Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 370 pages. 11.69x8.25x11.69 inches. In Stock.
Langue: anglais
Edité par The Institution of Engineering and Technology, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 131,43
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Ajouter au panierEtat : New.
Langue: anglais
Edité par Institution of Engineering and Technology, GB, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
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Ajouter au panierHardback. Etat : 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.
Langue: anglais
Edité par The Institution of Engineering and Technology, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 138,73
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 160,94
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Ajouter au panierEtat : New.
Langue: anglais
Edité par The Institution of Engineering and Technology, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 142,22
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Ajouter au panierEtat : New.
Langue: anglais
Edité par The Institution of Engineering and Technology, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Ajouter au panierEtat : New.
Langue: anglais
Edité par The Institution of Engineering and Technology, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 146
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Vendeur : Chiron Media, Wallingford, Royaume-Uni
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Ajouter au panierhardcover. Etat : New.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Vendeur : Chiron Media, Wallingford, Royaume-Uni
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Ajouter au panierHardcover. Etat : New.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 168,61
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Langue: anglais
Edité par Institution of Engineering and Technology, GB, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-Unis
EUR 142,24
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Ajouter au panierHardback. Etat : 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.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 170,25
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 171,92
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Ajouter au panierEtat : New. In.
Langue: anglais
Edité par Inst of Engineering & Technology, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 179,05
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Ajouter au panierHardcover. Etat : Brand New. 1st edition. 400 pages. 9.25x6.25x1.25 inches. In Stock.
Langue: anglais
Edité par INSTITUTION OF ENGINEERING & T, 2023
ISBN 10 : 1839533358 ISBN 13 : 9781839533358
Vendeur : moluna, Greven, Allemagne
EUR 160,43
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Ajouter au panierEtat : 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.
Vendeur : preigu, Osnabrück, Allemagne
EUR 157,95
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Ajouter au panierTaschenbuch. Etat : 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.
Vendeur : Buchpark, Trebbin, Allemagne
EUR 125,11
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 397 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 237,98
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Ajouter au panierEtat : New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 241,03
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Ajouter au panierEtat : New. 2023rd edition NO-PA16APR2015-KAP.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 188,90
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Ajouter au panierTaschenbuch. Etat : 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.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 257,20
Quantité disponible : 3 disponible(s)
Ajouter au panierEtat : New.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 190,49
Quantité disponible : 1 disponible(s)
Ajouter au panierBuch. Etat : 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.