The Intersection Of 6g Ai Machine Learning And Embedded Systems
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The Intersection of 6G, AI/Machine Learning, and Embedded Systems
This comprehensive guide to the emerging areas and synergistic relationships among the domains of 6G, machine learning, and embedded systems offers readers a detailed analysis of their converging paths and contributions to the development of intelligent wireless systems. Readers will gain a solid understanding of the principles and technologies behind 6G, machine learning, and embedded systems. They will learn how these three areas intertwine and why this intersection is pivotal for the next generation of wireless technologies. The contributors to this volume present a thorough and detailed analysis of this technology, highlighting its promising features, underlying technologies, and potential applications. The book first explores various applications of machine learning algorithms in areas such as network optimization, resource allocation, interference management, and intelligent data processing and analysis. Design considerations and challenges are presented, and case studies of innovative applications, such as smart cities, autonomous vehicles, healthcare, and industrial automation, are examined. The book concludes with a discussion of future trends and opportunities in this rapidly evolving field. Readers will benefit from the theoretical foundations and practical insights presented within and will be prepared to address future challenges and opportunities in these three fields. This book is a valuable resource for academic researchers and industry professionals working in the fields of wireless communication, machine learning, embedded systems, and artificial intelligence.
AI Model Design and Data Management for Disease Prediction
Author: Muniasamy, Anandhavalli
language: en
Publisher: IGI Global
Release Date: 2025-07-09
The design of artificial intelligence (AI) models for disease prediction advances fields that combine medical expertise, data science, and computational power to improve diagnostic accuracy and patient outcomes. The design of predictive models is central to this process, tailored to analyze complex healthcare data. Effective data management in healthcare involves the collection, integration, and storage of high-quality clinical and biomedical datasets. Ensuring data privacy and addressing biases are challenges that must be navigated to develop reliable and ethical AI systems. Thoughtful model design and effective data management strategies may ensure earlier detection, personalized treatment, and better resource allocation in modern healthcare systems. AI Model Design and Data Management for Disease Prediction explores the integration of intelligent technologies into medical prediction and diagnosis. It examines the usage of AI for enhanced healthcare data management. This book covers topics such as data science, medical imaging, and prediction models, and is a useful resource for computer engineers, medical professionals, academicians, researchers, and data scientists.
Embedded Artificial Intelligence
This book explores the role of embedded AI in revolutionizing industries such as healthcare, transportation, manufacturing, and retail. It begins by introducing the fundamentals of AI and embedded systems and specific challenges and opportunities. A key focus of this book is developing efficient and effective algorithms and models for embedded AI systems, as embedded systems have limited processing power, memory, and storage. It discusses a variety of techniques for optimizing algorithms and models for embedded systems, including hardware acceleration, model compression, and quantization. Key features: • Explores security experiments in emerging post‐CMOS technologies using AI, including side channel attack‐resistant embedded systems. • Discusses different hardware and software platforms available for developing embedded AI applications, as well as the various techniques used to design and implement these systems. • Considers ethical and societal implications of embedded AI vis‐a‐vis the need for responsible development and deployment of embedded AI systems. • Focuses on application‐based research and case studies to develop embedded AI systems for real‐life applications. • Examines high‐end parallel systems to run complex AI algorithms and comprehensive functionality while maintaining portability and power efficiency. This reference book is for students, researchers, and professionals interested in embedded AI and relevant branches of computer science, electrical engineering, or artificial intelligence.