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020			_a9783031459528
024	7		_a10.1007/978-3-031-45952-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050	0	4	_aR858-859.7 _b2024
245	0	0	_aNature-Inspired Methods for Smart Healthcare Systems and Medical Data _cedited by Ahmed M Anter, Mohamed Elhoseny, Anuradha D Thakare
250			_afirst edition 2024
264		1	_aCham _c2024 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
505	0		_aChapter. 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			_aThis 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.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-45952-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
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988			_aSpringer_Computer_2024
999			_c397770 _d397770