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


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Цена: 25155.00р.
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Автор: Singh, Java
Название:  Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI)
ISBN: 9789811914751
Издательство: Springer
Классификация:


ISBN-10: 9811914753
Обложка/Формат: Hardback
Страницы: 137
Вес: 0.44 кг.
Дата издания: 10.04.2022
Серия: Intelligent systems reference library
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 60 tables, color; 63 illustrations, color; 10 illustrations, black and white; xv, 137 p. 73 illus., 63 illus. in color.
Размер: 235 x 155
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
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.


Автор: Nagrath, Preeti
Название: Smart Distributed Embedded Systems for Healthcare Applications
ISBN: 1032183470 ISBN-13(EAN): 9781032183473
Издательство: Taylor&Francis
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Цена: 20671.00 р.
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Описание: This book discusses the applications and optimization of emerging smart technologies in the field of healthcare. It further explains different modeling scenarios of the latest technologies in the healthcare system and compares the results to better understand the nature and progress of diseases in the human body, which would ultimately lead to early diagnosis and better treatment and cure of diseases with the help of distributed technology. Covers the implementation models using technologies such as artificial intelligence, machine learning, and deep learning with distributed systems for better diagnosis and treatment of diseases.

Gives in-depth review of technological advancements like advanced sensing technologies such as plasmonic sensors, usage of RFIDs, and electronic diagnostic tools in the field of healthcare engineering. Discusses possibilities of augmented reality and virtual reality interventions for providing unique solutions in medical science, clinical research, psychology, and neurological disorders. Highlights the future challenges and risks involved in the application of smart technologies such as cloud computing, fog computing, IOT, and distributed computing in healthcare.

Confers to utilize the AI and ML and associated aids in healthcare sectors in the post-Covid 19 period to revitalize the medical setup. Contributions included in the book will motivate technological developers and researchers to develop new algorithms and protocols in the healthcare field. It will serve as a vast platform for gaining knowledge regarding healthcare delivery, health- care management, healthcare in governance, and health monitoring approaches using distributed environments.

It will serve as an ideal reference text for graduate students and researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical fields.

Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

Автор: Rothman Denis
Название: Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
ISBN: 1800208138 ISBN-13(EAN): 9781800208131
Издательство: Неизвестно
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Цена: 10114.00 р.
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Описание: In today`s era of AI, accurately interpreting and communicating trustworthy, fair, and secure AI findings have become a crucial skill to master. This book bridges the gap between AI`s pitfalls and potential by helping you build the ability to leverage machine learning with Python to visualize and integrate 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.
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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 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.
Principles and Methods of Explainable Artificial Intelligence in Healthcare

Автор: Akash Kumar Bhoi, Alfonso Gonza?lez Briones, P Naga Srinivasu, Victor Hugo C. de Albuquerque
Название: Principles and Methods of Explainable Artificial Intelligence in Healthcare
ISBN: 1668437910 ISBN-13(EAN): 9781668437919
Издательство: Mare Nostrum (Eurospan)
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Цена: 67914.00 р.
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Описание: Discusses explainable artificial intelligence and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. The book also encompasses computational vision processing techniques that handle complex.

Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

Автор: Mishra Pradeepta
Название: Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
ISBN: 1484271572 ISBN-13(EAN): 9781484271575
Издательство: Springer
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Цена: 9083.00 р.
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Описание: Intermediate-Advanced

Reasoning Web. Explainable Artificial Intelligence

Автор: Markus Kr?tzsch; Daria Stepanova
Название: Reasoning Web. Explainable Artificial Intelligence
ISBN: 3030314227 ISBN-13(EAN): 9783030314224
Издательство: Springer
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Цена: 8104.00 р.
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Описание: This volume contains lecture notes of the 15th Reasoning Web Summer School (RW 2019), held in Bolzano, Italy, in September 2019. The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.

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.
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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 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.

Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems

Автор: Mar Marcos; Jose M. Juarez; Richard Lenz; Grzegorz
Название: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems
ISBN: 3030374459 ISBN-13(EAN): 9783030374457
Издательство: Springer
Рейтинг:
Цена: 6986.00 р.
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Описание: This book constitutes revised selected papers from the AIME 2019 workshops KR4HC/ProHealth 2019, the Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, and TEAAM 2019, the Workshop on Transparent, Explainable and Affective AI in Medical Systems.

Explainable Artificial Intelligence for Cyber Security: Next Generation Artificial Intelligence

Автор: Ahmed Mohiuddin, Islam Sheikh Rabiul, Anwar Adnan
Название: Explainable Artificial Intelligence for Cyber Security: Next Generation Artificial Intelligence
ISBN: 3030966291 ISBN-13(EAN): 9783030966294
Издательство: Springer
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Цена: 22359.00 р.
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Описание: This book presents that explainable artificial intelligence (XAI) is going to replace the traditional artificial, machine learning, deep learning algorithms which work as a black box as of today. To understand the algorithms better and interpret the complex networks of these algorithms, XAI plays a vital role. In last few decades, we have embraced AI in our daily life to solve a plethora of problems, one of the notable problems is cyber security. In coming years, the traditional AI algorithms are not able to address the zero-day cyber attacks, and hence, to capitalize on the AI algorithms, it is absolutely important to focus more on XAI. Hence, this book serves as an excellent reference for those who are working in cyber security and artificial intelligence.

Explainable Human-AI Interaction

Автор: Sreedharan
Название: Explainable Human-AI Interaction
ISBN: 303103757X ISBN-13(EAN): 9783031037573
Издательство: Springer
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Цена: 7685.00 р.
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Описание: To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human`s task and goal models, as well as the human`s model of the AI agent`s task and goal models.


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