Machine Learning and Probabilistic Graphical Models for Decision Support Systems

This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.

Kim Phuc Tran is an Associate Professor of Artificial Intelligence and Data Science at ENSAIT & GEMTEX,
University of Lille, France, and a Senior Scientific Advisor at Dong A University, Vietnam. He obtained a Ph.D. in
Automation and Applied Informatics at the University of Nantes, and an HDR (Dr. Habil.) in Computer Science and
Automation at the University of Lille, France. His research focuses on Artificial Intelligence and applications. He has
published more than 60 papers in SCIE peer-reviewed international journals and proceedings of international conferences. He edited 3 books with Springer Nature and CRC Press, Taylor & Francis Group.