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Emerging Trends in Machine Learning, Data Science, and Internet of Things (Part 2)

Editors: Manoj Kumar, Sachin Kumar Gupta, Navnath Kale, Pramod Ganjewar, Sandeep Pande

Emerging Trends in Machine Learning, Data Science, and Internet of Things (Part 2)

ISBN: 979-8-89881-517-2
eISBN: 979-8-89881-516-5 (Online)

Introduction

Emerging Trends in Machine Learning, Data Science, and Internet of Things Volumes 1 & 2 explores the technologies shaping the future of intelligent systems. Covering Machine Learning, Data Science, IoT, Blockchain, Cloud Computing, and Artificial Intelligence, the book focuses on designing and applying secure, data-driven solutions across healthcare, agriculture, transportation, education, cybersecurity, and digital content management.

It blends cutting-edge research with practical implementations, including AI-driven disease prediction, anomaly detection for network security, sentiment analysis, smart IoT applications, and blockchain-enabled digital authentication. The volumes highlight both theoretical foundations and real-world case studies, offering actionable insights for researchers and industry professionals alike.


Key Features

  • - Comprehensive coverage of AI, ML, IoT, and Data Science applications.
  • - Case studies on healthcare, agriculture, cybersecurity, and digital systems.
  • - Practical implementations and comparative analyses of models.
  • - Interdisciplinary insights, including AR, blockchain, and smart systems.
  • - Focuses on both theory and real-world application.

Target Readership :

Researchers, academics, postgraduate students and professionals in Computer Engineering, IT, and Data Science.

Foreword

The rapid advancement of artificial intelligence and data-driven technologies continues to redefine the boundaries of innovation across multiple sectors. This volume reflects the dynamic and interdisciplinary nature of modern research in Machine Learning, Data Science, Blockchain, and the Internet of Things. The collection of chapters presented here demonstrates not only technical sophistication but also a clear commitment to addressing real-world societal challenges.

Several contributions in this book illustrate how machine learning techniques are being translated into impactful applications. From augmented reality-based educational tools and intelligent traffic classification to smart braking systems for vehicle safety and predictive models for heart disease detection, the research emphasizes practical implementations that enhance safety, well-being, and user engagement. The integration of wellness analytics and labor welfare authentication systems further highlights the role of AI in promoting inclusive and human-centered technological solutions.

Equally significant are the chapters devoted to secure and decentralized digital ecosystems. The exploration of blockchain-based healthcare data security, decentralized marketplaces for educators, and digital content verification systems reflects the growing importance of transparency, trust, and data integrity in the digital age. The inclusion of automated web and video summarization tools and smartphone usage analytics demonstrates how data science can provide meaningful insights into human behavior and information consumption.

The work on IoT-enabled healthcare monitoring and smart agricultural management underscores the transformative power of interconnected systems in improving quality of life and enabling informed decision-making. These contributions collectively reinforce the need for responsible, secure, and scalable AI systems.

I commend the editors and authors for compiling a volume that captures the spirit of innovation and interdisciplinary collaboration. This book will serve as an important resource for researchers, practitioners, and students seeking to advance intelligent technologies for the benefit of society.

Manish Kumar
Department of Information Technology
Indian Institute of Information Technology
Allahabad, India