Artificial Intelligence Techniques for Analysing Sensitive Data in Medical Cyber-Physical Systems (Hardcover)
Idioma: inglés
Editorial: Springer International Publishing AG, Cham, 2025
- Tapa dura
- Nuevo
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de IberLibro desde 12 de octubre de 2005
Condición: Nuevo
EUR 233,37
Cantidad disponible: 1 disponible
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Hardcover. This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. The advances in Internet of things and artificial intelligence (AI) have witnessed great progress on healthcare technologies in several application domains. In particular, the interconnection between the physical spaces, characterized by physical devices able to collect users health information, with the cyberspace, also known as the virtual space, has fostered the development of intelligent Medical Cyber-Physical Systems (MCPSs) with the capability to deliver real-time healthcare services. On the other hand, the potential innovation that these technologies bring to improve patient care, by remotely analysing health parameters using medical devices, advanced smart sensors, and AI, is hampered by security and privacy challenges related to the managed sensitive data. Starting from the state of the art on AI and ML for medical applications and digital health, an accurate analysis of privacy and security risks associated with the use of the MCPSs is presented. Then, Digital Twins are introduced as a significant technique to enhance decision-making through learning and reasoning of collected on-field real-time data. Moreover, decentralized healthcare data management approaches based on federated learning, tiny machine learning, and blockchain technologies have been introduced to shift control and responsibility of healthcare data management from individual centralized entities to a more distributed structure, preserving privacy and security. Finally, the application of AI-based security monitoring approaches in healthcare is discussed. In this book, both theoretical and practical approaches are used to allow readers to understand complex topics and concepts easily also through real-life scenarios. This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
N° de ref. del artículo 9783031707742
- Título
- Artificial Intelligence Techniques for Analysing Sensitive Data in Medical Cyber-Physical Systems (Hardcover)
- Autor
- Massimo Ficco
- Editorial
- Springer International Publishing AG, Cham
- Año de publicación
- 2025
- Estado
- new
- Encuadernación
- Hardcover
- Idioma
- inglés
- ISBN 10
- 3031707745
- ISBN 13
- 9783031707742
This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. The advances in Internet of things and artificial intelligence (AI) have witnessed great progress on healthcare technologies in several application domains. In particular, the interconnection between the physical spaces, characterized by physical devices able to collect users’ health information, with the cyberspace, also known as the virtual space, has fostered the development of intelligent Medical Cyber-Physical Systems (MCPSs) with the capability to deliver real-time healthcare services. On the other hand, the potential innovation that these technologies bring to improve patient care, by remotely analysing health parameters using medical devices, advanced smart sensors, and AI, is hampered by security and privacy challenges related to the managed sensitive data.
Starting from the state of the art on AI and ML for medical applications and digital health, an accurate analysis of privacy and security risks associated with the use of the MCPSs is presented. Then, Digital Twins are introduced as a significant technique to enhance decision-making through learning and reasoning of collected on-field real-time data. Moreover, decentralized healthcare data management approaches based on federated learning, tiny machine learning, and blockchain technologies have been introduced to shift control and responsibility of healthcare data management from individual centralized entities to a more distributed structure, preserving privacy and security. Finally, the application of AI-based security monitoring approaches in healthcare is discussed.
In this book, both theoretical and practical approaches are used to allow readers to understand complex topics and concepts easily also through real-life scenarios.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Massimo Ficco is Full Professor at the Computer Science Department of the University of Salerno, Italy. His major research interests include security and reliability aspects of critical infrastructures. Currently, his scientific research and dissemination activities concern the use of machine learning and artificial intelligence in the context of malware analysis and IoT systems. Currently, he heads the laboratory of Internet of Things (IoTResearch).
Gianni D'Angelo is an Associate Professor at the University of Salerno, Italy. His research interests concern with the development and implementation of algorithms based on Artificial Intelligence, Deep Learning, Machine Learning, Explainable Artificial Intelligence, and parallel programming applied in various scientific and industrial fields.
“Acerca de” puede pertenecer a otra edición de este título.
Grand Eagle Retail
Bensenville, IL, Estados Unidos de America
Vendedor de IberLibro desde 12 de octubre de 2005
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