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共 777 条记录，以下是681-690 订阅

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Handwritten Character Recognition of MODI Script using Convolutional Neural Network Based Feature Extraction Method and Support Vector machine Classifier

Handwritten Character Recognition of MODI Script using Convo...

引用

IEEE international conference on signal and Image processing (ICSIP)

作者： Solley Joseph Jossy George CHRIST (Deemed to be University) Bangalore India Carmel College for Women Goa India

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Deep learning based algorithms are used in various pattern recognition tasks, including character recognition. Convolutional Neural Network (CNN) is effectively implemented for character recognition and is one of the best performing deep learning models. CNN can be used for character recognition directly or it can be used for extracting features in the character recognition process. Implementation of a feature extraction method using CNN autoencoder for MODI script character recognition is discussed in the paper. the extracted features are then subjected to Support Vector machine (SVM) for the purpose of classification. the On-the-fly data augmentation method is used to add variability and generalization of the data set. MODI Script is an ancient Indian script and was used for writing Marathi until 1950. Various libraries and temples in India and abroad have a large collection of MODI documents. Character recognition related research of MODI script is still in infancy and research and development is necessary to extract the information from MODI manuscripts stored in various libraries. the performance of the proposed method, which uses CNN autoencoder as a feature extractor and an SVM based classifier gives very high accuracy and is better compared to the most accurate MODI character recognition method reported so far.

关键词： Support vector machines Image segmentation Writing Feature extraction Character recognition Convolutional neural networks MODIS

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Private-Preserving Analysis of Network Traffic 5th

Private-Preserving Analysis of Network Traffic

引用

5th international conference on signal and Information processing, Networking and Computers (ICSINC)

作者： Tang, Yang Ma, Yan Beijing Univ Posts & Telecommun Network Informat Ctr Beijing Peoples R China

ISBN: (纸本)9789811371233;9789811371226

Network traffic analysis has important applications in many fields, such as the quality of service provisioning, network monitoring, network optimization, and so on. We can get regional network situation by analyze all parties' data, however, the cooperation among multiple parties suffers an extra drag from privacy and regulation issues. In this work, we apply secure multiparty computation methods to analyze more than three parties' traffic data without revealing the secrets themselves. We present a clustering method based on IP network prefix and apply it to the traffic. through the results of clustering, we can get subnets with the most considerable traffic in the network, and make relevant network settings for these subnets.

关键词： Secure multi-party computation Netstream Unsupervised machine learning Clustering

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Conventional machine learning Techniques with Features Engineering for Preventive Larynx Cancer Detection

Conventional Machine Learning Techniques with Features Engin...

引用

international conference on Advanced Technologies for signal and Image processing (ATSIP)

作者： Anis Ben Aicha Higher School of Communications of Tunis (SUPCOM) LR11TIC04 COSIM research Laboratory University of Carthage Faculty of Sciences of Bizerte (FSB) Tunisia

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

Larynx cancer is developed from precancerous state. Some precancerous lesions such as Keratosis, Leukoplakia, Ery-throlplakia, Papiloma virus, etc., can be transformed into a cancer if they are note treated in time. In this paper, we propose a non-intrusive technique to detect precancerous lesions at an earlier stage. Hence, these lesions can be treated as soon as possible. the idea is based on the analysis of the human voice in order to detect pertinent acoustic features able to discriminate pathological voices with precancerous lesions from normal ones. We have tested a large number of speech acoustic features. A feature engineering methodology leads us to choose the most pertinent features. To detect mentioned lesions, several classification techniques are tested. Experimental results show the validity of the idea.

关键词： Feature extraction Lesions Cancer Larynx Pathology Databases Support vector machines

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Developing a Smart IoT Solution to Monitor on-Bed Movement Patterns

Developing a Smart IoT Solution to Monitor on-Bed Movement P...

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international conference on Information Technology (INCIT)

作者： Prakrankiat Youngkong Worawit Panpanyatep Kitti thamrongaphichartkul Institute of Field Robotics King Mongkut's University of Technology Thonburi

ISBN: (数字)9781728166940

ISBN: (纸本)9781728193212

IoT devices and systems become a part of modern living. they are mostly used to monitor daily activities, especially related to personal health and fitness. In fact, it is getting more crucial during the COVID-19 pandemic. In this study, a smart monitoring and alarming IoT system called “NEF” was modified to recognize on-bed movement patterns including prone position applying different machine learning techniques. On-bed movement patterns were collected from 7 subjects. Considering only prone and supine positions, the models obtained from multilayer perceptron was the best. However, random forest yielded the highest overall correctly classified percentage. Further investigation is likely to include beddings such as pillows and blankets.

关键词： Radio frequency Monitoring COVID-19 Stress signal processing Robots Hardware

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Efficient Statistical learning Framework with Applications to Human Activity and Facial Expression Recognition

Efficient Statistical Learning Framework with Applications t...

引用

international conference on Advanced Technologies for signal and Image processing (ATSIP)

作者： Fatma Najar Sami Bourouis Marwan Alshar’e Roobaea Alroobaea Nizar Bouguila Ali H. Al Badi Ines Channoufi ENIT Laboratoire RISC Robotique Informatique et Systèmes Complexes Université de Tunis El Manar Tunis Tunisie ENIT Laboratoire: LR-SITI Signal Image et Technologies de 1’Information Université de Tunis E1 Manar Tunis Tunisie Taif University Taif Saudi Arabia Faculty of Computing Sciences Gulf College Muscat Oman The Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada Centre for Postgraduate Studies and Research Gulf College Muscat Oman

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

In this paper, we address the problem of human activities and facial expression recognition by investigating the effectiveness of Bayesian inference methods. Indeed, a novel method termed as Bayesian learning for finite multivariate generalized Gaussian mixture model is developed. the multivariate generalized Gaussian distribution is encouraged by its ability to model a large range of data and its shape flexibility. Our main contribution in this work is to develop a Markov Chain Monte Carlo within Metropolis-Hastings algorithm for proposed generative model. In this research, we tackle also some key issues related to machine learning and pattern recognition such as the statistical model's parameters estimation. We demonstrate the merits of our developed learning framework over two challenging applications that concern human activity recognition and facial expression recognition.

关键词： Bayes methods Face recognition Visualization Mixture models Hidden Markov models Feature extraction Vocabulary

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Event Detection and Classification Algorithm using Wavelet and machine learning Technique for Vibration fence PIDS

Event Detection and Classification Algorithm using Wavelet a...

引用

IEEE international conference on Computing, Communication and Security (ICCCS)

作者： Sushil Bahuguna Nitish Kumar Nawaj Shikalgar Sangeeta Dhage Anand Laddha P. P. Marathe Control Instrumentation Division Bhabha Atomic Research Centre Mumbai India Security Electronics & Software System Division Bhabha Atomic Research Centre Mumbai India Electronics & Instrumentation Group Bhabha Atomic Research Centre Mumbai India

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

the paper presents a design of a real-time algorithm to detect and classify an event that is occurring on a perimeter fence based on the Triboelectric sensor. the intrusion events like climbing, cutting the fence etc. produces vibrations on the fence. these vibrations are detected by Triboelectric cable which runs alongside the fence. Event detection and classification algorithm has been designed based on Wavelet and machine learning techniques. Field data were collected for basic simulated events such as hand climb, metal tap and wood tap. these signals are pre-processed first before passing to a further part of the algorithm. the Discrete Wavelet Transform (DWT) is used here to extract features from the pre-processed data. these features are used to detect and further classify events using supervised machine learning methods like Support Vector machine (SVM), decision trees and k-Nearest Neighbours (k NN). the accuracy and complexity of these methods were compared, and based on this analysis a machine learning method has been selected for designing the classification algorithm. the model trained by the selected classification method is further used to derive the formulation required for the classification. the derived formulation is used in designing a real-time algorithm for event detection and classification. the algorithm has been implemented on Digital signal Processor (DSP) - Field Programmable Gate Array (FPGA) based hardware to detect and classify the events occurring on the fence in real-time.

关键词： Discrete wavelet transforms Feature extraction Real-time systems Classification algorithms Support vector machines Metals signal processing algorithms

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Activity-based music classifier: A supervised machine learning approach for curating activity-based playlists 1

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5th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018

作者： Achar, B. P. Aniruddha Aiyappa, N.D. Akshaj, B. thippeswamy, M.N. Pillay, N. Department of CSE NMIT Yelahanka Bangalore560064 India School of Engineering UKZN Durban4041 South Africa

ISBN: (数字)9789811360015

ISBN: (纸本)9789811360008

Classification of musical tracks and creation of playlists to match four primary activities such as Sleep, Party, Dinner and Workout, using concepts of machine learning (ML) and musical information retrieval (MIR), is proposed in this paper. A data set of songs using features extracted through digital signal processing (DSP) is developed for training. In this work, several prominent and distinguishing features of individual musical tracks are employed. the ML algorithms used to classify the data set are: super vector machine (SVM), kth nearest neighbour, neural networks and voting classifiers. the results show that the highest accuracy can be attained when classification is performed using the voting classifier compared to other algorithms. the increase in accuracy can be attributed to the voting classifier’s ability to improve the individual classes’ accuracy by utilizing multiple classifier outputs. © Springer Nature Singapore Pte Ltd 2019.

关键词： Discrete wavelet transforms

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Intelligent User Profile Prediction in Radio Access Network 5th

Intelligent User Profile Prediction in Radio Access Network

引用

5th international conference on signal and Information processing, Networking and Computers (ICSINC)

作者： Qiu, Yaxing Wang, Xidong Wang, Fengjun Bian, Sen CMCC China Mobile Res Inst Green Commun Res Ctr Beijing Peoples R China

ISBN: (纸本)9789811371233;9789811371226

With increasingly dense deployment of base stations, power consumption, resource utilization, interference management, and user experience optimization in mobile network have become more and more important challenges. the effective prediction of temporal and spatial data traffic distribution, en-powered by intelligent user profile prediction, will be essential. this paper investigates user mobility and user service patterns prediction by generating refined user trajectory and 2-D service feature models. Specifically, an improved Bayesian trajectory prediction strategy coupled with time-domain features extraction used for user service pattern prediction is proposed. Large scale field test using 2300 active mobile devices (users) across 1600 cells in a live network showed promising results of 85% trajectory prediction accuracy and 70% service pattern prediction accuracy. the accurate prediction of user-level behavior pattern is of great significance not only for the improvement of network energy-efficiency, but also for the guarantee of user experience and the optimization of network utilization.

关键词： Mobility prediction Service pattern prediction machine learning

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Cross-Subject EEG signal Classification with Deep Neural Networks Applied to Motor Imagery 1

引用

5th international conference on Mobile, Secure and Programmable Networking (MSPN)

作者： Riyad, Mouad Khalil, Mohammed Adib, Abdellah LIM II FSTM Networks Telecoms & Multimedia Team BP 146 Mohammadia 20650 Morocco

ISBN: (数字)9783030228859

ISBN: (纸本)9783030228859;9783030228842

the Brain-Computer Interface (BCI) is a system able to serve as a mean of communication between machine and human where the brainwaves are the control signals acquired by electroencephalography (EEG). One of the most used brainwaves is the sensorimotor rhythm (SMR) which appears for real or imagined motor movement. In general, EEG signals need feature extraction methods and classification algorithms to interpret the raw signals. Deep learning approaches;however, permit the processing of the raw data without any transformation. In this paper, we present a deep learning neural network architecture to classify SMR signals due to its success for some previous works and to visualize the learned features. the architecture is composed of three parts. the first part contains a temporal convolution operation followed by a spatial convolution one. the second part contains recurrent layers. Finally, we use a dense layer to assign the signal to its class. the model is trained with Adam optimizer algorithm. Also, we use various regularization techniques such as dropout to prevent learning problem like overfitting. To evaluate the performance of the proposed architecture, the well known Dataset IIa of the BCI Competition IV is used. As a result, we get equivalent results to those ones of EEGNet.

关键词： EEG BCI Convolutional neural network Recurrent neural network Hybrid network

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5th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018

5th International Conference on Emerging Research in Computi...

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5th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018

ISBN: (纸本)9789811360008

the proceedings contain 56 papers. the special focus in this conference is on Emerging Research in Computing, Information, Communication and Applications. the topics include: A review on feature selection algorithms;a review on ensembles-based approach to overcome class imbalance problem;"college explorer" an authentication guaranteed information display and management system;activity-based music classifier: A supervised machine learning approach for curating activity-based playlists;new password embedding technique using elliptic curve over finite field;performance of wind energy conversion system during fault condition and power quality improvement of grid-connected wecs by facts (upfc);comprehensive survey on hadoop security;descriptive data analysis of real estate using cube technology;temporal information retrieval and its application: A survey;A feasibility study and simulation of 450 kw grid connected solar pv system at NMIT, Bangalore;a survey on multi-resolution methods for de-noising medical images;performance analysis of vedic multiplier with different square root bk adders;analysis of traffic characteristics of skype video calls over the internet;smart tourist guide (touristo);thefted vehicle identification system and smart ambulance system in VANETs;Performance study of openMP and hybrid programming models on CPU–GPU cluster;signature analysis for forgery detection;optimal sensor deployment and battery life enhancement strategies to employ smart irrigation solutions for indian agricultural sector;smart waste monitoring using wireless sensor networks;Digital filter technique used in signal processing for analysing of ECG signal;prediction of a dam’s hazard level: A case study from South Africa;geofencing-based accident avoidance notification for road safety;smart sensing for vehicular approach.

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