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共 1778 条记录，以下是1481-1490 订阅

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Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)

Electromygraphy (EMG) signal based hand gesture recognition ...

引用

international conference on Mechatronics (ICOM)

作者： Md. Rezwanul Ahsan Muhammad Ibn Ibrahimy Othman O. Khalifa Electrical & Computer Engineering Department International Islamic University Malaysia Kuala Lumpur Malaysia

Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. this biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based reliable and efficient hand gesture identification can help to develop good human computer interface which in turn will increase the quality of life of the disabled or aged people. the purpose of this paper is to describe the process of detecting different predefined hand gestures (left, right, up and down) using artificial neural network (ANN). ANNs are particularly useful for complex pattern recognition and classification tasks. the capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. the EMG pattern signatures are extracted from the signals for each movement and then ANN utilized to classify the EMG signals based on features. A back-propagation (BP) network with Levenberg-Marquardt training algorithm has been used for the detection of gesture. the conventional and most effective time and time-frequency based features (namely MAV, RMS, VAR, SD, ZC, SSC and WL) have been chosen to train the neural network.

关键词： Electromyography Artificial neural networks Feature extraction Training Noise Support vector machine classification Neurons

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the Rock Physical Phase Partition Method of Multilayer Sandstone Reservoir Based on Fuzzy Inference Networks

The Rock Physical Phase Partition Method of Multilayer Sands...

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2011 4th IEEE international conference on Computer Science and Information Technology(ICCSIT 2011)

作者： JunHui PAN Hui WANG Department of Computer and Information Technology North East Petroleum University Daqing,Heilongjiang,China

A kind of discrimination method based on weighted fuzzy inference networks is put forward in this paper according to the rock physical phase division problem of heterogeneous multilayer sandstone *** this method,the fuzziness of rock physical phase classification and meeting character of characteristic parameters are fully considered,and the cumulated expert experience in research are combined with the quantitative indexes which reflect the rock physical phase *** rock physical phase classification types and deviding indexes are defined by the core analysis data of coring well and explaining results of expert,then the classification standard pattern library of rock physical phase is *** rock physical appearance discrimination model of heterogeneous multilayer sandstone reservoir based on weighted fuzzy inference networks is established by adopting the adaptive learning *** experimental results demonstrate that the method has good application effect by processing the actual data from Daqing oilfield.

关键词： rock physical phase heterogeneous sandstone reservoir fuzzy inference network pattern recognition

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Cost effective depression patient thought record categorization via self-taught learning 11

Cost effective depression patient thought record categorizat...

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Proceedings of the 4th international conference on PErvasive Technologies Related to Assistive Environments

作者： Hua Wang Heng Huang Monica Basco Molly Lopez Fillia Makedon University of Texas at Arlington TX The University of Texas at Austin TX

ISBN: (纸本)9781450307727

Automatic patient though record categorization (TR) is important in Cognitive Behavior therapy (CBT), which is an useful augmentation of standard clinic treatment for Major Depressive Disorder (MDD). Because both collection and labeling of TR data are expensive, it is cost prohibitive to require a large amount of TR data, as well as their cor-responding category labels, to train a classification model with high classification accuracy. As in practice we only have very limited amount of labeled and unlabeled training TR data, traditional semi-supervised learning methods and transfer learning methods, which are the most commonly used strategies to deal with the lack of training data in statistical learning, can not work well in the task of automatic TR categorization. With the recognition of these challenges, in this paper we propose to approach the TR categorization problem from a new perspective via self-taught learning, an emerging topic in machine learning. Self-taught learning is a special type of transfer learning, instead of requiring labeled data from an auxiliary domain that are relevant to the classification task of interest as in traditional transfer learning methods, it learns the inherent structures of the auxiliary data and does not require their labels. Consequently, we may learn a classifier using the limited amount of labeled TR texts to achieve decent classification accuracy, with the assistance from the large amount text data obtained from some cheap, or even no-cost, resources. that is, a cost effective TR categorization system can be built up, which is of particular use in practical diagnosis and training of new therapists. We demonstrate the proposed method in the task of classifying the real depression homework texts, where promising experimental results validate the effectiveness of our new method.

关键词： cost effective classification self-taught learning major depressive disorder thought record cognitive behavior therapy

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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 ...

引用

4th international Power Engineering and Optimization conference, PEOCO 2010

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

ISBN: (纸本)9781424471287

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. ©2010 IEEE.

关键词： pattern recognition

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Logic-based representation, reasoning and machine learning for event recognition 10

Logic-based representation, reasoning and machine learning f...

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4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

作者： Artikis, Alexander Paliouras, Georgios Portet, François Skarlatidis, Anastasios Institute of Informatics and Telecommunications NCSR Demokritos Athens 15310 Greece Laboratoire D'informatique de Grenoble Grenoble Universités 38400 Saint Martin d'Hères France Department of Information and Communication Systems Engineering University of the Aegean Greece

ISBN: (纸本)9781605589275

Today's organisations require techniques for automated transformation of the large data volumes they collect during their operations into operational knowledge. this requirement may be addressed by employing event recognition systems that detect activities/events of special significance within an organisation, given streams of 'low-level' information that is very difficult to be utilised by humans. Numerous event recognition systems have been proposed in the literature. recognition systems with a logic-based representation of event structures, in particular, have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they haven proven to be efficient and scalable, and they are supported by machine learning tools automating the construction and refinement of event structures. In this paper we review representative approaches of logic-based event recognition, and discuss open research issues of this field. © 2010 ACM.

关键词： Metadata

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pattern recognition Using Artificial Neural Network: A Review

引用

4th international conference on Information Security and Assurance

作者： Kim, Tai-hoon Hannam Univ Multimedia Engn Dept Taejon South Korea

ISBN: (纸本)9783642133640

Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. the design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. the objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

关键词： pattern recognition correlation Neural Network

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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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4th international conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Islam, Md Tawhidul Shaikh, Mostafa Nayak, Abhaya Ranganathan, Shoba Macquarie Univ Biotechnol Res Inst Dept Chem & Biomol Sci Sydney NSW Australia Univ Tokyo Dept Informat & Commun Engn Tokyo Japan Macquarie Univ Dept Comp Sydney NSW Australia

ISBN: (纸本)9781424447138

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 F-score of 86% for the task of biomarker information extraction comparing

关键词： Biomarker relationship extraction text mining literature mining

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Proceedings of the 4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

Proceedings of the 4th ACM International Conference on Distr...

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4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

ISBN: (纸本)9781605589275

the proceedings contain 38 papers. the topics discussed include: context aware computing and its utilization in event-based systems;logic-based representation, reasoning and machine learning for event recognition;an event view model and DSL for engineering an event-based SOA monitoring infrastructure;an RFID architecture based on an event-oriented component model;content-based rendezvous with upgraph combination;event semantics in event dissemination architectures for massive multiuser virtual environments;complex event processing synergies with predictive analytics;event processing for large-scale distributed games;reliable fault-tolerant sensors for distributed systems;business-oriented development methodology for complex event processing: demonstration of an integrated approach for process monitoring;placement of replicated tasks for distributed stream processing systems;and an approach for iterative event pattern recommendation.

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Feature Selection Using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network

Feature Selection Using Sequential Forward Selection and cla...

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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： Marcano-Cedeno, A. Quintanilla-Dominguez, J. Cortina-Januchs, M. G. Andina, D. Tech Univ Madrid Grp Automat Signals & Commun Madrid Spain

ISBN: (纸本)9781424452262

the feature selection has been widely used to reduce the data dimensionality. data reduction improve the classification performance, the approximation function, and pattern recognition systems in terms of speed, accuracy and simplicity. A strategy to reduce the number of features in local search are the sequential search algorithms. In this work is presented a feature selection method based on Sequential Forward Selection (SFS) and Feed Forward Neural Network (FFNN) to estimate the prediction error as a selection criterion. three well-known database have been used to test the SFS-FFNN with Artificial Metaplasticity on Perceptron Multilayer (AMMLP). the AMMLP is a new method applied for classification of patterns. the results obtained by SFS-FFNN with AMMLP in classification accuracy are superior than obtained by conventional BP algorithm and other recent feature selection algorithms applied to the same database. By these reasons the proposed method SFS-FFNN with AMMLP is an interesting alternative to reduce the data dimensionality and provide a high accuracy.

关键词： artificial metaplasticity neural network artificial metaplasticity on perceptron multilayer data analysis data dimensionality data reduction feature extraction feed forward neural network multilayer perceptrons pattern recognition systems sequential forward selection

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Sentiment Mining within Social Media for Topic Identification

Sentiment Mining within Social Media for Topic Identificatio...

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4th Annual IEEE international conference on Semantic Computing (ICSC)

作者： Ostrowski, David Alfred Ford Motor Co Res & Innovat Ctr Syst Analyt Dearborn MI 48121 USA

ISBN: (纸本)9780769541549

Social media has demonstrated itself to be a proven source of information towards the marketing of products. this unique source of data provides a rapid means of customer feedback that is used to support a number of business areas. Towards this purpose, we describe a methodology for the identification of topics associated with customer sentiment. this process first employs a Fisher Classification based approach towards sentiment analysis. By considering specific mutual information and word frequency distribution, topics are then identified within sentiment categories. the goal is to provide overall trends in sentiment along with associated subject matter (ie. why) as it supports a company's business. We demonstrate this methodology against data collected among a particular product line as obtained from Twitter advanced search.

关键词： Fisher classification machine learning Social Media Analytics Twitter advanced search Web Mining customer feedback customer satisfaction customer sentiment data mining data source mutual information pattern classification product marketing sentiment analysis sentiment category sentiment mining social media topic identification word frequency distribution

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