Explainable Artificial Intelligence for Intelligent Transportation Systems
Language: English
Published by Taylor and Francis Ltd, GB, 2025
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
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Add to basketItem description from seller
Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS.FEATURES:Provides the necessary background for newcomers to the field (both academics and interested practitioners)Presents a timely snapshot of explainable and interpretable models in ITS applicationsDiscusses ethical, societal, and legal implications of adopting XAI in the context of ITSIdentifies future research directions and open problems.…
Seller Inventory # LU-9781032348537
- Title
- Explainable Artificial Intelligence for Intelligent Transportation Systems
- Author
- Amina Adadi
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 2025
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1032348534
- ISBN 13
- 9781032348537
- Item weight
- 540 grams
Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS.
FEATURES:
- Provides the necessary background for newcomers to the field (both academics and interested practitioners)
- Presents a timely snapshot of explainable and interpretable models in ITS applications
- Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS
- Identifies future research directions and open problems
"Synopsis" may belong to another edition of this title.
About the Author
Amina Adadi is an assistant professor of Computer Science at Moulay Ismail University, Morocco. She has published several papers including refereed IEEE/Springer/Elsevier journal articles, conference papers, and book chapters. She has served and continues to serve on executive and technical program committees of numerous international conferences such as IEEE IRASET, ESETI, and WITS. Her research interests include Explainable AI, Data Efficient Models: Data Augmentation, Few-shot learning, Self-supervised learning, Transfer Learning, Blockchain, and Smart Contracts.
Afaf Bouhoute holds a Ph.D., a Master's degree in information systems, networking, and multimedia, and a bachelor's degree in computer science, all from the faculty of science, Sidi Mohamed Ben Abdellah University, Fez, Morocco. She regularly serves in the technical and program committees of numerous international conferences such as ISCV, WINCOM, ICECOCS, and ICDS. She also served as a co-chair of the First International Workshop on Cooperative Vehicle Networking (CVNET 2020), which was organized in conjunction with EAI ADHOCNETS 2020. Her research interests span different techniques and algorithms for modeling and analysis of driving behavior, with a focus on their application in cooperative intelligent transportation systems.
"About the title" may belong to another edition of this title.
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