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Sinopsis
In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials.
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Van Thanh Tien Nguyen received his master’s degrees in both mechanical engineering and linguistics from Viet Nam National University Ho Chi Minh City, Bach Khoa University, and HCMC University of Social Sciences and Humanities in 2012 and 2020, respectively. He holds a Ph.D. degree in Industrial Engineering and Management from the National Kaohsiung University of Science and Technology, Taiwan. He has published over 61 journal papers and conference papers, served as a reviewer for more than 75 SCI/Scopus Journals with over 1010 review reports, and handled as an Academic Editor for several Q1 Journals with over 65 scientific manuscripts. He has experience studying and working in labs as a researcher/professional in many countries, such as Korea, Thailand, Russia, and Taiwan. He is currently working as a lecturer for the Industrial University of Ho Chi Minh City, Vietnam. His areas of interest are machine learning (AI), compliant mechanisms optimization design, numerical computation, MCDM, and Supply chain management. The concentration studies conducted by this individual have significantly influenced his field, as evidenced by his Scopus H-index of 16 and 553 citations (updated as of December 31, 2023).
Thi Minh Nhut Vo received her M.Sc degree at National Kaohsiung University of Science and Technology (NKUST), Taiwan. She is currently pursuing the Ph.D. program in Industrial Engineering and Management at National Kaohsiung University of Science and Technology, Taiwan. Prior to coming to NKUST, she worked in the banking sector, jewelry industry, Information Technology, and E-commerce. She is now working as a self-publishing author with many books about lean management and other fields. Her areas of interest are the Internet of Things, Blockchain, cloud computing, machine learning (AI), green energy, logistics, E-Commerce, and numerical computation.
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Hardcover. Condición: new. Hardcover. In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. Offering valuable insights into leadership pathways within higher education contexts, the book presents a diverse collection of perspectives and experiences to inform readers about the complexities surrounding AI-driven decision-making. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials. Explores a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798369306390
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