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Full Description
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.
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.
