Название: Artificial Intelligence in Prescriptive Analytics: Innovations in Decision Analysis, Intelligent Optimization, and Data-Driven Decisions
Автор: Witold Pedrycz, Gilberto Rivera, Eduardo Fernández, Gustavo Javier Meschino
Издательство: Springer
Год: 2024
Страниц: 544
Язык: английский
Формат: pdf (true)
Размер: 32.5 MB

Considering the advances of the different approaches and applications in the last years, and even in the last months, this is a particular moment in history to transform every data-driven decision-making process with the power of Artificial Intelligence (AI). This book reveals, through concrete case studies and original application ideas, how cutting-edge AI techniques are revolutionizing industries such as finance, health care, and manufacturing. It invites us to discover how machine learning, decision analysis, and intelligent optimization are changing, directly or indirectly, almost all aspects of our daily lives. This comprehensive book offers practical insights and real-world applications for professionals, researchers, and students alike. It helps to learn how to apply AI for smarter, data-driven decisions in areas like supply chain management, risk assessment, and even personalized medicine. Be inspired by the chapters of this book and unlock the full potential of AI in your field!

The book opens with “Part I: Decision Analysis,” analyzing the power of AI to enhance traditional decision-making processes. Novel methodologies like multicriteria ordinal classification are applied to real-world financial data, demonstrating the potential for improved resource management. We also see AI’s impact on infrastructure planning, investment selection, and higher education management, offering readers a quick overview of how widely this is applied.

“Part II: Intelligent Optimization” explores the AI’s optimization capabilities. From internet shopping experiences to searching for optimizing delivery routes with drones, AI algorithms show their ability to tackle intricate problems with precision and adaptability. The automation of complex tasks, such as topic generation for government requests, highlights the potential for AI to enhance efficiency and transparency in administrative processes.

The concluding part, “Part III: Data-Driven Decision,” exemplifies the convergence of AI and data analysis for informed decision-making. In this section, authors examine diverse applications, from predicting the impact of sustainability practices on family businesses to harnessing learning analytics for improved reading comprehension. AI’s potential in financial predictions, garment fit classification, and even stress analysis of engineering structures emphasizes its versatility and adaptability across domains.

These three parts collectively make evident the impact of AI on prescriptive analytics, offering a glimpse into how the current data-driven insights and intelligent optimization converge to empower decision-makers.