Explainable artificial intelligence trustworthy (24 résultats)

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Etat : New.

Etat : As New. Unread book in perfect condition.

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Hardback. Etat : New. A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data. Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks. Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation. This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence. Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.…

Etat : As New. Unread book in perfect condition.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

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Etat : New. In English.

Hardback. Etat : New. A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data. Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks. Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation. This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence. Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.…

Hardcover. Etat : Brand New. 400 pages. 9.21x6.14x9.21 inches. In Stock.

Hardback. Etat : New. A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data. Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks. Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation. This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence. Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.…

Buch. Etat : Neu. Neuware - A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data. Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks. Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation. This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence. Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.…

Hardback. Etat : New. A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data. Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks. Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation. This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence. Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.…

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to the field of explainable artificial intelligence (XAI), which aims to make AI models more transparent and trustworthy. It explores how XAI can enhance trust and confidence in AI models and their decisions across various innovative applications in fields such as healthcare, finance, and engineering, where AI can significantly impact quality of life.Readers will discover emerging trends related to XAI such as large language models, generative AI, and natural language processing that are transforming the landscape of AI research and applications. Featuring an interdisciplinary overview, the book examines the state of the art, challenges, and opportunities in XAI, accompanied by clear examples and detailed explanations of its methods and techniques.The book also offers a balanced perspective on the limitations and trade-offs of XAI and outlines future directions and opportunities for both research and practice. This book is intended for anyone who wants to learn more about XAI and understand how it can enhance trust in AI models.…

paperback. Etat : VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Explainable Artificial Intelligence: Methods for Trustworthy AI Systems provides acomprehensive exploration of how artificial intelligence can become more transparent,interpretable, fair, and trustworthy. The book introduces the foundations and evolution ofExplainable AI (XAI), highlighting the growing need for transparency, accountability, andhuman trust in intelligent systems. It examines key explainability techniques, includingintrinsically interpretable models, feature attribution, model-agnostic methods, and local andglobal explanations. Special attention is given to deep learning, transformers, large languagemodels, generative AI, and foundation models. The book also addresses fairness, ethics, privacy,governance, and regulatory compliance, alongside practical tools, frameworks, evaluationmetrics, and human-centered assessment approaches. Through applications in healthcare,finance, cybersecurity, insurance, and critical infrastructure, it demonstrates the practical value oftrustworthy AI. The book concludes by examining emerging trends and future researchdirections, making it a valuable resource for researchers, professionals, educators, and studentsexploring responsible and explainable AI.…

Taschenbuch. Etat : Neu. Explainable Artificial Intelligence | Methods for Trustworthy AI Systems | Vinaykumar Chitukoori | Taschenbuch | Englisch | 2026 | Notion Press | EAN 9798906737731 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Etat : new. Questo è un articolo print on demand.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces readers to the field of explainable artificial intelligence (XAI), which aims to make AI models more transparent and trustworthy. It explores how XAI can enhance trust and confidence in AI models and their decisions across various innovative applications in fields such as healthcare, finance, and engineering, where AI can significantly impact quality of life.Readers will discover emerging trends related to XAI such as large language models, generative AI, and natural language processing that are transforming the landscape of AI research and applications. Featuring an interdisciplinary overview, the book examines the state of the art, challenges, and opportunities in XAI, accompanied by clear examples and detailed explanations of its methods and techniques.The book also offers a balanced perspective on the limitations and trade-offs of XAI and outlines future directions and opportunities for both research and practice. This book is intended for anyone who wants to learn more about XAI and understand how it can enhance trust in AI models. 415 pp. Englisch.…

Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces readers to the field of explainable artificial intelligence (XAI), which aims to make AI models more transparent and trustworthy. It explores how XAI can enhance trust and confidence in AI models and their decisions across various innovative applications in fields such as healthcare, finance, and engineering, where AI can significantly impact quality of life.Readers will discover emerging trends related to XAIsuch as large language models, generative AI, and natural language processingthat are transforming the landscape of AI research and applications. Featuring an interdisciplinary overview, the book examines the state of the art, challenges, and opportunities in XAI, accompanied by clear examples and detailed explanations of its methods and techniques.The book also offers a balanced perspective on the limitations and trade-offs of XAI and outlines future directions and opportunities for both research and practice. This book is intended for anyone who wants to learn more about XAI and understand how it can enhance trust in AI models.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 432 pp. Englisch.…