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This book presents selected papers from the International Conference on Explainable Intelligence in Digital Twins (EIDT 2025) held during November 12 14, at Ho Chi Minh City, Vietnam. The integration of explainable intelligence in digital twins offers significant advantages, enhancing transparency, trust, and informed decision-making across various industries, including manufacturing, healthcare, transportation, and smart cities. By combining real-time data with AI-driven analytics, digital twins enable predictive maintenance, process optimization, transparent communication, and system automation. While these technologies have immense potential to revolutionize digital ecosystems, they also pose significant challenges, such as bias in AI models, scalability of explainable methods, and trade-offs between performance and interpretability. To address these critical issues, the International Conference on Explainable Intelligence in Digital Twins (EIDT) was established as a dedicated platform for research scholars and engineers to discuss, innovate, and collaborate on advancing transparent, interpretable, and trustworthy AI-driven digital twins. EIDT fosters cutting-edge research and real-world applications that drive the next generation of explainable intelligence in digital twin networks and systems.
List of contents
Explainable Intelligence in Digital Twins: State-Of-The-Art and Open Challenges.- QLoRA and RAG Application in Building Admissions Support Chatbot: Performance Evaluation and Knowledge Update Ability.- AI-Driven Hand Gesture Recognition for Intelligent Human-Computer Interaction in Healthcare.- A Novel Approach to Analyze the Correlation Between Assessment Methods and CLO Learning Outcomes in Higher Education.- Enhancing Speaker Identification with Hybrid Deep Learning Models and Multi-Feature Fusion.
About the author
Nhu-Ngoc Dao is an assistant professor at the Department of Computer Science and Engineering, Sejong University, Seoul, Korea. He received his M.S. and Ph.D. degrees in computer science at the School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea, in 2016 and 2019, respectively. He received the B.S. degree in electronics and telecommunications from the Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam, in 2009. Prior to joining Sejong University, he was a visiting researcher with the University of Newcastle, Callaghan, NSW, Australia, in 2019 and a postdoc researcher with the Institute of Computer Science, University of Bern, Switzerland, from 2019 to 2020. He was a visiting professor at Chung-Ang University from 2023 to 2024. He is currently an editor of the ICT Express, Scientific Reports, and PLOS ONE journals. Dr. Dao is a senior member of IEEE and a member of ACM.
Vinh Truong Hoang received the master’s degree from the University of Montpellier, in 2009, and the Ph.D. degree in computer science from the University of the Littoral Opal Coast, France. He is currently an assistant professor and the head of the Image Processing and Computer Graphics Department, Ho Chi Minh City Open University, Vietnam. His research interests include image analysis and feature selection.
Fadi Dornaika received the M.S. degree in signal, image, and speech processing, and the Ph.D. degree in computer science from the Grenoble Institute of Technology, Grenoble, France, in 1992 and 1995, respectively. With a diverse research background, he has held research positions in Europe, China, and Canada. Currently, based on Stanford University’s ranking, he is recognized among the top 2% of scholars, determined by his citations in career-long data updated to the end of 2022 and the impact in the single year 2022. With a prolific publication record of more than 350 papers, his contributions span the fields of computer vision and machine learning. His research focuses on areas such as deep learning, supervised learning, multiview clustering, semi-supervised learning, and graph neural networks.
Summary
This book presents selected papers from the International Conference on Explainable Intelligence in Digital Twins (EIDT 2025) held during November 12–14, at Ho Chi Minh City, Vietnam. The integration of explainable intelligence in digital twins offers significant advantages, enhancing transparency, trust, and informed decision-making across various industries, including manufacturing, healthcare, transportation, and smart cities. By combining real-time data with AI-driven analytics, digital twins enable predictive maintenance, process optimization, transparent communication, and system automation. While these technologies have immense potential to revolutionize digital ecosystems, they also pose significant challenges, such as bias in AI models, scalability of explainable methods, and trade-offs between performance and interpretability. To address these critical issues, the International Conference on Explainable Intelligence in Digital Twins (EIDT) was established as a dedicated platform for research scholars and engineers to discuss, innovate, and collaborate on advancing transparent, interpretable, and trustworthy AI-driven digital twins. EIDT fosters cutting-edge research and real-world applications that drive the next generation of explainable intelligence in digital twin networks and systems.
