Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains. It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.
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Amir H. Gandomi, PhD, is a leading researcher in global optimization and big data analytics, currently serving as a Professor of Data Science and an ARC DECRA Fellow at the University of Technology Sydney (UTS). With over 450 journal publications and 60,000 citations, he is among the most cited researchers worldwide. Dr. Gandomi has authored 14 books and received numerous accolades, including the IEEE TCSC Award and the Achenbach Medal. His editorial roles span several prestigious journals, and he is a sought-after keynote speaker in the fields of artificial intelligence and genetic programming. Previously, he held academic positions at the Stevens Institute of Technology and Michigan State University, where he contributed significantly to advancing knowledge in machine learning and evolutionary computation.
Dr. Levente Kovács received his Ph.D. in biomedical engineering from the Budapest University of Technology and Economics, Hungary, and the Habilitation degree (Hons.) from Óbuda University. He is currently a Professor with Óbuda University and also serves as the Vice Dean for Education of the John von Neumann Faculty of Informatics and Head of the Physiological Controls Research Center. He was a János Bolyai Research Fellow with the Hungarian Academy of Sciences, from 2012 to 2015. His fields of interest are modern control theory and physiological controls. Within these subjects, he has published more than 250 articles in international journals and refereed international conference papers. Dr. Kovács is a recipient of the highly prestigious ERC StG grant of the European Union.
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