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.
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Dr. Danish Ather is an accomplished book editor with a robust academic and professional background. Currently as Professor at Amity University in Tashkent, Uzbekistan, Dr. Ather brings over 18 years of experience in teaching, research, and administration. He holds dual Ph.D. degrees in Computer Science and Computer Science & Engineering. His editorial expertise is highlighted through his role as Editor of the proceedings submitted yearly to IEEE USA for the SMART Series conferences. Dr. Ather has authored a book titled “Level up Your Programming with Core Dragon” and published 54 research papers in international journals and conferences, with 15 indexed in Scopus. He is a Senior Member of the IEEE Society and has received numerous awards, including a Certificate of Appreciation from the Ministry of Digital Technologies, Republic of Uzbekistan. His technical proficiency and dedication to advancing knowledge in fields such as IoT, AI, and programming make him a valuable asset in the realm of academic publishing.
Dr. Ng Khai Mun currently serves as the Acting Deputy Vice Chancellor (Academic & Research) at Infrastructure University Kuala Lumpur, Malaysia. Before becoming Acting Deputy Vice Chancellor, he was the Dean of the Faculty of Engineering, Science & Technology. With over 15 years in academia, Dr. Ng has significantly contributed to engineering education and research. A Chartered Engineer with the Engineering Council UK, and a Professional Technologist with the Malaysia Board of Technologies, he actively engages in academic and industry collaborations. His scholarly work includes multiple journal publications, conference proceedings, and a registered copyright for a solar absorber system. He has been involved in various funded research projects. His research expertise spans renewable energy systems, thermo fluids, and sustainable engineering solutions.He has served as a reviewer for high-impact journals such as Solar Energy and the Journal of Building Engineering, as well as a panel evaluator for research funding proposals.Beyond his academic and research commitments, Dr. Ng is dedicated to fostering innovation and industry-driven research collaborations.
Sachin Jain is working as an Associate Professor in the Department of Computer Science and Engineering at Ajay Kumar Garg Engineering College, Ghaziabad. He completed his B.Tech in Computer Science and Engineering in 2007 from UPTU, Lucknow and M.Tech in Computer Science and Engineering in 2010 from GGSIPU, Delhi. He has completed his Ph.D in Computer Science and Engineering from Sharda University Greater Noida in April, 2025. He is a UGC-NET qualified teacher and has total 16 years of teaching experience. His research interests lie in Artificial Intelligence, Deep Learning and Machine Learning. He has published several research papers in Peer-Reviewed Journals and Conference proceedings. Dr. Sachin Jain has filed several patents for his work.
Vishal Jain is pursuing Postdoc from Kuala Lumpur University of Science & Technology (KLUST) (formerly known as Infrastructure University Kuala Lumpur (IUKL)), Malaysia. He is presently working as a Professor (CSE) at the Department of Computer Science & Engineering, School of Engineering & Technology, Vivekananda Institute of Professional Studies - Technical Campus, New Delhi. Before that, he worked as Professor at Sharda University, Greater Noida and Associate Professor at Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi. He has associated as a member of the Faculty Board, Board of Planning and Management, Executive Council, Academic Council, Curriculum Development and Review Committee and Academic Audit of various higher education Institutions. His research areas include machine learning, information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, sensor networks and network security.
Dr. Vinay Kukreja currently holds the distinguished position of Professor and Director (Research) at the Office of Research Publications (CURIN), Chitkara University, Punjab. With an illustrious academic career spanning 18 years, he has guided numerous Ph.D. and Master of Engineering students. His scholarly accomplishments are equally impressive, encompassing over 600 articles indexed in Scopus, an h-index of 48, more than 6,000 citations, and upwards of 50 patents, reflecting his significant impact on academia and research. An accomplished author, he has contributed to the academic landscape with three authored books and four edited volumes. Demonstrating his innovative prowess, he secured the prestigious first prize at the 2018 SIH Hackathon, a competition supported by India’s Ministry of Housing & Urban Affairs. Prof. Kukreja’s research interests are both diverse and cutting-edge, encompassing machine learning, deep learning, agile software development, image processing, data analysis, and structural equation modeling. His achievements underscore his dedication to advancing knowledge and his unwavering commitment to academic and intellectual excellence.
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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 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798337333120
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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 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798337333120
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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 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9798337333120
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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 Demand. Bestandsnummer des Verkäufers 134040613
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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. Bestandsnummer des Verkäufers 9798337333120
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