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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Ganesh, Anirudh Jadhav, Ashwin R. Pragadeesh, K. A. Cibi Vellore Inst Univ Vellore Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. the complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. there have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Convolutional Neural Networks, and Deep Belief Networks in the classification of Handwritten Kannada numerals, and conclusively compared the results obtained. the proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Kannada characters.

关键词： Deep Belief Networks Convolutional Neural Networks LeNet pattern recognition Restricted boltzmann machine Deep learning

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Design and Evaluation of Reinforcement Learning Based AI Agent: A Case Study in Gaming 8th

Design and Evaluation of Reinforcement Learning Based AI Age...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Jayashree, P. Ramakrishnan, K. Anna Univ Dept Comp Technol Madras Inst Technol Madras 600044 Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

Soft computing is extensively used in the field of computer games to create AI agents for computers. A case study of reinforcement learning is presented, by designing an AI agent for chopsticks game, with a probabilistic algorithm devised to make use of past game experience as its only tool to guide itself to victory. It has been experimentally verified that the AI agent's performance increases with learning and nears saturation beyond a point of learning. Constant order space and time complexity is achieved with proper design of knowledge base.

关键词： AI agent Chopsticks Reinforcement learning Probabilistic model Soft computing

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A Cloud Based Environment for Big Data Analytics in Healthcare 8th

A Cloud Based Environment for Big Data Analytics in Healthca...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Chauhan, Ritu Jangade, Rajesh Mudunuru, Vimal K. Amity Univ Noida Uttar Pradesh India

ISBN: (纸本)9783319606187;9783319606170

A large amount of modern healthcare data is generated through imaging, Electronic Health Report (EHR), sensor based technology and other various healthcare processes. An elaborative perspective in technological advancement has enabled practitioners to answer questions for governance and future decision making. However, very few tools exist to critically analyze such big data for future knowledge discovery. We can further say that cloud computing technology can be a benchmark to substantiate big data which may lead to discover of hidden patterns and trends to enhance knowledge for progression of disease. this paper approached various aspects of cloud based services to enable big data analytic in healthcare data management system.

关键词： Cloud computing Big data Analytics as service Healthcare

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uPAD: Unsupervised Privacy-Aware Anomaly Detection in High Performance computing Systems 8th

uPAD: Unsupervised Privacy-Aware Anomaly Detection in High P...

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8th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Ghiasvand, Siavash Tech Univ Dresden Dresden Germany

ISBN: (纸本)9789897583513

Rapid growing complexity of HPC systems in response to demand for higher computing performance, results in higher probability of failures. Early detection of failures significantly reduces the damages caused by failure via impeding their propagation through system. Various anomaly detection mechanism are proposed to detect failures in their early stages. Insufficient amount of failure samples in addition to privacy concerns extremely limits the functionality of available anomaly detection approaches. Advances in machine learning techniques, significantly increased the accuracy of unsupervised anomaly detection methods, addressing the challenge of insufficient failure samples. However, available approaches are either domain specific, inaccurate, or require comprehensive knowledge about the underlying system. Furthermore, processing certain monitoring data such as system logs raises high privacy concerns. In addition, noises in monitoring data severely impact the correctness of data analysis. this work proposes an unsupervised and privacy-aware approach for detecting abnormal behaviors in general HPC systems. Preliminary results indicate high potentials of autoencoders for automatic detection of abnormal behaviors in HPC systems via analyzing anonymized system logs using fast-trainable noise-resistant models.

关键词： pattern Detection Time Series Analysis Anomaly Detection Neural Networks Noise Mitigation

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Advanced Deep Neural Networks for pattern recognition: An Experimental Study 8th

Advanced Deep Neural Networks for Pattern Recognition: An Ex...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Padmanabhan, Jayashree Premkumar, J. Melvin Jose Anna Univ Madras Inst Technol Madras 600044 Tamil Nadu India Google Inc Software Engn Intern Mountain View CA 94043 USA

ISBN: (纸本)9783319606187;9783319606170

Difficulty in pattern recognition is perceptible and neural networks approach the problem by way of learning from similar known patterns. Interest in Neural Networks started in the early 1980s when they were deemed to effectively model the human thought process. Speech recognition which first used Artificial Neural Networks (ANNs) to model the states of a Hidden Markov Models (HMMs) later started using Gaussian Mixture Models (GMMs). GMM-HMM systems have been the standard until recently when a new concept of Deep Neural Networks (DNNs) pre-trained using Restricted Boltzmann Machines (RBMs) came into existence. the discriminative capability of the resulting DNN is found to improve the performance of the recognition systems. the experimental work with DNN for recognizing patterns in handwriting and speech corpus has been carried out. In this work we implemented Deep Neural Networks for the above tasks and the pre trained DNN has been used for extracting bottleneck features and hereby improving the performance of the baseline systems with respect to recognition errors.

关键词： Deep Neural Networks Boltzmann machine Speech recognition Artificial Neural Networks Machine learning

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Human Posture recognition using Artificial Neural Networks 8

Human Posture Recognition using Artificial Neural Networks

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IEEE 8th international Advance computing conference (IACC)

作者： Kale, Hrishikesh Mandke, Prathamesh Mahajan, Hrishikesh Deshpande, Vedant COEP Dept Elect & Telecommun Engn Pune Maharashtra India

ISBN: (纸本)9781538666784

this paper proposes the use of artificial neural networks(ANNs) to classify human postures, using an invasive(intrusive) approach, into 6 categories namely standing, sitting, sleeping and bending - forward and backward. Human posture recognition has numerous applications in the field of healthcare analysis like patient monitoring, lifestyle analysis, elderly care etc. Most importantly, our solution is capable of classifying the aforementioned postures in real-time, by wirelessly(Wi-Fi) acquiring and processing the sensor data on a Raspberry-Pi device with minimal lag. A data-set of 44,800 samples was collected - from 3 subjects - which was used to train and test the neural *** experimenting and testing with a plethora of network architectures, an optimal neural network architecture(6-9-6) with suitable hyper-parameters was determined which gave an overall accuracy of 97.589%.

关键词： pattern-recognition neural-networks posture-recognition wearable-sensors accelerometers embedded systems real time movement classification sensor positioning

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Bangla Handwritten Basic Character recognition Using Deep Convolutional Neural Network 8

Bangla Handwritten Basic Character Recognition Using Deep Co...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior computing (ABC)

作者： Saha, Chandrika Faisal, Rahat Hossain Rahman, Md Mostafijur Univ Barishal Dept Comp Sci & Engn Barishal Bangladesh

ISBN: (纸本)9781728107882

Bangla is one of the mostly spoken languages all over the world, nonetheless, efforts on Bangla handwritten character recognition is not adequate. the use of Deep Convolutional Neural Network (DCNN) based classifiers has become a triumph over the state of art machine learning techniques. Using DCNN model to classify Bangla isolated basic characters can provide better output because of its ability to detect many hidden features from an image. In this paper, a new DCNN model, namely, BBCNet-15 is proposed for Bangla handwritten basic character recognition. the proposed model consists of 6 convolution layers, 6 max pooling layers, 2 fully connected layers followed by the softmax output unit. To avoid overfitting dropout regularization technique is used. the implementation of the proposed DCNN model is evaluated on a benchmark dataset, CMATERdb 3.1.2 which contains 50 character classes including 39 consonants and 11 vowels. the experimental evaluation of BBCNet-15 on the dataset provides a recognition accuracy of 96.40% which outperforms some prominent techniques in existence.

关键词： Deep learning Convolutional Neural Network pattern recognition Optical Character recognition Bangla Handwritten Character recognition

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Solving Machine Part Cell Formation Problem Using Genetic Algorithm Based Evolutionary computing 8th

Solving Machine Part Cell Formation Problem Using Genetic Al...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Sowmiya, N. Valarmathi, B. Gupta, N. Srinivasa VIT Univ Sch Informat Technol & Engn Vellore Tamil Nadu India VIT Univ Sch Informat Technol & Engn Dept Software & Syst Engn Vellore Tamil Nadu India VIT Univ Sch Mech Engn Vellore Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

Machine part cell formation is the group technology problem, in which the parts with near similar machining requirements are grouped into part families and the corresponding machines into machine cells. In this paper, a genetic algorithm with a fine tuning procedure is proposed to solve the group technology problem considering only one process plan for each part. the grouping efficacy achieved by the proposed method is comparable to the existing methods in general and better for 11.42% of the datasets.

关键词： Genetic algorithm Clustering Cell formation Cellular manufacturing Grouping efficacy

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Foreword from the editors of the special issue

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pattern recognition and Image Analysis 2008年第4期18卷 515-517页

作者： Zhuravlev, Yu.I. Gurevich, I.B. Romanov, E.M. Niemann, H. Ivanov, V.A. Dorodnicyn Computing Center Russian Academy of Sciences Moscow Russia Mari State Technical University Yoshkar-Ola Russia Friedrich-Alexander University of Erlangen Nuremberg Germany

No abstract available

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A New Algorithm for SVM Incremental Learning

A New Algorithm for SVM Incremental Learning

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2006 8th international conference on Signal Processing

作者： Xiaodan Wang Chunying Zheng Chongming Wu Wei Wang Department of Computer Engineering Air Force Engineering University

Based on analyzing the relationship between the Karush-Kuhn-Tucker(KKT) conditions of support vector machine and the distribution of the training samples,the possible changes of support vector set

ISBN: (纸本)0780397371

Based on analyzing the relationship between the Karush-Kuhn-Tucker(KKT) conditions of support vector machine and the distribution of the training samples,the possible changes of support vector set

关键词： Support vector machines Support vector machine classification Machine learning Algorithm design and analysis Distributed computing Military computing pattern recognition Machine learning algorithms Kernel Lagrangian functions

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