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(Bharati Vidyapeeth (Deemed to be University), India)
Dr. Babasaheb Dnyandeo Patil is an Assistant Professor in the Department of Computer Applications at Bharati Vidyapeeth (Deemed to be University), Pune, Institute of Management and Rural Development Administration, Sangli, India. He has over 24 years of academic experience and 2.5 years of Industrial Experience. He holds B.Sc., MCA, MCM, M.Phil., and Ph.D. degrees in Computer Application. His research interests include Software Engineering, Cloud Computing, Data Science, Information Security, ERP, Digital Transformation, and e-Governance. He has published over 21 research papers in reputed national and international journals and conferences. He is the author of two books and has contributed four book chapters in emerging areas of computer science. Dr. Patil holds six patents with associated copyrights and serves as a reviewer for reputed international journals and conferences. He is a member of the Decision Sciences Institute (Houston, USA) and is actively engaged in research, innovation, and academic collaborations in computer science and information technology.
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.