K. G. Srinivasa - Böcker

It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples.

The Internet of Educational Things - Enhancing Students’ Engagement and Learning Performance delves into the transformative potential of the Internet of Things (IoT) within education.

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.

In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.

This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. 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;

This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. 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;

The book discusses Society 5.0 which fills the gap between cyber and physical space by providing a balanced environment between economic and social needs. Part B discusses various integrated fields in Society 5.0, such as super-smart healthcare system, super-smart hospitality system, smart building, and transport management system.

The book discusses Society 5.0 which fills the gap between cyber and physical space by providing a balanced environment between economic and social needs. The book is divided into two parts; part A focuses on various concepts related to Society 5.0 such as cyber space, physical space, information management and digital transformation. Part B discusses various integrated fields in Society 5.0, such as super-smart healthcare system, super-smart hospitality system, smart building, and transport management system. It also illustrates the concepts of big data, real-time analytics for smart Society 5.0 with an insight of real-time case studies.