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.
Table of Contents
1. Introduction to Deep Learning: Concepts, Applications, and Challenges2. 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
Authors
Amir Hossein Gandomi University of Technology Sydney, Australia.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.
Seyedali Mirjalili Professor and Founding Director, Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, Australia. Dr. Mirjalili has gained international recognition for his contributions to nature-inspired artificial intelligence techniques. He has been on the list of the top 1% of highly-cited researchers since 2019, and the Web of Science named him one of the most influential researchers in the world. In 2022 and 2023, The Australian newspaper recognized him as a global leader in Artificial Intelligence and a national leader in the Evolutionary Computation and Fuzzy Systems fields. He also holds a post as Adjunct Principal Research Fellow at the Institute for Integrated and Intelligent Systems, Griffith University (Australia). He serves as a senior member of IEEE and covers editorial positions at several top AI-related journals published by Elsevier, including Engineering Applications of Artificial Intelligence, Applied Soft Computing, Neurocomputing, Advances in Engineering Software, Computers in Biology and Medicine, Healthcare Analytics, and Decision Analytics. Levente Kovacs �buda University, Budapest, Hungary.Dr. Levente Kov�cs received his Ph.D. in biomedical engineering from the Budapest University of Technology and Economics, Hungary, and the Habilitation degree (Hons.) from �buda University. He is currently a Professor with �buda 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 J�nos 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. Kov�cs is a recipient of the highly prestigious ERC StG grant of the European Union.
