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020			_a9783030990794
024	7		_a10.1007/978-3-030-99079-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A43 _b2022 EB
245	0	0	_aIntegrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems _cedited by Essam Halim Houssein, Mohamed Abd Elaziz, Diego Oliva, Laith Abualigah
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (IX, 497 páginas) _b221 ilustraciones, 177 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-9503 _v1038
505	0		_aCombined Optimization Algorithms for Incorporating DG in Distribution Systems -- Intelligent computational models for cancer diagnosis: A Comprehensive Review -- Elitist-Ant System metaheuristic for ITC 2021- Sports Timetabling -- Swarm intelligence algorithms-based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review -- Aggregation of Semantically Similar News Articles with the help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies -- Integration of Machine Learning and Optimization Techniques for Cardiac Health Recognition -- Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review -- Big Data Analysis using Hybrid Meta-heuristic Optimization Algorithm and MapReduce Framework -- Deep Neural Network for Virus Mutation Prediction: A Comprehensive Review -- 2D Target/Anomaly Detection in Time Series Drone Images using Deep Few-Shot Learning in Small Training Dataset -- Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons -- Early Detection of Coronary Artery Disease Using a PSO-based Neuroevolution Model -- Review for meta-heuristic optimization propels machine learning computations execution on spam comment area under digital security aegis region -- Solving reality based optimization trajectory problems with different metaphor inspired metaheuristic algorithms -- Parameter Tuning of PID controller Based on Arithmetic Optimization Algorithm in IOT systems -- Testing and Analysis of Predictive Capabilities of Machine Learning Algorithms -- AI Based Technologies for Digital and Banking Fraud During COVID -19 -- Gradient-Based Optimizer for structural optimization problems -- Aquila Optimizer based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing.
520			_aThis book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities.
988			_aSpringer_Robotics_2022
650		7	_2embne _9151819 _aAlgoritmos computacionales
700	1		_aHoussein, Essam Halim _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aAbd Elaziz, Mohamed _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aOliva, Diego _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9681352
700	1		_aAbualigah, Laith _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030990787
776	0	8	_iPrinted edition: _z9783030990800
776	0	8	_iPrinted edition: _z9783030990817
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-99079-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2022 _dz _eIG _zSI