About the Book
As multicriteria decision-making (MCDM) continues to grow and evolve, machine learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design.
Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is a comprehensive resource that bridges the gap between ML and MCDM. It offers a practical approach by demonstrating the application of ML and MCDM algorithms to real-world problems. Through case studies and examples, it showcases the effectiveness of these techniques in optimal design. The book also provides a comparative analysis of conventional MCDM algorithms and machine learning techniques, enabling readers to make informed decisions about their use in different scenarios. It also delves into emerging trends, providing insights into future directions and potential opportunities. The book covers a wide range of topics, including the definition of optimal design, MCDM algorithms, supervised and unsupervised ML techniques, deep learning techniques, and more, making it a valuable resource for professionals and researchers in various fields.
Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is designed for professionals, researchers, and practitioners in engineering, computer science, sustainability, and related fields. It is also a valuable resource for students and academics who wish to expand their knowledge of machine learning applications in multicriteria decision-making. By offering a blend of theoretical insights and practical examples, this guide aims to inspire further research and application of machine learning in multidimensional decision-making environments.
Table of Contents:
1. Innovations in Technical Methodologies - Advancing Decision-Making and Optimization. 2. Review of Fuzzy Systems for Multi-Criteria Optimization Tools: Applications in Engineering Design. 3. Optimizing Ti-6Al-4V Milling Under MQL Conditions Using SVR, NSGA-II & TOPSIS. 4. Decision of 3D Printing Parameters for Optimum Tensile Strength Using the Taguchi-based Response Surface Method. 5. An Enhanced Network Optimization using the Max product for Multi-Criteria Decision Making. 6. Optimizing surface roughness of H13 steel machined by wire EDM technique. 7. Impact toughness of PBT/PA6 composite reinforced with glass fibers. 8. The effect of chamber temperature on the flexural strength of thermoplastic polyurethane plastic via FDM technology. 9. Enhancement in Underwater Imagery Using Multi-Criteria Decision Making with Machine Learning Techniques. 10. Optimal Site Selection of Electric Vehicle Charging Station Based on AHP-VIKOR method. 11. Optimum Indices on Topological Intuitionistic Fuzzy Graph. 12. Advancements in Multi-Criteria Decision Making: Exploring Innovative Approaches. 13. Overview of Machine Learning Techniques for Multi-Criteria Decision-Making. 14. Multi-Criteria Decision-Making Analysis on Selection of Electric Vehicle Power Station Location Using Neutrosophic TOPSIS Method. 15. MCDM Modeling using Machine Learning via Spherical Neutrosophic Similarlity Measures. 16. A Study On Machine Learning Twig Graphs On The Hyper Wiener Index Of Complete Graph. 17. Enhancing Multi-Criteria Decision Making through Cryptographic Security Systems. 18. AI-Powered Decision-Making Applications for Sustainable Development. 19. Interface for the Empirical Analysis of Artificial Intelligent Algorithms for Better Decision Making. 20. Multi-Criterion Analysis of Fusion Sort: A Hybrid Approach to Sorting Algorithms. 21. Cruising through the choices: Unraveling destination decision-making dilemmas with social networks – A dynamic exploration via MCDM technique. 22. Analysis of Outcome-Based Education among Students by MCDM Algorithm. 23. Identifying Best Teacher Awardee using MCDM Algorithm. 24. Lumpy Skin Disease Prediction Using Machine Learning.
About the Author :
Tien V.T. Nguyen, a member of the IEEE, is a highly accomplished individual with an impressive educational background. He obtained a master's degree in mechanical engineering and linguistics from prestigious institutions such as Viet Nam National University Ho Chi Minh City, Bach Khoa University, and HCMC University of Social Sciences and Humanities in 2012 and 2020, respectively. Additionally, he holds a Ph.D. in industrial engineering and management from the National Kaohsiung University of Science and Technology in Taiwan.
Throughout his career, Tien has made significant contributions to his field, having published over 61 journal papers and conference papers. He has also served as a reviewer for more than 75 SCI/Scopus Journals, providing over 1010 review reports. Furthermore, he has acted as an Academic Editor for several Q1 Journals, handling over 65 scientific manuscripts.
Tien's professional experience extends beyond academia, as he has studied and worked in various countries including South Korea, Thailand, Russia, and Taiwan. Currently, he serves as a Lecturer at the Industrial University of Ho Chi Minh City in Vietnam.
His areas of expertise include machine learning (AI), compliant mechanisms optimization design, numerical computation, MCDM, and Supply chain management. Tien's research has had a significant impact on his field, as evidenced by his Scopus H-index of 17 and 646 citations as of April 2024.
Nhut T. M. Vo, a member of the IEEE, is a versatile professional with a diverse background. She received her M.Sc. degree from the National Kaohsiung University of Science and Technology (NKUST), Taiwan, where she is currently pursuing a Ph.D. degree in industrial engineering and management. Her professional journey has taken her through various sectors, including banking, the jewelry industry, information technology, and e-commerce, enriching her understanding of different industries. She is also a self-publishing author with many books about lean management and other fields. Her research interests span various topics, including the Internet of Things, blockchain, cloud computing, machine learning (AI), green energy, logistics, e-commerce, and numerical computation.
Van Chinh Truong is not just a Faculty of Mechanical Engineering at the Industrial University of Ho Chi Minh City, Vietnam, but a dedicated educator. Dr. Truong has also been actively involved in research and academia, having participated in several research projects. He has successfully developed and implemented various technologies, significantly contributing to the industry. But his true passion lies in inspiring and educating future generations of engineers, a commitment that shines through his work and contributions to the field of mechanical engineering.
Van-Thu Nguyen is a lecturer at Ho Chi Minh University of Technology and Education in Vietnam. He has a Ph.D. from the National Kaohsiung University of Science and Technology, Taiwan, and has published over 50 SCIE journal papers. His areas of expertise include manufacturing material science and mechanical processing. He is a highly respected researcher and educator in his field.
