This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint--coupled with case studies, statistical analyses, and expert insights--the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Pronaya Bhattacharya received the Ph.D. degree from Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India. He is currently an associate professor with the Computer Science and Engineering Department, Amity School of Engineering and Technology, Amity University, Kolkata, India. He has over ten years of teaching experience. He has authored or co-authored more than 130 research papers in leading SCI journals and top core IEEE COMSOC A* conferences. He is a recipient of nine Best Paper Awards from Springer ICRIC-2019, IEEE-ICIEM-2021, IEEE-ECAI-2021, Springer COMS2-2021, and IEEE-ICIEM-2022. He is a reviewer of 25 reputed SCI journals, such as IEEE Internet of Things Journal, IEEE Transactions on Industrial Informatics, IEEE Transactions of Vehicular Technology, IEEE Journal of Biomedical and Health Informatics, IEEE Access, IEEE Network, ETT (Wiley), IJCS (Wiley), MTAP (Springer), OSN (Elsevier), and WPC (Springer).
Haipeng Liu received his Bachelor and Master in Engineering degrees from Zhejiang University, China, in 2012 and 2015, respectively, and Doctor of Philosophy in Medical Sciences from the Chinese University of Hong Kong, in 2018. From 2019 to 2020, he was a research fellow with the Medical Technology Research Center, Anglia Ruskin University. Since 2020, he has been a research fellow with Coventry University, UK. He is the author of over 60 journal articles and 10 conference papers. His research interests include computational modelling of cardiovascular system, physiological measurement and biosignal processing, wearable nanosensor development and healthcare application, and AI-assisted diagnostics. He has a prolific profile with an H-index of 18 and i10-index of 26.
Dr. Pushan Kumar Dutta is an Assistant Professor Grade III at Amity University Kolkata, specializing in Electronics and Communication Engineering. With a Ph.D. in Electronics and Tele- Communication Engineering from Jadavpur University and Post Doctorate, Erasmus Mundus Scholar, European Union Leaders Program with full Scholarship, University of Oradea, 2015-2016, Dr. Dutta has established himself as a prominent researcher and educator in his field. His research interests span data mining, AI, edge computing, and predictive analytics, with a focus on applications in smart cities, healthcare, and sustainable development. Dr. Dutta's scholarly contributions include over 108 articles in Scopus-indexed journals and conferences, as well as numerous IEEE Xplore and Springer Lecture Notes publications. A prolific editor, Dr. Dutta has curated 30+ books for prestigious publishers in 2023-24 such as Springer, IET, Elsevier, Emerald Publishers, IGI Global, Bentham Publisher, River Publisher, Degruyter, apple academic press and Routledge and Taylor and Francis. He is managing a book series in CRC on Sustainable Industrial Engineering systems. This expertise has earned him recognition as a reviewer for leading academic publishers. Dr. Dutta's achievements include mentoring award-winning students, receiving the 'Mentor of Change' recognition from NITI Aayog and young faculty award, and delivering keynote speeches at international conferences. His commitment to innovative teaching and learning is evident through his copyrighted work titled 'Innovative Digital Teaching and Learning for Professional Readiness' and he has also published two Indian patents. He is a Threws Fellow member, a senior member at Indian Institute of Engineering(India) and participated to win an International White paper contest on Johnson Control.
Joel J. P. C. Rodrigues [S'01, M'06, SM'06, F'20] is a professor at the Federal University of Piauí (UFPI), Brazil, and senior researcher at the Instituto de Telecomunicações, Portugal. He received the Academic Title of Aggregated Professor in informatics engineering from UBI, the Habilitation in computer science and engineering from the University of Haute Alsace, France, a Ph.D. degree in informatics engineering and an M.Sc. degree from the UBI, and a five-year BSc degree (licentiate) in informatics engineering from the University of Coimbra, Portugal. His main research interests include IoT and sensor networks, e-health technologies vehicular communications, and mobile and ubiquitous computing.
He is the editor-in-chief of the International Journal on E-Health and Medical Communications and an editorial board member of several high-reputed journals (IEEE Network, IEEE Wireless Communications, IEEE IoT Journal, IEEE Open Journal of the Communications Society, etc.).
Gautam Sethi has cemented his reputation as a preeminent researcher in the fields of Pharmacology and Toxicology, with an array of accolades and distinctions recognizing his contributions. Notably, he was named the World's Most Highly Cited Researcher in these particular fields for both 2020 and 2021. His extraordinary contributions have been well-documented by the National University of Singapore (NUS), where he has also been lauded for his decade-long service. His achievements are buttressed by rigorous metrics and global rankings. He has also continuously featured in Stanford University's World's Top 2% Scientists List from 2020 onwards, which recognizes the most cited scientists in various disciplines, in the fields of oncology. He has been also bestowed with two of the highest Indian diasporic awards namely Hind Rattan award 2020 and Nav Rattan award 2023 by the NRI Welfare Society of India in recognition of his outstanding contributions and achievements in the field of pharmacology.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint-coupled with case studies, statistical analyses, and expert insights-the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI. N° de réf. du vendeur 9783031757709
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint-coupled with case studies, statistical analyses, and expert insights-the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI. 476 pp. Englisch. N° de réf. du vendeur 9783031757709
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint-coupled with case studies, statistical analyses, and expert insights-the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 488 pp. Englisch. N° de réf. du vendeur 9783031757709
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Hardcover. Etat : new. Hardcover. This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpointcoupled with case studies, statistical analyses, and expert insightsthe book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783031757709
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Hardcover. Etat : new. Hardcover. This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpointcoupled with case studies, statistical analyses, and expert insightsthe book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9783031757709
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