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Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2010年第6期34卷 446-452页

作者： Herold, Julia Zhou, Luxian Abouna, Sylvie Pelengaris, Stella Epstein, David Khan, Michael Nattkemper, Tim W. Univ Bielefeld Biodata Min & Appl Neuroinformat Grp D-33615 Bielefeld Germany Univ Warwick Dept Biol Sci Biomed Res Inst Coventry CV4 7AL W Midlands England Univ Warwick Sch Med Coventry CV4 7AL W Midlands England Univ Warwick Math Inst Coventry CV4 7AL W Midlands England

the challenging problem of computational bioimage analysis receives growing attention from life sciences. Fluorescence microscopy is capable of simultaneously visualizing multiple molecules by staining with different fluorescent dyes. In the analysis of the result multichannel images, segmentation of ROIs resembles only a first step which must be followed by a second step towards the analysis of the ROI's signals in the different channels. In this paper we present a system that combines image segmentation and information visualization principles for an integrated analysis of fluorescence micrographs of tissue samples. the analysis aims at the detection and annotation of cells of the Islets of Langerhans and the whole pancreas, which is of great importance in diabetes studies and in the search for new anti-diabetes treatments. the system operates with two modules. the automatic annotation module applies supervised machine learning for cell detection and segmentation. the second information visualization module can be used for an interactive classification and visualization of cell types following the link-and-brush principle for filtering. We can compare the results obtained with our system with results obtained manually by an expert, who evaluated a set of example images three times to account for his intra-observer variance. the comparison shows that using our system the images can be evaluated with high accuracy which allows a considerable speed up of the time-consuming evaluation process. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Fluorescence microscopy Bioimage informatics Image processing pattern recognition machine vision Information visualization Exploratory data analysis (EDA) alpha- and beta-cell counting

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An evaluation of neural network ensembles and model selection for time series prediction

An evaluation of neural network ensembles and model selectio...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 international Joint conference on Neural Networks, IJCNN 2010

作者： Barrow, Devon K. Crone, Sven F. Kourentzes, Nikolaos Department of Management Science Lancaster University Management School Lancaster LA1 4YX United Kingdom

ISBN: (纸本)9781424469178

Ensemble methods represent an approach to combine a set of models, each capable of solving a given task, but which together produce a composite global model whose accuracy and robustness exceeds that of the individual models. Ensembles of neural networks have traditionally been applied to machine learning and pattern recognition but more recently have been applied to forecasting of time series data. Several methods have been developed to produce neural network ensembles ranging from taking a simple average of individual model outputs to more complex methods such as bagging and boosting. Which ensemble method is best;what factors affect ensemble performance, under what data conditions are ensembles most useful and when is it beneficial to use ensembles over model selection are a few questions which remain unanswered. In this paper we present some initial findings using neural network ensembles based on the mean and median applied to forecast synthetic time series data. We vary factors such as the number of models included in the ensemble and how the models are selected, whether randomly or based on performance. We compare the performance of different ensembles to model selection and present the results. © 2010 IEEE.

关键词： pattern recognition

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Constructing Feature-based Ensemble Classifiers for Real-World machines Fault Diagnosis

Constructing Feature-based Ensemble Classifiers for Real-Wor...

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36th Annual conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE international conference on E-learning in Industrial Electronics/IES Industry Forum

作者： de Oliveira, Marcelo V. Wandekokem, Estefhan D. Mendel, Eduardo Fabris, Fabio Varejao, Flavio M. Rauber, thomas W. Batista, Rodrigo J. Univ Espirito Santo Dept Comp Sci BR-29060970 Vitoria ES Brazil Petr Brasileiro SA PETROBRAS Inspect & Maintenance Engn Espirito Santo Explorat & Prod Business Unit Vitoria Spain

ISBN: (纸本)9781424452262

this paper presents the results achieved by fault classifier ensembles based on supervised learning for diagnosing faults on oil rigs motor pumps. the main goal is to apply two feature-based ensemble construction methods to a real-world problem. Recent studies have shown that the use of ensembles of classifiers that are accurate and at the same time have diversifying results can improve the final classification accuracy, compared to a single accurate classifier. the diversification performed by the methods presented in this work is obtained by varying the feature set each classifier uses. We show results obtained with the established genetic algorithm GEFS and a recently developed approach called BSFS, which has lower computational cost. We rely on a database of real data, with 2000 acquisitions of vibration signals extracted from operational motor pumps. Our results show that the ensemble methods had a higher classification accuracy solving a real-world fault diagnosis problem than single classifiers. Also, we present promising results in our experiments with both algorithms, that successfully solves the problem.

关键词： condition monitoring fault diagnosis feature-based ensemble classifiers genetic algorithm genetic algorithms learning (artificial intelligence) machine fault diagnosis mechanical engineering computing oil rigs motor pumps pattern classification pumps supervised learning vibration signals vibrations

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Artificial immune system based machine learning for voltage stability prediction in power system

Artificial immune system based machine learning for voltage ...

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international Power Engineering and Optimization conference (PEOCO)

作者： S. I. Suliman T. K. Abdul Rahman Faculty of Electrical Engineering Universiti Teknologi Mara Malaysia

Voltage instability has recently become a challenging problem for many power system operators. this phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. this paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. the proposed technique was tested on the IEEE 30 bus power system and the results shows that fast performance with accurate evaluation for voltage stability condition has been obtained.

关键词： Stability criteria Power system stability Artificial neural networks Indexes pattern recognition

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Sunday school-work times of teachers in Tasmania, Australia

Sunday school-work times of teachers in Tasmania, Australia

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international conference on Distance learning and Education (ICDLE)

作者： Elkana Ngwenya Australian Maritime College University of Tasmania Australia

In most occupations there has been an increasing tendency to work unsociable hours, as a result of reforms. In the case of teaching, these unsociable hours have included after hours work and weekend work, and have created significant intensification of work. Little is known, however, about the Sunday work ethic that results from the intensification of teachers' work. In this study, data on teachers' workloads are examined for correlations between Sunday times and time allocated to other days of the week. An Ordinary Least Squares (OLS) regression equation that relates Sunday time-use to teacher and school characteristics is estimated. Results suggest that selected school and teacher characteristics affect the number of hours expended on Sunday school work by teachers in Tasmania. the absence of breaks during regular school time, and the extent to which teachers are overloaded, are particularly significant in increasing Sunday work time of teachers in Tasmania. these results have implications for teacher workloads, and future school reforms in Tasmania.

关键词： Educational institutions pattern recognition

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Extracting Biomarker Information applying Natural Language Processing and machine learning

Extracting Biomarker Information applying Natural Language P...

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the 4th international conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Md Tawhidul Islam Mostafa Shaikh Abhaya Nayak Shoba Ranganathan Department of Chemistry and Biomolecular Science Biotechnology Research InstituteMacquarie Universi Dept.Of Information and Comm.Engineering University of Tokyo Tokyo Japan Department of Computing Macquarie University Sydney Australia Department of Chemistry and Biomolecular Science Biotechnology Research Institute Macquarie Univer

In this paper, we detail an approach to a very specific task of information extraction namely, extracting biomarker information in biomedical literature. Starting with the abstract of a given publication, we first identify the evaluative sentence(s) among other sentences by recognizing words and phrases in the text belonging to semantic categories of interest to bio-medical entities (i.e., semantic category recognition). For the entities like, protein, gene and disease, we determine whether the statement refers to biomarker relationship (i.e., assertion classification). Finally, we identify the biomarker relationship among the biomedical entities (i.e., semantic relationship classification). the system, Biomarker Information Extraction Tool (BIET) implements machine learning-based biomarker extraction using support vector machines (SVM). the system is trained and tested on a corpus of oncology related PubMed/MEDLINE literatures hand-annotated with biomarker information. We investigate the effectiveness of different features for this task and examine the amount of training data needed to learn the biomarker relationship with the entities. Our system achieved an average Fscore of 86% for the task of biomarker information extraction comparing to the human annotated dataset (i.e. gold standard) scores.

关键词： data mining Biomarkers Natural language processing machine learning Text recognition Support vector machines Support vector machine classification Proteins Diseases System testing

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A Svm-Based Algorithm for Automatic Species Classification of a Marine Diatom Genus Coscinodiscus Ehrenberg

A Svm-Based Algorithm for Automatic Species Classification o...

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international conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Jinfei Luo Qiaoqi Luo Yahui Gao Changping Chen Junrong Liang Chenhui Yang Sch. of Life Sci. Xiamen Univ. Xiamen China Sch. of Inf. Sci. & Technol. Xiamen Univ. Xiamen China

Coscinodiscus Ehrenberg is a large and ecologically important diatom genus with plentiful species in marine phytoplankton and with a variety of round shapes and ornamentation. these properties can be measured by computer image pre-segmentation and feature extraction with threshold methods. However, it proves to be complicated task because of the high spatial variability of ornamentation properties. Researchers have shown a Teach-program and a number of library functions operating on sample image lists (SIL's) and operating on classifiers (CF's) to solve the problem. In this paper, we present Coscinodiscus Ehrenberg ornamentation classifier algorithm called support vector machines (SVMs) to derive a new set of SIL's and CF's. the principal purpose of SVMs is Coscinodiscus Ehrenberg images pattern recognition approach. A pattern is in this context always the SIL's contained in a sub-rectangle of some given (possibly larger) image. For the same classifier this sub-rectangle must always have the same dimensions, while the query image to be searched may be arbitrarily large. the training is done by preparing SIL's for the pattern taxa in question and feeding them to CF's created. Our classifier generation with preprocessing code optimization achieves a AAAAA preprocessing code, a 0.981 learning success, a 100% computational complexity. Train with SIL's achieves 212 samples, 17 taxa, a 0.472% error rate and Test with Query image searching achieves 253 samples, 17 taxa, a 15.81% error rate. the experiments demonstrate that the proposed method is very robust to the threshold segmentation and ornamentation feature extraction of Coscinodiscus Ehrenberg images, and is effective and useful for species classification of Coscinodiscus Ehrenberg.

关键词： Feature extraction Error analysis Shape Libraries Support vector machines Support vector machine classification pattern recognition Computational complexity Testing Robustness

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Mutual Information based recognition of Pulse Signal in TCM

Mutual Information based Recognition of Pulse Signal in TCM

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the 4th international conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Yong Shen Chiheng Zhou Chunming Xia Jianjun Yan Rui Guo Haixia Yan Fufeng Li Yiqin Wang Center for Mechatronics Engineering East China University of Science and Technology Shanghai 200237 Center for TCM information science and technology Shanghai University of TCM Shanghai 201203 P.R.Ch

this study aims at utilizing mutual information algorithm to make an objective and quantitative research for the pulses in Traditional Chinese Medicine (TCM) diagnosis. the normal pulse signals, slippery pulse signals and taut pulse signals were collected from the outpatients by Shanghai University of TCM. Some typical pulse signals from all these classes were selected as the templates to be matched with the new pulse signals by means of mutual information, and then which class the new pulse signal should be classified was determined through the maximum mutual information. Finally, the satisfactory results were obtained which proof that it was appropriate to use mutual information to recognize pulse signals. the biggest advantage of this way is simple, since the inputted pulse signals should not be extracted features. And the time-domain features were extracted from the pulse signals to be classified. the results from the two different ways show the classification method presented is efficient based on Mutual Information.

关键词： Mutual information Feature extraction Random variables data mining Support vector machines Support vector machine classification Neural networks Medical diagnostic imaging Time domain analysis pattern recognition

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thalamic Bursts Mediate pattern recognition

Thalamic Bursts Mediate Pattern Recognition

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4th international IEEE/EMBS conference on Neural Engineering

作者： Jandel, Magnus Swedish Def Res Agcy Stockholm Sweden

ISBN: (纸本)9781424420728

A new functional model for burst firing in the dorsal thalamus is proposed where thalamocortical pattern recognition systems, based on kernel machine principles, are connected by burst signaling. the systems include input trapping in the dorsal thalamus, cortical learning state memory and processing in the thalamic reticular nucleus. Misclassified events are captured as training examples in the waking state and the pattern recognition systems are trained by extensive thalamic bursting in deep sleep.

关键词： thalamus thalamic reticular nucleus neocortex pattern recognition kernel machine support vector

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CLASSIFICATION OF FACIAL EXPRESSIONS USING data MINING AND machine learning ALGORIthMS 4

CLASSIFICATION OF FACIAL EXPRESSIONS USING DATA MINING AND M...

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4th Iberian conference on Information Systems and Technologies

作者： Faria, Brigida Monica Lau, Nuno Reis, Luis Paulo Univ Aveiro Dept Elect Telecomunicacoes & Informat Aveiro Portugal IPP ESTSP Oporto Portugal IEETA Aveiro Portugal Univ Porto Fac Engn DEI Oporto Portugal Univ Porto LIACC Oporto Portugal

ISBN: (纸本)9789899624702

machine Facial recognition is a problem that consists in exploring still or video images of a scene to identify or verify one or more persons in the scene using stored databases of faces. Research in Facial recognition is multidisciplinary including disciplines like pattern recognition, image processing and analysis, computer graphics, machine learning and data mining. Sometimes applications include not only facial recognition but also facial expression characteristics identification and classification like blinking of the eyes, open/close mouth among others. this paper presents a comparative study of three algorithms for classification basic facial expressions.

关键词： data Mining OneR Naive Bayes Support Vector machines Facial recognition

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