U: Design Methods to Enhance AI-Driven Decision Making ebook

Name: U: Design Methods to Enhance AI-Driven Decision Making ebook
SKU: GC-7331968362
Availability: InStock

$161.00

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. Additional ISBNs 9798369306406Using Traditional Design Methods to Enhance AI-Driven Decision Making is written by Nguyen Tien and published by Information Science Reference. ISBNs for Using Traditional Design Methods to Enhance AI-Driven Decision Making are 9798369306413, and the print ISBNs are 9798369306390, . Additional ISBNs include 9798369306406.

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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. Additional ISBNs 9798369306406Using Traditional Design Methods to Enhance AI-Driven Decision Making is written by Nguyen Tien and published by Information Science Reference. ISBNs for Using Traditional Design Methods to Enhance AI-Driven Decision Making are 9798369306413, and the print ISBNs are 9798369306390, . Additional ISBNs include 9798369306406.