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Machine learning algorithms and applications / edited by Mettu Srinivas, G. Sucharitha, Anjanna Matta

データ種別	電子ブック
出版者	(Hoboken : Wiley : Scrivener Publishing)
出版年	2021
大きさ	1 online resource (1 volume)
著者標目	Srinivas, Mettu editor Sucharitha, G. editor Matta, Anjanna editor

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0005245	Wiley Online Library: Complete oBooks				9781119769262

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一般注記	Print version record Intro -- Table of Contents -- Title Page -- Copyright -- Acknowledgments -- Preface -- Part 1: Machine Learning for Industrial Applications -- 1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services -- 1.1 Introduction -- 1.2 Literature Survey -- 1.3 Implementation Details -- 1.4 Results and Discussions -- 1.5 Conclusion -- References -- 2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning -- 2.1 Introduction -- 2.2 Conventional Silkworm Egg Detection Approaches -- 2.3 Proposed Method -- 2.4 Dataset Generation -- 2.5 Results -- 2.6 Conclusion -- Acknowledgment -- References -- 3 A Wind Speed Prediction System Using Deep Neural Networks -- 3.1 Introduction -- 3.2 Methodology -- 3.3 Results and Discussions -- 3.4 Conclusion -- References -- 4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Preliminaries -- 4.4 Proposed Model -- 4.5 Experiments -- 4.6 Results -- 4.7 Conclusion -- References -- 5 Sakshi Aggarwal, Navjot Singh and K.K. Mishra -- 5.1 Genesis -- 5.2 The Big Picture: Artificial Neural Network -- 5.3 Delineating the Cornerstones -- 5.4 Deep Learning Architectures -- 5.5 Why is CNN Preferred for Computer Vision Applications? -- 5.6 Unravel Deep Learning in Medical Diagnostic Systems -- 5.7 Challenges and Future Expectations -- 5.8 Conclusion -- References -- 6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Proposed Model for Credit Scoring -- 6.4 Results and Discussion -- 6.5 Conclusion -- References -- 7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Feature Agglomeration Clustering 7.4 Proposed Methodology -- 7.5 Results and Analysis -- 7.6 Conclusion -- References -- Part 2: Machine Learning for Healthcare Systems -- 8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier -- 8.1 Introduction -- 8.2 Materials and Methods -- 8.3 Results and Discussion -- 8.4 Conclusion -- References -- 9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data -- 9.1 Introduction -- 9.2 Related Works -- 9.3 An Overview of Gravitational Search Algorithm -- 9.4 Proposed Model -- 9.5 Simulation Results -- 9.6 Conclusion -- References -- Part 3: Machine Learning for Security Systems -- 10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching -- 10.1 Introduction -- 10.2 Preliminary Details -- 10.3 Experiments and Results -- 10.4 Conclusions -- References -- 11 Fake Social Media Profile Detection -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Methodology -- 11.4 Experimental Results -- 11.5 Conclusion and Future Work -- Acknowledgment -- References -- 12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Methods and Materials -- 12.4 Results -- 12.5 Conclusion -- Acknowledgements -- References -- 13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Proposed Method -- 13.4 Experimental Results -- 13.5 Conclusion -- Acknowledgement -- References -- Part 4: Machine Learning for Classification and Information Retrieval Systems -- 14 AnimNet: An Animal Classification Network using Deep Learning -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Proposed Methodology -- 14.4 Results -- 14.5 Conclusion -- References -- 15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis 15.1 Introduction -- 15.2 Related Work -- 15.3 The Proposed System -- 15.4 Result Analysis -- 15.5 Conclusion -- References -- 16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding -- 16.1 Introduction -- 16.2 Related Work -- 16.3 Proposed Approach -- 16.4 Experimental Setup -- 16.5 Results -- 16.6 Conclusion -- References -- 17 Image Anonymization Using Deep Convolutional Generative Adversarial Network -- 17.1 Introduction -- 17.2 Background Information -- 17.3 Image Anonymization to Prevent Model Inversion Attack -- 17.4 Results and Analysis -- 17.5 Conclusion -- References -- Index -- End User License Agreement Machine Learning Algorithms is for machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program John Wiley and Sons Wiley Online Library: Complete oBooks HTTP:URL=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119769262
件　名	LCSH:Machine learning LCSH:Computer algorithms MESH:Algorithms MESH:Machine Learning CSHF:Apprentissage automatique CSHF:Algorithmes FREE:algorithms FREE:Computer algorithms FREE:Machine learning
分　類	LCC:Q325.5 DC23:006.3/1
書誌ID	EB00004534
ISBN	9781119769262