Advanced Intelligence Methods for Data Science and Optimization
Will be published approx. on 1. September 2026
Book
Paperback/Softback
250 pages
978-0-443-28940-8 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Weight
450 gr
ISBN-13
978-0-443-28940-8 (9780443289408)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence and
optimization, recognized as the No. 1 AI researcher on Stanford University's prestigious World's Top Scientists list since 2023. He founded the Centre for Artificial Intelligence Research and
Optimization in 2019 and serves as a Professor of AI at Torrens University Australia, with distinguished professorships in Hungary and the Czech Republic. With more than 600 research
publications, 130,000 citations, and an H-index of 125, Prof. Mirjalili is among the top 1% of highly cited researchers worldwide. His contributions include developing AI algorithms widely applied in science and industry and delivering influential talks, including a TED Talk on AI's transformative potential. Prof. Mirjalili is a strong advocate for responsible and inclusive AI, and he has collaborated with industry and government on ethical AI tools. As a senior member of IEEE and an editor for leading AI journals, he significantly contributed to the advancements of fundamental and applied research in the field. Recognized as a top research leader by The
Australian for five years, his insights have earned significant media attention, which showcases
his influence as a global thought leader. Dr. Levente Kovacs received his Ph.D. in biomedical engineering from the Budapest University of Technology and Economics, Hungary, and the Habilitation degree (Hons.) from Obuda University. He is currently a Professor with Obuda 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 Janos 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. Kovacs is a recipient of the highly prestigious ERC StG grant of the European Union.
Dr. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence and
optimization, recognized as the No. 1 AI researcher on Stanford University's prestigious World's Top Scientists list since 2023. He founded the Centre for Artificial Intelligence Research and
Optimization in 2019 and serves as a Professor of AI at Torrens University Australia, with distinguished professorships in Hungary and the Czech Republic. With more than 600 research
publications, 130,000 citations, and an H-index of 125, Prof. Mirjalili is among the top 1% of highly cited researchers worldwide. His contributions include developing AI algorithms widely applied in science and industry and delivering influential talks, including a TED Talk on AI's transformative potential. Prof. Mirjalili is a strong advocate for responsible and inclusive AI, and he has collaborated with industry and government on ethical AI tools. As a senior member of IEEE and an editor for leading AI journals, he significantly contributed to the advancements of fundamental and applied research in the field. Recognized as a top research leader by The
Australian for five years, his insights have earned significant media attention, which showcases
his influence as a global thought leader. Dr. Levente Kovacs received his Ph.D. in biomedical engineering from the Budapest University of Technology and Economics, Hungary, and the Habilitation degree (Hons.) from Obuda University. He is currently a Professor with Obuda 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 Janos 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. Kovacs is a recipient of the highly prestigious ERC StG grant of the European Union.
Content
1. Introduction to Deep Learning: Concepts, Applications, and Challenges
2. Evolutionary Optimization Techniques: Principles, Algorithms, and Real-World Applications
3. Reinforcement Learning for Decision Making in Complex Environments
4. Natural Language Processing: Techniques and Applications in Text Mining
5. Time Series Forecasting: Methods and Evaluation Metrics
6. Multi-Objective Optimization for Real-World Decision Making
7. Advanced Analytics for Large-Scale Data: Techniques and Tools
8. Image and Video Processing using Deep Learning: Applications and Challenges
9. Bayesian Optimization: Methods and Applications
10. Fuzzy Logic and its Applications in Data Science and Optimization
11. Quantum Computing for Data Science: Principles and Applications
12. Swarm Intelligence: Models, Algorithms, and Applications
13. Graph Theory and its Applications in Data Science and Optimization
14. Convex Optimization: Theory and Algorithms
15. Game Theory and its Applications in Data Science and Optimization
16. Clustering Techniques for Big Data: Methods and Applications
17. Anomaly Detection Techniques: Principles, Algorithms, and Applications
18. Differential Evolution: Principles, Variants, and Applications
19. Robust Optimization: Theory, Methods, and Applications
20. Neural Architecture Search: Concepts, Techniques, and Challenges
2. Evolutionary Optimization Techniques: Principles, Algorithms, and Real-World Applications
3. Reinforcement Learning for Decision Making in Complex Environments
4. Natural Language Processing: Techniques and Applications in Text Mining
5. Time Series Forecasting: Methods and Evaluation Metrics
6. Multi-Objective Optimization for Real-World Decision Making
7. Advanced Analytics for Large-Scale Data: Techniques and Tools
8. Image and Video Processing using Deep Learning: Applications and Challenges
9. Bayesian Optimization: Methods and Applications
10. Fuzzy Logic and its Applications in Data Science and Optimization
11. Quantum Computing for Data Science: Principles and Applications
12. Swarm Intelligence: Models, Algorithms, and Applications
13. Graph Theory and its Applications in Data Science and Optimization
14. Convex Optimization: Theory and Algorithms
15. Game Theory and its Applications in Data Science and Optimization
16. Clustering Techniques for Big Data: Methods and Applications
17. Anomaly Detection Techniques: Principles, Algorithms, and Applications
18. Differential Evolution: Principles, Variants, and Applications
19. Robust Optimization: Theory, Methods, and Applications
20. Neural Architecture Search: Concepts, Techniques, and Challenges