Ng khai mun (25 Ergebnisse)
Harnessing Deep Q-Networks and Computational Intelligence for Smart Healthcare Decision-Making
Ather, Danish (EDT); Mun, Ng Khai (EDT); Jain, Sachin (EDT); Jain, Vishal (EDT); Kukreja, Vinay (EDT)
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Harnessing Deep Q-Networks and Computational Intelligence for Smart Healthcare Decision-Making
Ather, Danish (EDT); Mun, Ng Khai (EDT); Jain, Sachin (EDT); Jain, Vishal (EDT); Kukreja, Vinay (EDT)
- Softcover
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Harnessing Deep Q-Networks and Computational Intelligence for Smart Healthcare Decision-Making
Ather, Danish (EDT); Mun, Ng Khai (EDT); Jain, Sachin (EDT); Jain, Vishal (EDT); Kukreja, Vinay (EDT)
- Softcover
Anbieter: GreatBookPrices, Columbia, MD, USAGreatBookPrices
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Harnessing Deep Q-Networks and Computational Intelligence for Smart Healthcare Decision-Making
Ather, Danish (EDT); Mun, Ng Khai (EDT); Jain, Sachin (EDT); Jain, Vishal (EDT); Kukreja, Vinay (EDT)
- Softcover
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- Softcover
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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
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Paperback. Zustand: New.
- Softcover
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Paperback. Zustand: new. Paperback. The integration of reinforcement learning techniques like Deep Q-Networks (DQNs)with computational intelligence is revolutionizing healthcare by enabling more accurate, timely, and personalized medical decision-making. These AI strategies enhance diagnostics, treatment planning, and patient mo…nitoring by leveraging real-time data from sources such as IoT devices and medical imaging. This shift not only improves patient outcomes but also supports more efficient use of healthcare resources. As AI becomes more embedded in clinical practice, it plays a critical role in transforming healthcare into a more data-driven, adaptive, and patient-centered system. Applied AI and Computational Intelligence in Diagnostics and Decision-Making explores the fusion of reinforcement learning, particularly DQNs, with computational intelligence to enhance decision-making in healthcare. It delves into AI-driven diagnostics, personalized treatment plans, and real-time monitoring, leveraging deep learning and IoT integration. Covering topics such as artificial intelligence, neural networks, and remote patient monitoring, this book is an excellent resource for AI researchers, data scientists, machine learning engineers, computational intelligence specialists, medical professionals, radiologists, clinicians, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print-on-Demand
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EUR 235,60
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Paperback. Zustand: new. Paperback. In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder the analysis of sensitive health information. F…ederated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
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EUR 269,86
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Hardcover. Zustand: new. Hardcover. The integration of reinforcement learning techniques like Deep Q-Networks (DQNs)with computational intelligence is revolutionizing healthcare by enabling more accurate, timely, and personalized medical decision-making. These AI strategies enhance diagnostics, treatment planning, and patient mo…nitoring by leveraging real-time data from sources such as IoT devices and medical imaging. This shift not only improves patient outcomes but also supports more efficient use of healthcare resources. As AI becomes more embedded in clinical practice, it plays a critical role in transforming healthcare into a more data-driven, adaptive, and patient-centered system. Applied AI and Computational Intelligence in Diagnostics and Decision-Making explores the fusion of reinforcement learning, particularly DQNs, with computational intelligence to enhance decision-making in healthcare. It delves into AI-driven diagnostics, personalized treatment plans, and real-time monitoring, leveraging deep learning and IoT integration. Covering topics such as artificial intelligence, neural networks, and remote patient monitoring, this book is an excellent resource for AI researchers, data scientists, machine learning engineers, computational intelligence specialists, medical professionals, radiologists, clinicians, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Hardcover
- Print-on-Demand
Anbieter: CitiRetail, Stevenage, Vereinigtes KönigreichCitiRetail
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EUR 274,67
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Hardcover. Zustand: new. Hardcover. In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder the analysis of sensitive health information. F…ederated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print-on-Demand
Anbieter: preigu, Osnabrück, Deutschlandpreigu
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EUR 281,90
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Taschenbuch. Zustand: Neu. Applied AI and Computational Intelligence in Diagnostics and Decision-Making | Danish Ather (u. a.) | Taschenbuch | Englisch | 2025 | IGI Global | EAN 9798337333120 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Deman…d.
- Softcover
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Anbieter: preigu, Osnabrück, Deutschlandpreigu
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EUR 288,70
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Taschenbuch. Zustand: Neu. Enabling Collaborative Health Intelligence With Federated Learning | Ng Khai Mun (u. a.) | Taschenbuch | Englisch | 2025 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337333076 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Pri…nt on Demand.
- Softcover
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The integration of reinforcement learning techniques like Deep Q-Networks (DQNs)with computational intelligence is revolutionizing healthcare by enabling more accurate, timely, and personalized medical decision-making. These AI strategies…enhance diagnostics, treatment planning, and patient monitoring by leveraging real-time data from sources such as IoT devices and medical imaging. This shift not only improves patient outcomes but also supports more efficient use of healthcare resources. As AI becomes more embedded in clinical practice, it plays a critical role in transforming healthcare into a more data-driven, adaptive, and patient-centered system. Applied AI and Computational Intelligence in Diagnostics and Decision-Making explores the fusion of reinforcement learning, particularly DQNs, with computational intelligence to enhance decision-making in healthcare. It delves into AI-driven diagnostics, personalized treatment plans, and real-time monitoring, leveraging deep learning and IoT integration. Covering topics such as artificial intelligence, neural networks, and remote patient monitoring, this book is an excellent resource for AI researchers, data scientists, machine learning engineers, computational intelligence specialists, medical professionals, radiologists, clinicians, and more.
- Softcover
- Print-on-Demand
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 354,20
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.
- Hardcover
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Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The integration of reinforcement learning techniques like Deep Q-Networks (DQNs)with computational intelligence is revolutionizing healthcare by enabling more accurate, timely, and personalized medical decision-making. These AI strategies enhance… diagnostics, treatment planning, and patient monitoring by leveraging real-time data from sources such as IoT devices and medical imaging. This shift not only improves patient outcomes but also supports more efficient use of healthcare resources. As AI becomes more embedded in clinical practice, it plays a critical role in transforming healthcare into a more data-driven, adaptive, and patient-centered system. Applied AI and Computational Intelligence in Diagnostics and Decision-Making explores the fusion of reinforcement learning, particularly DQNs, with computational intelligence to enhance decision-making in healthcare. It delves into AI-driven diagnostics, personalized treatment plans, and real-time monitoring, leveraging deep learning and IoT integration. Covering topics such as artificial intelligence, neural networks, and remote patient monitoring, this book is an excellent resource for AI researchers, data scientists, machine learning engineers, computational intelligence specialists, medical professionals, radiologists, clinicians, and more.
- Hardcover
- Print-on-Demand
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 415,93
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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder… the analysis of sensitive health information. Federated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students.
