applications machine learning digital (38 résultats)

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Etat : Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

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Hardback. 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.

Hardback. 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.

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Hardcover. Etat : New.

Etat : 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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Hardcover. Etat : Brand New. 1st edition. 400 pages. 9.25x6.25x1.25 inches. In Stock.

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Taschenbuch. 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.

Buch. 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.

Etat : New.

Buch. Etat : 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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Etat : New. 2023rd edition NO-PA16APR2015-KAP.

Etat : Brand New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

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Hardcover. Etat : Brand New. 241 pages. 9.25x6.10x0.79 inches. In Stock.

Hardcover. Etat : Brand New. 300 pages. 9.45x6.69x0.79 inches. In Stock.

Softcover. Etat : New. Etat de la jaquette : no dj. First. 'Machine Learning Applications in Digital Analytics' examines how machine learning algorithms enhance the interpretation of complex digital data. The book covers various models and techniques used to predict user behavior, segment audiences, and optimize digital campaigns. It emphasizes the integration of machine learning into analytics workflows to automate insights and improve decision-making. This publication is a valuable resource for data scientists and marketers aiming to leverage advanced algorithms for competitive advantage. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Etat : New. Etat de la jaquette : no dj. First. The publication 'Machine Learning Applications in Detecting Digital Fabrication Anomalies' investigates the role of artificial intelligence in ensuring the integrity of digital manufacturing processes. It discusses various machine learning algorithms tailored to identify irregularities and defects in fabricated components. The book emphasizes the importance of automation and data-driven techniques in maintaining quality control within digital fabrication environments. Its insights aim to enhance reliability, reduce errors, and promote trust in advanced manufacturing systems through innovative analytical approaches. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Etat : New. Etat de la jaquette : no dj. First. The digital age is characterized by vast amounts of data flowing through complex systems. 'Machine Learning Applications in Digital Analytics' offers a comprehensive exploration of the complex relationships between machine learning, data analysis, and digital analytics. By examining the latest techniques and tools in machine learning, this book provides a nuanced understanding of the challenges and opportunities presented by digital analytics. From predictive modeling to clustering analysis, this book equips readers with the knowledge and skills necessary to design, implement, and analyze machine learning applications in digital analytics. By applying the principles outlined in this book, businesses and organizations can unlock the full potential of their data, drive innovation, and create new revenue streams. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Etat : New. Etat de la jaquette : no dj. First. Circuit Design for High-Speed Digital Applications with Machine Learning offers a comprehensive exploration of the intersection of circuit design and machine learning, providing a nuanced understanding of the techniques used to develop high-speed digital circuits and optimize performance. Through in-depth analysis and theoretical frameworks, readers will gain a deeper understanding of the complex interplay between circuit design and machine learning, enabling them to develop innovative solutions for high-speed digital applications. This book provides a unique blend of theoretical foundations and practical applications, making it an essential resource for engineers and researchers seeking to push the boundaries of high-speed digital circuit design. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.