This book aims to gather high-quality research papers on developing theories, frameworks, architectures, and algorithms for solving complex challenges in smart healthcare applications for real industry use. It explores the recent theoretical and practical applications of metaheuristics and optimization in various smart healthcare contexts. The book also discusses the capability of optimization techniques to obtain optimal parameters in ML and DL technologies. It provides an open platform for academics and engineers to share their unique ideas and investigate the potential convergence of existing systems and advanced metaheuristic algorithms. The book's outcome will enable decision-makers and practitioners to select suitable optimization approaches for scheduling patients in crowded environments with minimized human errors. The healthcare system aims to improve the lives of disabled, elderly, sick individuals, and children. IoT-based systems simplify decision-making and task automation, offering an automated foundation. Nature-inspired metaheuristics and mining algorithms are crucial for healthcare applications, reducing costs, increasing efficiency, enabling accurate data analysis, and enhancing patient care. Metaheuristics improve algorithm performance and address challenges in data mining and ML, making them essential in healthcare research. Real-time IoT-based healthcare systems can be modeled using an IoT-based metaheuristic approach to generate optimal solutions. Metaheuristics are powerful technologies for optimization problems in healthcare systems. They balance exact methods, which guarantee optimal solutions but require significant computational resources, with fast but low-quality greedy methods. Metaheuristic algorithms find better solutions while minimizing computational time. The scientific community is increasingly interested in metaheuristics, incorporating techniques from AI, operations research, and soft computing. New metaheuristics offer efficient ways to address optimization problems and tackle unsolved challenges. They can be parameterized to control performance and adjust the trade-off between solution quality and resource utilization. Metaheuristics manage the trade-off between performance and solution quality, making them highly applicable to real-time applications with pragmatic objectives.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

This book encapsulates and occupies recent advances and state-of-the-art applications of nature-inspired computing (NIC) techniques in the field of bioinformatics and computational biology, which would aid medical sciences in various clinical applications. This edited volume covers fundamental applications, scope, and future perspectives of NIC techniques in bioinformatics including genomic profiling, gene expression data classification, DNA computation, systems and network biology, solving personalized therapy complications, antimicrobial resistance in bacterial pathogens, and computer-aided drug design, discovery, and therapeutics. It also covers the role of NIC techniques in various diseases and disorders, including cancer detection and diagnosis, breast cancer, lung disorder detection, disease biomarkers, and potential therapeutics identifications.

This book aims to provide a detailed understanding of IoMT-supported applications while engaging premium smart computing methods and improved algorithms in the field of computer science. It contains thirteen chapters discussing various applications under the umbrella of the Internet of Medical Things. These applications geared towards IoMT cloud analysis, machine learning, computer vision and deep learning have enabled the evaluation of the proposed solutions.

Experts in Medicine are under new pressures of advancing their studies while also reducing the impact they leave on the environment. Researchers within the fields of bio-neuro informatics, healthcare, engineering, and medical sciences require a dynamic platform that bridges the realms of academia, science, industry, and innovation. Green AI-Powered Intelligent Systems for Disease Prognosis facilitates a crossroads for a diverse audience interested in these two seldom coalesced concepts. Academicians, scientists, researchers, professionals, decision-makers, and even aspiring scholars all find a space to contribute, collaborate, and learn within the platform that this book provides. The book's thematic coverage is unequivocally compelling; by exploring the intersections of bio-neuro informatics, healthcare, engineering, and medical sciences, it captures the spirit of interdisciplinary research. It delves into well-established domains while also casting a spotlight on emerging trends that have the potential to reshape our understanding of these fields. Two prominent tracks form the backbone of the book's content. The first covers the Bioinformatics and Data Mining of Biological Data (BiDMBD), and unravels the intricacies of biomedical computation, signal analysis, clinical decision support, and health data mining. This approach holds a treasure trove of insights into the mechanisms of health data acquisition, clinical informatics, and the representation of healthcare knowledge. The second covers Biomedical Informatics and is a symposium of computational modeling, genomics, and proteomics. Here, the fusion of data science with medical sciences takes center stage.

In the dynamic landscape of bioinformatics and blockchain technology, a profound challenge is evident: ensuring secure exchange and analysis of complex biological data while maintaining data integrity and ownership. Traditional methods fall short in seamlessly transferring genomic data, spurring the fusion of blockchain innovation and optimization algorithms as a groundbreaking solution. Biology-Inspired Optimization Techniques in Blockchain Systems directly addresses the data integrity and ownership dilemma in bioinformatics and blockchain. Despite the intricacies of genomic data, blockchain's potential solution faces obstacles like data volume and slow transactions. These challenges are adeptly overcome through optimization algorithms. The book, authored by experts in bioinformatics, blockchain, and optimization, offers a comprehensive guide, showcasing how blockchain architecture and biological data intricacies can harmonize. It provides a blueprint for using blockchain to store genomic variants and aligned reads. This work empowers developers, data scientists, and researchers to overcome technological barriers, redefining the landscape of bioinformatics and beyond.

b”IoT-Enabled Smart Healthcare Systems, Services and ApplicationsExplore the latest healthcare applications of cutting-edge technologies In IoT-Enabled Smart Healthcare Systems, Services and Applications, an accomplished team of researchers delivers an insightful and comprehensive exploration of the roles played by cutting-edge technologies in modern healthcare delivery. The distinguished editors have included resources from a diverse array of learned experts in the field that combine to create a broad examination of a rapidly developing field. With a particular focus on Internet of Things (IoT) technologies, readers will discover how new technologies are impacting healthcare applications from remote monitoring systems to entire healthcare delivery methodologies. After an introduction to the role of emerging technologies in smart health care, this volume includes treatments of ICN-Fog computing, edge computing, security and privacy, IoT architecture, vehicular ad-hoc networks (VANETs), and patient surveillance systems, all in the context of healthcare delivery. Readers will also find: A thorough introduction to ICN-Fog computing for IoT based healthcare, including its architecture and challenges Comprehensive explorations of Internet of Things enabled software defined networking for edge computing in healthcare Practical discussions of a review of e-healthcare systems in India and Thailand, as well as the security and privacy issues that arise through the use of smart healthcare systems using Internet of Things devices In-depth examinations of the architecture and applications of an Internet of Things based healthcare system Perfect for healthcare practitioners and allied health professionals, hospital administrators, and technology professionals, IoT-Enabled Smart Healthcare Systems, Services and Applications is an indispensable addition to the libraries of healthcare regulators and policymakers seeking a one-stop resource that explains cutting-edge technologies in modern healthcare.