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Verlag: Taylor and Francis Ltd, GB, 2025
ISBN 10: 1032345225 ISBN 13: 9781032345222
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence.The wide variety of topics covered include:Artificial Intelligence in healthcareMachine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19Big data analytics solutions for healthcare data processingReliable biomedical applications using AI modelsIntelligent IoT in healthcareThe book explains fundamental concepts as well as the advanced use cases, illustrating how to apply emerging technologies such as machine learning, AI models, and data informatics into practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academicians and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain.
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Verlag: Taylor and Francis Ltd, GB, 2025
ISBN 10: 1032345225 ISBN 13: 9781032345222
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence.The wide variety of topics covered include:Artificial Intelligence in healthcareMachine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19Big data analytics solutions for healthcare data processingReliable biomedical applications using AI modelsIntelligent IoT in healthcareThe book explains fundamental concepts as well as the advanced use cases, illustrating how to apply emerging technologies such as machine learning, AI models, and data informatics into practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academicians and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain.
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In den WarenkorbZustand: New. Madhusudhan H. S. is an Associate Professor in the Department of Computer Science and Engineering at Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.Satish Kumar T. is an Associate Professor in the Department of Comput.
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Verlag: Taylor & Francis Ltd, London, 2025
ISBN 10: 1032345225 ISBN 13: 9781032345222
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence.The wide variety of topics covered include:Artificial Intelligence in healthcareMachine learning solutions for such disease as diabetes, arthritis, cardiovascular disease, and COVID-19Big data analytics solutions for healthcare data processingReliable biomedical applications using AI modelsIntelligent IoT in healthcareThe book explains fundamental concepts as well as the advanced use cases, illustrating how to apply emerging technologies such as machine learning, AI models, and data informatics into practice to tackle challenges in the field of healthcare with real-world scenarios. Chapters contributed by noted academicians and professionals examine various solutions, frameworks, applications, case studies, and best practices in the healthcare domain. Focuses on fundamental concepts of gathering, processing, analyzing the dataset from rich healthcare and biomedical sources. Covers interdisciplinary techniques such as data science, deep learning, statistics, big data analytics, smart devices, computer vision and IoT. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Verlag: Auerbach Publications 2024-04-01, 2024
ISBN 10: 1032559969 ISBN 13: 9781032559964
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: Brand New. 232 pages. 9.19x6.13x9.21 inches. In Stock.
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In den WarenkorbZustand: New. 1st edition NO-PA16APR2015-KAP.
Verlag: Taylor & Francis Ltd, London, 2024
ISBN 10: 1032345896 ISBN 13: 9781032345895
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. A new era of complexity science is emerging, in which nature- and bio-inspired principles are being applied to provide solutions. At the same time, the complexity of systems is increasing due to such models like the Internet of Things (IoT) and fog computing. Will complexity science, applying the principles of nature, be able to tackle the challenges posed by highly complex networked systems?Bio-Inspired Optimization in Fog and Edge Computing: Principles, Algorithms, and Systems is an attempt to answer this question. It presents innovative, bio-inspired solutions for fog and edge computing and highlights the role of machine learning and informatics. Nature- or biological-inspired techniques are successful tools to understand and analyze a collective behavior. As this book demonstrates, algorithms, and mechanisms of self-organization of complex natural systems have been used to solve optimization problems, particularly in complex systems that are adaptive, ever-evolving, and distributed in nature.The chapters look at ways of enhancingto enhance the performance of fog networks in real-world applications using nature-based optimization techniques. They discuss challenges and provide solutions to the concerns of security, privacy, and power consumption in cloud data center nodes and fog computing networks. The book also examines how:The existing fog and edge architecture is used to provide solutions to future challenges.A geographical information system (GIS) can be used with fog computing to help users in an urban region access prime healthcare.An optimization framework helps in cloud resource management.Fog computing can improve the quality, quantity, long-term viability, and cost-effectiveness in agricultural production.Virtualization can support fog computing, increase resources to be allocated, and be applied to different network layers.The combination of fog computing and IoT or cloud computing can help healthcare workers predict and analyze diseases in patients. The book examines the enhancement of the performance of fog networks for real-world applications using nature-based optimization techniques. It looks at the challenges in cloud datacenter nodes and fog computing networks and provides solutions. It covers such issues as security, privacy, and power consumption. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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In den WarenkorbZustand: New.
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Erstausgabe
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In den WarenkorbZustand: New. 2024. 1st Edition. paperback. . . . . .
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In den WarenkorbZustand: As New. Unread book in perfect condition.