K. G. Srinivasa – författare

The Internet of Educational Things - Enhancing Students’ Engagement and Learning Performance delves into the transformative potential of the Internet of Things (IoT) within education. This comprehensive guide explores how IoT technology can revolutionize traditional teaching methods and learning environments, fostering more interactive, adaptive, and data-driven experiences. The book covers a wide range of topics, including the development of IoT-enabled classrooms, intelligent tutoring systems, and online labs. By leveraging real-time data and advanced analytics, educators can personalize learning paths, enhance student engagement, and optimize resource allocation. Practical applications, real-world examples, and case studies illustrate the benefits and challenges of incorporating IoT in educational settings, making it a valuable resource for students, teachers, researchers, and policymakers.

The book provides practical implementation strategies and addresses critical issues such as data privacy, cybersecurity, and ethical considerations. It thoroughly examines the latest technologies, including AI, AR, VR, and digital twins, and their integration with IoT to create futuristic learning environments. The book’s unique contribution lies in its emphasis on securing IoT systems and its recommendations for overcoming infrastructure readiness and staff training obstacles. By presenting a forward-looking perspective on the role of IoT in education, this book aims to equip stakeholders with the knowledge and tools necessary to create innovative, inclusive, and secure learning ecosystems that prepare students for the future.

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. It details the techniques used in collection, storage and analysis of data and their usage in different healthcare solutions. It also discusses the techniques of predictive analysis in early diagnosis of critical diseases. The edited book is divided into four parts – part A discusses introduction to artificial intelligence and machine learning in healthcare; part B highlights different analytical techniques used in healthcare; part C provides various security and privacy mechanisms used in healthcare; and finally, part D exemplifies different tools used in visualization and data analytics.