Principles and Methods of Explainable Artificial Intelligence in Healthcare, Akash Kumar Bhoi, Alfonso Gonza?lez Briones, P Naga Srinivasu, Victor Hugo C. de Albuquerque
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.
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.
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.
This book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques.
This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI--in short, to anyone who is interested in problems involving fuzziness and AI in general.
Описание: With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making.This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.
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.
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.
Автор: Markus Kr?tzsch; Daria Stepanova Название: Reasoning Web. Explainable Artificial Intelligence ISBN: 3030314227 ISBN-13(EAN): 9783030314224 Издательство: Springer Рейтинг: Цена: 8104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Название: Explainable artificial intelligence for smart cities ISBN: 1032001127 ISBN-13(EAN): 9781032001128 Издательство: Taylor&Francis Рейтинг: Цена: 15310.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The objective of this book is to focus on Explainable Artificial Intelligence (XAI) in smart city development. This book provides a timely, global reference source about cutting edge research efforts to ensure the XAI factor in smart city-oriented developments.
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.
Автор: Kamath Uday, Liu John Название: Explainable Artificial Intelligence: An Introduction to Xai ISBN: 3030833550 ISBN-13(EAN): 9783030833558 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 1. Introduction to Interpretability and Explainability.- 2. Pre-Model Interpretability and Explainability.- 3. Model Visualization Techniques and Traditional Interpretable Algorithms.- 4. Model Interpretability: Advances in Interpretable Machine Learning.- 5. Post-hoc Interpretability and Explanations.- 6. Explainable Deep Learning.- 7. Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision.- 8. XAI: Challenges and Future.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
