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This book presents recent advancements in research, new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models. The book undertakes to stimulate scientific exchange, ideas, and experiences in the field of DSS applications.
List of contents
1. Introduction to Machine Learning and Probabilistic Graphical Models for Decision Support Systems 2. Decision Support Systems for Healthcare based on Probabilistic Graphical Models: A Survey and Perspective 3. Decision Support Systems for Anomaly Detection with the Applications in Smart Manufacturing: A Survey and Perspective 4. Decision Support System for Complex Systems Risk Assessment with Bayesian Networks 5. Decision Support System using LSTM with Bayesian Optimization for Predictive Maintenance: Remaining Useful Life Prediction 6. Decision Support Systems for Textile Manufacturing Process with Machine Learning 7. Anomaly Detection Enables Cybersecurity with Machine Learning Techniques 8. Machine Learning for Compositional Data Analysis in Support of the Decision Making Process 9. Decision Support System with Genetic Algorithm for Economic Statistical Design of Nonparametric Control Chart 10. Jamming Detection in Electromagnetic Communication with Machine Learning: A Survey and Perspective 11. Intellectual Support with Machine Learning for Decision-making in Garment Manufacturing Industry: A Review 12. Enabling Smart Supply Chain Management with Artificial Intelligence
About the author
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.
Summary
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.
