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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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pattern recognition approach for the automatic classification of data from impact acoustics

Pattern recognition approach for the automatic classificatio...

引用

10th IASTED international conference on Artificial Intelligence and Soft computing, ASC 2006

作者： Yella, Siril Gupta, Naren K. Dougherty, Mark Department of Computer Engineering Dalarna University Borlange 784 41 Sweden School of Engineering Napier University Edinburgh EH10 5DT United Kingdom

ISBN: (纸本)0889866104

this paper addresses and deals with the problem of automating condition monitoring of wood in the transportation domain. Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. these inspections are mostly done intuitively by skilled personnel. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Data resulting from impact acoustics tests made on wooden beams has been used. the relation between condition of the wooden beam and their respective emissions has been analyzed experimentally applying different feature extraction techniques. Combining the usage of traditional frequency extraction techniques like the magnitude of the signal together with famous speechrecognition techniques like Cepstral Coefficients, Linear Predictive Coding yield good results. Effect of using classifiers like Gaussian Mixture Models and Learning Vector Quantization has been tested and compared. In the current case Gaussian mixture model seem to achieve higher classification rates than Learning Vector Quantization model.

关键词： Condition monitoring

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10th international conference on Frontier computing, FC 2020

10th International Conference on Frontier Computing, FC 2020

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10th international conference on Frontier computing, FC 2020

ISBN: (纸本)9789811601149

the proceedings contain 300 papers. the special focus in this conference is on Frontier computing. the topics include: Dynamic Modeling and Control Method of Manipulator Based on Model recognition;design of Programmable Soft Starter Control System;power Load Forecasting Model Based on Improved Parallel Fuzzy Kernel Clustering Algorithm;the Prototype System of Support Vector Machine (SVM) Based on Big Data international Shipping Index Prediction;big Data Technology in Computer Information System;target recognition and Grab Location Based on Machine Vision and Deep Learning;point Cloud Data Self-adaptive Partition and Triangular Surface Reconstruction;application of Information Platform in Smart Logistics System;trace Car Based on Super Capacitor;Comparison of Influenza Disease Prediction Using ARIMA and LSTM Models for Central Taiwan;online Teaching Platform in Vocational College English Education;application of Adaptable College English Innovation Teaching in the Network Environment;problems and Countermeasures of Information Technology in College English Teaching;brief Analysis of Indian Cybersecurity Policy;advantages and Disadvantages of India and America in Cybersecurity Cooperation;new Model of Educational Information Service in the Era of Big Data;exploration on the Construction of Wisdom Teaching of Universities in the "Internet +" Era;on the Scalability Analysis of computing-Offloading Edge computing;theoretical Basis of P and NP Problem;Electrical Control and PLC Application Technology;the Design and Implementation of Dynamic Costume Projection System;automatic Control Technology and Integrated Design of Electromechanical Control System;evolution and Frontiers Visualization Analysis of international Cooperation in Vaccine R application Analysis of Computer-Aided Design in Clothing Design Teaching.

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Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network 10

Mirror Mosaicking: A Novel Approach to Achieve High-performa...

引用

10th international conference on Sensor Networks (SENSORNETS)

作者： Chaudhri, S. N. Rajput, N. S. Indian Inst Technol BHU Varanasi 221005 UP India

ISBN: (纸本)9789897584893

Limited dimensionality of the dataset obtained from an electronic nose (EN) is due to the number of elements in the sensor array used generally in the range of 4-8 elements only. Further, large number of sensor data can be generated by sampling the sensor responses both during the transient and steady states. the lower-dimensionality of sensor data prohibits the use of a convolutional neural network (CNN)-based pattern recognition techniques because the kernels of a CNN cannot be used on the obtained sample vectors to extract the features. In this paper, we have proposed a novel approach to enhance the data dimensionality keeping the sensor response characteristics absolutely unaltered. By leveraging the concept of mirror mosaicking technique, we have upscaled the input sample vectors into a 6x6 2-D input arrays to train the shallow CNN. Using the proposed approach, all the 16-unknown steady-state test samples classified accurately which are not used during the training. Moreover, the parameters of the classification report viz., Precision, Recall, and F1 score also obtained with a fraction value of 1.00. the proposed technique is a generic approach that can be used to classify various low-dimensional datasets obtained from various sensor arrays in various fields.

关键词： Mirror Mosaicking Gas Sensor Array Electronic Nose Gas Classification pattern recognition Convolutional Neural Network

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Web Application System of Handwritten Text recognition 5

Web Application System of Handwritten Text Recognition

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Bodnia, Yevhen Kozulia, Mariia Natl Tech Univ Kharkiv Polytech Inst Kirpichova Str 2 Kharkiv Ukraine

the problem of text recognition is becoming increasingly important due to the active introduction of digital computing and the widespread use of word processors. pattern recognition is one of the most difficult from a mathematical point of view and one of the most popular areas of artificial intelligence programming. In the work is researched approaches and methods of solving text recognition problem, improved the performance of the available algorithms for text recognition and created algorithmic software. According to the analysis, neural networks were selected for handwriting recognition. the main advantage of using neural networks is a good generalization ability, the ability to use context analysis and recognize a symbol based on the surrounding symbols. the software implementation features of Hopfield and convolutional neural network, genetic algorithm, which were chosen as effective methods for recognizing handwritten text, were considered. Algorithmic software and web application that uses these methods for the task of handwritten text recognition is developed.

关键词： Handwritten text recognition pattern recognition recognition methods neural network genetic algorithm convolutional neural network Hopfield neural network machine learning data models

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Riemannian SOS-Polynomial Normalizing Flows 1

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42nd German conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual computing for Biology and Medicine, VCBM 2020

作者： Schwarz, Jonathan Draxler, Felix Köthe, Ullrich Schnörr, Christoph Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg Germany Image and Pattern Analysis Group Heidelberg University Heidelberg Germany Visual Learning Lab Heidelberg University Heidelberg Germany

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

Sum-of-Squares polynomial normalizing flows have been proposed recently, without taking into account the convexity property and the geometry of the corresponding parameter space. We develop two gradient flows based on the geometry of the parameter space of the cone of SOS-polynomials. Few proof-of-concept experiments using non-Gaussian target distributions validate the computational approach and illustrate the expressiveness of SOS-polynomial normalizing flows. © 2021, Springer Nature Switzerland AG.

关键词： Polynomials

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Influence Maximization in Social Networks using Hurst exponent based Diffusion Model 10

Influence Maximization in Social Networks using Hurst expone...

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10th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Saxena, Bhawna Saxena, Vikas Jaypee Inst Informat Technol Dept Comp Sci & Engg & IT A-10Sect 62 Noida 201309 UP India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127910

Influence maximization (IM) in online social networks (OSNs) has been extensively studied in the past few years, owing to its potential of impacting online marketing. IM aims at solving the problem of selecting a small set of influential nodes, who can lead to maximum influence spread across a social network. An integral part of IM is the modelling of the underlying diffusion process, which has a substantial impact on the spread achieved by any seed set. In this paper, Hurst-based diffusion model for IM has been proposed, under which node's activation depends upon the nature of self-similarity exhibited in its past activity pattern. Assessment of the self-similarity trend exhibited by a node's activity pattern, has been done using Hurst exponent (H). On the basis of the results achieved, the proposed model has been found to perform significantly better than two widely popular diffusion models, Independent Cascade and Linear threshold, which are often used for IM in OSNs.

关键词： influence maximization information diffusion social network self-similarity Hurst exponent

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Attention Detection using Electro-oculography Signals in E-learning Environment 10

Attention Detection using Electro-oculography Signals in E-l...

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10th IEEE international conference on Intelligent computing and Information Systems, ICICIS 2021

作者： Abdo, Yassmina Yahya, Ehab Ismail, Hussein Saleh, Mostafa Reda, Radwa Shedeed, Howida A. Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9781665440769

Recently, on account of the Covid-19 pandemic online learning has become a main strategy of learning in any educational environment. the main problem is the lack of focus or attention during a student watching an online lecture or video due to ways of distraction that can occur and that's what interrupts the students and make them lose their focus. In this research paper, we propose the attention detection system using an Electro-oculogram (EOG) signal generated by eye movement. the first goal of this study was detecting the un-focusing periods in the video. the proposed process of recognizing the un-focusing period depends on recognizing the eye movement direction. 6 classes of eye movements were classified in this study: left, right, up, down, blinking, and no movement. the process started by collecting our benchmark dataset from 50 subjects, which was considered the largest dataset at all for eye movements, using a special hardware device. the pre-processing step was down to filter the signals from any noise that may be caused by the surrounding environment or involuntary movements using the band-pass filter. Finally, the classification step was down. three models of deep learning were examined for the best classifier: Convolution Neural Network (CNN), Inception Network, and VGG Network. Our experiments achieved the best results with the Inception model, reaching an average accuracy of 93.63%. the second goal of this study was to recognize the Attention-deficit hyperactivity disorder (ADHD) pattern in the student's recording. the model succeeded to recognize this disordered pattern. © 2021 IEEE.

关键词： Students

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Online pattern recognition of Time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble 11

Online Pattern Recognition of Time-series Gas Sensor Data wi...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems - Technology and Applications (IDAACS)

作者： Pareek, Vishakha Chaudhury, Santanu Singh, Sanjay CSIR Cent Elect Engn Res Inst Pilani 333031 Rajasthan India Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India Indian Inst Technol Delhi Delhi 110016 India

ISBN: (纸本)9781665426053

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. the presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. the responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. the Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. the network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. the proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.

关键词： 2D-CNN pattern recognition Ensemble Gas Sensor

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Static Malware Analysis in Encrypted Domain 10

Static Malware Analysis in Encrypted Domain

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10th international conference on Information and Communication Technology Convergence (ICTC) - ICT Convergence Leading the Autonomous Future

作者： Hwang, Taeyoon Yoon, Ji Won Korea Univ Seoul South Korea

ISBN: (纸本)9781728108933

In the cloud computing environments, a server can handle a lot of data which an individuals cannot process. therefore, many users upload data to the server and receive the results after computing the uploaded data. In the case of malware detection, the user uploads the suspicious file to the server and checks the file. However, if the file being examined is a document, there is a vulnerability that the data will be exposed to the server. therefore, In this paper, we propose a computational matching algorithm in encrypted domain. We apply this technique to actual malware detection using the homomorphic encryption. And we measure the detection speed of string signatures with varying lengths and the time to detect string signatures from real malware.

关键词： Homomorphic Encryption String signature pattern matching Malware detection

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Intelligent computing theory 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： De-Shuang Huang Vitoantonio Bevilacqua Prashan Premaratne

ISBN: (数字)9783319093338

ISBN: (纸本)9783319093321

this book – in conjunction with the volumes LNAI 8589 and LNBI 8590 – constitutes the refereed proceedings of the 10th international conference on Intelligent computing, ICIC 2014, held in Taiyuan, China, in August 2014. the 92 papers of this volume were carefully reviewed and selected from numerous submissions. the papers are organized in topical sections such as evolutionary computation and learning; swarm intelligence and optimization; machine learning; social and natural computing; neural networks; biometrics recognition; image processing; information security; virtual reality and human-computer interaction; knowledge discovery and data mining; signal processing; pattern recognition; biometric system and security for intelligent computing.

关键词： Artificial Intelligence Image Processing and Computer Vision pattern recognition Computational Biology/Bioinformatics Information Systems Applications (incl. Internet) Computation by Abstract Devices

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