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Hardcover. Condition: new. Hardcover. This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Hardcover. Condition: new. Hardcover. This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Gebunden. Condition: New. Dr. Lalitha Krishnasamy received B.Tech IT from CSI College of Engineering, The Nilgris in 2005 and M.Tech IT from Anna University of Technology in 2009 and completed PhD in Wireless Sensor Networks in 2019. Currently working as an Assoc.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise.
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ISBN 10: 3031946367 ISBN 13: 9783031946363
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Hardcover. Condition: new. Hardcover. This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main aim of Healthcare 4.0: Health Informatics and Precision Data Management is to improve the services given by the healthcare industry and to bring meaningful patient outcomes, Informatics involved by applying the data, information and knowledge in the healthcare domain.
Language: English
Published by Taylor & Francis, Chapman And Hall/CRC, 2022
ISBN 10: 1032108606 ISBN 13: 9781032108605
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main aim of Healthcare 4.0: Health Informatics and Precision Data Management is to improve the services given by the healthcare industry and to bring meaningful patient outcomes by applying the data, information and knowledge in the healthcare domain.Features:Improves the quality of health data of a patientPresents a wide range of opportunities and renewed possibilities for healthcare systemsGives a way for carefully and meticulously tracking the provenance of medical recordsAccelerates the process of disease-oriented data and medical data arbitrationBrings meaningful patient health outcomesEradicates delayed clinical communicationsHelps the research intellectuals to step down further toward the disease and clinical data storageCreates more patient-centered servicesThe precise focus of this handbook is on the potential applications and use of data informatics in healthcare, including clinical trials, tailored ailment data, patient and ailment record characterization and health records management. 298 pp. Englisch.
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Published by Springer, Berlin, Springer Nature Switzerland, Springer, 2025
ISBN 10: 3031946367 ISBN 13: 9783031946363
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is a comprehensive resource that delves into the integration of advanced artificial intelligence techniques within the context of modern industrial practices. It systematically explores how distributed deep learning methodologies can be effectively combined with explainable AI to enhance transparency in Industry 4.0 applications. In recent years, neural networks and other deep learning models have produced remarkable outcomes in a variety of fields, including image recognition, natural language processing, and decision-making. Concerns have been raised regarding the transparency and interpretability of these models as a result of their increasing intricacy. The demand for methodologies and approaches associated with explainable artificial intelligence (XAI) has consequently increased. The primary aim of XAI is to enhance the transparency and comprehensibility of deep learning model decision-making processes for stakeholders, irrespective of their technical expertise. 424 pp. Englisch.