Explainable Artificial Intelligence for Intelligent Transportation Systems
暫譯: 可解釋的人工智慧在智慧交通系統中的應用
Adadi, Amina, Bouhoute, Afaf
商品描述
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.
商品描述(中文翻譯)
可解釋的人工智慧（Explainable AI）方法已被提出以解決這個問題，這些方法能夠在保持性能的同時，產生人類可解釋的機器學習模型表示。這些方法有潛力提高公眾對基於人工智慧的智慧交通系統（ITS）的接受度和信任度。
作者簡介
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.
作者簡介(中文翻譯)
Amina Adadi 是摩洛哥穆萊伊斯梅爾大學的計算機科學助理教授。她發表了多篇論文，包括經過審核的 IEEE/Springer/Elsevier 期刊文章、會議論文和書籍章節。她曾擔任並持續在多個國際會議的執行和技術程序委員會中服務，如 IEEE IRASET、ESETI 和 WITS。她的研究興趣包括可解釋的人工智慧（Explainable AI）、數據高效模型（Data Efficient Models）：數據增強（Data Augmentation）、少量學習（Few-shot learning）、自我監督學習（Self-supervised learning）、轉移學習（Transfer Learning）、區塊鏈（Blockchain）和智能合約（Smart Contracts）。
Afaf Bouhoute 擁有博士學位、信息系統、網絡和多媒體的碩士學位，以及計算機科學的學士學位，均來自摩洛哥菲斯的西迪穆罕默德本阿卜杜拉大學科學學院。她定期在多個國際會議的技術和程序委員會中服務，如 ISCV、WINCOM、ICECOCS 和 ICDS。她還曾擔任第一屆國際合作車輛網絡研討會（CVNET 2020）的共同主席，該研討會與 EAI ADHOCNETS 2020 同時舉辦。她的研究興趣涵蓋不同的技術和算法，用於駕駛行為的建模和分析，並專注於其在合作智能交通系統中的應用。
