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: The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria decision tools can make deep neural networks more interpretable - and even, in many cases, more efficient.
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.
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.
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.
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: This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine.
Автор: Mishra Название: Explainable AI Recipes ISBN: 1484290283 ISBN-13(EAN): 9781484290286 Издательство: Springer Рейтинг: Цена: 4890.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. What You Will Learn * Create code snippets and explain machine learning models using Python * Leverage deep learning models using the latest code with agile implementations * Build, train, and explain neural network models designed to scale * Understand the different variants of neural network models Who This Book Is For AI engineers, data scientists, and software developers interested in XAI
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: Edited By Bhavnesh Kumar, Bhanu Pratap, Vivek Shri Название: Artificial Intelligence for Solar Photovoltaic Systems ISBN: 1032054417 ISBN-13(EAN): 9781032054414 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a clear explanation of the application of artificial intelligence to solve the challenges in solar photovoltaic technology. It introduces the readers about new AI-based approaches and technologies helpful in managing and operating solar photovoltaic systems effectively.
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.
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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