Advancements in Artificial Intelligence and Machine Learning

Introduction

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations.

This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI’s transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI’s impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems.

Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI’s role in healthcare and automation, including an AI-assisted system for women’s safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries.

Key Features:

• -A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters.
• -A focus on contemporary challenges and solutions in AI and ML across industries.
• -Research-driven insights from experts and practitioners in the field.
• -Practical discussions on AI-driven automation, security, and intelligent decision-making systems.

Readership:

This book is an essential read for researchers, academicians, professionals, and students in AI, ML, computer science, and engineering. Primary readers include AI and ML enthusiasts, researchers, and industry practitioners seeking to expand their knowledge, while secondary audiences comprise policymakers, business leaders, and technology enthusiasts eager to understand AI's impact on modern industries.