Saltar al contenido principal
Elsevier

Advanced Intelligence Methods for Data Science and Optimization

  • 1 Edición - 1 de septiembre de 2026
  • Última edición
  • Editores: Amir Hossein Gandomi, Seyedali Mirjalili, Levente Kovacs
  • Idioma: Inglés

Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learni… Leer más

Descripción

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.

Puntos claves

  • Provides comprehensive coverage of advanced intelligence methods
  • Includes real-world examples and case studies that illustrate the application of these methods across a wide range of fields
  • Begins with an introduction to Deep Learning concepts and quickly moves to the most leading-edge topics in computational intelligence, all with an application to data science techniques

De interès para

Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the fields of data science, machine learning, and optimization. The primary audience also includes data analysts and software engineers

Índice

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

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 1 de septiembre de 2026
  • Idioma: Inglés

Sobre los editores

AG

Amir Hossein Gandomi

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.

Afiliaciones y experiencia
University of Technology Sydney, Australia

SM

Seyedali Mirjalili

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.
Afiliaciones y experiencia
Professor and Founding Director, Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Ultimo, NSW, Australia

LK

Levente Kovacs

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.

Afiliaciones y experiencia
Óbuda University, Budapest, Hungary