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001447291 001__ 1447291
001447291 003__ OCoLC
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001447291 020__ $$a9783030990794$$q(electronic bk.)
001447291 020__ $$a3030990796$$q(electronic bk.)
001447291 020__ $$z9783030990787
001447291 020__ $$z3030990788
001447291 0247_ $$a10.1007/978-3-030-99079-4$$2doi
001447291 035__ $$aSP(OCoLC)1325717871
001447291 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCQ
001447291 049__ $$aISEA
001447291 050_4 $$aQA76.9.A43
001447291 08204 $$a005.1$$223/eng/20220607
001447291 24500 $$aIntegrating meta-heuristics and machine learning for real-world optimization problems /$$cEssam H. Houssein, Mohamed Abd Elaziz, Diego Oliva, Laith Abualigah, editors.
001447291 264_1 $$aCham :$$bSpringer,$$c[2022]
001447291 264_4 $$c©2022
001447291 300__ $$a1 online resource :$$billustrations (chiefly color).
001447291 336__ $$atext$$btxt$$2rdacontent
001447291 337__ $$acomputer$$bc$$2rdamedia
001447291 338__ $$aonline resource$$bcr$$2rdacarrier
001447291 4901_ $$aStudies in computational intelligence ;$$vvolume 1038
001447291 5050_ $$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.
001447291 506__ $$aAccess limited to authorized users.
001447291 520__ $$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.
001447291 588__ $$aDescription based on print version record.
001447291 650_0 $$aMetaheuristics.
001447291 650_0 $$aMachine learning.
001447291 650_0 $$aMathematical optimization.
001447291 650_6 $$aMétaheuristiques.$$0(CaQQLa)201-0313163
001447291 650_6 $$aApprentissage automatique.$$0(CaQQLa)201-0131435
001447291 650_6 $$aOptimisation mathématique.$$0(CaQQLa)201-0007680
001447291 655_0 $$aElectronic books.
001447291 7001_ $$aHoussein, Essam H.,$$eeditor.
001447291 7001_ $$aAbd Elaziz, Mohamed,$$eeditor.
001447291 7001_ $$aOliva, Diego,$$eeditor.
001447291 7001_ $$aAbualigah, Laith,$$eeditor.
001447291 77608 $$iPrint version:$$tIntegrating meta-heuristics and machine learning for real-world optimization problems.$$dCham : Springer, 2022$$z9783030990787$$w(OCoLC)1308484295
001447291 830_0 $$aStudies in computational intelligence ;$$vv. 1038
001447291 852__ $$bebk
001447291 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-99079-4$$zOnline Access$$91397441.1
001447291 909CO $$ooai:library.usi.edu:1447291$$pGLOBAL_SET
001447291 980__ $$aBIB
001447291 980__ $$aEBOOK
001447291 982__ $$aEbook
001447291 983__ $$aOnline
001447291 994__ $$a92$$bISE