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Présentation Explainable Artificial Intelligence For Intelligent Transportation Systems de Format Relié
- Livre Sciences de la vie et de la terre
Résumé :
Explainable AI 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....
Biographie: 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.
Sommaire: Section I Towards explainable ITS. 1. Explainable AI for Intelligent Transportation Systems: Are we there yet? Amina Adadi and Afaf Bouhoute. Section II Interpretable methods for ITS applications. 2. Towards Safe, Explainable, and Regulated Autonomous Driving Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, and Randy Goebel. 3. Explainable Machine Learning Method for Predicting Road Traffic Accident Injury Severity in Addis Ababa city based on a New Graph Feature Selection Technique Yassine Akhiat , Younes Bouchlaghem, Ahmed Zinedine, and Mohamed Chahhou. 4. COVID-19 pandemic effects on traffic crash patterns and in- juries in Barcelona, Spain: An interpretable approach Ahmad Aiash and Francesc Robuste. 5. Advances in Explainable Reinforcement Learning: an Intelligent Transportation Systems perspective Rudy Milani, Maximilian Moll and Stefan Pickl. 6. Road Traffic Data Collection: Handling Missing Data Abdelilah Mbarek, Mouna Jiber, Ali Yahyaouy, and Abdelouahed Sabri. 7. Explainability of surrogate models for traffic signal control Pawel Gora, Dominik Bogucki, and M. Latif Bolum. 8. Intelligent Techniques and Explainable Artificial Intelligence for Vessel Traffic Service: A Survey Meng Joo Er, Huibin Gong, Chuang Ma, Wenxiao Gao. 9. An Explainable Model for Detection and Recognition of Traffic Road Signs Anass Barodi, Abdelkarim Zemmouri, Abderrahim Bajit, Mohammed Benbrahim, and Ahmed Tamtaoui. 10. An Interpretable Detection of Transportation Mode Consider- ing GPS, Spatial, and Contextual Data based on Ensemble Machine Learning Sajjad Sowlati, Rahim Ali Abbaspour, and Chehreghan. 11. Blockchain and Explainable AI for Trustworthy Autonomous Vehicles Ouassima Markouh, Amina Adadi, Mohammed Berrada. Section III Ethical, social and legal implications of XAI in ITS. 12. Ethical Decision-Making Under Different Perspective-Taking Scenarios and Demographic Characteristics: The Case of Autonomous Vehicles Kareem Othman.
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