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.
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.
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: 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;
L`ouvrage porte sur la « trajectivite », un terme emprunte a la mesologie d`Augustin Berque et qui se definit comme un mouvement d`aller-retour entre les « moments » de la reception et de la creation. Posant les rapports a la litterature sous l`angle phenomenologique, il ouvre une perspective nouvelle aux etudes litteraires et traductologiques.
Описание: This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine.
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 Рейтинг: Цена: 10340.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning· Explanation Methods in Deep Learning· Learning Functional Causal Models with Generative Neural Networks· Learning Interpreatable Rules for Multi-Label Classification· Structuring Neural Networks for More Explainable Predictions· Generating Post Hoc Rationales of Deep Visual Classification Decisions· Ensembling Visual Explanations· Explainable Deep Driving by Visualizing Causal Attention· Interdisciplinary Perspective on Algorithmic Job Candidate Search· Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations
Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy.
In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering.
This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source.
Автор: Bohr, Adam Название: Artificial Intelligence In Healthcare ISBN: 0128184388 ISBN-13(EAN): 9780128184387 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Artificial Intelligence in Healthcare Data is more than a comprehensive introduction to artificial intelligence and machine learning. The book is split into two sections with an introduction to current healthcare data challenges that is followed by specific applications and case studies. The editors explore how AI is used as a tool in the analysis of healthcare data, specifically focusing on machine learning, deep learning, natural language processing. data privacy, cybersecurity and the ethics. Other sections explore how AI tools can help to interrogate data across a range of healthcare applications, including AI driven wearables and sensors and AI assisted surgery.
This book will be useful for researchers, graduate students and practitioners in computer science, data science, bioinformatics, health informatics, biomedical engineering and clinical engineering.
Описание: With this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company.
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