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(Dr. D. Y. Patil College of Engineering and Innovation, India)
Arivanantham Thangavelu is serving as an Assistant Professor in the Department of Computer Engineering at Dr. D. Y. Patil College of Engineering and Innovation, Varale, Pune, Maharashtra. He obtained his Bachelor of Engineering (B.E.) in Electronics and Communication Engineering (ECE) from Madurai Kamaraj University and his Master of Science (M.S.) in Computer Science and Engineering (CSE) from Bharathiar University, Coimbatore, Tamil Nadu, in 2004. He is currently pursuing a part-time Ph.D. in Computer Science and Engineering at Monad University, Uttar Pradesh. With 19 years of teaching experience, He has made significant contributions to academics, research, and innovation. He has authored three books, served as the editor of one book, published three book chapters, secured three copyrights, and filed five patents. He has also published 15 research papers in reputed national and international journals and conference proceedings. Throughout his teaching career, he has been committed to nurturing students' academic excellence and professional development in the fields of Computer Engineering and Information Technology. His research interests include Machine Learning, Data Science, Artificial Intelligence, and related emerging technologies.
Artificial Intelligence and IoT for Intelligent Prediction and Decision Support in Real-World Application presents a comprehensive exploration of how Artificial Intelligence (AI) and the Internet of Things (IoT) are transforming predictive analytics and intelligent decision-making across diverse application domains. The book covers fundamental concepts, machine learning and deep learning techniques, cloud and edge computing frameworks, explainable AI, and real-time data analytics for building intelligent prediction systems. Through twenty chapters, the book highlights practical applications in healthcare, transportation, electric vehicles, environmental monitoring, smart cities, agriculture, industrial automation, finance, energy management, and higher education. It demonstrates how AI-driven predictive models leverage historical and real-time data to improve forecasting accuracy, optimize resource utilization, automate decision-making, and enhance operational efficiency. The volume also discusses emerging technologies, implementation challenges, ethical considerations, and future research directions, making it a valuable reference for researchers, academicians, students, engineers, industry professionals, and policymakers interested in intelligent predictive systems and data-driven decision support.