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Digital Watermarking for Machine Learning Model: Techniques, Protocols and Applications
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Add to basketPaperback. Condition: Brand New. 370 pages. 11.69x8.25x11.69 inches. In Stock.
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by The Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Condition: New.
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by Institution of Engineering and Technology, GB, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Hardback. Condition: New. Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use. This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how ML techniques can be applied to process an individual patient's medical data to swiftly aid diagnosis. Written by an international team of experts, the book presents several applications of machine learning in the healthcare sector, including health system planning, optimisation and preparedness, outlining the benefits and challenges of coordination and data sharing. Machine learning has many applications in processing patient data and topics such as arrhythmia detection, image-guided microsurgery and early detection of Alzheimer's disease are discussed in depth. The book also looks at machine learning applications exploiting wearable sensors for real-time analysis and concepts around enhancing physical performance. Suitable for an audience of computer scientists, healthcare engineers and those involved with digital medicine, this book brings together a plethora of machine learning applications from across the board of the healthcare services.
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by The Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Condition: As New. Unread book in perfect condition.
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by The Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Applications of Machine Learning in Digital Healthcare (Healthcare Technologies)
Language: English
Published by The Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by The Institution of Engineering and Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications
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Digital Twins: Internet of Things, Machine Learning, and Smart Manufacturing (Smart Computing Applications, 8)
Language: English
Published by De Gruyter 2023-08-21, 2023
ISBN 10: 3110778785 ISBN 13: 9783110778786
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Digital Twins: Internet of Things, Machine Learning, and Smart Manufacturing (Smart Computing Applications, 8)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Digital Watermarking for Machine Learning Model : Techniques, Protocols and Applications
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by Institution of Engineering and Technology, GB, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Hardback. Condition: New. Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use. This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how ML techniques can be applied to process an individual patient's medical data to swiftly aid diagnosis. Written by an international team of experts, the book presents several applications of machine learning in the healthcare sector, including health system planning, optimisation and preparedness, outlining the benefits and challenges of coordination and data sharing. Machine learning has many applications in processing patient data and topics such as arrhythmia detection, image-guided microsurgery and early detection of Alzheimer's disease are discussed in depth. The book also looks at machine learning applications exploiting wearable sensors for real-time analysis and concepts around enhancing physical performance. Suitable for an audience of computer scientists, healthcare engineers and those involved with digital medicine, this book brings together a plethora of machine learning applications from across the board of the healthcare services.
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by INSTITUTION OF ENGINEERING & T, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
US$ 159.97
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Add to basketCondition: New. KlappentextThis edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how machine learning techniques can be appl.
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by Inst of Engineering & Technology, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
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Add to basketHardcover. Condition: Brand New. 1st edition. 400 pages. 9.25x6.25x1.25 inches. In Stock.
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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 397 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Condition: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
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Condition: New. 2023rd edition NO-PA16APR2015-KAP.
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Taschenbuch. Condition: Neu. Digital Watermarking for Machine Learning Model | Techniques, Protocols and Applications | Lixin Fan (u. a.) | Taschenbuch | xvi | Englisch | 2024 | Springer | EAN 9789811975561 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Applications of Machine Learning in Digital Healthcare
Language: English
Published by Institution Of Engineering & Technology Jun 2023, 2023
ISBN 10: 1839533358 ISBN 13: 9781839533358
Buch. Condition: Neu. Neuware - This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how machine learning techniques can be applied to help individual patients.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model's owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model's owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
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