In recent years, the application of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart healthcare has been increasing. We are approaching a world where connected smart devices tell people when they need to visit a doctor because these devices will be able to detect health problems and discover symptoms of illness that may need medical care. AI-collaborative IoT technologies can help medical professionals with decision-making. These technologies can also help develop a sustainable and smart healthcare system. AI and IoT Technology and Applications for Smart Healthcare Systems helps readers understand complex scientific topics in a simple and accessible way. It introduces the world of AI-collaborative IoT physics, explaining how this technology behaves at the smallest level and how this can revolutionize healthcare. The book shows how IoT technology and AI can work together to make computers more powerful and capable of solving complex problems in the healthcare sector. Exploring the effect of AI-collaborative technology on IoT technologies, the book discusses how IoT can benefit from AI algorithms to enable machines to learn, make decisions, and process information more efficiently. Because smart machines create more perceptive devices and systems, the application of this technology raises important ethical questions about privacy, security, and the responsible development of healthcare IoT technology, which this book covers. The book also provides insight into the potential applications of these technologies not only in the healthcare industry but also in related fields, such as smart transportation, smart manufacturing, and smart cities.

The healthcare industry has reached its full capacity due to the outbreak of COVID-19. Its global influence has brought attention to the utmost capabilities and limitations of healthcare facilities worldwide. The Internet of Things (IoT) and cloud services can effectively handle the immense healthcare demands that have never been seen before. The scarcity of healthcare personnel and limited resources necessitate the adoption of emerging technology to bolster healthcare delivery. IoT and cloud computing present ample promise in situations like this, as they may be utilized for monitoring, diagnostics, support, and intelligent decision-making. Revolutionizing Healthcare Systems Through Cloud Computing and IoT explores the concepts of cloud computing-based healthcare systems, IoT-based healthcare systems, and cloud-IoT-based healthcare systems. It delves into the significance and benefits of cloud-IoT-based healthcare systems. Covering topics such as disease screening, smart monitoring, and healthcare policy, this book is an excellent resource for researchers, scientists, engineers, graduate and postgraduate students, healthcare professionals and administrators, educators, and more.

The effectiveness of federated learning in high‐performance information systems and informatics‐based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‐based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications. Features: • Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy. • Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy. • Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area. • Analyses the need for a personalized federated learning framework in cloud‐edge and wireless‐edge architecture for intelligent IoT applications. • Comprises real‐life case illustrations and examples to help consolidate understanding of topics presented in each chapter. This book is recommended for anyone interested in federated learning‐based intelligent algorithms for smart communications.

As the demand for advanced technologies to revolutionize patient care intensifies, the medical industry faces a pressing need to confront challenges hindering the assimilation of AI-enhanced healthcare systems. Issues such as data interoperability, ethical considerations, and the translation of AI advancements into practical clinical applications pose formidable hurdles that demand immediate attention. It is within this context of challenges and opportunities that the book, Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems promises to pave the way for a transformative era in healthcare. The book serves as a comprehensive guide for academic scholars, researchers, and healthcare professionals navigating the dynamic landscape of data-driven, AI-enhanced healthcare. By showcasing the latest advancements, the book empowers its readers to not only comprehend the existing frontiers in data sciences and healthcare technologies but also to actively contribute to overcoming obstacles. Through detailed case studies and practical guidance, the publication equips its audience with the skills necessary to implement AI in various clinical settings.

In today’s digital age, the healthcare industry is undergoing a paradigm shift towards embracing innovative technologies to enhance patient care, improve efficiency, and ensure data security. With the increasing adoption of electronic health records, telemedicine, and AI-driven diagnostics, robust cybersecurity measures and advanced data management strategies have become paramount. Protecting sensitive patient information from cyber threats is critical and maintaining effective data management practices is essential for ensuring the integrity, accuracy, and availability of vast amounts of healthcare data. Cybersecurity and Data Management Innovations for Revolutionizing Healthcare delves into the intersection of healthcare, data management, cybersecurity, and emerging technologies. It brings together a collection of insightful chapters that explore the transformative potential of these innovations in revolutionizing healthcare practices around the globe. Covering topics such as advanced analytics, data breach detection, and privacy preservation, this book is an essential resource for healthcare professionals, researchers, academicians, healthcare professionals, data scientists, cybersecurity experts, and more.

The integration of artificial intelligence (AI), quantum computing, and semiconductor technology offers improved innovation to redefine computational power and capabilities. As AI drives advances in machine learning and data processing, quantum computing revolutionizes problem-solving with its ability to handle complex calculations at improved speeds. Advancements in semiconductor technology push the limits of processing efficiency and miniaturization. Continued exploration on this convergence may accelerate breakthroughs in various fields such as cryptography, material science, and healthcare. Integration of AI, Quantum Computing, and Semiconductor Technology explores the intersection of artificial intelligence (AI) and semiconductor technology within the context of quantum computing. It offers a comprehensive analysis of the current advancements, challenges, and potential applications resulting from this convergence. This book covers topics such as cyber security, healthcare monitoring, and machine learning, and is a useful resource for computer engineers, energy scientists, business owners, healthcare administrators, environmental scientists, academicians, and researchers.

Explore the latest innovations in oral surgery with this comprehensive guide. Focusing on advanced procedures and technologies, this book aims to improve patient outcomes and provide oral surgeons with the tools to stay at the forefront of their field.

In the post-COVID-19 healthcare landscape, the demand for smart healthcare solutions and precision medicine systems has grown significantly. To address these challenges, the book AI and IoT-Based Technologies for Precision Medicine provides a comprehensive resource for doctors, researchers, engineers, and students. By leveraging AI and IoT technologies, the book equips healthcare professionals with advanced tools and methodologies for predictive disease analysis, informed decision-making, and other aspects of precision medicine. This resource bridges the gap between theory and practice, exploring concepts like machine learning, deep learning, computer vision, AI-integrated applications, IoT-based technologies, healthcare data analytics, and biotechnology applications. Through this, the book empowers healthcare practitioners to pioneer innovative solutions that enhance efficiency, accuracy, and security in medical practices. AI and IoT-Based Technologies for Precision Medicine not only offer insights into the potential of AI-powered applications and IoT-equipped techniques in smart healthcare but also foster collaboration among healthcare scholars and professionals. This authoritative guide encourages knowledge sharing and collaboration to harness the transformative potential of AI and IoT, leading to revolutionary advancements in medical practices and healthcare services. With this book as a guide, readers can navigate the evolving landscape of high-tech medicine, taking confident steps toward a cutting-edge and precise medical ecosystem.

Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes. Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes.