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Nature-inspired methods for smart healthcare systems and medical data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nature-inspired methods for smart healthcare systems and medical dataedited by Ahmed M. Anter, Mohamed Elhoseny, Anuradha D. Thakare.
other author:	Anter, Ahmed M.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xxiii, 250 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Medical technology.
Online resource:	https://doi.org/10.1007/978-3-031-45952-8
ISBN:	9783031459528$q(electronic bk.)

Nature-inspired methods for smart healthcare systems and medical data
Nature-inspired methods for smart healthcare systems and medical data[electronic resource] /edited by Ahmed M. Anter, Mohamed Elhoseny, Anuradha D. Thakare. - Cham :Springer Nature Switzerland :2024. - xxiii, 250 p. :ill. (some col.), digital ;24 cm.

Chapter. 1. A review of methods employed for forensic human identification -- Chapter. 2. AI based Medicine Intake Tracker -- Chapter. 3. Analysis of Genetic Mutations using Nature-Inspired Optimization Methods and Classification Approach -- Chapter. 4. Applications of Blockchain: A Healthcare Use Case -- Chapter. 5. Comprehensive Methodology of Contact Tracing Techniques to Reduce Pandemic Infectious Diseases Spread -- Chapter. 6. High-impact applications of IoT system-based metaheuristics -- Chapter. 7. IoT-based eHealth solutions for aging with special emphasis on aging-related inflammatory diseases: prospects and challenges -- Chapter. 8. Leveraging Meta-Heuristics in Improving Health Care Delivery: A Comprehensive Overview -- Chapter. 9. Metaheuristics algorithms for complex disease prediction -- Chapter. 10. Printed rGO-based temperature sensor for wireless body area network applications -- Chapter. 11. Recent advanced in healthcare data privacy techniques -- Chapter. 12. The ability of the CFD approach to investigate the fluid and wall hemodynamics of cerebral stenosis and aneurysm.

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.

ISBN: 9783031459528$q(electronic bk.)

Standard No.: 10.1007/978-3-031-45952-8doiSubjects--Topical Terms:

226338
Medical technology.

LC Class. No.: R855.3

Dewey Class. No.: 610.285

Nature-inspired methods for smart healthcare systems and medical data
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505 0 $a Chapter. 1. A review of methods employed for forensic human identification -- Chapter. 2. AI based Medicine Intake Tracker -- Chapter. 3. Analysis of Genetic Mutations using Nature-Inspired Optimization Methods and Classification Approach -- Chapter. 4. Applications of Blockchain: A Healthcare Use Case -- Chapter. 5. Comprehensive Methodology of Contact Tracing Techniques to Reduce Pandemic Infectious Diseases Spread -- Chapter. 6. High-impact applications of IoT system-based metaheuristics -- Chapter. 7. IoT-based eHealth solutions for aging with special emphasis on aging-related inflammatory diseases: prospects and challenges -- Chapter. 8. Leveraging Meta-Heuristics in Improving Health Care Delivery: A Comprehensive Overview -- Chapter. 9. Metaheuristics algorithms for complex disease prediction -- Chapter. 10. Printed rGO-based temperature sensor for wireless body area network applications -- Chapter. 11. Recent advanced in healthcare data privacy techniques -- Chapter. 12. The ability of the CFD approach to investigate the fluid and wall hemodynamics of cerebral stenosis and aneurysm.
520 $a 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.
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