Advanced Intelligence Methods for Data Science and Optimization

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 andoptimization, 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 andOptimization 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 researchpublications, 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 TheAustralian for five years, his insights have earned significant media attention, which showcaseshis influence as a global thought leader. 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.

• 1. Introduction to Deep Learning: Concepts, Applications, and Challenges2. Evolutionary Optimization Techniques: Principles, Algorithms, and Real-World Applications3. Reinforcement Learning for Decision Making in Complex Environments4. Natural Language Processing: Techniques and Applications in Text Mining5. Time Series Forecasting: Methods and Evaluation Metrics6. Multi-Objective Optimization for Real-World Decision Making7. Advanced Analytics for Large-Scale Data: Techniques and Tools8. Image and Video Processing using Deep Learning: Applications and Challenges9. Bayesian Optimization: Methods and Applications10. Fuzzy Logic and its Applications in Data Science and Optimization11. Quantum Computing for Data Science: Principles and Applications12. Swarm Intelligence: Models, Algorithms, and Applications13. Graph Theory and its Applications in Data Science and Optimization14. Convex Optimization: Theory and Algorithms15. Game Theory and its Applications in Data Science and Optimization16. Clustering Techniques for Big Data: Methods and Applications17. Anomaly Detection Techniques: Principles, Algorithms, and Applications18. Differential Evolution: Principles, Variants, and Applications19. Robust Optimization: Theory, Methods, and Applications20. Neural Architecture Search: Concepts, Techniques, and Challenges