Using Traditional Design Methods To Enhance Ai Driven Decision Making
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Using Traditional Design Methods to Enhance AI-Driven Decision Making
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.
Using Traditional Design Methods to Enhance AI-Driven Decision Making
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.
AI-Driven Innovation in Healthcare Data Analytics
As the healthcare industry continues to rely on data to enhance patient outcomes and streamline operations, artificial intelligence (AI) becomes a powerful tool for complex dataset analysis using improved speed and accuracy. From predictive modeling in disease outbreak management to personalized treatment plans for individual patient profiles, AI technologies are reshaping clinical decision-making and resource allocation. Harnessing the potential of machine learning and advanced analytics may allow healthcare providers to uncover insights that drive innovation, improve patient care, and optimize operational efficiency. AI-Driven Innovation in Healthcare Data Analytics explores the intersection of AI and healthcare data analytics. It examines the application of AI-driven techniques, including machine learning, deep learning, and data mining, in addressing complex challenges in healthcare management. This book covers topics such as data science, medical diagnosis, and patient care, and is a useful resource for healthcare professionals, data scientists, computer engineers, business owners, academicians, and researchers.