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Explainable Artificial Intelligence for Biomedical Applications, 


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Цена: 17609.00р.
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Название:  Explainable Artificial Intelligence for Biomedical Applications
ISBN: 9788770228497
Издательство: Taylor&Francis
Классификация:




ISBN-10: 8770228493
Обложка/Формат: Hardback
Страницы: 380
Вес: 0.93 кг.
Дата издания: 14.12.2023
Серия: River publishers series in biomedical engineering
Иллюстрации: 22 tables, black and white; 45 line drawings, color; 18 line drawings, black and white; 102 halftones, color; 7 halftones, black and white; 147 illustrations, color; 25 illustrations, black and white
Размер: 161 x 243 x 27
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Поставляется из: Европейский союз


Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI)

Автор: Singh, Java
Название: Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI)
ISBN: 9811914753 ISBN-13(EAN): 9789811914751
Издательство: Springer
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Цена: 25155.00 р.
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Описание: The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. The book explains both positive as well as negative findings obtained by explainable AI techniques.

Explainable Artificial Intelligence (Xai) in Healthcare

Автор: Kose, Utku ; Sengoz, Nilgun ; Chen, XI ; Saucedo,
Название: Explainable Artificial Intelligence (Xai) in Healthcare
ISBN: 1032543701 ISBN-13(EAN): 9781032543703
Издательство: Taylor&Francis
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Цена: 16843.00 р.
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xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Paper

Автор: Holzinger Andreas, Goebel Randy, Fong Ruth
Название: xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Paper
ISBN: 3031040821 ISBN-13(EAN): 9783031040825
Издательство: Springer
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Цена: 5589.00 р.
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Описание: This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.

Explainable Artificial Intelligence for Intelligent Transportation Systems

Автор: Gaur
Название: Explainable Artificial Intelligence for Intelligent Transportation Systems
ISBN: 3031096460 ISBN-13(EAN): 9783031096464
Издательство: Springer
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Цена: 22359.00 р.
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Описание: Transportation typically entails crucial “life-death” choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.

Medical Data Analysis and Processing using Explainable Artificial Intelligence

Автор: Edited By Om Prakash Jena, Mrutyunjaya Panda, Utku
Название: Medical Data Analysis and Processing using Explainable Artificial Intelligence
ISBN: 1032191120 ISBN-13(EAN): 9781032191126
Издательство: Taylor&Francis
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Цена: 24499.00 р.
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Explainable Artificial Intelligence

Автор: Longo
Название: Explainable Artificial Intelligence
ISBN: 3031440692 ISBN-13(EAN): 9783031440694
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This three-volume set constitutes the refereed proceedings of the First World Conference on Explainable Artificial Intelligence, xAI 2023, held in Lisbon, Portugal, in July 2023. The 94 papers presented were thoroughly reviewed and selected from the 220 qualified submissions. They are organized in the following topical sections: Part I: Interdisciplinary perspectives, approaches and strategies for xAI; Model-agnostic explanations, methods and techniques for xAI, Causality and Explainable AI; Explainable AI in Finance, cybersecurity, health-care and biomedicine. Part II: Surveys, benchmarks, visual representations and applications for xAI; xAI for decision-making and human-AI collaboration, for Machine Learning on Graphs with Ontologies and Graph Neural Networks; Actionable eXplainable AI, Semantics and explainability, and Explanations for Advice-Giving Systems. Part III: xAI for time series and Natural Language Processing; Human-centered explanations and xAI for Trustworthy and Responsible AI; Explainable and Interpretable AI with Argumentation, Representational Learning and concept extraction for xAI.

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Автор: Wojciech Samek; Gr?goire Montavon; Andrea Vedaldi;
Название: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
ISBN: 3030289532 ISBN-13(EAN): 9783030289539
Издательство: Springer
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Цена: 10340.00 р.
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Описание:

The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner.The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance

Автор: Rutkowski Tom
Название: Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance
ISBN: 3030755207 ISBN-13(EAN): 9783030755201
Издательство: Springer
Цена: 22359.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations.

Explainable, Transparent Autonomous Agents and Multi-Agent Systems: Second International Workshop, Extraamas 2020, Auckland, New Zealand, May 9-13, 20

Автор: Calvaresi Davide, Najjar Amro, Winikoff Michael
Название: Explainable, Transparent Autonomous Agents and Multi-Agent Systems: Second International Workshop, Extraamas 2020, Auckland, New Zealand, May 9-13, 20
ISBN: 3030519236 ISBN-13(EAN): 9783030519230
Издательство: Springer
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Цена: 6986.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book constitutes the proceedings of the Second International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2020, which was due to be held in Auckland, New Zealand, in May 2020.

Explainable Artificial Intelligence for Intelligent Transportation Systems

Автор: Gaur
Название: Explainable Artificial Intelligence for Intelligent Transportation Systems
ISBN: 3031096436 ISBN-13(EAN): 9783031096433
Издательство: Springer
Рейтинг:
Цена: 22359.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Transportation typically entails crucial “life-death” choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.

Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance

Автор: Rutkowski
Название: Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance
ISBN: 3030755231 ISBN-13(EAN): 9783030755232
Издательство: Springer
Рейтинг:
Цена: 22359.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations.

Explainable, Transparent Autonomous Agents and Multi-Agent Systems

Автор: Davide Calvaresi; Amro Najjar; Michael Schumacher;
Название: Explainable, Transparent Autonomous Agents and Multi-Agent Systems
ISBN: 303030390X ISBN-13(EAN): 9783030303907
Издательство: Springer
Рейтинг:
Цена: 6986.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book constitutes the proceedings of the First International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems, EXTRAAMAS 2019, held in Montreal, Canada, in May 2019. The 12 revised and extended papers presented were carefully selected from 23 submissions. explainable agent simulations;


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