Preface

The accelerated progress in Artificial Intelligence (AI), Big Data, and Industry 4.0 technologies has triggered significant transformations across multiple domains, particularly in healthcare and manufacturing. AI-powered advancements are enhancing diagnostic precision, enabling predictive analytics, and driving automation, thereby improving patient outcomes and optimizing industrial operations. The fusion of AI with Big Data analytics offers a powerful framework to tackle critical healthcare challenges, including early disease detection, precision medicine, and personalized treatment planning. In parallel, the advent of Industry 4.0 and the Industrial Internet of Things (IIoT) is reshaping manufacturing systems, leading to improved efficiency, productivity, and intelligent process control.

This book, AI, Big Data, and Healthcare Data-Driven Applications, presents a curated collection of research contributions that explore state-of-the-art developments in AI and Big Data, with a strong emphasis on healthcare and manufacturing applications. The chapters cover a broad spectrum of topics, including predictive analytics, AI-driven diagnostic systems, deep learning techniques, IoT-enabled health monitoring, and the integration of robotics within modern manufacturing environments.

The volume highlights several key scientific themes:

Key scientific themes include:

• Predictive and Descriptive Analytics in Healthcare – Demonstrating how AI-based analytics enhance diagnostic accuracy and support effective treatment planning.

• AI Innovations in Predictive Diabetes Care – Utilizing machine learning models for early detection and improved management of diabetes.

• Deep Learning in Medical Imaging – Exploring advanced techniques for pneumonia detection using X-ray images and deep learning applications in endodontics.

• IoT in Healthcare Monitoring – Examining the role of IoT technologies in monitoring conditions such as sleep apnea and enabling real-time health tracking.

• Biotechnology Safety and Security Measures – Evaluating global biosecurity frameworks through qualitative analysis and clustering approaches.

• Healthcare Transformation in India – Analyzing the impact of digital health initiatives, including e-hospitals, online registration systems, and AI-driven services.

• Mental Health and AI – Investigating AI-based tools for detecting and monitoring depression, particularly in patients with coexisting conditions like hypertension and diabetes.

• Industry 4.0 in Manufacturing – Assessing smart manufacturing systems, automation strategies, and the influence of IIoT on modern industrial practices.

Designed as a comprehensive resource, this book caters to researchers, healthcare practitioners, industry professionals, and students interested in the convergence of AI, Big Data, and applied technologies. By integrating insights from diverse disciplines, it undersco-

res the transformative potential of AI in enhancing healthcare delivery and advancing manufacturing systems.

As AI and Big Data continue to redefine conventional paradigms, this volume serves as a timely reference, offering valuable perspectives on innovation and technological progress. It aims to inspire further research, foster interdisciplinary collaboration, and support the development of intelligent solutions for complex global challenges.