000			07638cam a2200673 i 4500
001			on1266222675
003			OCoLC
005			20240523125543.0
006			m o d
007			cr cnu---unuuu
008			210831s2021 nju o 000 0 eng d
040			_aDG1 _beng _erda _epn _cDG1 _dOCLCO _dOCLCF _dDG1 _dUKAHL _dUKMGB _dN$T _dOCLCO _dOCLCQ _dSFB _dOCLCQ _dUPM _dOCLCQ _dWSU _dOCLCO _dOCLCL
015			_aGBC1D7804 _2bnb
016	7		_a020300524 _2Uk
020			_a9781119769262 _q(electronic bk. ; _qoBook)
020			_a1119769264 _q(electronic bk. ; _qoBook)
020			_a1119769248
020			_a9781119769248 _q(electronic bk.)
020			_z9781119768852
020			_z1119768853
024	7		_a10.1002/9781119769262 _2doi
029	1		_aAU@ _b000069952149
029	1		_aUKMGB _b020300524
035			_a(OCoLC)1266222675
037			_a9781119769248 _bWiley
050		4	_aQ325.5
082	0	4	_a006.3/1 _223
049			_aMAIN
245	0	0	_aMachine learning algorithms and applications / _cedited by Mettu Srinivas, G. Sucharitha, Anjanna Matta.
264		1	_aHoboken : _bWiley : _bScrivener Publishing, _c2021.
300			_a1 online resource (1 volume)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aPrint version record.
505	0		_aIntro -- 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.
505	8		_a7.4 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.
505	8		_a15.1 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.
520			_aMachine 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.
590			_aJohn Wiley and Sons _bWiley Online Library: Complete oBooks
650		0	_aMachine learning.
650		0	_aComputer algorithms.
650		2	_aAlgorithms
650		2	_aMachine Learning
650		6	_aApprentissage automatique.
650		6	_aAlgorithmes.
650		7	_aalgorithms. _2aat
650		7	_aComputer algorithms _2fast
650		7	_aMachine learning _2fast
700	1		_aSrinivas, Mettu, _eeditor.
700	1		_aSucharitha, G., _eeditor.
700	1		_aMatta, Anjanna, _eeditor.
758			_ihas work: _aMachine Learning Algorithms and Applications (Text) _1https://id.oclc.org/worldcat/entity/E39PD3gcgbhRW9Fm9GJY8j4bYd _4https://id.oclc.org/worldcat/ontology/hasWork
776	0	8	_iPrint version: _tMachine learning algorithms and applications. _dHoboken : Wiley-Scrivener, 2021 _z9781119768852 _w(OCoLC)1264404859
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119769262
938			_aAskews and Holts Library Services _bASKH _nAH39137260
938			_aEBSCOhost _bEBSC _n2994454
994			_a92 _bINLUM
999			_c12825 _d12825