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Evolutionary Machine Learning Techniques: Algorithms and Applications by Seyedal

Description: Evolutionary Machine Learning Techniques by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah Estimated delivery 3-12 business days Format Paperback Condition Brand New Description This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields. Author Biography Dr. Seyedali Mirjalili is a lecturer at Griffith College, Griffith University, and internationally recognised for his advances in nature-inspired artificial intelligence (AI) techniques. He is the author of five books, 100 journal articles, 20 conference papers, and 20 book chapters. With over 10000 citations and H-index of 40, he is one of the most influential AI researchers in the world. From Google Scholar metrics, he is globally the 3rd most cited researcher in Engineering Optimisation and Robust Optimisation using AI techniques. He has been the keynote speaker of several international conferences and is serving as an associate editor of top AI journals including Applied Soft Computing, Applied Intelligence, IEEE Access, Advances in Engineering Software, and Applied Intelligence. Hossam Faris is a Professor in the Information Technology Department at King Abdullah II School for Information Technology at The University of Jordan, Jordan. Hossam Faris received his B.A. and M.Sc. degrees in computer science from the Yarmouk University and Al-Balqa` Applied University in 2004 and 2008, respectively, in Jordan. He was awarded a full-time competition-based scholarship from the Italian Ministry of Education and Research to peruse his Ph.D. degrees in e-Business at the University of Salento, Italy, where he obtained his Ph.D. degree in 2011. In 2016, he worked as a postdoctoral researcher with the GeNeura team at the Information and Communication Technologies Research Center (CITIC), University of Granada, Spain. His research interests include applied computational intelligence, evolutionary computation, knowledge systems, data mining, semantic web, and ontologies.Dr. Aljarah is an Associate Professor of BIG Data Mining and Computational Intelligence at The University of Jordan—Department of Information Technology, Jordan. Currently, he is the Director Assistant to International Affairs Unit at The University of Jordan. He obtained the bachelor degree in computer science from the Yarmouk University, Jordan, 2003. He also obtained his master degree in computer science and information systems from the Jordan University of Science and Technology, Jordan, in 2006. He participated in many conferences in the fields of data mining, machine learning, and big data such as CEC, GECCO, NTIT, CSIT, IEEE NABIC, CASON, and BigData Congress. Furthermore, he contributed in many projects in USA such as Vehicle Class Detection System (VCDS), Pavement Analysis Via Vehicle Electronic Telemetry (PAVVET), and Farm Cloud Storage System (CSS) projects. He has published more than 35 papers in refereed international conferences and journals. His research focuses on data mining, machine learning, big data, MapReduce, Hadoop, swarm intelligence, evolutionary computation, social network analysis (SNA), and large-scale distributed algorithms. Details ISBN 9813299924 ISBN-13 9789813299924 Title Evolutionary Machine Learning Techniques Author Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah Format Paperback Year 2020 Pages 286 Edition 1st Publisher Springer Verlag, Singapore GE_Item_ID:137905613; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. 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Price: 218.07 USD

Location: Fairfield, Ohio

End Time: 2024-10-26T02:06:51.000Z

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Evolutionary Machine Learning Techniques: Algorithms and Applications by Seyedal

Item Specifics

Restocking Fee: No

Return shipping will be paid by: Buyer

All returns accepted: Returns Accepted

Item must be returned within: 30 Days

Refund will be given as: Money Back

ISBN-13: 9789813299924

Book Title: Evolutionary Machine Learning Techniques

Number of Pages: X, 286 Pages

Publication Name: Evolutionary Machine Learning Techniques : Algorithms and Applications

Language: English

Publisher: Springer

Publication Year: 2020

Subject: Engineering (General), Intelligence (Ai) & Semantics, Applied

Type: Textbook

Item Weight: 16.1 Oz

Item Length: 9.3 in

Author: Hossam Faris

Subject Area: Mathematics, Computers, Technology & Engineering

Item Width: 6.1 in

Series: Algorithms for Intelligent Systems Ser.

Format: Trade Paperback

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