or watermark works; they represent features that are essential to the character of works and that remain fairly consistent in the works even when works are paraphrased. These features can be directly extracted from the contents of the works on demand and can be used to recognize works that would not be correctly identified either in the absence of pre-existing labels or by verbatim-copy detectors.

Automatic recognition of lactating sow behaviors through depth image processing

USDA-ARS?s Scientific Manuscript database

Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shiftin...

A semi-automatic traffic sign detection, classification, and positioning system

NASA Astrophysics Data System (ADS)

Creusen, I. M.; Hazelhoff, L.; de With, P. H. N.

2012-01-01

The availability of large-scale databases containing street-level panoramic images offers the possibility to perform semi-automatic surveying of real-world objects such as traffic signs. These inventories can be performed significantly more efficiently than using conventional methods. Governmental agencies are interested in these inventories for maintenance and safety reasons. This paper introduces a complete semi-automatic traffic sign inventory system. The system consists of several components. First, a detection algorithm locates the 2D position of the traffic signs in the panoramic images. Second, a classification algorithm is used to identify the traffic sign. Third, the 3D position of the traffic sign is calculated using the GPS position of the photographs. Finally, the results are listed in a table for quick inspection and are also visualized in a web browser.

Neural Network Classification of Receiver Functions as a Step Towards Automatic Crustal Parameter Determination

NASA Astrophysics Data System (ADS)

Jemberie, A.; Dugda, M. T.; Reusch, D.; Nyblade, A.

2006-12-01

Neural networks are decision making mathematical/engineering tools, which if trained properly, can do jobs automatically (and objectively) that normally require particular expertise and/or tedious repetition. Here we explore two techniques from the field of artificial neural networks (ANNs) that seek to reduce the time requirements and increase the objectivity of quality control (QC) and Event Identification (EI) on seismic datasets. We explore to apply the multiplayer Feed Forward (FF) Artificial Neural Networks (ANN) and Self- Organizing Maps (SOM) in combination with Hk stacking of receiver functions in an attempt to test the extent of the usefulness of automatic classification of receiver functions for crustal parameter determination. Feed- forward ANNs (FFNNs) are a supervised classification tool while self-organizing maps (SOMs) are able to provide unsupervised classification of large, complex geophysical data sets into a fixed number of distinct generalized patterns or modes. Hk stacking is a methodology that is used to stack receiver functions based on the relative arrival times of P-to-S converted phase and next two reverberations to determine crustal thickness H and Vp-to-Vs ratio (k). We use receiver functions from teleseismic events recorded by the 2000- 2002 Ethiopia Broadband Seismic Experiment. Preliminary results of applying FFNN neural network and Hk stacking of receiver functions for automatic receiver functions classification as a step towards an effort of automatic crustal parameter determination look encouraging. After training a FFNN neural network, the network could classify the best receiver functions from bad ones with a success rate of about 75 to 95%. Applying H? stacking on the receiver functions classified by this FFNN as the best receiver functions, we could obtain crustal thickness and Vp/Vs ratio of 31±4 km and 1.75±0.05, respectively, for the crust beneath station ARBA in the Main Ethiopian Rift. To make comparison, we applied Hk stacking on the receiver functions which we ourselves classified as the best set and found that the crustal thickness and Vp/Vs ratio are 31±2 km and 1.75±0.02, respectively.

The patient inflating valve in anaesthesia and resuscitation breathing systems.

PubMed

Fenton, P M; Bell, G

2013-03-01

Patient inflating valves combined with self-inflating bags are known to all anaesthetists as resuscitation devices and are familiar as components of draw-over anaesthesia systems. Their variants are also commonplace in transfer and home ventilators. However, the many variations in structure and function have led to difficulties in their optimal use, definition and classification. After reviewing the relevant literature, we defined a patient inflating valve as a one-way valve that closes an exit port to enable lung inflation, also permitting exhalation and spontaneous breathing, the actions being automatic. We present a new classification based on the mechanism of valve opening/closure; namely elastic recoil of a flexible flap/diaphragm, sliding spindle opened by a spring/magnet or a hollow balloon collapsed by external pressure. The evolution of these valves has been driven by the difficulties documented in critical incidents, which we have used along with information from modern International Organization for Standardization standards to identify 13 ideal properties, the top six of which are non-jamming, automatic, no bypass effect, no rebreathing or air entry at patient end, low resistance, robust and easy to service. The Ambu and the Laerdal valves have remained popular due to their simplicity and reliability. Two new alternatives, the Fenton and Diamedica valves, offer the benefits of location away from the patient while retaining a small functional dead space. They also offer the potential for greater use of hybrid continuous flow/draw-over systems that can operate close to atmospheric pressure. The reliable application of positive end-expiratory pressure/continuous positive airway pressure remains a challenge.

Improving Student Question Classification

ERIC Educational Resources Information Center

Heiner, Cecily; Zachary, Joseph L.

2009-01-01

Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This paper analyzes 411 questions from an introductory Java programming course by reducing the natural…

Sources of error in estimating truck traffic from automatic vehicle classification data

DOT National Transportation Integrated Search

1998-10-01

Truck annual average daily traffic estimation errors resulting from sample classification counts are computed in this paper under two scenarios. One scenario investigates an improper factoring procedure that may be used by highway agencies. The study...

6 CFR 7.28 - Automatic declassification.

Code of Federal Regulations, 2011 CFR

2011-01-01

... years after the date of its original classification with the exception of specific information exempt... information whenever the information exempted does not identify a confidential human source or human... Classification Appeals Panel (ISCAP) for approval. (d) Declassification guides that narrowly and precisely define...

6 CFR 7.28 - Automatic declassification.

Code of Federal Regulations, 2014 CFR

2014-01-01

... years after the date of its original classification with the exception of specific information exempt... information whenever the information exempted does not identify a confidential human source or human... Classification Appeals Panel (ISCAP) for approval. (d) Declassification guides that narrowly and precisely define...

6 CFR 7.28 - Automatic declassification.

Code of Federal Regulations, 2013 CFR

2013-01-01

... years after the date of its original classification with the exception of specific information exempt... information whenever the information exempted does not identify a confidential human source or human... Classification Appeals Panel (ISCAP) for approval. (d) Declassification guides that narrowly and precisely define...

6 CFR 7.28 - Automatic declassification.

Code of Federal Regulations, 2012 CFR

2012-01-01

... years after the date of its original classification with the exception of specific information exempt... information whenever the information exempted does not identify a confidential human source or human... Classification Appeals Panel (ISCAP) for approval. (d) Declassification guides that narrowly and precisely define...

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using CASE tools to write engineering specifications

NASA Astrophysics Data System (ADS)

Henry, James E.; Howard, Robert W.; Iveland, Scott T.

1993-08-01

There are always a wide variety of obstacles to writing and maintaining engineering documentation. To combat these problems, documentation generation can be linked to the process of engineering development. The same graphics and communication tools used for structured system analysis and design (SSA/SSD) also form the basis for the documentation. The goal is to build a living document, such that as an engineering design changes, the documentation will `automatically' revise. `Automatic' is qualified by the need to maintain textual descriptions associated with the SSA/SSD graphics, and the need to generate new documents. This paper describes a methodology and a computer aided system engineering toolset that enables a relatively seamless transition into document generation for the development engineering team.

Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

2018-04-01

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

Enhancing navigation in biomedical databases by community voting and database-driven text classification

PubMed Central

Duchrow, Timo; Shtatland, Timur; Guettler, Daniel; Pivovarov, Misha; Kramer, Stefan; Weissleder, Ralph

2009-01-01

Background The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them. Results Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly. Conclusion Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases. The system can be accessed at . PMID:19799796

77 FR 13026 - Energy Conservation Program: Energy Conservation Standard for Automatic Commercial Ice Makers

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-05

... written comments on these subjects. This document announces an extension of the time period for submitting... preliminary technical support document (preliminary TSD) for automatic commercial ice makers. The comment... published a Federal Register notice announcing the availability of its preliminary technical support...

Gradient Evolution-based Support Vector Machine Algorithm for Classification

NASA Astrophysics Data System (ADS)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

Analysis of steranes and triterpanes in geolipid extracts by automatic classification of mass spectra

NASA Technical Reports Server (NTRS)

Wardroper, A. M. K.; Brooks, P. W.; Humberston, M. J.; Maxwell, J. R.

1977-01-01

A computer method is described for the automatic classification of triterpanes and steranes into gross structural type from their mass spectral characteristics. The method has been applied to the spectra obtained by gas-chromatographic/mass-spectroscopic analysis of two mixtures of standards and of hydrocarbon fractions isolated from Green River and Messel oil shales. Almost all of the steranes and triterpanes identified previously in both shales were classified, in addition to a number of new components. The results indicate that classification of such alkanes is possible with a laboratory computer system. The method has application to diagenesis and maturation studies as well as to oil/oil and oil/source rock correlations in which rapid screening of large numbers of samples is required.

Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.

PubMed

Delavarian, Mona; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Dibajnia, Parvin

2011-07-12

Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using "Prtools", we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Automatic classification of seismic events within a regional seismograph network

NASA Astrophysics Data System (ADS)

Tiira, Timo; Kortström, Jari; Uski, Marja

2015-04-01

A fully automatic method for seismic event classification within a sparse regional seismograph network is presented. The tool is based on a supervised pattern recognition technique, Support Vector Machine (SVM), trained here to distinguish weak local earthquakes from a bulk of human-made or spurious seismic events. The classification rules rely on differences in signal energy distribution between natural and artificial seismic sources. Seismic records are divided into four windows, P, P coda, S, and S coda. For each signal window STA is computed in 20 narrow frequency bands between 1 and 41 Hz. The 80 discrimination parameters are used as a training data for the SVM. The SVM models are calculated for 19 on-line seismic stations in Finland. The event data are compiled mainly from fully automatic event solutions that are manually classified after automatic location process. The station-specific SVM training events include 11-302 positive (earthquake) and 227-1048 negative (non-earthquake) examples. The best voting rules for combining results from different stations are determined during an independent testing period. Finally, the network processing rules are applied to an independent evaluation period comprising 4681 fully automatic event determinations, of which 98 % have been manually identified as explosions or noise and 2 % as earthquakes. The SVM method correctly identifies 94 % of the non-earthquakes and all the earthquakes. The results imply that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of confidence. The tool helps to reduce work-load in manual seismic analysis by leaving only ~5 % of the automatic event determinations, i.e. the probable earthquakes for more detailed seismological analysis. The approach presented is easy to adjust to requirements of a denser or wider high-frequency network, once enough training examples for building a station-specific data set are available.

Automatic Classification of High Resolution Satellite Imagery - a Case Study for Urban Areas in the Kingdom of Saudi Arabia

NASA Astrophysics Data System (ADS)

Maas, A.; Alrajhi, M.; Alobeid, A.; Heipke, C.

2017-05-01

Updating topographic geospatial databases is often performed based on current remotely sensed images. To automatically extract the object information (labels) from the images, supervised classifiers are being employed. Decisions to be taken in this process concern the definition of the classes which should be recognised, the features to describe each class and the training data necessary in the learning part of classification. With a view to large scale topographic databases for fast developing urban areas in the Kingdom of Saudi Arabia we conducted a case study, which investigated the following two questions: (a) which set of features is best suitable for the classification?; (b) what is the added value of height information, e.g. derived from stereo imagery? Using stereoscopic GeoEye and Ikonos satellite data we investigate these two questions based on our research on label tolerant classification using logistic regression and partly incorrect training data. We show that in between five and ten features can be recommended to obtain a stable solution, that height information consistently yields an improved overall classification accuracy of about 5%, and that label noise can be successfully modelled and thus only marginally influences the classification results.

Automatic Picking of Foraminifera: Design of the Foraminifera Image Recognition and Sorting Tool (FIRST) Prototype and Results of the Image Classification Scheme

NASA Astrophysics Data System (ADS)

de Garidel-Thoron, T.; Marchant, R.; Soto, E.; Gally, Y.; Beaufort, L.; Bolton, C. T.; Bouslama, M.; Licari, L.; Mazur, J. C.; Brutti, J. M.; Norsa, F.

2017-12-01

Foraminifera tests are the main proxy carriers for paleoceanographic reconstructions. Both geochemical and taxonomical studies require large numbers of tests to achieve statistical relevance. To date, the extraction of foraminifera from the sediment coarse fraction is still done by hand and thus time-consuming. Moreover, the recognition of morphotypes, ecologically relevant, requires some taxonomical skills not easily taught. The automatic recognition and extraction of foraminifera would largely help paleoceanographers to overcome these issues. Recent advances in automatic image classification using machine learning opens the way to automatic extraction of foraminifera. Here we detail progress on the design of an automatic picking machine as part of the FIRST project. The machine handles 30 pre-sieved samples (100-1000µm), separating them into individual particles (including foraminifera) and imaging each in pseudo-3D. The particles are classified and specimens of interest are sorted either for Individual Foraminifera Analyses (44 per slide) and/or for classical multiple analyses (8 morphological classes per slide, up to 1000 individuals per hole). The classification is based on machine learning using Convolutional Neural Networks (CNNs), similar to the approach used in the coccolithophorid imaging system SYRACO. To prove its feasibility, we built two training image datasets of modern planktonic foraminifera containing approximately 2000 and 5000 images each, corresponding to 15 & 25 morphological classes. Using a CNN with a residual topology (ResNet) we achieve over 95% correct classification for each dataset. We tested the network on 160,000 images from 45 depths of a sediment core from the Pacific ocean, for which we have human counts. The current algorithm is able to reproduce the downcore variability in both Globigerinoides ruber and the fragmentation index (r2 = 0.58 and 0.88 respectively). The FIRST prototype yields some promising results for high-resolution paleoceanographic studies and evolutionary studies.

10 CFR 1045.40 - Marking requirements.

Code of Federal Regulations, 2010 CFR

2010-01-01

... DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review... holder that it contains RD or FRD information, the level of classification assigned, and the additional... classification level of the document, the following notices shall appear on the front of the document, as...

10 CFR 1045.40 - Marking requirements.

Code of Federal Regulations, 2011 CFR

2011-01-01

... DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review... holder that it contains RD or FRD information, the level of classification assigned, and the additional... classification level of the document, the following notices shall appear on the front of the document, as...

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Optimizing Input/Output Using Adaptive File System Policies

NASA Technical Reports Server (NTRS)

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

1996-01-01

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

Using machine learning techniques to automate sky survey catalog generation

NASA Technical Reports Server (NTRS)

Fayyad, Usama M.; Roden, J. C.; Doyle, R. J.; Weir, Nicholas; Djorgovski, S. G.

1993-01-01

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

An automatically updateable web publishing solution: taking document sharing and conversion to enterprise level

NASA Astrophysics Data System (ADS)

Rahman, Fuad; Tarnikova, Yuliya; Hartono, Rachmat; Alam, Hassan

2006-01-01

This paper presents a novel automatic web publishing solution, Pageview (R). PageView (R) is a complete working solution for document processing and management. The principal aim of this tool is to allow workgroups to share, access and publish documents on-line on a regular basis. For example, assuming that a person is working on some documents. The user will, in some fashion, organize his work either in his own local directory or in a shared network drive. Now extend that concept to a workgroup. Within a workgroup, some users are working together on some documents, and they are saving them in a directory structure somewhere on a document repository. The next stage of this reasoning is that a workgroup is working on some documents, and they want to publish them routinely on-line. Now it may happen that they are using different editing tools, different software, and different graphics tools. The resultant documents may be in PDF, Microsoft Office (R), HTML, or Word Perfect format, just to name a few. In general, this process needs the documents to be processed in a fashion so that they are in the HTML format, and then a web designer needs to work on that collection to make them available on-line. PageView (R) takes care of this whole process automatically, making the document workflow clean and easy to follow. PageView (R) Server publishes documents, complete with the directory structure, for online use. The documents are automatically converted to HTML and PDF so that users can view the content without downloading the original files, or having to download browser plug-ins. Once published, other users can access the documents as if they are accessing them from their local folders. The paper will describe the complete working system and will discuss possible applications within the document management research.

Automatic Certification of Kalman Filters for Reliable Code Generation

NASA Technical Reports Server (NTRS)

Denney, Ewen; Fischer, Bernd; Schumann, Johann; Richardson, Julian

2005-01-01

AUTOFILTER is a tool for automatically deriving Kalman filter code from high-level declarative specifications of state estimation problems. It can generate code with a range of algorithmic characteristics and for several target platforms. The tool has been designed with reliability of the generated code in mind and is able to automatically certify that the code it generates is free from various error classes. Since documentation is an important part of software assurance, AUTOFILTER can also automatically generate various human-readable documents, containing both design and safety related information. We discuss how these features address software assurance standards such as DO-178B.

Effectiveness of Global Features for Automatic Medical Image Classification and Retrieval – the experiences of OHSU at ImageCLEFmed

PubMed Central

Kalpathy-Cramer, Jayashree; Hersh, William

2008-01-01

In 2006 and 2007, Oregon Health & Science University (OHSU) participated in the automatic image annotation task for medical images at ImageCLEF, an annual international benchmarking event that is part of the Cross Language Evaluation Forum (CLEF). The goal of the automatic annotation task was to classify 1000 test images based on the Image Retrieval in Medical Applications (IRMA) code, given a set of 10,000 training images. There were 116 distinct classes in 2006 and 2007. We evaluated the efficacy of a variety of primarily global features for this classification task. These included features based on histograms, gray level correlation matrices and the gist technique. A multitude of classifiers including k-nearest neighbors, two-level neural networks, support vector machines, and maximum likelihood classifiers were evaluated. Our official error rates for the 1000 test images were 26% in 2006 using the flat classification structure. The error count in 2007 was 67.8 using the hierarchical classification error computation based on the IRMA code in 2007. Confusion matrices as well as clustering experiments were used to identify visually similar classes. The use of the IRMA code did not help us in the classification task as the semantic hierarchy of the IRMA classes did not correspond well with the hierarchy based on clustering of image features that we used. Our most frequent misclassification errors were along the view axis. Subsequent experiments based on a two-stage classification system decreased our error rate to 19.8% for the 2006 dataset and our error count to 55.4 for the 2007 data. PMID:19884953

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

NASA Astrophysics Data System (ADS)

Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, AdaBoost, and Decision Tree) and is a good technique for automatic classification of exoplanet-transit-like signal.

Comparison of SAM and OBIA as Tools for Lava Morphology Classification - A Case Study in Krafla, NE Iceland

NASA Astrophysics Data System (ADS)

Aufaristama, Muhammad; Hölbling, Daniel; Höskuldsson, Ármann; Jónsdóttir, Ingibjörg

2017-04-01

The Krafla volcanic system is part of the Icelandic North Volcanic Zone (NVZ). During Holocene, two eruptive events occurred in Krafla, 1724-1729 and 1975-1984. The last eruptive episode (1975-1984), known as the "Krafla Fires", resulted in nine volcanic eruption episodes. The total area covered by the lavas from this eruptive episode is 36 km2 and the volume is about 0.25-0.3 km3. Lava morphology is related to the characteristics of the surface morphology of a lava flow after solidification. The typical morphology of lava can be used as primary basis for the classification of lava flows when rheological properties cannot be directly observed during emplacement, and also for better understanding the behavior of lava flow models. Although mapping of lava flows in the field is relatively accurate such traditional methods are time consuming, especially when the lava covers large areas such as it is the case in Krafla. Semi-automatic mapping methods that make use of satellite remote sensing data allow for an efficient and fast mapping of lava morphology. In this study, two semi-automatic methods for lava morphology classification are presented and compared using Landsat 8 (30 m spatial resolution) and SPOT-5 (10 m spatial resolution) satellite images. For assessing the classification accuracy, the results from semi-automatic mapping were compared to the respective results from visual interpretation. On the one hand, the Spectral Angle Mapper (SAM) classification method was used. With this method an image is classified according to the spectral similarity between the image reflectance spectrums and the reference reflectance spectra. SAM successfully produced detailed lava surface morphology maps. However, the pixel-based approach partly leads to a salt-and-pepper effect. On the other hand, we applied the Random Forest (RF) classification method within an object-based image analysis (OBIA) framework. This statistical classifier uses a randomly selected subset of training samples to produce multiple decision trees. For final classification of pixels or - in the present case - image objects, the average of the class assignments probability predicted by the different decision trees is used. While the resulting OBIA classification of lava morphology types shows a high coincidence with the reference data, the approach is sensitive to the segmentation-derived image objects that constitute the base units for classification. Both semi-automatic methods produce reasonable results in the Krafla lava field, even if the identification of different pahoehoe and aa types of lava appeared to be difficult. The use of satellite remote sensing data shows a high potential for fast and efficient classification of lava morphology, particularly over large and inaccessible areas.

Automatic mathematical modeling for real time simulation program (AI application)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Purinton, Steve

1989-01-01

A methodology is described for automatic mathematical modeling and generating simulation models. The major objective was to create a user friendly environment for engineers to design, maintain, and verify their models; to automatically convert the mathematical models into conventional code for computation; and finally, to document the model automatically.

An automatic indexing method for medical documents.

PubMed Central

Wagner, M. M.

1991-01-01

This paper describes MetaIndex, an automatic indexing program that creates symbolic representations of documents for the purpose of document retrieval. MetaIndex uses a simple transition network parser to recognize a language that is derived from the set of main concepts in the Unified Medical Language System Metathesaurus (Meta-1). MetaIndex uses a hierarchy of medical concepts, also derived from Meta-1, to represent the content of documents. The goal of this approach is to improve document retrieval performance by better representation of documents. An evaluation method is described, and the performance of MetaIndex on the task of indexing the Slice of Life medical image collection is reported. PMID:1807564

28 CFR 17.28 - Automatic declassification.

Code of Federal Regulations, 2012 CFR

2012-07-01

... declassified not later than 25 years after the date of its original classification with the exception of... information exempted does not identify a confidential human source or human intelligence source. (c) Proposed... submit it to the Executive Secretary of the Interagency Security Classification Appeals Panel. (d...

28 CFR 17.28 - Automatic declassification.

Code of Federal Regulations, 2014 CFR

2014-07-01

... declassified not later than 25 years after the date of its original classification with the exception of... information exempted does not identify a confidential human source or human intelligence source. (c) Proposed... submit it to the Executive Secretary of the Interagency Security Classification Appeals Panel. (d...

28 CFR 17.28 - Automatic declassification.

Code of Federal Regulations, 2011 CFR

2011-07-01

... declassified not later than 25 years after the date of its original classification with the exception of... information exempted does not identify a confidential human source or human intelligence source. (c) Proposed... submit it to the Executive Secretary of the Interagency Security Classification Appeals Panel. (d...

28 CFR 17.28 - Automatic declassification.

Code of Federal Regulations, 2013 CFR

2013-07-01

... declassified not later than 25 years after the date of its original classification with the exception of... information exempted does not identify a confidential human source or human intelligence source. (c) Proposed... submit it to the Executive Secretary of the Interagency Security Classification Appeals Panel. (d...

28 CFR 17.28 - Automatic declassification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... declassified not later than 25 years after the date of its original classification with the exception of... information exempted does not identify a confidential human source or human intelligence source. (c) Proposed... submit it to the Executive Secretary of the Interagency Security Classification Appeals Panel. (d...

Sorting Olive Batches for the Milling Process Using Image Processing

PubMed Central

Puerto, Daniel Aguilera; Martínez Gila, Diego Manuel; Gámez García, Javier; Gómez Ortega, Juan

2015-01-01

The quality of virgin olive oil obtained in the milling process is directly bound to the characteristics of the olives. Hence, the correct classification of the different incoming olive batches is crucial to reach the maximum quality of the oil. The aim of this work is to provide an automatic inspection system, based on computer vision, and to classify automatically different batches of olives entering the milling process. The classification is based on the differentiation between ground and tree olives. For this purpose, three different species have been studied (Picudo, Picual and Hojiblanco). The samples have been obtained by picking the olives directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks. The proposed methodology has been validated successfully, obtaining good classification results. PMID:26147729

Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

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

Andonov, Rumen; Djidjev, Hristo Nikolov; Klau, Gunnar W.

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifiesmore » up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.« less

Fidelity of Automatic Speech Processing for Adult and Child Talker Classifications.

PubMed

VanDam, Mark; Silbert, Noah H

2016-01-01

Automatic speech processing (ASP) has recently been applied to very large datasets of naturalistically collected, daylong recordings of child speech via an audio recorder worn by young children. The system developed by the LENA Research Foundation analyzes children's speech for research and clinical purposes, with special focus on of identifying and tagging family speech dynamics and the at-home acoustic environment from the auditory perspective of the child. A primary issue for researchers, clinicians, and families using the Language ENvironment Analysis (LENA) system is to what degree the segment labels are valid. This classification study evaluates the performance of the computer ASP output against 23 trained human judges who made about 53,000 judgements of classification of segments tagged by the LENA ASP. Results indicate performance consistent with modern ASP such as those using HMM methods, with acoustic characteristics of fundamental frequency and segment duration most important for both human and machine classifications. Results are likely to be important for interpreting and improving ASP output.

Fidelity of Automatic Speech Processing for Adult and Child Talker Classifications

PubMed Central

2016-01-01

Automatic speech processing (ASP) has recently been applied to very large datasets of naturalistically collected, daylong recordings of child speech via an audio recorder worn by young children. The system developed by the LENA Research Foundation analyzes children's speech for research and clinical purposes, with special focus on of identifying and tagging family speech dynamics and the at-home acoustic environment from the auditory perspective of the child. A primary issue for researchers, clinicians, and families using the Language ENvironment Analysis (LENA) system is to what degree the segment labels are valid. This classification study evaluates the performance of the computer ASP output against 23 trained human judges who made about 53,000 judgements of classification of segments tagged by the LENA ASP. Results indicate performance consistent with modern ASP such as those using HMM methods, with acoustic characteristics of fundamental frequency and segment duration most important for both human and machine classifications. Results are likely to be important for interpreting and improving ASP output. PMID:27529813

Automatic Identification of Critical Follow-Up Recommendation Sentences in Radiology Reports

PubMed Central

Yetisgen-Yildiz, Meliha; Gunn, Martin L.; Xia, Fei; Payne, Thomas H.

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports. PMID:22195225

Automatic classification of killer whale vocalizations using dynamic time warping.

PubMed

Brown, Judith C; Miller, Patrick J O

2007-08-01

A set of killer whale sounds from Marineland were recently classified automatically [Brown et al., J. Acoust. Soc. Am. 119, EL34-EL40 (2006)] into call types using dynamic time warping (DTW), multidimensional scaling, and kmeans clustering to give near-perfect agreement with a perceptual classification. Here the effectiveness of four DTW algorithms on a larger and much more challenging set of calls by Northern Resident whales will be examined, with each call consisting of two independently modulated pitch contours and having considerable overlap in contours for several of the perceptual call types. Classification results are given for each of the four algorithms for the low frequency contour (LFC), the high frequency contour (HFC), their derivatives, and weighted sums of the distances corresponding to LFC with HFC, LFC with its derivative, and HFC with its derivative. The best agreement with the perceptual classification was 90% attained by the Sakoe-Chiba algorithm for the low frequency contours alone.

Automatic identification of critical follow-up recommendation sentences in radiology reports.

PubMed

Yetisgen-Yildiz, Meliha; Gunn, Martin L; Xia, Fei; Payne, Thomas H

2011-01-01

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. Using information technology can improve communication and improve patient safety. In this paper, we describe a text processing approach that uses natural language processing (NLP) and supervised text classification methods to automatically identify critical recommendation sentences in radiology reports. To increase the classification performance we enhanced the simple unigram token representation approach with lexical, semantic, knowledge-base, and structural features. We tested different combinations of those features with the Maximum Entropy (MaxEnt) classification algorithm. Classifiers were trained and tested with a gold standard corpus annotated by a domain expert. We applied 5-fold cross validation and our best performing classifier achieved 95.60% precision, 79.82% recall, 87.0% F-score, and 99.59% classification accuracy in identifying the critical recommendation sentences in radiology reports.

An Automatic User-Adapted Physical Activity Classification Method Using Smartphones.

PubMed

Li, Pengfei; Wang, Yu; Tian, Yu; Zhou, Tian-Shu; Li, Jing-Song

2017-03-01

In recent years, an increasing number of people have become concerned about their health. Most chronic diseases are related to lifestyle, and daily activity records can be used as an important indicator of health. Specifically, using advanced technology to automatically monitor actual activities can effectively prevent and manage chronic diseases. The data used in this paper were obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an efficient Adaboost-Stump running on a smartphone to classify five common activities: cycling, running, sitting, standing, and walking and achieved a satisfactory classification accuracy of 98%. We designed an online learning method, and the classification model requires continuous training with actual data. The parameters in the model then become increasingly fitted to the specific user, which allows the classification accuracy to reach 95% under different use environments. In addition, this paper also utilized the OpenCL framework to design the program in parallel. This process can enhance the computing efficiency approximately ninefold.

Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

DOE PAGES

Andonov, Rumen; Djidjev, Hristo Nikolov; Klau, Gunnar W.; ...

2015-10-09

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifiesmore » up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.« less

Investigating the Potential of Deep Neural Networks for Large-Scale Classification of Very High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Postadjian, T.; Le Bris, A.; Sahbi, H.; Mallet, C.

2017-05-01

Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.

Performance analysis of distributed applications using automatic classification of communication inefficiencies

DOEpatents

Vetter, Jeffrey S.

2005-02-01

The method and system described herein presents a technique for performance analysis that helps users understand the communication behavior of their message passing applications. The method and system described herein may automatically classifies individual communication operations and reveal the cause of communication inefficiencies in the application. This classification allows the developer to quickly focus on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, the method and system described herein trace the message operations of Message Passing Interface (MPI) applications and then classify each individual communication event using a supervised learning technique: decision tree classification. The decision tree may be trained using microbenchmarks that demonstrate both efficient and inefficient communication. Since the method and system described herein adapt to the target system's configuration through these microbenchmarks, they simultaneously automate the performance analysis process and improve classification accuracy. The method and system described herein may improve the accuracy of performance analysis and dramatically reduce the amount of data that users must encounter.

Can multilinguality improve Biomedical Word Sense Disambiguation?

PubMed

Duque, Andres; Martinez-Romo, Juan; Araujo, Lourdes

2016-12-01

Ambiguity in the biomedical domain represents a major issue when performing Natural Language Processing tasks over the huge amount of available information in the field. For this reason, Word Sense Disambiguation is critical for achieving accurate systems able to tackle complex tasks such as information extraction, summarization or document classification. In this work we explore whether multilinguality can help to solve the problem of ambiguity, and the conditions required for a system to improve the results obtained by monolingual approaches. Also, we analyze the best ways to generate those useful multilingual resources, and study different languages and sources of knowledge. The proposed system, based on co-occurrence graphs containing biomedical concepts and textual information, is evaluated on a test dataset frequently used in biomedicine. We can conclude that multilingual resources are able to provide a clear improvement of more than 7% compared to monolingual approaches, for graphs built from a small number of documents. Also, empirical results show that automatically translated resources are a useful source of information for this particular task. Copyright © 2016 Elsevier Inc. All rights reserved.

Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections

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

Morton, April M; Omitaomu, Olufemi A; Kotikot, Susan

A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.

17 CFR 200.507 - Declassification dates on derivative documents.

Code of Federal Regulations, 2011 CFR

2011-04-01

... derivative documents. 200.507 Section 200.507 Commodity and Securities Exchanges SECURITIES AND EXCHANGE... of National Security Information and Material § 200.507 Declassification dates on derivative... derivative document that derives its classification from the approved use of the classification guide of...

Automatic classification of the sub-techniques (gears) used in cross-country ski skating employing a mobile phone.

PubMed

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-10-31

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automatic Extraction of JPF Options and Documentation

NASA Technical Reports Server (NTRS)

Luks, Wojciech; Tkachuk, Oksana; Buschnell, David

2011-01-01

Documenting existing Java PathFinder (JPF) projects or developing new extensions is a challenging task. JPF provides a platform for creating new extensions and relies on key-value properties for their configuration. Keeping track of all possible options and extension mechanisms in JPF can be difficult. This paper presents jpf-autodoc-options, a tool that automatically extracts JPF projects options and other documentation-related information, which can greatly help both JPF users and developers of JPF extensions.

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

Besides Precision & Recall: Exploring Alternative Approaches to Evaluating an Automatic Indexing Tool for MEDLINE

PubMed Central

Névéol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

Objective This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. Materials and methods The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Results Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. Conclusions The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis. PMID:17238409

Besides precision & recall: exploring alternative approaches to evaluating an automatic indexing tool for MEDLINE.

PubMed

Neveol, Aurélie; Zeng, Kelly; Bodenreider, Olivier

2006-01-01

This paper explores alternative approaches for the evaluation of an automatic indexing tool for MEDLINE, complementing the traditional precision and recall method. The performance of MTI, the Medical Text Indexer used at NLM to produce MeSH recommendations for biomedical journal articles is evaluated on a random set of MEDLINE citations. The evaluation examines semantic similarity at the term level (indexing terms). In addition, the documents retrieved by queries resulting from MTI index terms for a given document are compared to the PubMed related citations for this document. Semantic similarity scores between sets of index terms are higher than the corresponding Dice similarity scores. Overall, 75% of the original documents and 58% of the top ten related citations are retrieved by queries based on the automatic indexing. The alternative measures studied in this paper confirm previous findings and may be used to select particular documents from the test set for a more thorough analysis.

Automatic script identification from images using cluster-based templates

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

Hochberg, J.; Kerns, L.; Kelly, P.

We have developed a technique for automatically identifying the script used to generate a document that is stored electronically in bit image form. Our approach differs from previous work in that the distinctions among scripts are discovered by an automatic learning procedure, without any handson analysis. We first develop a set of representative symbols (templates) for each script in our database (Cyrillic, Roman, etc.). We do this by identifying all textual symbols in a set of training documents, scaling each symbol to a fixed size, clustering similar symbols, pruning minor clusters, and finding each cluster`s centroid. To identify a newmore » document`s script, we identify and scale a subset of symbols from the document and compare them to the templates for each script. We choose the script whose templates provide the best match. Our current system distinguishes among the Armenian, Burmese, Chinese, Cyrillic, Ethiopic, Greek, Hebrew, Japanese, Korean, Roman, and Thai scripts with over 90% accuracy.« less

32 CFR 2001.24 - Additional requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

...,” “Secret,” and “Confidential” shall not be used to identify classified national security information. (b) Transmittal documents. A transmittal document shall indicate on its face the highest classification level of... Removed or Upon Removal of Attachments, This Document is (Classification Level) (c) Foreign government...

Distinguish self- and hetero-perceived stress through behavioral imaging and physiological features.

PubMed

Spodenkiewicz, Michel; Aigrain, Jonathan; Bourvis, Nadège; Dubuisson, Séverine; Chetouani, Mohamed; Cohen, David

2018-03-02

Stress reactivity is a complex phenomenon associated to multiple and multimodal expressions. Response to stressors has an obvious survival function and may be seen as an internal regulation to adapt to threat or danger. The intensity of this internal response can be assessed as the self-perception of the stress response. In species with social organization, this response also serves a communicative function, so-called hetero-perception. Our study presents multimodal stress detection assessment - a new methodology combining behavioral imaging and physiological monitoring for analyzing stress from these two perspectives. The system is based on automatic extraction of 39 behavioral (2D+3D video recording) and 62 physiological (Nexus-10 recording) features during a socially evaluated mental arithmetic test. The analysis with machine learning techniques for automatic classification using Support Vector Machine (SVM) show that self-perception and hetero-perception of social stress are both close but different phenomena: self-perception was significantly correlated with hetero-perception but significantly differed from it. Also, assessing stress with SVM through multimodality gave excellent classification results (F1 score values: 0.9±0.012 for hetero-perception and 0.87±0.021 for self-perception). In the best selected feature subsets, we found some common behavioral and physiological features that allow classification of both self- and hetero-perceived stress. However, we also found the contributing features for automatic classifications had opposite distributions: self-perception classification was mainly based on physiological features and hetero-perception was mainly based on behavioral features. Copyright © 2017. Published by Elsevier Inc.

GMM-based speaker age and gender classification in Czech and Slovak

NASA Astrophysics Data System (ADS)

Přibil, Jiří; Přibilová, Anna; Matoušek, Jindřich

2017-01-01

The paper describes an experiment with using the Gaussian mixture models (GMM) for automatic classification of the speaker age and gender. It analyses and compares the influence of different number of mixtures and different types of speech features used for GMM gender/age classification. Dependence of the computational complexity on the number of used mixtures is also analysed. Finally, the GMM classification accuracy is compared with the output of the conventional listening tests. The results of these objective and subjective evaluations are in correspondence.

The decision tree approach to classification

NASA Technical Reports Server (NTRS)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Document Exploration and Automatic Knowledge Extraction for Unstructured Biomedical Text

NASA Astrophysics Data System (ADS)

Chu, S.; Totaro, G.; Doshi, N.; Thapar, S.; Mattmann, C. A.; Ramirez, P.

2015-12-01

We describe our work on building a web-browser based document reader with built-in exploration tool and automatic concept extraction of medical entities for biomedical text. Vast amounts of biomedical information are offered in unstructured text form through scientific publications and R&D reports. Utilizing text mining can help us to mine information and extract relevant knowledge from a plethora of biomedical text. The ability to employ such technologies to aid researchers in coping with information overload is greatly desirable. In recent years, there has been an increased interest in automatic biomedical concept extraction [1, 2] and intelligent PDF reader tools with the ability to search on content and find related articles [3]. Such reader tools are typically desktop applications and are limited to specific platforms. Our goal is to provide researchers with a simple tool to aid them in finding, reading, and exploring documents. Thus, we propose a web-based document explorer, which we called Shangri-Docs, which combines a document reader with automatic concept extraction and highlighting of relevant terms. Shangri-Docsalso provides the ability to evaluate a wide variety of document formats (e.g. PDF, Words, PPT, text, etc.) and to exploit the linked nature of the Web and personal content by performing searches on content from public sites (e.g. Wikipedia, PubMed) and private cataloged databases simultaneously. Shangri-Docsutilizes Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) [4] and Unified Medical Language System (UMLS) to automatically identify and highlight terms and concepts, such as specific symptoms, diseases, drugs, and anatomical sites, mentioned in the text. cTAKES was originally designed specially to extract information from clinical medical records. Our investigation leads us to extend the automatic knowledge extraction process of cTAKES for biomedical research domain by improving the ontology guided information extraction process. We will describe our experience and implementation of our system and share lessons learned from our development. We will also discuss ways in which this could be adapted to other science fields. [1] Funk et al., 2014. [2] Kang et al., 2014. [3] Utopia Documents, http://utopiadocs.com [4] Apache cTAKES, http://ctakes.apache.org

A Review of Equation of State Models, Chemical Equilibrium Calculations and CERV Code Requirements for SHS Detonation Modelling

DTIC Science & Technology

2009-10-01

parameters for a large number of species. These authors provide many sample calculations with the JCZS database incorporated in CHEETAH 2.0, including...FORM (highest classification of Title, Abstract, Keywords) DOCUMENT CONTROL DATA (Security classification of title, body of abstract and...CLASSIFICATION OF FORM 13. ABSTRACT (a brief and factual summary of the document. It may also appear elsewhere in the body of the document itself

Computing symmetrical strength of N-grams: a two pass filtering approach in automatic classification of text documents.

PubMed

Agnihotri, Deepak; Verma, Kesari; Tripathi, Priyanka

2016-01-01

The contiguous sequences of the terms (N-grams) in the documents are symmetrically distributed among different classes. The symmetrical distribution of the N-Grams raises uncertainty in the belongings of the N-Grams towards the class. In this paper, we focused on the selection of most discriminating N-Grams by reducing the effects of symmetrical distribution. In this context, a new text feature selection method named as the symmetrical strength of the N-Grams (SSNG) is proposed using a two pass filtering based feature selection (TPF) approach. Initially, in the first pass of the TPF, the SSNG method chooses various informative N-Grams from the entire extracted N-Grams of the corpus. Subsequently, in the second pass the well-known Chi Square (χ(2)) method is being used to select few most informative N-Grams. Further, to classify the documents the two standard classifiers Multinomial Naive Bayes and Linear Support Vector Machine have been applied on the ten standard text data sets. In most of the datasets, the experimental results state the performance and success rate of SSNG method using TPF approach is superior to the state-of-the-art methods viz. Mutual Information, Information Gain, Odds Ratio, Discriminating Feature Selection and χ(2).

Automated aural classification used for inter-species discrimination of cetaceans.

PubMed

Binder, Carolyn M; Hines, Paul C

2014-04-01

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

NASA Astrophysics Data System (ADS)

Wang, Liming; Zhang, Kai; Liu, Xiyang; Long, Erping; Jiang, Jiewei; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Li, Wangting; Lin, Haotian

2017-01-01

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

PubMed

Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

2008-01-01

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

Diagnostic support for glaucoma using retinal images: a hybrid image analysis and data mining approach.

PubMed

Yu, Jin; Abidi, Syed Sibte Raza; Artes, Paul; McIntyre, Andy; Heywood, Malcolm

2005-01-01

The availability of modern imaging techniques such as Confocal Scanning Laser Tomography (CSLT) for capturing high-quality optic nerve images offer the potential for developing automatic and objective methods for diagnosing glaucoma. We present a hybrid approach that features the analysis of CSLT images using moment methods to derive abstract image defining features. The features are then used to train classifers for automatically distinguishing CSLT images of normal and glaucoma patient. As a first, in this paper, we present investigations in feature subset selction methods for reducing the relatively large input space produced by the moment methods. We use neural networks and support vector machines to determine a sub-set of moments that offer high classification accuracy. We demonstratee the efficacy of our methods to discriminate between healthy and glaucomatous optic disks based on shape information automatically derived from optic disk topography and reflectance images.

Automatic counting and classification of bacterial colonies using hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

Detection and counting of bacterial colonies on agar plates is a routine microbiology practice to get a rough estimate of the number of viable cells in a sample. There have been a variety of different automatic colony counting systems and software algorithms mainly based on color or gray-scale pictu...

Automatic Classification of Question & Answer Discourse Segments from Teacher's Speech in Classrooms

ERIC Educational Resources Information Center

Blanchard, Nathaniel; D'Mello, Sidney; Olney, Andrew M.; Nystrand, Martin

2015-01-01

Question-answer (Q&A) is fundamental for dialogic instruction, an important pedagogical technique based on the free exchange of ideas and open-ended discussion. Automatically detecting Q&A is key to providing teachers with feedback on appropriate use of dialogic instructional strategies. In line with this, this paper studies the…

Hybrid Automatic Building Interpretation System

NASA Astrophysics Data System (ADS)

Pakzad, K.; Klink, A.; Müterthies, A.; Gröger, G.; Stroh, V.; Plümer, L.

2011-09-01

HABIS (Hybrid Automatic Building Interpretation System) is a system for an automatic reconstruction of building roofs used in virtual 3D building models. Unlike most of the commercially available systems, HABIS is able to work to a high degree automatically. The hybrid method uses different sources intending to exploit the advantages of the particular sources. 3D point clouds usually provide good height and surface data, whereas spatial high resolution aerial images provide important information for edges and detail information for roof objects like dormers or chimneys. The cadastral data provide important basis information about the building ground plans. The approach used in HABIS works with a multi-stage-process, which starts with a coarse roof classification based on 3D point clouds. After that it continues with an image based verification of these predicted roofs. In a further step a final classification and adjustment of the roofs is done. In addition some roof objects like dormers and chimneys are also extracted based on aerial images and added to the models. In this paper the used methods are described and some results are presented.

Automatic Web-based Calibration of Network-Capable Shipboard Sensors

DTIC Science & Technology

2007-09-01

Server, Java , Applet, and Servlet . 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION OF THIS PAGE...49 b. Sensor Applet...........................................................................49 3. Java Servlet ...Table 1. Required System Environment Variables for Java Servlet Development. ......25 Table 2. Payload Data Format of the POST Requests from

A Framework for Automated Marmoset Vocalization Detection And Classification

DTIC Science & Technology

2016-09-08

recent push to automate vocalization monitoring in a range of mammals. Such efforts have been used to classify bird songs [11], African elephants [12... Elephant ( Loxodonta africana ) Vocalizations,” vol. 117, no. 2, pp. 956–963, 2005. [13] J. C. Brown, “Automatic classification of killer whale

AstroCV: Astronomy computer vision library

NASA Astrophysics Data System (ADS)

González, Roberto E.; Muñoz, Roberto P.; Hernández, Cristian A.

2018-04-01

AstroCV processes and analyzes big astronomical datasets, and is intended to provide a community repository of high performance Python and C++ algorithms used for image processing and computer vision. The library offers methods for object recognition, segmentation and classification, with emphasis in the automatic detection and classification of galaxies.

Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

NASA Astrophysics Data System (ADS)

Bayoudh, Meriam; Roux, Emmanuel; Richard, Gilles; Nock, Richard

2015-03-01

The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

PubMed

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-10-01

To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%. A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

PubMed Central

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-01-01

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors’ classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors’ automatic classification and manual segmentation were 91.6% ± 2.0%. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. PMID:23039675

The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.

PubMed

Aktaruzzaman, M; Migliorini, M; Tenhunen, M; Himanen, S L; Bianchi, A M; Sassi, R

2015-05-01

The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.

A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

PubMed

Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

2014-06-01

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified). © 2013 Elsevier B.V. All rights reserved.

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Information must contain the identity of the source document or the classification guide, including the agency.../Exemption) Classifier: (Name/Title/Number) (2) For Restricted Data documents: (i) Identity of the classifier. The identity of the classifier must be shown by completion of the “Derivative Classifier” line. The...

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Information must contain the identity of the source document or the classification guide, including the agency.../Exemption) Classifier: (Name/Title/Number) (2) For Restricted Data documents: (i) Identity of the classifier. The identity of the classifier must be shown by completion of the “Derivative Classifier” line. The...

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Information must contain the identity of the source document or the classification guide, including the agency.../Exemption) Classifier: (Name/Title/Number) (2) For Restricted Data documents: (i) Identity of the classifier. The identity of the classifier must be shown by completion of the “Derivative Classifier” line. The...

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

A classification model of Hyperion image base on SAM combined decision tree

NASA Astrophysics Data System (ADS)

Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

2009-10-01

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model heightens 9.9%.

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

PubMed Central

Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

2016-01-01

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient. PMID:27176486

Automatic detection and classification of obstacles with applications in autonomous mobile robots

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Rosas-Miranda, Dario I.

2016-04-01

Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

High-throughput automatic defect review for 300mm blank wafers with atomic force microscope

NASA Astrophysics Data System (ADS)

Zandiatashbar, Ardavan; Kim, Byong; Yoo, Young-kook; Lee, Keibock; Jo, Ahjin; Lee, Ju Suk; Cho, Sang-Joon; Park, Sang-il

2015-03-01

While feature size in lithography process continuously becomes smaller, defect sizes on blank wafers become more comparable to device sizes. Defects with nm-scale characteristic size could be misclassified by automated optical inspection (AOI) and require post-processing for proper classification. Atomic force microscope (AFM) is known to provide high lateral and the highest vertical resolution by mechanical probing among all techniques. However, its low throughput and tip life in addition to the laborious efforts for finding the defects have been the major limitations of this technique. In this paper we introduce automatic defect review (ADR) AFM as a post-inspection metrology tool for defect study and classification for 300 mm blank wafers and to overcome the limitations stated above. The ADR AFM provides high throughput, high resolution, and non-destructive means for obtaining 3D information for nm-scale defect review and classification.

A new blood vessel extraction technique using edge enhancement and object classification.

PubMed

Badsha, Shahriar; Reza, Ahmed Wasif; Tan, Kim Geok; Dimyati, Kaharudin

2013-12-01

Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.

Convolutional neural networks with balanced batches for facial expressions recognition

NASA Astrophysics Data System (ADS)

Battini Sönmez, Elena; Cangelosi, Angelo

2017-03-01

This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

Classification of forensic autopsy reports through conceptual graph-based document representation model.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2018-06-01

Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD. Copyright © 2018 Elsevier Inc. All rights reserved.

Automatic identification of artifacts in electrodermal activity data.

PubMed

Taylor, Sara; Jaques, Natasha; Chen, Weixuan; Fedor, Szymon; Sano, Akane; Picard, Rosalind

2015-01-01

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

Meta-Learning Approach for Automatic Parameter Tuning: A Case Study with Educational Datasets

ERIC Educational Resources Information Center

Molina, M. M.; Luna, J. M.; Romero, C.; Ventura, S.

2012-01-01

This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification.…

Land use surveys by means of automatic interpretation of LANDSAT system data

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Lombardo, M. A.; Novo, E. M. L. D.; Niero, M.; Foresti, C.

1981-01-01

Analyses for seven land-use classes are presented. The classes are: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation, and natural vegetation. The automatic classification of LANDSAT MSS data using a maximum likelihood algorithm shows a 39% average error of emission and a 3.45 error of commission for the seven classes.

Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

PubMed Central

Hauschild, Anne-Christin; Kopczynski, Dominik; D’Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-01-01

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME). We manually generated a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors’ results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications. PMID:24957992

Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches.

PubMed

Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-04-16

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring

PubMed Central

Bello, Juan Pablo; Farnsworth, Andrew; Robbins, Matt; Keen, Sara; Klinck, Holger; Kelling, Steve

2016-01-01

Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants’ flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research. PMID:27880836

Automatic Keyword Extraction from Individual Documents

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

Rose, Stuart J.; Engel, David W.; Cramer, Nicholas O.

2010-05-03

This paper introduces a novel and domain-independent method for automatically extracting keywords, as sequences of one or more words, from individual documents. We describe the method’s configuration parameters and algorithm, and present an evaluation on a benchmark corpus of technical abstracts. We also present a method for generating lists of stop words for specific corpora and domains, and evaluate its ability to improve keyword extraction on the benchmark corpus. Finally, we apply our method of automatic keyword extraction to a corpus of news articles and define metrics for characterizing the exclusivity, essentiality, and generality of extracted keywords within a corpus.

Automatic adventitious respiratory sound analysis: A systematic review.

PubMed

Pramono, Renard Xaviero Adhi; Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases.

Automatic adventitious respiratory sound analysis: A systematic review

PubMed Central

Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Background Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. Objective To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. Data sources A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Study selection Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Data extraction Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. Data synthesis A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Limitations Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. Conclusion A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases. PMID:28552969

Piezo-electric automatic vehicle classification system : Oregon Department of Transportation with Castle Rock Consultants for a SHRP Long Term Pavement Performance Site : final report.

DOT National Transportation Integrated Search

1991-07-01

Oregon has twelve pavement test sites that are part of the Strategic Highway Research Program (SHRP), Long Term Pavement Performance (LTPP) studies. Part of the data gathering on these sites involves vehicle weight and classification. This pilot proj...

Piezo-electric automatic vehicle classification system : Oregon Department of Transportation with Castle Rock Consultants for a SHRP Long Term Pavement Performance Site.

DOT National Transportation Integrated Search

1990-05-01

Oregon has twelve sites that are part of the Strategic Highway Research Program (SHRP), Long Term Pavement Performance (LTPP) studies. Part of the data gathering on these sites involves vehicle weight and classification. This pilot project was to hel...

An Ontology to Support the Classification of Learning Material in an Organizational Learning Environment: An Evaluation

ERIC Educational Resources Information Center

Valaski, Joselaine; Reinehr, Sheila; Malucelli, Andreia

2017-01-01

Purpose: The purpose of this research was to evaluate whether ontology integrated in an organizational learning environment may support the automatic learning material classification in a specific knowledge area. Design/methodology/approach: An ontology for recommending learning material was integrated in the organizational learning environment…

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.

Automatic classification of spectra from the Infrared Astronomical Satellite (IRAS)

NASA Technical Reports Server (NTRS)

Cheeseman, Peter; Stutz, John; Self, Matthew; Taylor, William; Goebel, John; Volk, Kevin; Walker, Helen

1989-01-01

A new classification of Infrared spectra collected by the Infrared Astronomical Satellite (IRAS) is presented. The spectral classes were discovered automatically by a program called Auto Class 2. This program is a method for discovering (inducing) classes from a data base, utilizing a Bayesian probability approach. These classes can be used to give insight into the patterns that occur in the particular domain, in this case, infrared astronomical spectroscopy. The classified spectra are the entire Low Resolution Spectra (LRS) Atlas of 5,425 sources. There are seventy-seven classes in this classification and these in turn were meta-classified to produce nine meta-classes. The classification is presented as spectral plots, IRAS color-color plots, galactic distribution plots and class commentaries. Cross-reference tables, listing the sources by IRAS name and by Auto Class class, are also given. These classes show some of the well known classes, such as the black-body class, and silicate emission classes, but many other classes were unsuspected, while others show important subtle differences within the well known classes.

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

An approach for automatic classification of grouper vocalizations with passive acoustic monitoring.

PubMed

Ibrahim, Ali K; Chérubin, Laurent M; Zhuang, Hanqi; Schärer Umpierre, Michelle T; Dalgleish, Fraser; Erdol, Nurgun; Ouyang, B; Dalgleish, A

2018-02-01

Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.

Automatic classification of radiological reports for clinical care.

PubMed

Gerevini, Alfonso Emilio; Lavelli, Alberto; Maffi, Alessandro; Maroldi, Roberto; Minard, Anne-Lyse; Serina, Ivan; Squassina, Guido

2018-06-07

Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia. The proposed system is built exploiting a training data set containing reports annotated by radiologists. Each report is classified according to the schema developed by radiologists and textual evidences are marked in the report. The annotations are then used to train different machine learning based classifiers. We present in this paper a method based on a cascade of classifiers which make use of a set of syntactic and semantic features. The resulting system is a novel hierarchical classification system for the given task, that we have experimentally evaluated. Copyright © 2018 Elsevier B.V. All rights reserved.

EEG Responses to Auditory Stimuli for Automatic Affect Recognition

PubMed Central

Hettich, Dirk T.; Bolinger, Elaina; Matuz, Tamara; Birbaumer, Niels; Rosenstiel, Wolfgang; Spüler, Martin

2016-01-01

Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies. PMID:27375410

A method for modeling co-occurrence propensity of clinical codes with application to ICD-10-PCS auto-coding.

PubMed

Subotin, Michael; Davis, Anthony R

2016-09-01

Natural language processing methods for medical auto-coding, or automatic generation of medical billing codes from electronic health records, generally assign each code independently of the others. They may thus assign codes for closely related procedures or diagnoses to the same document, even when they do not tend to occur together in practice, simply because the right choice can be difficult to infer from the clinical narrative. We propose a method that injects awareness of the propensities for code co-occurrence into this process. First, a model is trained to estimate the conditional probability that one code is assigned by a human coder, given than another code is known to have been assigned to the same document. Then, at runtime, an iterative algorithm is used to apply this model to the output of an existing statistical auto-coder to modify the confidence scores of the codes. We tested this method in combination with a primary auto-coder for International Statistical Classification of Diseases-10 procedure codes, achieving a 12% relative improvement in F-score over the primary auto-coder baseline. The proposed method can be used, with appropriate features, in combination with any auto-coder that generates codes with different levels of confidence. The promising results obtained for International Statistical Classification of Diseases-10 procedure codes suggest that the proposed method may have wider applications in auto-coding. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Semi-automatic knee cartilage segmentation

NASA Astrophysics Data System (ADS)

Dam, Erik B.; Folkesson, Jenny; Pettersen, Paola C.; Christiansen, Claus

2006-03-01

Osteo-Arthritis (OA) is a very common age-related cause of pain and reduced range of motion. A central effect of OA is wear-down of the articular cartilage that otherwise ensures smooth joint motion. Quantification of the cartilage breakdown is central in monitoring disease progression and therefore cartilage segmentation is required. Recent advances allow automatic cartilage segmentation with high accuracy in most cases. However, the automatic methods still fail in some problematic cases. For clinical studies, even if a few failing cases will be averaged out in the overall results, this reduces the mean accuracy and precision and thereby necessitates larger/longer studies. Since the severe OA cases are often most problematic for the automatic methods, there is even a risk that the quantification will introduce a bias in the results. Therefore, interactive inspection and correction of these problematic cases is desirable. For diagnosis on individuals, this is even more crucial since the diagnosis will otherwise simply fail. We introduce and evaluate a semi-automatic cartilage segmentation method combining an automatic pre-segmentation with an interactive step that allows inspection and correction. The automatic step consists of voxel classification based on supervised learning. The interactive step combines a watershed transformation of the original scan with the posterior probability map from the classification step at sub-voxel precision. We evaluate the method for the task of segmenting the tibial cartilage sheet from low-field magnetic resonance imaging (MRI) of knees. The evaluation shows that the combined method allows accurate and highly reproducible correction of the segmentation of even the worst cases in approximately ten minutes of interaction.

Automatic classification of bottles in crates

NASA Astrophysics Data System (ADS)

Aas, Kjersti; Eikvil, Line; Bremnes, Dag; Norbryhn, Andreas

1995-03-01

This paper presents a statistical method for classification of bottles in crates for use in automatic return bottle machines. For the automatons to reimburse the correct deposit, a reliable recognition is important. The images are acquired by a laser range scanner coregistering the distance to the object and the strength of the reflected signal. The objective is to identify the crate and the bottles from a library with a number of legal types. The bottles with significantly different size are separated using quite simple methods, while a more sophisticated recognizer is required to distinguish the more similar bottle types. Good results have been obtained when testing the method developed on bottle types which are difficult to distinguish using simple methods.

The integration of system specifications and program coding

NASA Technical Reports Server (NTRS)

Luebke, W. R.

1970-01-01

Experience in maintaining up-to-date documentation for one module of the large-scale Medical Literature Analysis and Retrieval System 2 (MEDLARS 2) is described. Several innovative techniques were explored in the development of this system's data management environment, particularly those that use PL/I as an automatic documenter. The PL/I data description section can provide automatic documentation by means of a master description of data elements that has long and highly meaningful mnemonic names and a formalized technique for the production of descriptive commentary. The techniques discussed are practical methods that employ the computer during system development in a manner that assists system implementation, provides interim documentation for customer review, and satisfies some of the deliverable documentation requirements.

Cross-over between discrete and continuous protein structure space: insights into automatic classification and networks of protein structures.

PubMed

Pascual-García, Alberto; Abia, David; Ortiz, Angel R; Bastolla, Ugo

2009-03-01

Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications. As a domain set, we have selected a consensus set of 2,890 domains decomposed very similarly in SCOP and CATH. As an alignment algorithm, we used a global version of MAMMOTH developed in our group, which is both rapid and accurate. As a similarity measure, we used the size-normalized contact overlap, and as a clustering algorithm, we used average linkage. The resulting automatic classification at the cross-over point was more consistent than expert ones with respect to the structure similarity measure, with 86% of the clusters corresponding to subsets of either SCOP or CATH superfamilies and fewer than 5% containing domains in distinct folds according to both SCOP and CATH. Almost 15% of SCOP superfamilies and 10% of CATH superfamilies were split, consistent with the notion of fold change in protein evolution. These results were qualitatively robust for all choices that we tested, although we did not try to use alignment algorithms developed by other groups. Folds defined in SCOP and CATH would be completely joined in the regime of large transitivity violations where clustering is more arbitrary. Consistently, the agreement between SCOP and CATH at fold level was lower than their agreement with the automatic classification obtained using as a clustering algorithm, respectively, average linkage (for SCOP) or single linkage (for CATH). The networks representing significant evolutionary and structural relationships between clusters beyond the cross-over point may allow us to perform evolutionary, structural, or functional analyses beyond the limits of classification schemes. These networks and the underlying clusters are available at http://ub.cbm.uam.es/research/ProtNet.php.

Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data

NASA Astrophysics Data System (ADS)

Bellakaout, A.; Cherkaoui, M.; Ettarid, M.; Touzani, A.

2016-06-01

Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive. This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others. This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.

10 CFR 1045.39 - Challenging classification and declassification determinations.

Code of Federal Regulations, 2013 CFR

2013-01-01

... holder of an RD or FRD document who, in good faith, believes that the RD or FRD document has an improper... classified the document. (b) Agencies shall establish procedures under which authorized holders of RD and FRD... involving RD or FRD may be appealed to the Director of Classification. In the case of FRD and RD related...

10 CFR 1045.39 - Challenging classification and declassification determinations.

Code of Federal Regulations, 2012 CFR

2012-01-01

... holder of an RD or FRD document who, in good faith, believes that the RD or FRD document has an improper... classified the document. (b) Agencies shall establish procedures under which authorized holders of RD and FRD... involving RD or FRD may be appealed to the Director of Classification. In the case of FRD and RD related...

10 CFR 1045.39 - Challenging classification and declassification determinations.

Code of Federal Regulations, 2014 CFR

2014-01-01

... holder of an RD or FRD document who, in good faith, believes that the RD or FRD document has an improper... classified the document. (b) Agencies shall establish procedures under which authorized holders of RD and FRD... involving RD or FRD may be appealed to the Director of Classification. In the case of FRD and RD related...

Automatic sleep stage classification using two-channel electro-oculography.

PubMed

Virkkala, Jussi; Hasan, Joel; Värri, Alpo; Himanen, Sari-Leena; Müller, Kiti

2007-10-15

An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5-6 Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18-30 Hz and alpha power 8-12 Hz was also used for wakefulness detection. Synchronous 1.5-6 Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohen's Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes.

Automatic classification of acetowhite temporal patterns to identify precursor lesions of cervical cancer

NASA Astrophysics Data System (ADS)

Gutiérrez-Fragoso, K.; Acosta-Mesa, H. G.; Cruz-Ramírez, N.; Hernández-Jiménez, R.

2013-12-01

Cervical cancer has remained, until now, as a serious public health problem in developing countries. The most common method of screening is the Pap test or cytology. When abnormalities are reported in the result, the patient is referred to a dysplasia clinic for colposcopy. During this test, a solution of acetic acid is applied, which produces a color change in the tissue and is known as acetowhitening phenomenon. This reaction aims to obtaining a sample of tissue and its histological analysis let to establish a final diagnosis. During the colposcopy test, digital images can be acquired to analyze the behavior of the acetowhitening reaction from a temporal approach. In this way, we try to identify precursor lesions of cervical cancer through a process of automatic classification of acetowhite temporal patterns. In this paper, we present the performance analysis of three classification methods: kNN, Naïve Bayes and C4.5. The results showed that there is similarity between some acetowhite temporal patterns of normal and abnormal tissues. Therefore we conclude that it is not sufficient to only consider the temporal dynamic of the acetowhitening reaction to establish a diagnosis by an automatic method. Information from cytologic, colposcopic and histopathologic disciplines should be integrated as well.

Computer-aided diagnosis system: a Bayesian hybrid classification method.

PubMed

Calle-Alonso, F; Pérez, C J; Arias-Nicolás, J P; Martín, J

2013-10-01

A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Robust automated classification of first-motion polarities for focal mechanism determination with machine learning

NASA Astrophysics Data System (ADS)

Ross, Z. E.; Meier, M. A.; Hauksson, E.

2017-12-01

Accurate first-motion polarities are essential for determining earthquake focal mechanisms, but are difficult to measure automatically because of picking errors and signal to noise issues. Here we develop an algorithm for reliable automated classification of first-motion polarities using machine learning algorithms. A classifier is designed to identify whether the first-motion polarity is up, down, or undefined by examining the waveform data directly. We first improve the accuracy of automatic P-wave onset picks by maximizing a weighted signal/noise ratio for a suite of candidate picks around the automatic pick. We then use the waveform amplitudes before and after the optimized pick as features for the classification. We demonstrate the method's potential by training and testing the classifier on tens of thousands of hand-made first-motion picks by the Southern California Seismic Network. The classifier assigned the same polarity as chosen by an analyst in more than 94% of the records. We show that the method is generalizable to a variety of learning algorithms, including neural networks and random forest classifiers. The method is suitable for automated processing of large seismic waveform datasets, and can potentially be used in real-time applications, e.g. for improving the source characterizations of earthquake early warning algorithms.

Automated software system for checking the structure and format of ACM SIG documents

NASA Astrophysics Data System (ADS)

Mirza, Arsalan Rahman; Sah, Melike

2017-04-01

Microsoft (MS) Office Word is one of the most commonly used software tools for creating documents. MS Word 2007 and above uses XML to represent the structure of MS Word documents. Metadata about the documents are automatically created using Office Open XML (OOXML) syntax. We develop a new framework, which is called ADFCS (Automated Document Format Checking System) that takes the advantage of the OOXML metadata, in order to extract semantic information from MS Office Word documents. In particular, we develop a new ontology for Association for Computing Machinery (ACM) Special Interested Group (SIG) documents for representing the structure and format of these documents by using OWL (Web Ontology Language). Then, the metadata is extracted automatically in RDF (Resource Description Framework) according to this ontology using the developed software. Finally, we generate extensive rules in order to infer whether the documents are formatted according to ACM SIG standards. This paper, introduces ACM SIG ontology, metadata extraction process, inference engine, ADFCS online user interface, system evaluation and user study evaluations.

The Influence of Intention on Masked Priming: A Study with Semantic Classification of Words

ERIC Educational Resources Information Center

Eckstein, Doris; Perrig, Walter J.

2007-01-01

Unconscious perception is commonly described as a phenomenon that is not under intentional control and relies on automatic processes. We challenge this view by arguing that some automatic processes may indeed be under intentional control, which is implemented in task-sets that define how the task is to be performed. In consequence, those prime…

Method of center localization for objects containing concentric arcs

NASA Astrophysics Data System (ADS)

Kuznetsova, Elena G.; Shvets, Evgeny A.; Nikolaev, Dmitry P.

2015-02-01

This paper proposes a method for automatic center location of objects containing concentric arcs. The method utilizes structure tensor analysis and voting scheme optimized with Fast Hough Transform. Two applications of the proposed method are considered: (i) wheel tracking in video-based system for automatic vehicle classification and (ii) tree growth rings analysis on a tree cross cut image.

Automatic photointerpretation for land use management in Minnesota

NASA Technical Reports Server (NTRS)

Swanlund, G. D. (Principal Investigator); Kirvida, L.; Cheung, M.; Pile, D.; Zirkle, R.

1974-01-01

The author has identified the following significant results. Automatic photointerpretation techniques were utilized to evaluate the feasibility of data for land use management. It was shown that ERTS-1 MSS data can produce thematic maps of adequate resolution and accuracy to update land use maps. In particular, five typical land use areas were mapped with classification accuracies ranging from 77% to over 90%.

Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image

NASA Astrophysics Data System (ADS)

Khazendar, Shan; Farren, Jessica; Al-Assam, Hisham; Sayasneh, Ahmed; Du, Hongbo; Bourne, Tom; Jassim, Sabah A.

2014-05-01

Ultrasound is an effective multipurpose imaging modality that has been widely used for monitoring and diagnosing early pregnancy events. Technology developments coupled with wide public acceptance has made ultrasound an ideal tool for better understanding and diagnosing of early pregnancy. The first measurable signs of an early pregnancy are the geometric characteristics of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurement involves multiple subjective decisions, in which dimensions are taken in three planes to establish what is known as Mean Sac Diameter (MSD). The manual measurement results in inter- and intra-observer variations, which may lead to difficulties in diagnosis. This paper proposes a fully automated diagnosis solution to accurately identify miscarriage cases in the first trimester of pregnancy based on automatic quantification of the MSD. Our study shows a strong positive correlation between the manual and the automatic MSD estimations. Our experimental results based on a dataset of 68 ultrasound images illustrate the effectiveness of the proposed scheme in identifying early miscarriage cases with classification accuracies comparable with those of domain experts using K nearest neighbor classifier on automatically estimated MSDs.

Mixture Model and MDSDCA for Textual Data

NASA Astrophysics Data System (ADS)

Allouti, Faryel; Nadif, Mohamed; Hoai An, Le Thi; Otjacques, Benoît

E-mailing has become an essential component of cooperation in business. Consequently, the large number of messages manually produced or automatically generated can rapidly cause information overflow for users. Many research projects have examined this issue but surprisingly few have tackled the problem of the files attached to e-mails that, in many cases, contain a substantial part of the semantics of the message. This paper considers this specific topic and focuses on the problem of clustering and visualization of attached files. Relying on the multinomial mixture model, we used the Classification EM algorithm (CEM) to cluster the set of files, and MDSDCA to visualize the obtained classes of documents. Like the Multidimensional Scaling method, the aim of the MDSDCA algorithm based on the Difference of Convex functions is to optimize the stress criterion. As MDSDCA is iterative, we propose an initialization approach to avoid starting with random values. Experiments are investigated using simulations and textual data.

Automatic Classification of the Sub-Techniques (Gears) Used in Cross-Country Ski Skating Employing a Mobile Phone

PubMed Central

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-01-01

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear. PMID:25365459

Automatic discrimination between safe and unsafe swallowing using a reputation-based classifier

PubMed Central

2011-01-01

Background Swallowing accelerometry has been suggested as a potential non-invasive tool for bedside dysphagia screening. Various vibratory signal features and complementary measurement modalities have been put forth in the literature for the potential discrimination between safe and unsafe swallowing. To date, automatic classification of swallowing accelerometry has exclusively involved a single-axis of vibration although a second axis is known to contain additional information about the nature of the swallow. Furthermore, the only published attempt at automatic classification in adult patients has been based on a small sample of swallowing vibrations. Methods In this paper, a large corpus of dual-axis accelerometric signals were collected from 30 older adults (aged 65.47 ± 13.4 years, 15 male) referred to videofluoroscopic examination on the suspicion of dysphagia. We invoked a reputation-based classifier combination to automatically categorize the dual-axis accelerometric signals into safe and unsafe swallows, as labeled via videofluoroscopic review. From these participants, a total of 224 swallowing samples were obtained, 164 of which were labeled as unsafe swallows (swallows where the bolus entered the airway) and 60 as safe swallows. Three separate support vector machine (SVM) classifiers and eight different features were selected for classification. Results With selected time, frequency and information theoretic features, the reputation-based algorithm distinguished between safe and unsafe swallowing with promising accuracy (80.48 ± 5.0%), high sensitivity (97.1 ± 2%) and modest specificity (64 ± 8.8%). Interpretation of the most discriminatory features revealed that in general, unsafe swallows had lower mean vibration amplitude and faster autocorrelation decay, suggestive of decreased hyoid excursion and compromised coordination, respectively. Further, owing to its performance-based weighting of component classifiers, the static reputation-based algorithm outperformed the democratic majority voting algorithm on this clinical data set. Conclusion Given its computational efficiency and high sensitivity, reputation-based classification of dual-axis accelerometry ought to be considered in future developments of a point-of-care swallow assessment where clinical informatics are desired. PMID:22085802

Advanced Fuel Properties; A Computer Program for Estimating Property Values

DTIC Science & Technology

1993-05-01

security considerations, contractual obligations, or notice on a specific document. REPORT DOCUMENTATION PAGE Fogu Approwd I OMB No. 0704-01=5 Ps NP...found in fuels. 14. SUBJECT TERMS 15. NUMBEROF PAGES 175 Fuel properties, Physical Propertie, Thermodynamnics, Predictions 16. PRICE CODE 17. SECURITY ...CLASSIFICATION is. SECURrrY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITFATION OF ABSTRACT OF REPORT OF THIS PAGE OF ABSTRACT Unclassified

Automatic classification of diseases from free-text death certificates for real-time surveillance.

PubMed

Koopman, Bevan; Karimi, Sarvnaz; Nguyen, Anthony; McGuire, Rhydwyn; Muscatello, David; Kemp, Madonna; Truran, Donna; Zhang, Ming; Thackway, Sarah

2015-07-15

Death certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV. Two classification methods are presented: i) a machine learning approach, where detailed features (terms, term n-grams and SNOMED CT concepts) are extracted from death certificates and used to train a set of supervised machine learning models (Support Vector Machines); and ii) a set of keyword-matching rules. These methods were used to identify the presence of diabetes, influenza, pneumonia and HIV in a death certificate. An empirical evaluation was conducted using 340,142 death certificates, divided between training and test sets, covering deaths from 2000-2007 in New South Wales, Australia. Precision and recall (positive predictive value and sensitivity) were used as evaluation measures, with F-measure providing a single, overall measure of effectiveness. A detailed error analysis was performed on classification errors. Classification of diabetes, influenza, pneumonia and HIV was highly accurate (F-measure 0.96). More fine-grained ICD-10 classification effectiveness was more variable but still high (F-measure 0.80). The error analysis revealed that word variations as well as certain word combinations adversely affected classification. In addition, anomalies in the ground truth likely led to an underestimation of the effectiveness. The high accuracy and low cost of the classification methods allow for an effective means for automatic and real-time surveillance of diabetes, influenza, pneumonia and HIV deaths. In addition, the methods are generally applicable to other diseases of interest and to other sources of medical free-text besides death certificates.

A new machine classification method applied to human peripheral blood leukocytes

NASA Technical Reports Server (NTRS)

Rorvig, Mark E.; Fitzpatrick, Steven J.; Vitthal, Sanjay; Ladoulis, Charles T.

1994-01-01

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

Multi-class SVM model for fMRI-based classification and grading of liver fibrosis

NASA Astrophysics Data System (ADS)

Freiman, M.; Sela, Y.; Edrei, Y.; Pappo, O.; Joskowicz, L.; Abramovitch, R.

2010-03-01

We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an anatomical MRI image and three T2*-W fMRI signal intensity time-course scans acquired during the breathing of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the resulting classification model with the leave-one out technique and compared it to both full multi-class SVM and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of more invasive approaches, such as biopsy or contrast-enhanced imaging.

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

PubMed

Agarwal, Shashank; Yu, Hong

2009-12-01

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

PubMed

Shen, Simon; Syal, Karan; Tao, Nongjian; Wang, Shaopeng

2015-12-01

We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.

A model for simulating the grinding and classification cyclic system of waste PCBs recycling production line.

PubMed

Yang, Deming; Xu, Zhenming

2011-09-15

Crushing and separating technology is widely used in waste printed circuit boards (PCBs) recycling process. A set of automatic line without negative impact to environment for recycling waste PCBs was applied in industry scale. Crushed waste PCBs particles grinding and classification cyclic system is the most important part of the automatic production line, and it decides the efficiency of the whole production line. In this paper, a model for computing the process of the system was established, and matrix analysis method was adopted. The result showed that good agreement can be achieved between the simulation model and the actual production line, and the system is anti-jamming. This model possibly provides a basis for the automatic process control of waste PCBs production line. With this model, many engineering problems can be reduced, such as metals and nonmetals insufficient dissociation, particles over-pulverizing, incomplete comminuting, material plugging and equipment fever. Copyright © 2011 Elsevier B.V. All rights reserved.

Automatic multimodal detection for long-term seizure documentation in epilepsy.

PubMed

Fürbass, F; Kampusch, S; Kaniusas, E; Koren, J; Pirker, S; Hopfengärtner, R; Stefan, H; Kluge, T; Baumgartner, C

2017-08-01

This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages. All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%. Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages. Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

NASA Astrophysics Data System (ADS)

Jürgens, Björn; Herrero-Solana, Victor

2017-04-01

Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

Automatic photointerpretation for land use management in Minnesota

NASA Technical Reports Server (NTRS)

Swanlund, G. D. (Principal Investigator); Pile, D. R.

1973-01-01

The author has identified the following significant results. The Minnesota Iron Range area was selected as one of the land use areas to be evaluated. Six classes were selected: (1) hardwood; (2) conifer; (3) water (including in mines); (4) mines, tailings and wet areas; (5) open area; and (6) urban. Initial classification results show a correct classification of 70.1 to 95.4% for the six classes. This is extremely good. It can be further improved since there were some incorrect classifications in the ground truth.

Contour classification in thermographic images for detection of breast cancer

NASA Astrophysics Data System (ADS)

Okuniewski, Rafał; Nowak, Robert M.; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Oleszkiewicz, Witold

2016-09-01

Thermographic images of breast taken by the Braster device are uploaded into web application which uses different classification algorithms to automatically decide whether a patient should be more thoroughly examined. This article presents the approach to the task of classifying contours visible on thermographic images of breast taken by the Braster device in order to make the decision about the existence of cancerous tumors in breast. It presents the results of the researches conducted on the different classification algorithms.

Use of Headings and Classifications by Physicians in Medical Narratives of EHRs

PubMed Central

Häyrinen, K.; Harno, K.; Nykänen, P.

2011-01-01

Objective The purpose of this study was to describe and evaluate patient care documentation by hospital physicians in EHRs and especially the use of national headings and classifications in these documentations Material and Methods The initial material consisted of a random sample of 3,481 medical narratives documented in EHRs during the period 2004-2005 in one department of a Finnish central hospital. The final material comprised a subset of 1,974 medical records with a focus on consultation requests and consultation responses by two specialist groups from 871 patients. This electronic documentation was analyzed using deductive content analyses and descriptive statistics. Results The physicians documented patient care in EHRs principally as narrative text. The medical narratives recorded by specialists were structured with headings in less than half of the patient cases. Consultation responses in general were more often structured with headings than consultation requests. The use of classifications was otherwise insignificant, but diagnoses were documented as ICD 10 codes in over 50% of consultation responses by both medical specialties. Conclusion There is an obvious need to improve the structuring of narrative text with national headings and classifications. According to the findings of this study, reason for care, patient history, health status, follow-up care plan and diagnosis are meaningful headings in physicians’ documentation. The existing list of headings needs to be analyzed within a consistent unified terminology system as a basis for further development. Adhering to headings and classifications in EHR documentation enables patient data to be shared and aggregated. The secondary use of data is expected to improve care management and quality of care. PMID:23616866

StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2

PubMed Central

Eftimov, Tome; Korošec, Peter; Koroušić Seljak, Barbara

2017-01-01

The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed—a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system. PMID:28587103

StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.

PubMed

Eftimov, Tome; Korošec, Peter; Koroušić Seljak, Barbara

2017-05-26

The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed-a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system.

A two-step automatic sleep stage classification method with dubious range detection.

PubMed

Sousa, Teresa; Cruz, Aniana; Khalighi, Sirvan; Pires, Gabriel; Nunes, Urbano

2015-04-01

The limitations of the current systems of automatic sleep stage classification (ASSC) are essentially related to the similarities between epochs from different sleep stages and the subjects' variability. Several studies have already identified the situations with the highest likelihood of misclassification in sleep scoring. Here, we took advantage of such information to develop an ASSC system based on knowledge of subjects' variability of some indicators that characterize sleep stages and on the American Academy of Sleep Medicine (AASM) rules. An ASSC system consisting of a two-step classifier is proposed. In the first step, epochs are classified using support vector machines (SVMs) spread into different nodes of a decision tree. In the post-processing step, the epochs suspected of misclassification (dubious classification) are tagged, and a new classification is suggested. Identification and correction are based on the AASM rules, and on misclassifications most commonly found/reported in automatic sleep staging. Six electroencephalographic and two electrooculographic channels were used to classify wake, non-rapid eye movement (NREM) sleep--N1, N2 and N3, and rapid eye movement (REM) sleep. The proposed system was tested in a dataset of 14 clinical polysomnographic records of subjects suspected of apnea disorders. Wake and REM epochs not falling in the dubious range, are classified with accuracy levels compatible with the requirements for clinical applications. The suggested correction assigned to the epochs that are tagged as dubious enhances the global results of all sleep stages. This approach provides reliable sleep staging results for non-dubious epochs. Copyright © 2015 Elsevier Ltd. All rights reserved.

An AdaBoost Based Approach to Automatic Classification and Detection of Buildings Footprints, Vegetation Areas and Roads from Satellite Images

NASA Astrophysics Data System (ADS)

Gonulalan, Cansu

In recent years, there has been an increasing demand for applications to monitor the targets related to land-use, using remote sensing images. Advances in remote sensing satellites give rise to the research in this area. Many applications ranging from urban growth planning to homeland security have already used the algorithms for automated object recognition from remote sensing imagery. However, they have still problems such as low accuracy on detection of targets, specific algorithms for a specific area etc. In this thesis, we focus on an automatic approach to classify and detect building foot-prints, road networks and vegetation areas. The automatic interpretation of visual data is a comprehensive task in computer vision field. The machine learning approaches improve the capability of classification in an intelligent way. We propose a method, which has high accuracy on detection and classification. The multi class classification is developed for detecting multiple objects. We present an AdaBoost-based approach along with the supervised learning algorithm. The combi- nation of AdaBoost with "Attentional Cascade" is adopted from Viola and Jones [1]. This combination decreases the computation time and gives opportunity to real time applications. For the feature extraction step, our contribution is to combine Haar-like features that include corner, rectangle and Gabor. Among all features, AdaBoost selects only critical features and generates in extremely efficient cascade structured classifier. Finally, we present and evaluate our experimental results. The overall system is tested and high performance of detection is achieved. The precision rate of the final multi-class classifier is over 98%.

Automatic Detection of Welding Defects using Deep Neural Network

NASA Astrophysics Data System (ADS)

Hou, Wenhui; Wei, Ye; Guo, Jie; Jin, Yi; Zhu, Chang'an

2018-01-01

In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

Land use in the Paraiba Valley through remotely sensed data. [Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Lombardo, M. A.; Novo, E. M. L. D.; Niero, M.; Foresti, C.

1980-01-01

A methodology for land use survey was developed and land use modification rates were determined using LANDSAT imagery of the Paraiba Valley (state of Sao Paulo). Both visual and automatic interpretation methods were employed to analyze seven land use classes: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation and natural vegetation. By means of visual interpretation, little spectral differences are observed among those classes. The automatic classification of LANDSAT MSS data using maximum likelihood algorithm shows a 39% average error of omission and a 3.4% error of inclusion for the seven classes. The complexity of land uses in the study area, the large spectral variations of analyzed classes, and the low resolution of LANDSAT data influenced the classification results.

Automatic EEG artifact removal: a weighted support vector machine approach with error correction.

PubMed

Shao, Shi-Yun; Shen, Kai-Quan; Ong, Chong Jin; Wilder-Smith, Einar P V; Li, Xiao-Ping

2009-02-01

An automatic electroencephalogram (EEG) artifact removal method is presented in this paper. Compared to past methods, it has two unique features: 1) a weighted version of support vector machine formulation that handles the inherent unbalanced nature of component classification and 2) the ability to accommodate structural information typically found in component classification. The advantages of the proposed method are demonstrated on real-life EEG recordings with comparisons made to several benchmark methods. Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities. Qualitative evaluation of the reconstructed EEG epochs also demonstrates that after artifact removal inherent brain activities are largely preserved.

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.

Autoclass: An automatic classification system

NASA Technical Reports Server (NTRS)

Stutz, John; Cheeseman, Peter; Hanson, Robin

1991-01-01

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

NASA Astrophysics Data System (ADS)

Negro Maggio, Valentina; Iocchi, Luca

2015-02-01

Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

Scanning electron microscope automatic defect classification of process induced defects

NASA Astrophysics Data System (ADS)

Wolfe, Scott; McGarvey, Steve

2017-03-01

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

An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

PubMed

Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

2016-01-01

Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods.

Automatic optical detection and classification of marine animals around MHK converters using machine vision

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

Brunton, Steven

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robustmore » principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.« less

Classification of time-series images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark.

PubMed

Singha, Suman; Vespe, Michele; Trieschmann, Olaf

2013-08-15

Today the health of ocean is in danger as it was never before mainly due to man-made pollutions. Operational activities show regular occurrence of accidental and deliberate oil spill in European waters. Since the areas covered by oil spills are usually large, satellite remote sensing particularly Synthetic Aperture Radar represents an effective option for operational oil spill detection. This paper describes the development of a fully automated approach for oil spill detection from SAR. Total of 41 feature parameters extracted from each segmented dark spot for oil spill and 'look-alike' classification and ranked according to their importance. The classification algorithm is based on a two-stage processing that combines classification tree analysis and fuzzy logic. An initial evaluation of this methodology on a large dataset has been carried out and degree of agreement between results from proposed algorithm and human analyst was estimated between 85% and 93% respectively for ENVISAT and RADARSAT. Copyright © 2013 Elsevier Ltd. All rights reserved.

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

PubMed

Kauppi, Jukka-Pekka; Martikainen, Kalle; Ruotsalainen, Ulla

2010-12-01

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns. Copyright © 2010 Elsevier Ltd. All rights reserved.

Computational Modeling of Emotions and Affect in Social-Cultural Interaction

DTIC Science & Technology

2013-10-02

acoustic and textual information sources. Second, a cross-lingual study was performed that shed light on how human perception and automatic recognition...speech is produced, a speaker’s pitch and intonational pattern, and word usage. Better feature representation and advanced approaches were used to...recognition performance, and improved our understanding of language/cultural impact on human perception of emotion and automatic classification. • Units

Computerized Interpretation of Dynamic Breast MRI

DTIC Science & Technology

2006-05-01

correction, tumor segmentation , extraction of computerized features that help distinguish between benign and malignant lesions, and classification. Our...for assessing tumor extent in 3D. The primary feature used for 3D tumor segmentation is the postcontrast enhancement vector. Tumor segmentation is a...Appendix B. 4. Investigation of methods for automatic tumor segmentation We developed an automatic method for assessing tumor extent in 3D. The

Wheat cultivation: Identifying and estimating area by means of LANDSAT data

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Mendonca, F. J.; Cottrell, D. A.; Tardin, A. T.; Lee, D. C. L.; Shimabukuro, Y. E.; Moreira, M. A.; Delima, A. M.; Maia, F. C. S.

1981-01-01

Automatic classification of LANDSAT data supported by aerial photography for identification and estimation of wheat growing areas was evaluated. Data covering three regions in the State of Rio Grande do Sul, Brazil were analyzed. The average correct classification of IMAGE-100 data was 51.02% and 63.30%, respectively, for the periods of July and of September/October, 1979.

Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

Vehicle detection in aerial surveillance using dynamic Bayesian networks.

PubMed

Cheng, Hsu-Yung; Weng, Chih-Chia; Chen, Yi-Ying

2012-04-01

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.

Classification of video sequences into chosen generalized use classes of target size and lighting level.

PubMed

Leszczuk, Mikołaj; Dudek, Łukasz; Witkowski, Marcin

The VQiPS (Video Quality in Public Safety) Working Group, supported by the U.S. Department of Homeland Security, has been developing a user guide for public safety video applications. According to VQiPS, five parameters have particular importance influencing the ability to achieve a recognition task. They are: usage time-frame, discrimination level, target size, lighting level, and level of motion. These parameters form what are referred to as Generalized Use Classes (GUCs). The aim of our research was to develop algorithms that would automatically assist classification of input sequences into one of the GUCs. Target size and lighting level parameters were approached. The experiment described reveals the experts' ambiguity and hesitation during the manual target size determination process. However, the automatic methods developed for target size classification make it possible to determine GUC parameters with 70 % compliance to the end-users' opinion. Lighting levels of the entire sequence can be classified with an efficiency reaching 93 %. To make the algorithms available for use, a test application has been developed. It is able to process video files and display classification results, the user interface being very simple and requiring only minimal user interaction.

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Label-indicator morpheme growth on LSTM for Chinese healthcare question department classification.

PubMed

Hu, Yang; Wen, Guihua; Ma, Jiajiong; Li, Danyang; Wang, Changjun; Li, Huihui; Huan, Eryang

2018-04-26

Current Chinese medicine has an urgent demand for convenient medical services. When facing a large number of patients, understanding patients' questions automatically and precisely is useful. Different from the high professional medical text, patients' questions contain only a small amount of descriptions regarding the symptoms, and the questions are slightly professional and colloquial. The aim of this paper is to implement a department classification system for patient questions. Patients' questions will be classified into 11 departments, such as surgery and others. This paper presents a morpheme growth model that enhances the memories of key elements in questions, and later extracts the "label-indicators" and germinates the expansion vectors around them. Finally, the model inputs the expansion vectors into a neural network to assign department labels for patients' questions. All compared methods are validated by experiments on three datasets that are composed of real patient questions. The proposed method has some ability to improve the performance of the classification. The proposed method is effective for the departments classification of patients questions and serves as a useful system for the automatic understanding of patient questions. Copyright © 2018. Published by Elsevier Inc.

Confidence level estimation in multi-target classification problems

NASA Astrophysics Data System (ADS)

Chang, Shi; Isaacs, Jason; Fu, Bo; Shin, Jaejeong; Zhu, Pingping; Ferrari, Silvia

2018-04-01

This paper presents an approach for estimating the confidence level in automatic multi-target classification performed by an imaging sensor on an unmanned vehicle. An automatic target recognition algorithm comprised of a deep convolutional neural network in series with a support vector machine classifier detects and classifies targets based on the image matrix. The joint posterior probability mass function of target class, features, and classification estimates is learned from labeled data, and recursively updated as additional images become available. Based on the learned joint probability mass function, the approach presented in this paper predicts the expected confidence level of future target classifications, prior to obtaining new images. The proposed approach is tested with a set of simulated sonar image data. The numerical results show that the estimated confidence level provides a close approximation to the actual confidence level value determined a posteriori, i.e. after the new image is obtained by the on-board sensor. Therefore, the expected confidence level function presented in this paper can be used to adaptively plan the path of the unmanned vehicle so as to optimize the expected confidence levels and ensure that all targets are classified with satisfactory confidence after the path is executed.

Context-based automated defect classification system using multiple morphological masks

DOEpatents

Gleason, Shaun S.; Hunt, Martin A.; Sari-Sarraf, Hamed

2002-01-01

Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. This invention includes novel digital image analysis techniques that generate unique feature vector descriptions of semiconductor defects as well as classifiers that use these descriptions to automatically categorize the defects into one of a set of pre-defined classes. Feature extraction techniques based on multiple-focus images, multiple-defect mask images, and segmented semiconductor wafer images are used to create unique feature-based descriptions of the semiconductor defects. These feature-based defect descriptions are subsequently classified by a defect classifier into categories that depend on defect characteristics and defect contextual information, that is, the semiconductor process layer(s) with which the defect comes in contact. At the heart of the system is a knowledge database that stores and distributes historical semiconductor wafer and defect data to guide the feature extraction and classification processes. In summary, this invention takes as its input a set of images containing semiconductor defect information, and generates as its output a classification for the defect that describes not only the defect itself, but also the location of that defect with respect to the semiconductor process layers.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

PubMed

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Yan, Bin; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

PubMed Central

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition. PMID:26380294

Methods for automatically analyzing humpback song units.

PubMed

Rickwood, Peter; Taylor, Andrew

2008-03-01

This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.

6 CFR 7.26 - Derivative classification.

Code of Federal Regulations, 2011 CFR

2011-01-01

... already classified, and marking the newly developed material consistent with the classification markings... classification decisions and carry forward to any newly created documents the pertinent classification markings. (d) Information classified derivatively from other classified information shall be classified and...

Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

PubMed Central

Guerra, Luis; McGarry, Laura M; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2011-01-01

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 71–82, 2011 PMID:21154911

Structural Validation of Nursing Terminologies

PubMed Central

Hardiker, Nicholas R.; Rector, Alan L.

2001-01-01

Objective: The purpose of the study is twofold: 1) to explore the applicability of combinatorial terminologies as the basis for building enumerated classifications, and 2) to investigate the usefulness of formal terminological systems for performing such classification and for assisting in the refinement of both combinatorial terminologies and enumerated classifications. Design: A formal model of the beta version of the International Classification for Nursing Practice (ICNP) was constructed in the compositional terminological language GRAIL (GALEN Representation and Integration Language). Terms drawn from the North American Nursing Diagnosis Association Taxonomy I (NANDA taxonomy) were mapped into the model and classified automatically using GALEN technology. Measurements: The resulting generated hierarchy was compared with the NANDA taxonomy to assess coverage and accuracy of classification. Results: In terms of coverage, in this study ICNP was able to capture 77 percent of NANDA terms using concepts drawn from five of its eight axes. Three axes—Body Site, Topology, and Frequency—were not needed. In terms of accuracy, where hierarchic relationships existed in the generated hierarchy or the NANDA taxonomy, or both, 6 were identical, 19 existed in the generated hierarchy alone (2 of these were considered suitable for incorporation into the NANDA taxonomy and 17 were considered inaccurate), and 23 appeared in the NANDA taxonomy alone (8 of these were considered suitable for incorporation into ICNP, 9 were considered inaccurate, and 6 reflected different, equally valid perspectives). Sixty terms appeared at the top level, with no indenting, in both the generated hierarchy and the NANDA taxonomy. Conclusions: With appropriate refinement, combinatorial terminologies such as ICNP have the potential to provide a useful foundation for representing enumerated classifications such as NANDA. Technologies such as GALEN make possible the process of building automatically enumerated classifications while providing a useful means of validating and refining both combinatorial terminologies and enumerated classifications. PMID:11320066

A Semi-supervised Heat Kernel Pagerank MBO Algorithm for Data Classification

DTIC Science & Technology

2016-07-01

financial predictions, etc. and is finding growing use in text mining studies. In this paper, we present an efficient algorithm for classification of high...video data, set of images, hyperspectral data, medical data, text data, etc. Moreover, the framework provides a way to analyze data whose different...also be incorporated. For text classification, one can use tfidf (term frequency inverse document frequency) to form feature vectors for each document

Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?

PubMed

Rossini, Paolo M; Buscema, Massimo; Capriotti, Massimiliano; Grossi, Enzo; Rodriguez, Guido; Del Percio, Claudio; Babiloni, Claudio

2008-07-01

It has been shown that a new procedure (implicit function as squashing time, IFAST) based on artificial neural networks (ANNs) is able to compress eyes-closed resting electroencephalographic (EEG) data into spatial invariants of the instant voltage distributions for an automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects with classification accuracy of individual subjects higher than 92%. Here we tested the hypothesis that this is the case also for the classification of individual normal elderly (Nold) vs. MCI subjects, an important issue for the screening of large populations at high risk of AD. Eyes-closed resting EEG data (10-20 electrode montage) were recorded in 171 Nold and in 115 amnesic MCI subjects. The data inputs for the classification by IFAST were the weights of the connections within a nonlinear auto-associative ANN trained to generate the instant voltage distributions of 60-s artifact-free EEG data. The most relevant features were selected and coincidently the dataset was split into two halves for the final binary classification (training and testing) performed by a supervised ANN. The classification of the individual Nold and MCI subjects reached 95.87% of sensitivity and 91.06% of specificity (93.46% of accuracy). These results indicate that IFAST can reliably distinguish eyes-closed resting EEG in individual Nold and MCI subjects. IFAST may be used for large-scale periodic screening of large populations at risk of AD and personalized care.

Automatic intelligibility classification of sentence-level pathological speech

PubMed Central

Kim, Jangwon; Kumar, Naveen; Tsiartas, Andreas; Li, Ming; Narayanan, Shrikanth S.

2014-01-01

Pathological speech usually refers to the condition of speech distortion resulting from atypicalities in voice and/or in the articulatory mechanisms owing to disease, illness or other physical or biological insult to the production system. Although automatic evaluation of speech intelligibility and quality could come in handy in these scenarios to assist experts in diagnosis and treatment design, the many sources and types of variability often make it a very challenging computational processing problem. In this work we propose novel sentence-level features to capture abnormal variation in the prosodic, voice quality and pronunciation aspects in pathological speech. In addition, we propose a post-classification posterior smoothing scheme which refines the posterior of a test sample based on the posteriors of other test samples. Finally, we perform feature-level fusions and subsystem decision fusion for arriving at a final intelligibility decision. The performances are tested on two pathological speech datasets, the NKI CCRT Speech Corpus (advanced head and neck cancer) and the TORGO database (cerebral palsy or amyotrophic lateral sclerosis), by evaluating classification accuracy without overlapping subjects’ data among training and test partitions. Results show that the feature sets of each of the voice quality subsystem, prosodic subsystem, and pronunciation subsystem, offer significant discriminating power for binary intelligibility classification. We observe that the proposed posterior smoothing in the acoustic space can further reduce classification errors. The smoothed posterior score fusion of subsystems shows the best classification performance (73.5% for unweighted, and 72.8% for weighted, average recalls of the binary classes). PMID:25414544

Advances of FishNet towards a fully automatic monitoring system for fish migration

NASA Astrophysics Data System (ADS)

Kratzert, Frederik; Mader, Helmut

2017-04-01

Restoring the continuum of river networks, affected by anthropogenic constructions, is one of the main objectives of the Water Framework Directive. Regarding fish migration, fish passes are a widely used measure. Often the functionality of these fish passes needs to be assessed by monitoring. Over the last years, we developed a new semi-automatic monitoring system (FishCam) which allows the contact free observation of fish migration in fish passes through videos. The system consists of a detection tunnel, equipped with a camera, a motion sensor and artificial light sources, as well as a software (FishNet), which helps to analyze the video data. In its latest version, the software is capable of detecting and tracking objects in the videos as well as classifying them into "fish" and "no-fish" objects. This allows filtering out the videos containing at least one fish (approx. 5 % of all grabbed videos) and reduces the manual labor to the analysis of these videos. In this state the entire system has already been used in over 20 different fish passes across Austria for a total of over 140 months of monitoring resulting in more than 1.4 million analyzed videos. As a next step towards a fully automatic monitoring system, a key feature is the automatized classification of the detected fish into their species, which is still an unsolved task in a fully automatic monitoring environment. Recent advances in the field of machine learning, especially image classification with deep convolutional neural networks, sound promising in order to solve this problem. In this study, different approaches for the fish species classification are tested. Besides an image-only based classification approach using deep convolutional neural networks, various methods that combine the power of convolutional neural networks as image descriptors with additional features, such as the fish length and the time of appearance, are explored. To facilitate the development and testing phase of this approach, a subset of six fish species of Austrian rivers and streams is considered in this study. All scripts and the data to reproduce the results of this study will be made publicly available on GitHub* at the beginning of the EGU2017 General Assembly. * https://github.com/kratzert/EGU2017_public/

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2012 CFR

2012-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions.

PubMed

Kolchinsky, A; Lourenço, A; Li, L; Rocha, L M

2013-01-01

Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.

Classification of document page images based on visual similarity of layout structures

NASA Astrophysics Data System (ADS)

Shin, Christian K.; Doermann, David S.

1999-12-01

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify a document's type in the absence of domain specific models. A document type or genre can be defined by the user based primarily on layout structure. Our classification approach is based on 'visual similarity' of the layout structure by building a supervised classifier, given examples of the class. We use image features, such as the percentages of tex and non-text (graphics, image, table, and ruling) content regions, column structures, variations in the point size of fonts, the density of content area, and various statistics on features of connected components which can be derived from class samples without class knowledge. In order to obtain class labels for training samples, we conducted a user relevance test where subjects ranked UW-I document images with respect to the 12 representative images. We implemented our classification scheme using the OC1, a decision tree classifier, and report our findings.

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.

PubMed

Go, Taesik; Byeon, Hyeokjun; Lee, Sang Joon

2018-04-30

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep Question Answering for protein annotation

PubMed Central

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/ PMID:26384372

Deep Question Answering for protein annotation.

PubMed

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/. © The Author(s) 2015. Published by Oxford University Press.

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

NASA Astrophysics Data System (ADS)

Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

2014-06-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

NASA Astrophysics Data System (ADS)

Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

2012-09-01

The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

PubMed Central

Keihaninejad, Shiva; Heckemann, Rolf A.; Gousias, Ioannis S.; Hajnal, Joseph V.; Duncan, John S.; Aljabar, Paul; Rueckert, Daniel; Hammers, Alexander

2012-01-01

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study. PMID:22523539

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

PubMed Central

Hou, Le; Samaras, Dimitris; Kurc, Tahsin M.; Gao, Yi; Davis, James E.; Saltz, Joel H.

2016-01-01

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN. PMID:27795661

Non-heuristic automatic techniques for overcoming low signal-to-noise-ratio bias of localization microscopy and multiple signal classification algorithm.

PubMed

Agarwal, Krishna; Macháň, Radek; Prasad, Dilip K

2018-03-21

Localization microscopy and multiple signal classification algorithm use temporal stack of image frames of sparse emissions from fluorophores to provide super-resolution images. Localization microscopy localizes emissions in each image independently and later collates the localizations in all the frames, giving same weight to each frame irrespective of its signal-to-noise ratio. This results in a bias towards frames with low signal-to-noise ratio and causes cluttered background in the super-resolved image. User-defined heuristic computational filters are employed to remove a set of localizations in an attempt to overcome this bias. Multiple signal classification performs eigen-decomposition of the entire stack, irrespective of the relative signal-to-noise ratios of the frames, and uses a threshold to classify eigenimages into signal and null subspaces. This results in under-representation of frames with low signal-to-noise ratio in the signal space and over-representation in the null space. Thus, multiple signal classification algorithms is biased against frames with low signal-to-noise ratio resulting into suppression of the corresponding fluorophores. This paper presents techniques to automatically debias localization microscopy and multiple signal classification algorithm of these biases without compromising their resolution and without employing heuristics, user-defined criteria. The effect of debiasing is demonstrated through five datasets of invitro and fixed cell samples.

Towards automated sleep classification in infants using symbolic and subsymbolic approaches.

PubMed

Kubat, M; Flotzinger, D; Pfurtscheller, G

1993-04-01

The paper addresses the problem of automatic sleep classification. A special effort is made to find a method of extracting reasonable descriptions of the individual sleep stages from sample measurements of EGG, EMG, EOG, etc., and from a classification of these measurements provided by an expert. The method should satisfy three requirements: classification accuracy, interpretability of the results, and the ability to select the relevant and discard the irrelevant variables. The solution suggested in this paper consists of a combination of the subsymbolic algorithm LVQ with the symbolic decision tree generator ID3. Results demonstrating the feasibility and utility of our approach are also presented.

77 FR 32010 - Applications (Classification, Advisory, and License) and Documentation

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-31

... DEPARTMENT OF COMMERCE Bureau of Industry and Security 15 CFR Part 748 Applications (Classification, Advisory, and License) and Documentation CFR Correction 0 In Title 15 of the Code of Federal... fourth column of the table, the two entries for ``National Semiconductor Hong Kong Limited'' are removed...

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.

PubMed

Olesen, Alexander Neergaard; Christensen, Julie A E; Sorensen, Helge B D; Jennum, Poul J

2016-08-01

Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen's kappa of 0.74 indicating substantial agreement between automatic and manual scoring.

17 CFR 200.506 - Derivative classification.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 2 2012-04-01 2012-04-01 false Derivative classification. 200...; CONDUCT AND ETHICS; AND INFORMATION AND REQUESTS Classification and Declassification of National Security Information and Material § 200.506 Derivative classification. Any document that includes paraphrases...

17 CFR 200.506 - Derivative classification.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 17 Commodity and Securities Exchanges 2 2013-04-01 2013-04-01 false Derivative classification. 200...; CONDUCT AND ETHICS; AND INFORMATION AND REQUESTS Classification and Declassification of National Security Information and Material § 200.506 Derivative classification. Any document that includes paraphrases...

17 CFR 200.506 - Derivative classification.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 3 2014-04-01 2014-04-01 false Derivative classification. 200...; CONDUCT AND ETHICS; AND INFORMATION AND REQUESTS Classification and Declassification of National Security Information and Material § 200.506 Derivative classification. Any document that includes paraphrases...

17 CFR 200.506 - Derivative classification.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 2 2011-04-01 2011-04-01 false Derivative classification. 200...; CONDUCT AND ETHICS; AND INFORMATION AND REQUESTS Classification and Declassification of National Security Information and Material § 200.506 Derivative classification. Any document that includes paraphrases...

Extracting biomedical events from pairs of text entities

PubMed Central

2015-01-01

Background Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processing for their registration into organized databases. This organization is instrumental for information retrieval, enabling to answer the advanced queries of researchers and practitioners in biology, medicine, and related fields. Hence, the massive data flow calls for efficient automatic methods of text-mining that extract high-level information, such as biomedical events, from biomedical text. The usual computational tools of Natural Language Processing cannot be readily applied to extract these biomedical events, due to the peculiarities of the domain. Indeed, biomedical documents contain highly domain-specific jargon and syntax. These documents also describe distinctive dependencies, making text-mining in molecular biology a specific discipline. Results We address biomedical event extraction as the classification of pairs of text entities into the classes corresponding to event types. The candidate pairs of text entities are recursively provided to a multiclass classifier relying on Support Vector Machines. This recursive process extracts events involving other events as arguments. Compared to joint models based on Markov Random Fields, our model simplifies inference and hence requires shorter training and prediction times along with lower memory capacity. Compared to usual pipeline approaches, our model passes over a complex intermediate problem, while making a more extensive usage of sophisticated joint features between text entities. Our method focuses on the core event extraction of the Genia task of BioNLP challenges yielding the best result reported so far on the 2013 edition. PMID:26201478

Computer Recognition of Facial Profiles

DTIC Science & Technology

1974-08-01

facial recognition 20. ABSTRACT (Continue on reverse side It necessary and Identify by block number) A system for the recognition of human faces from...21 2.6 Classification Algorithms ........... ... 32 III FACIAL RECOGNITION AND AUTOMATIC TRAINING . . . 37 3.1 Facial Profile Recognition...provide a fair test of the classification system. The work of Goldstein, Harmon, and Lesk [81 indicates, however, that for facial recognition , a ten class

Automatic Author Profiling of Online Chat Logs

DTIC Science & Technology

2007-03-01

CLASSIFICATION WITH PRIOR ..........91 1. All Test Data ................................91 2. Extracted Test Data: Teens and 20s ...........92 3...Extracted Test Data: Teens and 30s ...........92 4. Extracted Test Data: Teens and 40s ...........93 5. Extracted Test Data: Teens and 50s ...........93 6...Data ................................97 C. AGE: BINARY CLASSIFICATION WITH PRIOR .............98 1. Extracted Test Data: Teens and 20s ...........98 2

Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports

PubMed Central

Wilson, Richard A.; Chapman, Wendy W.; DeFries, Shawn J.; Becich, Michael J.; Chapman, Brian E.

2010-01-01

Background: Clinical records are often unstructured, free-text documents that create information extraction challenges and costs. Healthcare delivery and research organizations, such as the National Mesothelioma Virtual Bank, require the aggregation of both structured and unstructured data types. Natural language processing offers techniques for automatically extracting information from unstructured, free-text documents. Methods: Five hundred and eight history and physical reports from mesothelioma patients were split into development (208) and test sets (300). A reference standard was developed and each report was annotated by experts with regard to the patient’s personal history of ancillary cancer and family history of any cancer. The Hx application was developed to process reports, extract relevant features, perform reference resolution and classify them with regard to cancer history. Two methods, Dynamic-Window and ConText, for extracting information were evaluated. Hx’s classification responses using each of the two methods were measured against the reference standard. The average Cohen’s weighted kappa served as the human benchmark in evaluating the system. Results: Hx had a high overall accuracy, with each method, scoring 96.2%. F-measures using the Dynamic-Window and ConText methods were 91.8% and 91.6%, which were comparable to the human benchmark of 92.8%. For the personal history classification, Dynamic-Window scored highest with 89.2% and for the family history classification, ConText scored highest with 97.6%, in which both methods were comparable to the human benchmark of 88.3% and 97.2%, respectively. Conclusion: We evaluated an automated application’s performance in classifying a mesothelioma patient’s personal and family history of cancer from clinical reports. To do so, the Hx application must process reports, identify cancer concepts, distinguish the known mesothelioma from ancillary cancers, recognize negation, perform reference resolution and determine the experiencer. Results indicated that both information extraction methods tested were dependant on the domain-specific lexicon and negation extraction. We showed that the more general method, ConText, performed as well as our task-specific method. Although Dynamic- Window could be modified to retrieve other concepts, ConText is more robust and performs better on inconclusive concepts. Hx could greatly improve and expedite the process of extracting data from free-text, clinical records for a variety of research or healthcare delivery organizations. PMID:21031012

Development of an automated ultrasonic testing system

NASA Astrophysics Data System (ADS)

Shuxiang, Jiao; Wong, Brian Stephen

2005-04-01

Non-Destructive Testing is necessary in areas where defects in structures emerge over time due to wear and tear and structural integrity is necessary to maintain its usability. However, manual testing results in many limitations: high training cost, long training procedure, and worse, the inconsistent test results. A prime objective of this project is to develop an automatic Non-Destructive testing system for a shaft of the wheel axle of a railway carriage. Various methods, such as the neural network, pattern recognition methods and knowledge-based system are used for the artificial intelligence problem. In this paper, a statistical pattern recognition approach, Classification Tree is applied. Before feature selection, a thorough study on the ultrasonic signals produced was carried out. Based on the analysis of the ultrasonic signals, three signal processing methods were developed to enhance the ultrasonic signals: Cross-Correlation, Zero-Phase filter and Averaging. The target of this step is to reduce the noise and make the signal character more distinguishable. Four features: 1. The Auto Regressive Model Coefficients. 2. Standard Deviation. 3. Pearson Correlation 4. Dispersion Uniformity Degree are selected. And then a Classification Tree is created and applied to recognize the peak positions and amplitudes. Searching local maximum is carried out before feature computing. This procedure reduces much computation time in the real-time testing. Based on this algorithm, a software package called SOFRA was developed to recognize the peaks, calibrate automatically and test a simulated shaft automatically. The automatic calibration procedure and the automatic shaft testing procedure are developed.

A Computerized English-Spanish Correlation Index to Five Biomedical Library Classification Schemes Based on MeSH*

PubMed Central

Muench, Eugene V.

1971-01-01

A computerized English/Spanish correlation index to five biomedical library classification schemes and a computerized English/Spanish, Spanish/English listings of MeSH are described. The index was accomplished by supplying appropriate classification numbers of five classification schemes (National Library of Medicine; Library of Congress; Dewey Decimal; Cunningham; Boston Medical) to MeSH and a Spanish translation of MeSH The data were keypunched, merged on magnetic tape, and sorted in a computer alphabetically by English and Spanish subject headings and sequentially by classification number. Some benefits and uses of the index are: a complete index to classification schemes based on MeSH terms; a tool for conversion of classification numbers when reclassifying collections; a Spanish index and a crude Spanish translation of five classification schemes; a data base for future applications, e.g., automatic classification. Other classification schemes, such as the UDC, and translations of MeSH into other languages can be added. PMID:5172471

Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries.

PubMed

Lin, Ching-Heng; Wu, Nai-Yuan; Lai, Wei-Shao; Liou, Der-Ming

2015-01-01

Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents. Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines. The 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, p<0.0001), but a similar F-measure to that of the SAAP (0.89 vs 0.87). For the Procedure terms, the F-measure was not significantly different among the three pipelines. The combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.comFor numbered affiliation see end of article.

Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

NASA Astrophysics Data System (ADS)

Freitas, Nuno R.; Vieira, Pedro M.; Lima, Estevão; Lima, Carlos S.

2018-02-01

Correct classification of cystoscopy images depends on the interpreter’s experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

PubMed

Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

2014-01-01

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

Automatic Threshold Design for a Bound Document Scanner.

DTIC Science & Technology

1982-12-01

IS k- A AL O. N J MJt A ,4. TITLE foodSublitOio ). TYP R F EPOAT A PERIOD COVEREO Automatic Threshold De~i~n - ’W::d 1)o, i ,-r THESIS /DASSET’T...due to data uncertainty and other shortcomings in the scanner L * rather than in the ATC scheme. (Page count: 224) * Thesis Supervisor: Dr. J. F...permission to reproduce and distribute copies of this thesis document in whole or in part. Signature of Author Certified b y_ ___ -F . Reites, Thesis

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

EMG finger movement classification based on ANFIS

NASA Astrophysics Data System (ADS)

Caesarendra, W.; Tjahjowidodo, T.; Nico, Y.; Wahyudati, S.; Nurhasanah, L.

2018-04-01

An increase number of people suffering from stroke has impact to the rapid development of finger hand exoskeleton to enable an automatic physical therapy. Prior to the development of finger exoskeleton, a research topic yet important i.e. machine learning of finger gestures classification is conducted. This paper presents a study on EMG signal classification of 5 finger gestures as a preliminary study toward the finger exoskeleton design and development in Indonesia. The EMG signals of 5 finger gestures were acquired using Myo EMG sensor. The EMG signal features were extracted and reduced using PCA. The ANFIS based learning is used to classify reduced features of 5 finger gestures. The result shows that the classification of finger gestures is less than the classification of 7 hand gestures.

32 CFR 2001.22 - Derivative classification.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 6 2012-07-01 2012-07-01 false Derivative classification. 2001.22 Section 2001... Identification and Markings § 2001.22 Derivative classification. (a) General. Information classified derivatively on the basis of source documents or classification guides shall bear all markings prescribed in...

32 CFR 2001.22 - Derivative classification.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 6 2013-07-01 2013-07-01 false Derivative classification. 2001.22 Section 2001... Identification and Markings § 2001.22 Derivative classification. (a) General. Information classified derivatively on the basis of source documents or classification guides shall bear all markings prescribed in...

10 CFR 1045.37 - Classification guides.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 4 2014-01-01 2014-01-01 false Classification guides. 1045.37 Section 1045.37 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review of Documents Containing Restricted Data and Formerly Restricted Data § 1045.37 Classification guides...

10 CFR 1045.37 - Classification guides.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 4 2013-01-01 2013-01-01 false Classification guides. 1045.37 Section 1045.37 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review of Documents Containing Restricted Data and Formerly Restricted Data § 1045.37 Classification guides...

32 CFR 2001.22 - Derivative classification.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 6 2014-07-01 2014-07-01 false Derivative classification. 2001.22 Section 2001... Identification and Markings § 2001.22 Derivative classification. (a) General. Information classified derivatively on the basis of source documents or classification guides shall bear all markings prescribed in...

10 CFR 1045.37 - Classification guides.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 4 2012-01-01 2012-01-01 false Classification guides. 1045.37 Section 1045.37 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review of Documents Containing Restricted Data and Formerly Restricted Data § 1045.37 Classification guides...

10 CFR 1045.37 - Classification guides.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Classification guides. 1045.37 Section 1045.37 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review of Documents Containing Restricted Data and Formerly Restricted Data § 1045.37 Classification guides...

10 CFR 1045.37 - Classification guides.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 4 2011-01-01 2011-01-01 false Classification guides. 1045.37 Section 1045.37 Energy DEPARTMENT OF ENERGY (GENERAL PROVISIONS) NUCLEAR CLASSIFICATION AND DECLASSIFICATION Generation and Review of Documents Containing Restricted Data and Formerly Restricted Data § 1045.37 Classification guides...

32 CFR 2001.22 - Derivative classification.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 6 2011-07-01 2011-07-01 false Derivative classification. 2001.22 Section 2001... Identification and Markings § 2001.22 Derivative classification. (a) General. Information classified derivatively on the basis of source documents or classification guides shall bear all markings prescribed in...

19 CFR 177.8 - Issuance of rulings.

Code of Federal Regulations, 2012 CFR

2012-04-01

.... Any person engaging in a Customs transaction with respect to which a binding tariff classification ruling letter (including pre-entry classification decisions) has been issued under this part shall... tariff classification of merchandise shall set forth such classification in the documents or information...

19 CFR 177.8 - Issuance of rulings.

Code of Federal Regulations, 2011 CFR

2011-04-01

.... Any person engaging in a Customs transaction with respect to which a binding tariff classification ruling letter (including pre-entry classification decisions) has been issued under this part shall... tariff classification of merchandise shall set forth such classification in the documents or information...

19 CFR 177.8 - Issuance of rulings.

Code of Federal Regulations, 2013 CFR

2013-04-01

.... Any person engaging in a Customs transaction with respect to which a binding tariff classification ruling letter (including pre-entry classification decisions) has been issued under this part shall... tariff classification of merchandise shall set forth such classification in the documents or information...

19 CFR 177.8 - Issuance of rulings.

Code of Federal Regulations, 2014 CFR

2014-04-01

.... Any person engaging in a Customs transaction with respect to which a binding tariff classification ruling letter (including pre-entry classification decisions) has been issued under this part shall... tariff classification of merchandise shall set forth such classification in the documents or information...

TeraSCREEN: multi-frequency multi-mode Terahertz screening for border checks

NASA Astrophysics Data System (ADS)

Alexander, Naomi E.; Alderman, Byron; Allona, Fernando; Frijlink, Peter; Gonzalo, Ramón; Hägelen, Manfred; Ibáñez, Asier; Krozer, Viktor; Langford, Marian L.; Limiti, Ernesto; Platt, Duncan; Schikora, Marek; Wang, Hui; Weber, Marc Andree

2014-06-01

The challenge for any security screening system is to identify potentially harmful objects such as weapons and explosives concealed under clothing. Classical border and security checkpoints are no longer capable of fulfilling the demands of today's ever growing security requirements, especially with respect to the high throughput generally required which entails a high detection rate of threat material and a low false alarm rate. TeraSCREEN proposes to develop an innovative concept of multi-frequency multi-mode Terahertz and millimeter-wave detection with new automatic detection and classification functionalities. The system developed will demonstrate, at a live control point, the safe automatic detection and classification of objects concealed under clothing, whilst respecting privacy and increasing current throughput rates. This innovative screening system will combine multi-frequency, multi-mode images taken by passive and active subsystems which will scan the subjects and obtain complementary spatial and spectral information, thus allowing for automatic threat recognition. The TeraSCREEN project, which will run from 2013 to 2016, has received funding from the European Union's Seventh Framework Programme under the Security Call. This paper will describe the project objectives and approach.

Automatic analysis for neuron by confocal laser scanning microscope

NASA Astrophysics Data System (ADS)

Satou, Kouhei; Aoki, Yoshimitsu; Mataga, Nobuko; Hensh, Takao K.; Taki, Katuhiko

2005-12-01

The aim of this study is to develop a system that recognizes both the macro- and microscopic configurations of nerve cells and automatically performs the necessary 3-D measurements and functional classification of spines. The acquisition of 3-D images of cranial nerves has been enabled by the use of a confocal laser scanning microscope, although the highly accurate 3-D measurements of the microscopic structures of cranial nerves and their classification based on their configurations have not yet been accomplished. In this study, in order to obtain highly accurate measurements of the microscopic structures of cranial nerves, existing positions of spines were predicted by the 2-D image processing of tomographic images. Next, based on the positions that were predicted on the 2-D images, the positions and configurations of the spines were determined more accurately by 3-D image processing of the volume data. We report the successful construction of an automatic analysis system that uses a coarse-to-fine technique to analyze the microscopic structures of cranial nerves with high speed and accuracy by combining 2-D and 3-D image analyses.

Processing of Crawled Urban Imagery for Building Use Classification

NASA Astrophysics Data System (ADS)

Tutzauer, P.; Haala, N.

2017-05-01

Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Automatic Identification of Topic Tags from Texts Based on Expansion-Extraction Approach

ERIC Educational Resources Information Center

Yang, Seungwon

2013-01-01

Identifying topics of a textual document is useful for many purposes. We can organize the documents by topics in digital libraries. Then, we could browse and search for the documents with specific topics. By examining the topics of a document, we can quickly understand what the document is about. To augment the traditional manual way of topic…

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

PubMed Central

Burlina, Philippe; Billings, Seth; Joshi, Neil

2017-01-01

Objective To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Methods Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and “engineered” features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. Results The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). Conclusions This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification. PMID:28854220

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

PubMed

Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima

2017-01-01

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

Objective automated quantification of fluorescence signal in histological sections of rat lens.

PubMed

Talebizadeh, Nooshin; Hagström, Nanna Zhou; Yu, Zhaohua; Kronschläger, Martin; Söderberg, Per; Wählby, Carolina

2017-08-01

Visual quantification and classification of fluorescent signals is the gold standard in microscopy. The purpose of this study was to develop an automated method to delineate cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. A region of interest representing the lens epithelium was manually demarcated in each input image. Thereafter, individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding with an algorithm developed in Matlab™. Fluorescence signal was quantified within nuclei, cytoplasms and juxtaposed backgrounds. The classification of cells as labelled or not labelled was based on comparison of the fluorescence signal within cells with local background. The classification rule was thereafter optimized as compared with visual classification of a limited dataset. The performance of the automated classification was evaluated by asking 11 independent blinded observers to classify all cells (n = 395) in one lens image. Time consumed by the automatic algorithm and visual classification of cells was recorded. On an average, 77% of the cells were correctly classified as compared with the majority vote of the visual observers. The average agreement among visual observers was 83%. However, variation among visual observers was high, and agreement between two visual observers was as low as 71% in the worst case. Automated classification was on average 10 times faster than visual scoring. The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal with an accuracy comparable with the variability among visual observers. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

32 CFR 1636.2 - The claim of conscientious objection.

Code of Federal Regulations, 2010 CFR

2010-07-01

... CLASSIFICATION OF CONSCIENTIOUS OBJECTORS § 1636.2 The claim of conscientious objection. A claim to classification in Class 1-A-0 or Class 1-0, must be made by the registrant in writing. Claims and documents in... or after the Director has made a specific request for submission of such documents. All claims or...

32 CFR 1636.2 - The claim of conscientious objection.

Code of Federal Regulations, 2011 CFR

2011-07-01

... CLASSIFICATION OF CONSCIENTIOUS OBJECTORS § 1636.2 The claim of conscientious objection. A claim to classification in Class 1-A-0 or Class 1-0, must be made by the registrant in writing. Claims and documents in... or after the Director has made a specific request for submission of such documents. All claims or...

Bibliographic Classification Theory and Text Linguistics: Aboutness Analysis, Intertextuality and the Cognitive Act of Classifying Documents.

ERIC Educational Resources Information Center

Beghtol, Clare

1986-01-01

Explicates a definition and theory of "aboutness" and aboutness analysis developed by text linguist van Dijk; explores implications of text linguistics for bibliographic classification theory; suggests the elements that a theory of the cognitive process of classifying documents needs to encompass; and delineates how people identify…

The "Smart Dining Table": Automatic Behavioral Tracking of a Meal with a Multi-Touch-Computer.

PubMed

Manton, Sean; Magerowski, Greta; Patriarca, Laura; Alonso-Alonso, Miguel

2016-01-01

Studying how humans eat in the context of a meal is important to understanding basic mechanisms of food intake regulation and can help develop new interventions for the promotion of healthy eating and prevention of obesity and eating disorders. While there are a number of methodologies available for behavioral evaluation of a meal, there is a need for new tools that can simplify data collection through automatic and online analysis. Also, there are currently no methods that leverage technology to add a dimension of interactivity to the meal table. In this study, we examined the feasibility of a new technology for automatic detection and classification of bites during a laboratory meal. We used a SUR40 multi-touch tabletop computer, powered by an infrared camera behind the screen. Tags were attached to three plates, allowing their positions to be tracked, and the saturation (a measure of the infrared intensity) in the surrounding region was measured. A Kinect camera was used to record the meals for manual verification and provide gesture detection for when the bites were taken. Bite detections triggered classification of the source plate by the SUR40 based on saturation flux in the preceding time window. Five healthy subjects (aged 20-40 years, one female) were tested, providing a total sample of 320 bites. Sensitivity, defined as the number of correctly detected bites out of the number of actual bites, was 67.5%. Classification accuracy, defined as the number of correctly classified bites out of those detected, was 82.4%. Due to the poor sensitivity, a second experiment was designed using a single plate and a Myo armband containing a nine-axis accelerometer as an alternative method for bite detection. The same subjects were tested (sample: 195 bites). Using a simple threshold on the pitch reading of the magnetometer, the Myo data achieved 86.1% sensitivity vs. 60.5% with the Kinect. Further, the precision of positive predictive value was 72.1% for the Myo vs. 42.8% for the Kinect. We conclude that the SUR40 + Myo combination is feasible for automatic detection and classification of bites with adequate accuracy for a range of applications.

SAR-based change detection using hypothesis testing and Markov random field modelling

NASA Astrophysics Data System (ADS)

Cao, W.; Martinis, S.

2015-04-01

The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre- and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

Classification Accuracy Increase Using Multisensor Data Fusion

NASA Astrophysics Data System (ADS)

Makarau, A.; Palubinskas, G.; Reinartz, P.

2011-09-01

The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport objects, forest, roads, rail roads, etc.

Government Classification: An Overview.

ERIC Educational Resources Information Center

Brown, Karen M.

Classification of government documents (confidential, secret, top secret) is a system used by the executive branch to, in part, protect national security and foreign policy interests. The systematic use of classification markings with precise definitions was established during World War I, and since 1936 major changes in classification have…

22 CFR 41.12 - Classification symbols.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Foreign Relations DEPARTMENT OF STATE VISAS VISAS: DOCUMENTATION OF NONIMMIGRANTS UNDER THE IMMIGRATION AND NATIONALITY ACT, AS AMENDED Classification of Nonimmigrants § 41.12 Classification symbols. A visa... appropriate visa symbol to show the classification of the alien. The symbol shall be inserted in the space...

22 CFR 41.12 - Classification symbols.

Code of Federal Regulations, 2013 CFR

2013-04-01

... Foreign Relations DEPARTMENT OF STATE VISAS VISAS: DOCUMENTATION OF NONIMMIGRANTS UNDER THE IMMIGRATION AND NATIONALITY ACT, AS AMENDED Classification of Nonimmigrants § 41.12 Classification symbols. A visa... appropriate visa symbol to show the classification of the alien. The symbol shall be inserted in the space...

22 CFR 41.12 - Classification symbols.

Code of Federal Regulations, 2012 CFR

2012-04-01

... Foreign Relations DEPARTMENT OF STATE VISAS VISAS: DOCUMENTATION OF NONIMMIGRANTS UNDER THE IMMIGRATION AND NATIONALITY ACT, AS AMENDED Classification of Nonimmigrants § 41.12 Classification symbols. A visa... appropriate visa symbol to show the classification of the alien. The symbol shall be inserted in the space...

Denver RTD's computer aided dispatch/automatic vehicle location system : the human factors consequences

DOT National Transportation Integrated Search

1999-09-01

This report documents what happened to employees' work procedures when their employer when their employer installed Computer Aided Disptach/Automatic Vehicle Locator (CAD/AVL) technology to provide real-time surveillance of vehicles and to upgrade ra...

Automatic Commercial Permit Sets

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

Grana, Paul

Final report for Folsom Labs’ Solar Permit Generator project, which has successfully completed, resulting in the development and commercialization of a software toolkit within the cloud-based HelioScope software environment that enables solar engineers to automatically generate and manage draft documents for permit submission.

An Evaluation of Automatic Terminal Information Service (ATIS) Flight Deck Display Presentation Options

DOT National Transportation Integrated Search

1994-04-01

This document describes the first of three studies relating to human factors : aspects in the flight desk display of Automatic Terminal Information Servies : (ATIS). This research is being conducted by the Federal Aviation Administration : (FAA) Tech...

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

PubMed

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

2018-05-24

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

Cross-ontological analytics for alignment of different classification schemes

DOEpatents

Posse, Christian; Sanfilippo, Antonio P; Gopalan, Banu; Riensche, Roderick M; Baddeley, Robert L

2010-09-28

Quantification of the similarity between nodes in multiple electronic classification schemes is provided by automatically identifying relationships and similarities between nodes within and across the electronic classification schemes. Quantifying the similarity between a first node in a first electronic classification scheme and a second node in a second electronic classification scheme involves finding a third node in the first electronic classification scheme, wherein a first product value of an inter-scheme similarity value between the second and third nodes and an intra-scheme similarity value between the first and third nodes is a maximum. A fourth node in the second electronic classification scheme can be found, wherein a second product value of an inter-scheme similarity value between the first and fourth nodes and an intra-scheme similarity value between the second and fourth nodes is a maximum. The maximum between the first and second product values represents a measure of similarity between the first and second nodes.

Coding of procedures documented by general practitioners in Swedish primary care-an explorative study using two procedure coding systems

PubMed Central

2012-01-01

Background Procedures documented by general practitioners in primary care have not been studied in relation to procedure coding systems. We aimed to describe procedures documented by Swedish general practitioners in electronic patient records and to compare them to the Swedish Classification of Health Interventions (KVÅ) and SNOMED CT. Methods Procedures in 200 record entries were identified, coded, assessed in relation to two procedure coding systems and analysed. Results 417 procedures found in the 200 electronic patient record entries were coded with 36 different Classification of Health Interventions categories and 148 different SNOMED CT concepts. 22.8% of the procedures could not be coded with any Classification of Health Interventions category and 4.3% could not be coded with any SNOMED CT concept. 206 procedure-concept/category pairs were assessed as a complete match in SNOMED CT compared to 10 in the Classification of Health Interventions. Conclusions Procedures documented by general practitioners were present in nearly all electronic patient record entries. Almost all procedures could be coded using SNOMED CT. Classification of Health Interventions covered the procedures to a lesser extent and with a much lower degree of concordance. SNOMED CT is a more flexible terminology system that can be used for different purposes for procedure coding in primary care. PMID:22230095

Building a common pipeline for rule-based document classification.

PubMed

Patterson, Olga V; Ginter, Thomas; DuVall, Scott L

2013-01-01

Instance-based classification of clinical text is a widely used natural language processing task employed as a step for patient classification, document retrieval, or information extraction. Rule-based approaches rely on concept identification and context analysis in order to determine the appropriate class. We propose a five-step process that enables even small research teams to develop simple but powerful rule-based NLP systems by taking advantage of a common UIMA AS based pipeline for classification. Our proposed methodology coupled with the general-purpose solution provides researchers with access to the data locked in clinical text in cases of limited human resources and compact timelines.

Documentation and Detection of Colour Changes of Bas Relieves Using Close Range Photogrammetry

NASA Astrophysics Data System (ADS)

Malinverni, E. S.; Pierdicca, R.; Sturari, M.; Colosi, F.; Orazi, R.

2017-05-01

The digitization of complex buildings, findings or bas relieves can strongly facilitate the work of archaeologists, mainly for in depth analysis tasks. Notwithstanding, whether new visualization techniques ease the study phase, a classical naked-eye approach for determining changes or surface alteration could bring towards several drawbacks. The research work described in these pages is aimed at providing experts with a workflow for the evaluation of alterations (e.g. color decay or surface alterations), allowing a more rapid and objective monitoring of monuments. More in deep, a pipeline of work has been tested in order to evaluate the color variation between surfaces acquired at different époques. The introduction of reliable tools of change detection in the archaeological domain is needful; in fact, the most widespread practice, among archaeologists and practitioners, is to perform a traditional monitoring of surfaces that is made of three main steps: production of a hand-made map based on a subjective analysis, selection of a sub-set of regions of interest, removal of small portion of surface for in depth analysis conducted in laboratory. To overcome this risky and time consuming process, digital automatic change detection procedure represents a turning point. To do so, automatic classification has been carried out according to two approaches: a pixel-based and an object-based method. Pixel-based classification aims to identify the classes by means of the spectral information provided by each pixel belonging to the original bands. The object-based approach operates on sets of pixels (objects/regions) grouped together by means of an image segmentation technique. The methodology was tested by studying the bas-relieves of a temple located in Peru, named Huaca de la Luna. Despite the data sources were collected with unplanned surveys, the workflow proved to be a valuable solution useful to understand which are the main changes over time.

Putting Priors in Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2004-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

An Automatic Vehicle Classification System.

DTIC Science & Technology

1981-07-01

addi- tion, various portions of the system design can be used by other vehicle study projects, e.g. for projects concerned with vehicle speed or for...traffic study projects that require an axle counter or vehicle height indicator. A *4 UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE(W1en Data Enrerod...optoelectronic components as the basis for detection. Factors of vehicle length, height, and number of axles are used as identification characteristics. In

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Automated detection of breast cancer in resected specimens with fluorescence lifetime imaging

NASA Astrophysics Data System (ADS)

Phipps, Jennifer E.; Gorpas, Dimitris; Unger, Jakob; Darrow, Morgan; Bold, Richard J.; Marcu, Laura

2018-01-01

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N  =  20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Stock Market Index Data and indicators for Day Trading as a Binary Classification problem.

PubMed

Bruni, Renato

2017-02-01

Classification is the attribution of labels to records according to a criterion automatically learned from a training set of labeled records. This task is needed in a huge number of practical applications, and consequently it has been studied intensively and several classification algorithms are available today. In finance, a stock market index is a measurement of value of a section of the stock market. It is often used to describe the aggregate trend of a market. One basic financial issue would be forecasting this trend. Clearly, such a stochastic value is very difficult to predict. However, technical analysis is a security analysis methodology developed to forecast the direction of prices through the study of past market data. Day trading consists in buying and selling financial instruments within the same trading day. In this case, one interesting problem is the automatic individuation of favorable days for trading. We model this problem as a binary classification problem, and we provide datasets containing daily index values, the corresponding values of a selection of technical indicators, and the class label, which is 1 if the subsequent time period is favorable for day trading and 0 otherwise. These datasets can be used to test the behavior of different approaches in solving the day trading problem.

Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

NASA Astrophysics Data System (ADS)

Kachach, Redouane; Cañas, José María

2016-05-01

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images.

PubMed

Díaz, Gloria; González, Fabio A; Romero, Eduardo

2009-04-01

Visual quantification of parasitemia in thin blood films is a very tedious, subjective and time-consuming task. This study presents an original method for quantification and classification of erythrocytes in stained thin blood films infected with Plasmodium falciparum. The proposed approach is composed of three main phases: a preprocessing step, which corrects luminance differences. A segmentation step that uses the normalized RGB color space for classifying pixels either as erythrocyte or background followed by an Inclusion-Tree representation that structures the pixel information into objects, from which erythrocytes are found. Finally, a two step classification process identifies infected erythrocytes and differentiates the infection stage, using a trained bank of classifiers. Additionally, user intervention is allowed when the approach cannot make a proper decision. Four hundred fifty malaria images were used for training and evaluating the method. Automatic identification of infected erythrocytes showed a specificity of 99.7% and a sensitivity of 94%. The infection stage was determined with an average sensitivity of 78.8% and average specificity of 91.2%.

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

PubMed

Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

2018-06-29

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).

Delineation and geometric modeling of road networks

NASA Astrophysics Data System (ADS)

Poullis, Charalambos; You, Suya

In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

A Joint Time-Frequency and Matrix Decomposition Feature Extraction Methodology for Pathological Voice Classification

NASA Astrophysics Data System (ADS)

Ghoraani, Behnaz; Krishnan, Sridhar

2009-12-01

The number of people affected by speech problems is increasing as the modern world places increasing demands on the human voice via mobile telephones, voice recognition software, and interpersonal verbal communications. In this paper, we propose a novel methodology for automatic pattern classification of pathological voices. The main contribution of this paper is extraction of meaningful and unique features using Adaptive time-frequency distribution (TFD) and nonnegative matrix factorization (NMF). We construct Adaptive TFD as an effective signal analysis domain to dynamically track the nonstationarity in the speech and utilize NMF as a matrix decomposition (MD) technique to quantify the constructed TFD. The proposed method extracts meaningful and unique features from the joint TFD of the speech, and automatically identifies and measures the abnormality of the signal. Depending on the abnormality measure of each signal, we classify the signal into normal or pathological. The proposed method is applied on the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database which consists of 161 pathological and 51 normal speakers, and an overall classification accuracy of 98.6% was achieved.

A compressed sensing method with analytical results for lidar feature classification

NASA Astrophysics Data System (ADS)

Allen, Josef D.; Yuan, Jiangbo; Liu, Xiuwen; Rahmes, Mark

2011-04-01

We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting's unique ability to minimize or eliminate undesirable terrain data artifacts.

An automatic device for detection and classification of malaria parasite species in thick blood film.

PubMed

Kaewkamnerd, Saowaluck; Uthaipibull, Chairat; Intarapanich, Apichart; Pannarut, Montri; Chaotheing, Sastra; Tongsima, Sissades

2012-01-01

Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin blood films, which may not detect the existence of parasites due to the parasite scarcity on the thin blood film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick blood films, which contain more numbers of parasite per detection area, would address the previous limitation. The prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick blood films (40 blood films containing infected red blood cells and 20 control blood films of normal red blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative blood films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick blood films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%. This work presents an automatic device for both detection and classification of malaria parasite species on thick blood film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.

12 CFR 403.3 - Classification principles and authority.

Code of Federal Regulations, 2014 CFR

2014-01-01

... to any document is limited as follows and is nondelegable: Classification Classifier CONFIDENTIAL... 12 Banks and Banking 5 2014-01-01 2014-01-01 false Classification principles and authority. 403.3 Section 403.3 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION...

12 CFR 403.3 - Classification principles and authority.

Code of Federal Regulations, 2013 CFR

2013-01-01

... to any document is limited as follows and is nondelegable: Classification Classifier CONFIDENTIAL... 12 Banks and Banking 5 2013-01-01 2013-01-01 false Classification principles and authority. 403.3 Section 403.3 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION...

12 CFR 403.3 - Classification principles and authority.

Code of Federal Regulations, 2012 CFR

2012-01-01

... to any document is limited as follows and is nondelegable: Classification Classifier CONFIDENTIAL... 12 Banks and Banking 5 2012-01-01 2012-01-01 false Classification principles and authority. 403.3 Section 403.3 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION...

Railroad Classification Yard Technology Manual: Volume II : Yard Computer Systems

DOT National Transportation Integrated Search

1981-08-01

This volume (Volume II) of the Railroad Classification Yard Technology Manual documents the railroad classification yard computer systems methodology. The subjects covered are: functional description of process control and inventory computer systems,...

Fast title extraction method for business documents

NASA Astrophysics Data System (ADS)

Katsuyama, Yutaka; Naoi, Satoshi

1997-04-01

Conventional electronic document filing systems are inconvenient because the user must specify the keywords in each document for later searches. To solve this problem, automatic keyword extraction methods using natural language processing and character recognition have been developed. However, these methods are slow, especially for japanese documents. To develop a practical electronic document filing system, we focused on the extraction of keyword areas from a document by image processing. Our fast title extraction method can automatically extract titles as keywords from business documents. All character strings are evaluated for similarity by rating points associated with title similarity. We classified these points as four items: character sitting size, position of character strings, relative position among character strings, and string attribution. Finally, the character string that has the highest rating is selected as the title area. The character recognition process is carried out on the selected area. It is fast because this process must recognize a small number of patterns in the restricted area only, and not throughout the entire document. The mean performance of this method is an accuracy of about 91 percent and a 1.8 sec. processing time for an examination of 100 Japanese business documents.

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.

New insights into the classification and nomenclature of cortical GABAergic interneurons.

PubMed

DeFelipe, Javier; López-Cruz, Pedro L; Benavides-Piccione, Ruth; Bielza, Concha; Larrañaga, Pedro; Anderson, Stewart; Burkhalter, Andreas; Cauli, Bruno; Fairén, Alfonso; Feldmeyer, Dirk; Fishell, Gord; Fitzpatrick, David; Freund, Tamás F; González-Burgos, Guillermo; Hestrin, Shaul; Hill, Sean; Hof, Patrick R; Huang, Josh; Jones, Edward G; Kawaguchi, Yasuo; Kisvárday, Zoltán; Kubota, Yoshiyuki; Lewis, David A; Marín, Oscar; Markram, Henry; McBain, Chris J; Meyer, Hanno S; Monyer, Hannah; Nelson, Sacha B; Rockland, Kathleen; Rossier, Jean; Rubenstein, John L R; Rudy, Bernardo; Scanziani, Massimo; Shepherd, Gordon M; Sherwood, Chet C; Staiger, Jochen F; Tamás, Gábor; Thomson, Alex; Wang, Yun; Yuste, Rafael; Ascoli, Giorgio A

2013-03-01

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.

New insights into the classification and nomenclature of cortical GABAergic interneurons

PubMed Central

DeFelipe, Javier; López-Cruz, Pedro L.; Benavides-Piccione, Ruth; Bielza, Concha; Larrañaga, Pedro; Anderson, Stewart; Burkhalter, Andreas; Cauli, Bruno; Fairén, Alfonso; Feldmeyer, Dirk; Fishell, Gord; Fitzpatrick, David; Freund, Tamás F.; González-Burgos, Guillermo; Hestrin, Shaul; Hill, Sean; Hof, Patrick R.; Huang, Josh; Jones, Edward G.; Kawaguchi, Yasuo; Kisvárday, Zoltán; Kubota, Yoshiyuki; Lewis, David A.; Marín, Oscar; Markram, Henry; McBain, Chris J.; Meyer, Hanno S.; Monyer, Hannah; Nelson, Sacha B.; Rockland, Kathleen; Rossier, Jean; Rubenstein, John L. R.; Rudy, Bernardo; Scanziani, Massimo; Shepherd, Gordon M.; Sherwood, Chet C.; Staiger, Jochen F.; Tamás, Gábor; Thomson, Alex; Wang, Yun; Yuste, Rafael; Ascoli, Giorgio A.

2013-01-01

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts’ assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus. PMID:23385869

Analysis of Chi-square Automatic Interaction Detection (CHAID) and Classification and Regression Tree (CRT) for Classification of Corn Production

NASA Astrophysics Data System (ADS)

Susanti, Yuliana; Zukhronah, Etik; Pratiwi, Hasih; Respatiwulan; Sri Sulistijowati, H.

2017-11-01

To achieve food resilience in Indonesia, food diversification by exploring potentials of local food is required. Corn is one of alternating staple food of Javanese society. For that reason, corn production needs to be improved by considering the influencing factors. CHAID and CRT are methods of data mining which can be used to classify the influencing variables. The present study seeks to dig up information on the potentials of local food availability of corn in regencies and cities in Java Island. CHAID analysis yields four classifications with accuracy of 78.8%, while CRT analysis yields seven classifications with accuracy of 79.6%.

Automated enforcement of traffic signals : a literature review

DOT National Transportation Integrated Search

2001-08-13

This document presents the results of an extensive review of available documents regarding automated enforcement of traffic signal compliance. The literature regarding the operation and impacts of systems that automatically enforce driver compliance ...

Automatic Galaxy Classification via Machine Learning Techniques: Parallelized Rotation/Flipping INvariant Kohonen Maps (PINK)

NASA Astrophysics Data System (ADS)

Polsterer, K. L.; Gieseke, F.; Igel, C.

2015-09-01

In the last decades more and more all-sky surveys created an enormous amount of data which is publicly available on the Internet. Crowd-sourcing projects such as Galaxy-Zoo and Radio-Galaxy-Zoo used encouraged users from all over the world to manually conduct various classification tasks. The combination of the pattern-recognition capabilities of thousands of volunteers enabled scientists to finish the data analysis within acceptable time. For up-coming surveys with billions of sources, however, this approach is not feasible anymore. In this work, we present an unsupervised method that can automatically process large amounts of galaxy data and which generates a set of prototypes. This resulting model can be used to both visualize the given galaxy data as well as to classify so far unseen images.

An Automatic Segmentation Method Combining an Active Contour Model and a Classification Technique for Detecting Polycomb-group Proteinsin High-Throughput Microscopy Images.

PubMed

Gregoretti, Francesco; Cesarini, Elisa; Lanzuolo, Chiara; Oliva, Gennaro; Antonelli, Laura

2016-01-01

The large amount of data generated in biological experiments that rely on advanced microscopy can be handled only with automated image analysis. Most analyses require a reliable cell image segmentation eventually capable of detecting subcellular structures.We present an automatic segmentation method to detect Polycomb group (PcG) proteins areas isolated from nuclei regions in high-resolution fluorescent cell image stacks. It combines two segmentation algorithms that use an active contour model and a classification technique serving as a tool to better understand the subcellular three-dimensional distribution of PcG proteins in live cell image sequences. We obtained accurate results throughout several cell image datasets, coming from different cell types and corresponding to different fluorescent labels, without requiring elaborate adjustments to each dataset.

A Multi-Resolution Approach for an Automated Fusion of Different Low-Cost 3D Sensors

PubMed Central

Dupuis, Jan; Paulus, Stefan; Behmann, Jan; Plümer, Lutz; Kuhlmann, Heiner

2014-01-01

The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation. The requirements on the measuring devices are versatile, because spacious scenes have to be imaged with a high level of detail for selected objects. Thus, the used measuring systems are expensive and require an experienced operator. With the rise of low-cost 3D imaging systems, their integration into the digital documentation process is possible. However, common low-cost sensors have the limitation of a trade-off between range and accuracy, providing either a low resolution of single objects or a limited imaging field. Therefore, the use of multiple sensors is desirable. We show the combined use of two low-cost sensors, the Microsoft Kinect and the David laserscanning system, to achieve low-resolved scans of the whole scene and a high level of detail for selected objects, respectively. Afterwards, the high-resolved David objects are automatically assigned to their corresponding Kinect object by the use of surface feature histograms and SVM-classification. The corresponding objects are fitted using an ICP-implementation to produce a multi-resolution map. The applicability is shown for a fictional crime scene and the reconstruction of a ballistic trajectory. PMID:24763255

A multi-resolution approach for an automated fusion of different low-cost 3D sensors.

PubMed

Dupuis, Jan; Paulus, Stefan; Behmann, Jan; Plümer, Lutz; Kuhlmann, Heiner

2014-04-24

The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation. The requirements on the measuring devices are versatile, because spacious scenes have to be imaged with a high level of detail for selected objects. Thus, the used measuring systems are expensive and require an experienced operator. With the rise of low-cost 3D imaging systems, their integration into the digital documentation process is possible. However, common low-cost sensors have the limitation of a trade-off between range and accuracy, providing either a low resolution of single objects or a limited imaging field. Therefore, the use of multiple sensors is desirable. We show the combined use of two low-cost sensors, the Microsoft Kinect and the David laserscanning system, to achieve low-resolved scans of the whole scene and a high level of detail for selected objects, respectively. Afterwards, the high-resolved David objects are automatically assigned to their corresponding Kinect object by the use of surface feature histograms and SVM-classification. The corresponding objects are fitted using an ICP-implementation to produce a multi-resolution map. The applicability is shown for a fictional crime scene and the reconstruction of a ballistic trajectory.

Land cover classification of VHR airborne images for citrus grove identification

NASA Astrophysics Data System (ADS)

Amorós López, J.; Izquierdo Verdiguier, E.; Gómez Chova, L.; Muñoz Marí, J.; Rodríguez Barreiro, J. Z.; Camps Valls, G.; Calpe Maravilla, J.

Managing land resources using remote sensing techniques is becoming a common practice. However, data analysis procedures should satisfy the high accuracy levels demanded by users (public or private companies and governments) in order to be extensively used. This paper presents a multi-stage classification scheme to update the citrus Geographical Information System (GIS) of the Comunidad Valenciana region (Spain). Spain is the first citrus fruit producer in Europe and the fourth in the world. In particular, citrus fruits represent 67% of the agricultural production in this region, with a total production of 4.24 million tons (campaign 2006-2007). The citrus GIS inventory, created in 2001, needs to be regularly updated in order to monitor changes quickly enough, and allow appropriate policy making and citrus production forecasting. Automatic methods are proposed in this work to facilitate this update, whose processing scheme is summarized as follows. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution aerial images (0.5 m). Next, several automatic classifiers (decision trees, artificial neural networks, and support vector machines) are trained and combined to improve the final classification accuracy. Finally, the citrus GIS is automatically updated if a high enough level of confidence, based on the agreement between classifiers, is achieved. This is the case for 85% of the parcels and accuracy results exceed 94%. The remaining parcels are classified by expert photo-interpreters in order to guarantee the high accuracy demanded by policy makers.

A clinically viable capsule endoscopy video analysis platform for automatic bleeding detection

NASA Astrophysics Data System (ADS)

Yi, Steven; Jiao, Heng; Xie, Jean; Mui, Peter; Leighton, Jonathan A.; Pasha, Shabana; Rentz, Lauri; Abedi, Mahmood

2013-02-01

In this paper, we present a novel and clinically valuable software platform for automatic bleeding detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos for GI tract run about 8 hours and are manually reviewed by physicians to locate diseases such as bleedings and polyps. As a result, the process is time consuming and is prone to disease miss-finding. While researchers have made efforts to automate this process, however, no clinically acceptable software is available on the marketplace today. Working with our collaborators, we have developed a clinically viable software platform called GISentinel for fully automated GI tract bleeding detection and classification. Major functional modules of the SW include: the innovative graph based NCut segmentation algorithm, the unique feature selection and validation method (e.g. illumination invariant features, color independent features, and symmetrical texture features), and the cascade SVM classification for handling various GI tract scenes (e.g. normal tissue, food particles, bubbles, fluid, and specular reflection). Initial evaluation results on the SW have shown zero bleeding instance miss-finding rate and 4.03% false alarm rate. This work is part of our innovative 2D/3D based GI tract disease detection software platform. While the overall SW framework is designed for intelligent finding and classification of major GI tract diseases such as bleeding, ulcer, and polyp from the CE videos, this paper will focus on the automatic bleeding detection functional module.

GISentinel: a software platform for automatic ulcer detection on capsule endoscopy videos

NASA Astrophysics Data System (ADS)

Yi, Steven; Jiao, Heng; Meng, Fan; Leighton, Jonathon A.; Shabana, Pasha; Rentz, Lauri

2014-03-01

In this paper, we present a novel and clinically valuable software platform for automatic ulcer detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos take about 8 hours. They have to be reviewed manually by physicians to detect and locate diseases such as ulcers and bleedings. The process is time consuming. Moreover, because of the long-time manual review, it is easy to lead to miss-finding. Working with our collaborators, we were focusing on developing a software platform called GISentinel, which can fully automated GI tract ulcer detection and classification. This software includes 3 parts: the frequency based Log-Gabor filter regions of interest (ROI) extraction, the unique feature selection and validation method (e.g. illumination invariant feature, color independent features, and symmetrical texture features), and the cascade SVM classification for handling "ulcer vs. non-ulcer" cases. After the experiments, this SW gave descent results. In frame-wise, the ulcer detection rate is 69.65% (319/458). In instance-wise, the ulcer detection rate is 82.35%(28/34).The false alarm rate is 16.43% (34/207). This work is a part of our innovative 2D/3D based GI tract disease detection software platform. The final goal of this SW is to find and classification of major GI tract diseases intelligently, such as bleeding, ulcer, and polyp from the CE videos. This paper will mainly describe the automatic ulcer detection functional module.

Automatic identification of species with neural networks.

PubMed

Hernández-Serna, Andrés; Jiménez-Segura, Luz Fernanda

2014-01-01

A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.

Automatic emotional expression analysis from eye area

NASA Astrophysics Data System (ADS)

Akkoç, Betül; Arslan, Ahmet

2015-02-01

Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.