International Chair in Data Science & Explainable Artificial Intelligence

 

About International Chair in DS & XAI

The International Chair in Data Science and Explainable Artificial Intelligence is an academic initiative established in 2018 by Dong A University (UDA) through its International Research Institute for Artificial Intelligence and Data Science (IAD). Conceived from a long-term perspective, the Chair is supported by Dong A University through a 20-year funding and development commitment.

The Chair aims to provide a structured framework for research, education, student and researcher mobility, and international academic cooperation in the fields of Data Science, Applied Artificial Intelligence, Explainable Artificial Intelligence, Intelligent Systems, Digital Transformation, Smart Manufacturing, Healthcare, Education, and Innovation.

Since its establishment, the Chair has served as a transversal academic platform for the development of scientific collaborations, training activities, student exchanges, joint supervision, short courses, and international partnerships. Rather than being limited to a single discipline or research unit, it is designed to facilitate cooperation across academic fields, institutions, and countries.

The Chair also plays an important role in international academic mobility and talent development. It has supported exchange and internship opportunities for international students at Dong A University, including students from ENSAIT since 2020. At the same time, the Chair provides scholarships and structured opportunities for young Vietnamese researchers to pursue doctoral research at the University of Lille and other partner universities associated with its international network.

Beyond research, education, and mobility, the Chair supports the organization and sponsorship of international scientific conferences, workshops, seminars, and academic events. These activities contribute to strengthening scientific visibility, expanding international networks, and creating concrete opportunities for collaboration between researchers, students, universities, and industrial partners in Vietnam, France, and other countries.

In the context of the now officially recognized priority partnership between Dong A University and the University of Lille, the Chair is expected to play a strategic role in strengthening cooperation between Vietnam and France. Particular areas of cooperation include AI education, applied research, student and researcher mobility, joint certificates, doctoral pathways, co-supervision, short training programs, and collaborative academic and industrial projects.

At the present stage, the International Chair remains formally established and funded by Dong A University. A possible academic sponsorship or institutional support from the University of Lille is currently under discussion. Such sponsorship could provide a clearer institutional framework for existing and future collaborations. It would not, at this stage, imply a transfer of ownership of the Chair or a direct financial commitment from the University of Lille.

The Chair is not intended to replace existing laboratories, research units, or academic structures. Rather, it is conceived as a complementary international cooperation platform that can bring together relevant academic components, research teams, and institutional partners according to the scientific and educational objectives of each project. Potential contributors and partners include the University of Lille, ENSAIT, GEMTEX, Polytech Lille, IAE Lille, and other French and international institutions involved in future collaborative initiatives.

In the long term, one of the strategic objectives of the Chair is to contribute to the development of a joint international laboratory between Dong A University and the University of Lille and its French academic ecosystem. Such a laboratory could focus on Applied Artificial Intelligence, Explainable Artificial Intelligence, Data Science, Intelligent Systems, and Human-Centered Digital Transformation. The International Chair is therefore intended to provide an institutional and operational foundation from which progressively more ambitious joint initiatives can emerge.

Strategic Significance of the International Chair

The significance of the International Chair lies in its ability to transform academic cooperation into a long-term strategic and institutional platform. For Dong A University, the Chair is not only a research initiative but also an instrument for developing AI education, strengthening research capacity, attracting international partners, training young talent, supporting doctoral pathways, and increasing the University's academic visibility in Vietnam and internationally.

For the city of Da Nang, the Chair can contribute to positioning the city as an emerging hub for applied artificial intelligence, data science, digital transformation, smart industry, healthcare innovation, and international education. Through student exchanges, internships, scholarships, scientific events, and international collaborations, the Chair helps connect Da Nang with global academic, research, and industrial networks.

For international partners such as the University of Lille, ENSAIT, and other institutions involved in the Chair's ecosystem, the Chair provides an operational gateway to Vietnam. It offers a flexible framework for student and researcher mobility, joint certificates, co-supervision, doctoral pathways, applied research projects, short training programs, industrial collaboration, and future joint laboratory initiatives.

Its broader significance is therefore strategic. The Chair connects education, research, innovation, international mobility, scholarships, internships, scientific events, talent development, and regional development within a common long-term framework. By transforming individual collaborations into a structured institutional platform, it is designed to create concrete impact for students, young researchers, universities, companies, and society, while strengthening sustainable cooperation between Vietnam, France, and other international partners.

Ultimately, the International Chair provides a concrete foundation for future joint education programs, doctoral pathways, collaborative research initiatives, and, in the longer term, the establishment of a joint international laboratory.

Research Topics:

  • Embedded Artificial Intelligence
  • Embedded Federated Learning
  • FPGA Design for the Acceleration of AI Algorithms
  • Explainability of AI-based prediction
  • Trustworthy AI systems 
  • Explainable Anomaly Detection
  • Reinforcement Learning
  • Cybersecurity 
  • Decision Support Systems
  • Fairness of AI algorithms
  • Privacy-preserving data analytics
  • Auditing AI systems
  • Distributed/online optimization algorithms
  • Coupling optimization/simulation with AI
  • Data-driven supply chain network design
  • Wearable technologies
  • Smart Healthcare
  • Smart Manufacturing
  • Data Science
  • Digital Transformation
  • Inverse problems in medical imaging
  • Digital Twins in Healthcare
  • Digital Twin Application for Production Optimization
  • Ethical and Human-centered Artificial Intelligence
  • Fairness and ethical AI in fashion recommendation systems
  • Embedded AI for smart devices in textile manufacturing
  • Cybersecurity for smart textile manufacturing systems
  • Decision support systems for merchandisers, designers, and factory managers
  • AI for wearable technologies and smart textiles
  • Trustworthy AI for human-centered fashion innovation

Organizational structure:

Honorary Head & Chair Holder

 Senior Assoc. Prof. Kim Phuc TRAN

  University of Lille, ENSAIT, GEMTEX, France

-  Google Scholar:  https://scholar.google.fr/citations?hl=en&user=uGv7zzQAAAAJ
-  Personal webpage: https://www.gemtex.fr/gemtex-members/phuc-tran/

   Biography 

Dr. habil. Kim Phuc Tran, PhD, Senior Member of IEEE, is a French–Vietnamese senior expert in Explainable Artificial Intelligence (XAI), scientific advisor, and venture builder, with over 17 years of experience supporting industrial innovation, research evaluation, and decision-support systems across Europe, Asia, and broader international ecosystems.

He is currently a Senior Associate Professor (HDR) in Artificial Intelligence and Data Science at ENSAIT – University of Lille (France) and a senior researcher at the GEMTEX laboratory. Since 2017, he has also served as Senior Scientific Advisor at Dong A University and the International Research Institute for Artificial Intelligence and Digital Transformation (IAD), Vietnam, where he contributes to long-term AI research, innovation, and technology-transfer strategies at the interface between Europe and Southeast Asia.

In 2018, Dr. Tran was elected President of the Permanent External Scientific Advisory Board of Dong A University, positioning him at the highest strategic level of research and innovation governance. In this role, he oversees research quality, international partnerships, and strategic alignment with European and Asian industrial, regulatory, and societal priorities.

Dr. Tran is widely recognized as an independent expert and evaluator for major national and international research and innovation programs in France, Belgium, Canada, Israel, and other advanced research ecosystems. These activities span public research agencies, national funding bodies, doctoral–industrial collaboration schemes, and strategic university initiatives. In this capacity, he is regularly entrusted with the evaluation of complex and high-risk AI projects, assessing whether ambitious scientific and technical visions can realistically translate into deployable, trustworthy, and economically viable technologies under real-world industrial, regulatory, and market constraints.

Through these expert roles, he has developed a rare panoramic understanding of what differentiates investable and scalable AI technologies from purely academic prototypes, across multiple national innovation systems and funding cultures.

In parallel, Dr. Tran has accumulated extensive hands-on industrial experience as a long-term AI expert and strategic advisor for multiple European industrial groups, technology-oriented SMEs, and innovation-driven organizations operating in sectors such as advanced manufacturing, smart textiles, industrial analytics, and software-intensive systems. His contributions have supported the deployment of AI-driven analytics, explainable decision-support systems, quality monitoring solutions, and data-centric industrial platforms in real operational environments.

Alongside his research, evaluation, and industrial advisory activities, Dr. Tran has built a strong and sustained record in doctoral and postdoctoral supervision. To date, he has supervised and co-supervised more than 20 PhD candidates and postdoctoral researchers across Europe and Asia, covering topics such as Explainable and Trustworthy AI, federated and edge intelligence, industrial analytics, hybrid (physics–data) modeling, and AI-driven decision-support systems. Among them, 12 researchers have successfully completed their training and have since assumed key academic and strategic industrial positions, including appointments as Associate Professors, Directors of AI and Data Science, and senior technical leaders within large international industrial groups and technology-driven organizations. This track record reflects his ability to mentor researchers not only toward scientific excellence, but also toward leadership roles at the interface of research, industry, and strategic decision-making.

Beyond expert evaluation and industrial advisory, Dr. Tran is the Founder and Scientific Director of the International Chair in Data Science & Explainable AI (XAI Chair) at Dong A University. Conceived as a startup-oriented innovation platform inspired by European Industry 5.0 models and adapted to the fast-growing Asian innovation landscape, the XAI Chair leverages his dense European industrial network and strong Asian academic–industrial connections to generate applied XAI technologies, intellectual property, spin-offs, and cross-regional innovation pipelines connecting Europe and Asia with investors.

With over 100 peer-reviewed publications, multiple edited volumes with leading international publishers, and leadership roles in multi-million-euro collaborative research and innovation projects, Dr. Tran combines deep technical authority in Explainable and Trustworthy AI with proven experience in expert evaluation, cross-regional industrial collaboration, doctoral mentorship, and venture-oriented innovation.

His core mission is to transform explainable and trustworthy AI into deployable, credible, and investable technologies, aligned with the regulatory, industrial, and human constraints of both European and Asian markets.

Head of Chair & Scientific Director

- Assist. Prof. Kim Duc TRAN

Kim Duc Tran is currently Permanent Director of International Research Institute for Artificial Intelligence and Digital Transformation (IAD), and lecturer at Dong A University (UDA), Vietnam. He completed his doctoral training and received his Ph.D. from the MADIS-631 Graduate School, GEMTEX & ENSAIT, University of Lille. From 2018 until now, he has been the Local chair of the International Chair in Data Science and Explainable Artificial Intelligence. During his career, Kim Duc Tran has published over 30 papers in international journals and the proceedings of international conferences.

Permanent External Scientific Advisory Commission Members: 

  1. Prof. Cédric HEUCHENNE (HEC, University of Liège, Belgium)
  2. Prof. Xianyi ZENG (ENSAIT, GEMTEX National Laboratory, University of Lille, France)
  3. Prof. Damien SOULAT (ENSAIT, GEMTEX National Laboratory, University of Lille, France)
  4. Prof. Bruno AGARD (Department of Mathematics and Industrial Engineering, Ecole Polytechnique de Montréal, Canada)
  5. Prof. Hongmei HE (University of Salford, United Kingdom)
  6. Prof. Sidharta GAUTAMA (University of Ghent, Belgium)
  7.  Prof. Narayanaswamy BALAKRISHNAN (McMaster University, Canada)
  8.  Prof. Jaafar GABER (Université de Technologie de Belfort-Montbéliard (UTBM) FEMTO-ST Institute (CNRS UMR 6174), France)
  9.  Prof. Frederic VANDERHAEGEN (University of Polytechnic, Haute-de-France, France)
  10.  Prof. Abdessamad KOBI (University of Anger, France)
  11.  Prof. Rozenn Ravallec (University of Lille, France)
  12.  Senior Assoc. Prof. Laëtitia ROUX ( University of Lille, France)

Senior Members:

  1. Prof. Slim HAMMADI (Centrale Lille Institut France, Lab CRISTAL/OSL CNRS, and co-leader of the ELODI Chair)
  2.  Prof. Ludovic KOEHL (GEMTEX National Laboratory, University of Lille, France)
  3. Prof. Giovanni CELANO (University of Catania, Italy)
  4. Prof. Pascal BRUNIAUX (IAD, Dong A University, Vietnam)
  5. Prof. Thi Le Hoa VO (University of Rennes, France). 
  6. Prof. Arne JOHANNSSEN (Harz University of Applied Sciences, Germany)
  7.  Prof. Sébastien THOMASSEY (ENSAIT, GEMTEX National Laboratory, University of Lille, France) 
  8. Prof. Michael Khoo Boon CHONG (Universiti Sains Malaysia, Malaysia)
  9. Prof. Salah BOURENNANE (École Centrale Méditerranée, France)
  10. Prof. Hind BRIL EL HAOUZ (University of Lorraine, France)
  11.  Assoc. Prof. Mai Huong BUI (Ho Chi Minh City University of Technology – Vietnam National University Ho Chi Minh City)
  12.  Assoc. Prof. Ramla SADDEM (EiSINE School of Industrial and Digital Sciences Engineering, University of Reims Champagne-Ardenne (URCA) in France)
  13.  Assoc. Prof. Yingquian ZHANG (Eindhoven University of Technology, Netherland)
  14.  Assoc. Prof. Sondes CHAABANE (Univ. of Polytechnic , Haut-de-France, France)
  15.  Assist. Prof. Rehmat Ullah KHAN (School of Technologies, Cardiff Metropolitan University, UK)
  16.  Assist. Prof. Wei Lin TEOH (School of Mathematical and Computer Sciences at Heriot-Watt University Malaysia (HWUM), Malaysia)
  17. Assist. Prof. Athanasios ‘Sakis’ RAKITZIS (University of Piraeus, Greece, University of the Aegean)
  18.  Assoc. Prof. Zhenglei HE (Automation and Intelligent Manufacturing at the State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, China)
  19.  Dr. Ali RAZA (Honda Research Institute EU, Germany)
  20.  Dr. Moussab ORABI (Rosenberger Group, Germany)

Members:

  1. Prof. Khanh NGUYEN (Tarbes (ENIT), INP Toulouse, France)
  2.  Assoc. Prof. Guillaume TARTARE (ENSAIT, GEMTEX National Laboratory, University of Lille, France)
  3. Assoc. Prof. Aamir SAGHIR (Mirpur University of Science and Technology (MUST), Mirpur-10250(AJK), Pakistan)
  4. Assoc. Prof. Van Tan VU (University of Transport and Communications, Hanoi, Vietnam)
  5. Assist. Prof. Hai Canh VU (Université de Technologie de Compiègne, Compiègne, France)
  6. Assist. Prof. Thi Hien NGUYEN (Laboratoire AGM, UMR CNRS 8088, CY Cergy Paris Université, 95000 Cergy, France)
  7.  Assist. Prof. Asma AMDOUNI (Tunis University, Tunisia)
  8. Assist. Prof. Robab AFSHARI (University of Zanjan, Iran) 
  9.  Dr. Huu Du NGUYEN (Hanoi University of Science and Technology, Vietnam).
  10. Dr. Adel Ahmadi NADI (University of Waterloo, Canada)
  11. Dr. Muhammad IMRAN (Guangzhou University, China)
  12. Dr. Seyedeh Azadeh Fallah MORTEZANEJAD (Ferdowsi university of Mashhad, Iran)
  13. Dr. Quoc Thong NGUYEN (ISAT, Institut Supérieur de l’Automobile et des Transport, France )
  14. Dr. Fatima Sehar ZAIDI (Guangzhou University, China)
  15. Dr. Di SHA (Jiangsu Provincial Research Base for Food Safety, Jiangsu, China; Institute for Food Safety Risk Governance, Taiwan.)
  16. Dr. Thi Thuy Duong PHAM (IAD, Dong A University, Vietnam)
  17. PhD. Student Thi Thuy Van NGUYEN  (HEC, University of Liège, Belgium; ENSAIT, GEMTEX, the University of Lille France, IAD, Dong A University, Vietnam)
  18.  PhD. Student Phuong Bac TA (Soongsil University, Korea; IAD, Dong A University, Vietnam)
  19. PhD. Student Thu Ha DO (Université de Technologie de Compiègne, France; IAD, Dong A University)
  20. PhD. Student Hoang Nguyen LE (Department of Electrical - Electronics, IAD, Dong A University, Vietnam)
  21. PhD. Student Phuong Hanh TRAN (HEC, University of Liège, Belgium)
  22. PhD. Student. Viet Hieu TRAN (IT Dept. & IAD, Dong A University, Vietnam)
  23.  PhD. Student. Minh Anh LUONG (IT Dept. & IAD, Dong A University, Vietnam)
  24. MPH. MD. Tho NGUYEN (IAD, Dong A University, Vietnam)
  25. PhD. Student. Nguyen Anh LUONG (Dong A University, Vietnam)
  26.  PhD. Student Dac Hieu NGUYEN (IAD, Dong A University, Vietnam)
  27.  PhD. Student. Long Hai LE (EEE Dept. & IAD, Dong A University, Vietnam)
  28.  Eng. Thi Thu Huyen HO (National Chung Cheng University, Taiwan)
  29.  Pharm. Tan Bao Minh NGUYEN (IAD, Dong A University, Vietnam)

International Academic Exchange Program

  • 7/2019 - 1/2020: Dr. Huu Du Nguyen conducted a project: Multi-State models: inference and applications as a postdoc researcher under the supervision of Senior Assoc. Prof. Kim Phuc Tran and Prof. Cédric Heuchenne at University of Lille.
  • 1/2020 - 3/2021: Dr. Quoc Thong Nguyen carried out a project: Smart manufacturing with Artificial intelligence, IoT, and Big Data as a postdoc researcher under the supervision of Senior Assoc. Prof. Kim Phuc Tran and Prof. Cédric Heuchenne at University of Lille.
  • 4/2021-3/2022: Dr. Quoc Thong Nguyen conducted a project: AI and Machine Learning For Fashion Industry under supervisors Prof. Sébastien Thomassey and Senior Assoc. Prof. Kim Phuc Tran.
  • 5/2021-5/2022: Dr. Adel NADI conducted a project: Explainable Machine Learning for Anomaly Detection with Applications under supervisor Senior Assoc. Prof. Kim Phuc Tran.
  • 10/2022 - 9/2023: Engineer Thu Ha Do conducted the project: Digital Fashion with AI as a postdoc researcher at University of Lille under the supervision of Prof. Xianyi Zeng and Senior Assoc. Prof. Kim Phuc Tran.
  • 9/2022 - 9/2025: MS. Thuy Van Nguyen worked at University of Lille as a PhD. Student under the supervision of Assoc. Prof. Kim Phuc Tran and Prof. Cédric Heuchenne on the Project: Anomaly detection in IoT Multivariate Time Series data with Statistical and Machine Learning techniques.
  • 01/2024 - 2028: MS. Eng. Hoang Le Nguyen works at University of Lille as a PhD. Student under the supervision of Assoc. Prof. Kim Phuc Tran on the Project: Edge Artificial Intelligence for Smart Factory Applications in Industry 5.0. His PhD funding: Dong A University - International Research Institute for Artificial Intelligence and Data science - scholarship program for excellent lecturers-researchers at XAI &DS chair

Student Exchange Program:

Between Dong A University, Vietnam & University of Lille, France

  • 03/2023 - 05/2023 : Lukas VIALE, Emma PENDOLINO and Jeanne BONDONI

Research project:  "How to use ChatGPT in fashion" under the supervision of MBs. Eng. Kim Duc TRAN, MB. Tho NGUYEN, PhD. Student. Thu Ha DO and Dr. Ali RAZA. 

  • 01/2024 - 05/2024: Victoire MARCHEMIN, Apsara CHEA, Thelma DOMINICI, Eugénie DUFFAUT, Emma Tronel.

Research project: "How to use ChatGPT in Fashion" under the supervision of PhD. Student. Kim Duc TRAN, MB. Tho NGUYEN

  • 01/2025 - 05/2025: Elisa Casals, Lilla SORREL, Alix EMPISSE, Clémentine CHANTRY, Emma  LESCIEUX, Jeanne FONTIER, Louise OGIEZ, Capucine COSTES
  1. Research project: "Responsible Artificial Intelligence-Driven Decision Support Systems for Supply Chain Management in the Textile Industry" supervized by PhD. Student. Minh Anh Luong and PhD. Student. Kim Duc TRAN
  2. Research project: "The Role of Artificial Intelligence in Transforming Human Resources Management within the Textile Industry: Opportunities, Challenges, and Ethical Considerations in the Age of Industry 5.0" supervised by PhD. Student. Nguyen Anh LUONG and PhD. Student. Kim Duc TRAN
  3. Research project: "How ChatGPT Can Be Used for Fashion?" supervised by MPH. Tho NGUYEN
  4. Research project: "H&M Personalized Fashion Recommendations" supervised by PhD. Student. Kim Duc TRAN and MSc. Dac Hieu NGUYEN
  • 05/2025 - 07/2025: Alice DILIGENT and Olivia GONZALEZ. 

Research project: "Artificial intelligence and textile : Fashion Image-Based Product Recommendation system" supervised by PhD. Student. Kim Duc TRAN and MSc. Dac Hieu NGUYEN

  • 06/2025 - 07/2025: Joana MAIANI, Kim-Anh LE, Louis LACASSAGNE, Louise MORATILLE, Maëliss POULAT.
  1. Research project: "Predicting Diabetes with Machine Learning Methods" supervised by MPH. Tho NGUYEN
  2. Research project: "Identify customer segments based on the overall buying behavior of the client" supervised by PhD. Student. Kim Duc TRAN
  3. Research project: "Fashion Item Recognition using Deep Learning" supervised by MPH. Tho NGUYEN
  • 06/2025 - 08/2025: Louis Debret. 

Research project: "Fashion Item Recognition using Deep Learning" supervised by MPH. Tho NGUYEN

  • 01/2025 – 05/2026: Emilie DEKERLE, Zoé DENEUX, Floriane PHILIPPE, Zoé COQUART, Jinane BENZAKOUR, Fiona CAPON, Simon DEGRES, Victor METAYER
  1. Research project: “Fashion Retrieval & Similarity Search for Design”, supervised by Assist. Prof. Kim Duc TRAN & MPH. Tho NGUYEN
  2. Research project: “Sentiment Analysis and Review-based Recommendation System for Silk Product on E-Commerce Platform”, supervised by Assist. Prof. Kim Duc TRAN & MPH. Tho NGUYEN
  3. Research project: “Demand Forecasting & Production Planning Optimization for Textiles”, supervised by Assist. Prof. Kim Duc TRAN & MPH. Tho NGUYEN
  4. Research project: “Fabric defect detection with Computer Vision”, supervised by Assist. Prof. Kim Duc TRAN & MPH. Tho NGUYEN

Milestones 

Publications

1. Journals

2025

  1. J. W. Teoh, W. L. Teoh, M. B. C. Khoo, K. P.  Tran, M. H. Lee. (2025). An integrated optimal-GICP design for the SPRT control chart with estimated process parameters based on the average number of observations to signal, Quality Technology & Quantitative Management 22 (1), 84-104
  2. J. W. Teoh, W. L. Teoh, X. L. Hu, K. P. Tran, D. G. Godase. (2025). A new omnibus SPRT chart for monitoring process mean and variability based on the average number of observations to signal, Journal of Statistical Computation and Simulation 95 (1), 49-69.
  3.  J. Ivars, K. P. Tran, A. R. Labanieh, D. Soulat. (2025). Reliability modeling of tensile properties in recycled carbon fibers: a comparative fit of statistical distributions pre-and post-textile processing, Materials Today Communications, 111818
  4.  T. T. Van Nguyen, C. Heuchenne, K. D. Tran, G. Tartare, K. P. Tran. (2025). SVDD control charts based on MEWMA technique for monitoring compositional data, Computers & Industrial Engineering 201, 110865
  5.  J. A. Bairaktaris, A. Johannssen, K.P. Tran (2025). Security strategies for AI systems in Industry 4.0, Quality and Reliability Engineering International 41 (2), 897-915.
  6.  T. Q. D Pham, K. D. Tran, K. T. P. Nguyen, X. V. Tran, K. P. Tran. (2025). A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning, arXiv preprint arXiv:2503.05725

2024

1. Z. He, Z. Lu, X. Wang, Q. Xiong, K.P. Tran, S. Thomassey, X. Zeng, M. Hong, and Y. Man. (2024). Multiobjective Optimization of Papermaking Wastewater Treatment Processes under Economic, Energy, and Environmental Goals, Environmental Science & Technology, https://doi.org/10.1021/acs.est.4c03460

2. S. Rita, Q.T. Nguyen, A.L. Sandra, K.P. Tran, S. Thomassey. (2024). Evaluating the sales potential of new products using Machine Learning techniques and data collected from mobile applications, International Journal of Clothing Science and Technology, https://doi.org/10.1108/IJCST-07-2023-0099

3. Teoh, J. W., Teoh, W. L., Khoo, M. B., K. P. Tran, & Lee, M. H. (2024). An integrated optimal-GICP design for the SPRT control chart with estimated process parameters based on the average number of observations to signal. Quality Technology & Quantitative Management, 1-21. 

4. M. Orabi, K.P. Tran, S. Thomassey, P. Egger. (2024). Anomaly Detection in Smart Manufacturing: An Adaptive Adversarial Transformer based Model, Journal of Manufacturing Systems,   https://doi.org/10.1016/j.jmsy.2024.09.021

5. F. S. Zaidi, Hong-Liang Dai, Muhammad Imran, Kim Phuc Tran. (2024). Enhanced Hybrid LSTM and SLAR Modeling for In-Depth Analysis of Temporal and Spatial Patterns in Compositional Data for Environmental Monitoring, Process Safety and Environmental Protection..

6. M. Imran, H.L. Dai, F.S; Zaidi, X. Hu, K.P. Tran, and J. Sun.  (2024). Analyzing Out-of-Control Signals of T2 Control Chart for Compositional Data using Artificial Neural Networks, Expert Systems With Applications.

7. S. A. F. Mortezanejad, R. Wang, G. M. Borzadaran, R. Ding, K. P. Tran. (2024). Profile Control Chart based on Maximum Entropy: Accepted-December 2024, REVSTAT-Statistical Journal.

2023

  1.  M. Imran, H.L. Dai, F.S; Zaidi, X. Hu, K.P. Tran, Z. Abbas, & H. Z. Nazir. (2023). Incorporating principal component analysis into Hotelling T2 control chart for compositional data monitoring. Computers & Industrial Engineering, 109755.
  2. H. D. Nguyen, H. L. Nguyen,  N. H. Kieu, V. H. Nguyen,  T. H. Truong, & K. P. Tran. (2023). Trans-Lighter: A light-weight federated learning-based architecture for Remaining Useful Lifetime prediction. Computers in Industry, 148, 103888.
  3. A. Raza, K. P. Tran, L. Koehl, & S. Li. (2023). AnoFed: Adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description. Engineering Applications of Artificial Intelligence, 121, 106051.
  4. A. A. Nadi, B. S. Gildeh, J. Kazempoor, K. D. Tran, & K. P. Tran (2023). Cost-effective optimization strategies and sampling plan for Weibull quantiles under type-II censoring. Applied Mathematical Modelling, 116, 16-31.
  5. F.S. Zaidi, H.L Dai, M.  Imran, K.P. Tran. (2023). Analyzing Abnormal Pattern of Hotelling T2 Control Chart for Compositional Data using Artificial Neural Networks, Computers & Industrial Engineering, 109254.
  6. F. S. Zaidi, H.L Dai, M.  Imran, K.P. Tran. (2023). Monitoring Autocorrelated Compositional Data Vectors using an Enhanced Residuals Hotelling T2 Control Chart, Computers & Industrial Engineering.
  7.  A. Saghir, X. Hu, K.P. Tran, Z. Song. (2023). Optimal design and evaluation of adaptive EWMA monitoring schemes for Inverse Maxwell distribution, Computers & Industrial Engineering.

 2022

  1. H. D. Nguyen, K. P. Tran, S.Thomassey & M. Hamad. (2021). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management, 57, 102282.
  2. H. D. Nguyen, A. A. Nadi, K. D. Tran, P. Castagliola, G. Celano, & K. P. Tran. (2022). The Shewhart-type RZ control chart for monitoring the ratio of autocorrelated variables. International Journal of Production Research, 1-26.
  3. H. T. Truong, B. P. Ta, Q. A. Le, D. M. Nguyen, C. T. Le, H. X. Nguyen, H.T. Do, H.T. Nguyen & K. P. Tran. (2022). Light-weight federated learning-based anomaly detection for time-series data in industrial control systems. Computers in Industry, 140, 103692.
  4. A. Raza, K. P. Tran, L. Koehl & S. Li. (2022). Designing ECG monitoring healthcare system with federated transfer learning and explainable AI. Knowledge-Based Systems, 236, 107763.
  5. Z. He, K. P. Tran, S. Thomassey, X. Zeng, J. Xu & C. Yi. (2022). Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning. Journal of Manufacturing Systems, 62, 939-949.
  6.  A. Raza, S. Li , K. P. Tran, L. Koehl, S. Li & K. Ludovic. ​(2022). Detection of Poisoning Attacks with Anomaly Detection in Federated Learning for Healthcare Applications: A Machine Learning Approach. arXiv preprint arXiv:2207.08486
  7.   R. Afshari, A. A. Nadi, A. Johannssen, N. Chukhrova & K. P. Tran. (2022). The effects of measurement errors on estimating and assessing the multivariate process capability with imprecise characteristic. Computers & Industrial Engineering, 108563.

 2021

  1. T. H. Truong, P. B. Ta, M. L. Dao, D. L. Tran, M. D. Nguyen, A.Q. Le, T. C. Le, D. T. Bui &  K. P. Tran. (2021). Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach. Computers in Industry, 132, 103509.
  2. K. D. Tran, A. A. Nadi, T. H. Nguyen & K. P. Tran. (2021). One-sided Shewhart control charts for monitoring the ratio of two normal variables in short production runs. Journal of Manufacturing Processes, 69, 273-289.
  3. H. D. Nguyen, K. P. Tran & K. D. Tran. (2021). The effect of measurement errors on the performance of the Exponentially Weighted Moving Average control charts for the Ratio of Two Normally Distributed Variables. European Journal of Operational Research, 293(1), 203-218.
  4. Z. He, K. P. Tran, S. Thomassey, X.Zeng, J.Xu & C. Yi. (2021). A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Computers in Industry, 125, 103373.
  5. T. H. Truong, P. B. Ta, M. L. Dao, T. D. Bui,  D. L. Tran &  K. P. Tran. (2021). Lockedge: Low-complexity cyberattack detection in iot edge computing. IEEE Access, 9, 29696-29710.
  6. Q. T. Nguyen, V. Giner-Bosch, K. D. Tran, C. Heuchenne & K. P. Tran. (2021). One-sided variable sampling interval EWMA control charts for monitoring the multivariate coefficient of variation in the presence of measurement errors. The International Journal of Advanced Manufacturing Technology, 115(5), 1821-1851.
  7. A. Raza, K. P. Tran, L. Koehl, S. Li, X. Zeng, & K. Benzaidi. (2021). Lightweight Transformer in Federated Setting for Human Activity Recognition. arXiv preprint arXiv:2110.00244.

2020

  1. H. D. Nguyen, A. A. Nadi, K. D. Tran, P. Castagliola, G. Celano, & K. P. Tran. (2022). The Shewhart-type RZ control chart for monitoring the ratio of autocorrelated variables. International Journal of Production Research, 1-26.
  2. H. D. Nguyen, K. P. Tran, G. Celano, P. E. Maravelakis, and P. Castagliola. (2020). On the effect of the measurement error on Shewhart t and EWMA t control charts. The International Journal of Advanced Manufacturing Technology, Vol 107, no. 9 (2020): 4317-4332
  3. H. D. Nguyen, and K. P. Tran, Effect of the measurement errors on two one‐sided Shewhart control charts for monitoring the ratio of two normal variables. Quality and Reliability Engineering International, Vol 36, no. 5 (2020): 1731-1750
  4. P. H. Tran, C. Heuchenne, H. D. Nguyen, and H. Marie, "Monitoring coefficient of variation using one-sided run rules control charts in the presence of measurement errors.", Journal of Applied Statistics, Vol 48, issue 12 (2020): 2178-2204

2019

  1. K.P. Tran, H.D. Nguyen, P.H. Tran, C. Heuchenne, ”On the Performance of CUSUM control charts for monitoring the Coefficient of Variation with Measurement Errors”, The International Journal of Advanced Manufacturing Technology, Vol 104 (2019): 5-8, 1903-1917
  2. Q. T. Nguyen, K. P. Tran, P. Castagliola, G. Celano, and S. Lardjane, “One-sided synthetic control charts for monitoring the multivariate coefficient of variation.”, Journal of Statistical Computation and Simulation, Vol 89, no. 10 (2019): 1841-1862
  3. H. D. Nguyen, K. P. Tran, and H. L. Heuchenne, "CUSUM control charts with variable sampling interval for monitoring the ratio of two normal variables.", Quality and Reliability Engineering International, Vol 36, no. 2 (2019): 474-497
  4. H. D. Nguyen, K. P. Tran, and T. N. Goh, "Variable sampling interval control charts for monitoring the ratio of two normal variables.", Journal of Testing and Evaluation, Vol 48, no. 3 (2019): 2505-2529
  5. H. D. Nguyen, and E. Gouno, "Maximum likelihood and Bayesian inference for common-cause of failure model.", Reliability Engineering and System Safety, Vol 182 (2019): 56-62, ISSN 0951-8320
  6. H. D. Nguyen, Q. T. Nguyen, K. P. Tran, and D. P. Ho, "On the performance of VSI Shewhart control chart for monitoring the coefficient of variation in the presence of measurement errors.", The International Journal of Advanced Manufacturing Technology, Vol 104, no. 1 (2019): 211-243
  7. K. P. Tran, H. D. Nguyen, P. H. Tran, and C. Heuchenne, "On the performance of CUSUM control charts for monitoring the coefficient of variation with measurement errors.", The International Journal of Advanced Manufacturing Technology, Vol 104, no. 5 (2019): 1903-1917
  8. P.H. Tran, K.P. Tran, and A.C. Rakitzis, “A Synthetic median control chart for monitoring the process mean with measurement errors”, Quality and Reliability Engineering International, Vol 35, issue 4 (2019): 1100-1116
  9. Q. T. Nguyen, K. P. Tran, H. L. Heuchenne, T. H. Nguyen, and H. D. Nguyen, "Variable sampling interval Shewhart control charts for monitoring the multivariate coefficient of variation.", Applied Stochastic Models in Business and Industry, Vol 35, issue 5 (2019): 1253-1268
  10. H. D. Nguyen, K. P. Tran, and C. Heuchenne, "Monitoring the ratio of two normal variables using variable sampling interval exponentially weighted moving average control charts.", Quality and Reliability Engineering International, Vol 35, issue 1 (2019): 439-460
  11.  K. P. Tran, P. Castagliola, T. H. Nguyen, and A. Cuzol, "Design of a variable sampling interval EWMA median control chart", International Journal of Reliability, Quality and Safety Engineering, Vol. 26, no. 5 (2019): 1950021

2. Chapters 

2025

  1. A. Saghir, K. D. Tran, K. P. Tran. (2025). Explainable Machine Learning based Control Charts for High-Dimensional Non-Stationary Time Series Data in IoT Systems: Challenges, Methods, and Future Directions, Computational Techniques for Smart Manufacturing in Industry 5.0: Methods and Application, 166 184. DOI:10.1201/9781003399322-7

2024

  1.  H. C. Vu, K. D. Tran, V. H. Tran, K. P. Tran. (2024), Predictive Maintenance Optimization Based on Genetic Algorithms for Future Industrial Systems, Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges, (25-47), Springer Nature Switzerland.
  2.  M. Orabi, K. P. Tran, S. Thomassey, P. Egger. (2024), Anomaly Detection for Catalyzing Operational Excellence in Complex Manufacturing Processes: A Survey and Perspective, Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges, (49-78), Springer Nature Switzerland.
  3.  D. H Nguyen, T. H. Nguyen, K. D. Tran, K. P. Tran. (2024), Physics-Informed Machine Learning for Industrial Reliability and Safety Engineering: A Review and Perspective, Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges, (5-23), Springer Nature Switzerland.
  4. LH Nguyen, KD Tran, X Zeng, KP Tran. (2024). Human-Centered Edge Artificial Intelligence for Smart Factory Applications in Industry 5.0: A Review and Perspective, Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges, (79-100), Springer Nature Switzerland. 
  5.  T. H. Nguyen, A. Saghir, K. D. Tran, D. H. Nguyen, N. A. Luong, K. P. Tran. (2024). Safety and Reliability of Artificial Intelligence Systems, Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges, (185-199), Springer Nature Switzerland. 
  6. K. P. Tran. (2024). Introduction to Artificial Intelligence for Safety and Reliability Engineering, Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges, (185-199), Springer Nature Switzerland. 
  7.  T. Nguyen, D. H. Nguyen, Q. T. Nguyen, K. D. Tran, K. P. Tran. (2024) Human-Centered Edge AI and Wearable Technology for Workplace Health and Safety in Industry 5.0, Artificial Intelligence for Safety and Reliability Engineering: Methods, Applications, and Challenges,  (171-183), Springer Nature Switzerland.

     

2023

  1. H. D. Nguyen, and K. P. Tran. (2023), "Artificial Intelligence for Smart Manufacturing in Industry 5.0: Methods, Applications, and Challenges". Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges, 5-33. Springer, https://doi.org/10.1007/978-3-031-30510-8_2
  2. H. D. Nguyen, P. H. Tran, T. H. Do, and K. P. Tran. (2023), Quality Control for Smart Manufacturing in Industry 5.0. In Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges, 35-64. Cham: Springer International Publishing, https://doi.org/10.1007/978-3-031-30510-8_3
  3. P. H. Tran, H. D. Nguyen, C. Heuchenne, and K. P. Tran. (2023), Monitoring Coefficient of Variation Using CUSUM Control Charts. In Springer Handbook of Engineering Statistics (pp. 333-360). London: Springer London, https://doi.org/10.1007/978-1-4471-7503-2_18
  4. T. Nguyen, K. D. Tran, A. Raza, Q. T. Nguyen, H. M. Bui, and K. P. Tran. (2023), Wearable Technology for Smart Manufacturing in Industry 5.0. In Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges (pp. 225-254). Cham: Springer International Publishing, https://doi.org/10.1007/978-3-031-30510-8_11
  5. T. P. Bac, D. T. Ha, K. D. Tran, and K. P. Tran. (2023), Explainable Articial Intelligence for Cybersecurity in Smart Manufacturing. In Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges (pp. 199-223). Cham: Springer International Publishing, https://doi.org/10.1007/978-3-031-30510-8_10
  6. D. T. Ha, T. P. Bac, K. D. Tran, and K. P. Tran. (2023), Efficient and Trustworthy Federated Learning-Based Explainable Anomaly Detection: Challenges, Methods, and Future Directions. In Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges (pp. 145-166). Cham: Springer International Publishing, https://doi.org/10.1007/978-3-031-30510-8_8

2022

  1. P. H. Tran, A. Ahmadi Nadi, T. H. Nguyen, K. D. Tran & K. P. Tran. (2022). Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective. In Control Charts and Machine Learning for Anomaly Detection in Manufacturing (pp. 7-42). Springer, Cham.
  2. H. D. Nguyen, K. P. Tran, P. Castagliola & F. M. Megahed. (2022). Enabling Smart Manufacturing with Artificial Intelligence and Big Data: A Survey and Perspective. In Advanced Manufacturing Methods (pp. 1-26). CRC Press.

3. Conferences 

  1. TTV Nguyen, KP Tran, C Heuchenne, G Tartare. (2024). Poster" Anomaly Detection for IoT multivariate time series data with statistical and machine learning techniques, JRDMA 2024". 10th edition of the Journée Régionale des Doctorants en sciences de la Mer et de l’Automatique (JRDMA).
  2.  A. Saghir, H. Beniwal, K. D. Tran, A. Raza, L. Koehl, X. Zeng & K. P. Tran. (2024). Explainable Transformer-Based Anomaly Detection for Internet of Things Security Check for updates. The Seventh International Conference on Safety and Security with IoT: SaSeIoT 2023.
  3. T. H. Do, X. H. Nguyen, V. H. Nguyen, H. D. Nguyen, T. H. Truong & K. P. Tran. (2022, June). Explainable Anomaly Detection for Industrial Control System Cybersecurity. Proceedings of The IFAC 10th conference on MANUFACTURING MODELING, MANAGEMENT AND CONTROL, Nantes, France.
  4. F. Ouedraogo, C. Heuchenne, Q. T. Nguyen & H. Tran. (5–9 Sept, 2021). Data-Driven Approach for Credit Card Fraud Detection with Autoencoder and One-Class Classification Techniques, Proceedings of the Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems (APMS 2021), Nantes, France, Part I, https://doi.org/10.1007/978-3-030-85874-2_4
  5. K.P. Tran H. D. Nguyen, Q. T. Nguyen, and W. Chattinnawat. (2018) One-Sided synthetic control charts for monitoring the Coefficient of Variation with Measurement Errors, Proceedings of The IEEE International Conference on Industrial Engineering and Engineering Management, Bangkok, Thailand.
  6. K.P. Tran, P. Castagliola, T.H. Nguyen and A. Cuzol. (2018). The Efficiency of the VSI Exponentially Weighted Moving Average Median Control Chart. 24nd ISSAT International Conference on Reliability and Quality in Design. Toronto, Ontario, Canda.

4. Books

K.P. Tran (Ed.). (2024). Artificial Intelligence for Safety and Reliability Engineering (1st ed). Springer.

K.P. Tran (Ed.). (2023). Artificial Intelligence for Smart Manufacturing: Methods, Applications, and Challenges (1st ed). Springer.

K.P. Tran (Ed.). (2022). Machine Learning and Probabilistic Graphical Models for Decision Support Systems (1st ed.). CRC Press.

K.P. Tran (Ed.). (2021). Control Charts and Machine Learning for Anomaly Detection in Manufacturing (1st ed.). Springer.

 

 

Embedded Artificial Intelligence Platforms

   Federated learning-based anomaly detection testbed

  Light-weight federated learning-based anomaly detection for time-series data in industrial control systems

Federated learning-based anomaly detection is developed and studied by the collaboration of Future Internet Laboratory – HUST, IAD – UDA, and University of Lille – France.

Main goal is to design an AD system (FATRAF) that has a fast training time and is light weight to accommodate frequent learning update, while still either retaining the same or improving the detection performance in comparison with some existing AD solutions for ICSs in the literature.

Federated learning (FL)-based anomaly detection architecture was proposed a new hybrid solution of Autoencoder, Transformer and Fourier transform operating in a chain to enhance the detection performance, whilst reducing the training time and being more lightweight for more feasible deployment over edge devices of an IIoT-based industrial control system.

 

Figure 1. Federated learning (FL) – based anomaly detection architecture (FATRAF)

FATRAF comprises two main components:

  • Edge sites: In an IIoT-based smart factory monitored by industrial control systems, there are various manufacturing zones, in which sensor systems are installed to gather readings that signify operating states over time. Subsequently, the time-series data, as the local data, is transmitted wirelessly to edge devices in the vicinity of the corresponding manufacturing zones. Designated to monitor anomalies, these edge devices employ the local data as inputs for training its own local anomaly detection model - ATRAF (AE-Transformer-Fourier learning model). This deployment allows detecting anomalies timely right at the edge sites, making use of the computing capacity of edge devices, and distributing heavy computation tasks that could overload the cloud server.

Figure 2. ATRAF learning model operation during the testing process

  • Cloud Server: The cloud server undertakes two primary functions: system initialization and aggregation of local models sent from different edges. The whole process of model aggregation and global model updating down to all local models are called Federated Learning.

The procedure of FATRAF can be summarized into the following key stages:

  1. System Initialization: At the beginning, the cloud server establishes a global model with specific learning parameters and sending it to each edge device of corresponding edge site.
  2. Local Training: After receiving the initial configuration, by utilizing the on-site data collected from sensors, edge devices conduct a local training process for their Autoencoder blocks. Subsequently, at each edge site, the local training data is fed into the encoder part of the trained Autoencoder model so as to obtain corresponding embeddings or code sequences. These code sequences are then applied to train the Transformer-Fourier block.
  3. Local Model Update: After the local training, each edge device sends the learnable parameters of the Transformer-Fourier block wtr+1 to the cloud server for aggregation. Specifically, wtr+1 includes weight and bias matrices from WQ, WK, WV, WZ nodes in the Masked Self-attention sublayer and all linear transformations in the Position-wise Feedforward sublayer along with Final Feedforward Network.
  4. Model Aggregation: After deriving trained weights wtr+1 from all edge clients, the cloud server federates them and constructs a new global model version.
  5. Global Update: Finally, the cloud server broadcasts back the new configuration wt+1 to each edge device as to update the local models of Transformer-Fourier block. In the subsequent learning rounds, edge devices use the updated models to continue training and the communication process will repeat in order to optimize the local Transformer-Fourier models until the global learning model converges.

Essential devices for testbed in Federated-learning mode, the experiment is conducted using:

  1. CPU Intel Core i9-12900K
  2. GPU ROG STRIX RTX3090
  3. Edge device: raspberry pi 4 (quantify: 05) 

 

Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

Federated Transfer Learning and Explainable AI is developed and studied by the collaboration of ENSAIT, GEMTEX Laboratory – University of Lille, IAD – UDA, and School of Computing & Institute of Cyber Security for Society (iCSS), University of Kent, UK.

 

Figure 1.  Architecture of Federated Learning

 

Figure 2.  An overview of the proposed framework

 

Figure 3.  The architecture of the proposed denoising autoencoder

 

Figure 4. The architecture of the proposed denoising autoencoder

 

 

Figure 5. Overview of the proposed XAI module in our framework

Figure 6. Federated learning architecture in Healthcare system (i.e ECG)

Besides, we apply FL architecture in Heathcare (i.e. ECG) system. First phase, we develop methodology based on sample data. Then, we tend to collect data and extend our system by using IoT sensor (i.e. ECG) in person.

Figure 7. ECG signal displayed in IoT device

FPGA Platform Based on Xilinx Zynq UltraScale and MPSoC XCZU15EG FPGA Development Board

We intend this as a first step toward building a future FPGA-focused HPC community and ecosystem in Vietnam and the EU. We will run training events and develop materials to ensure accessibility. The physics-based testbed at EPCC's Advanced Compute Facility is publicly available. 

Figure 8. ALINX AXU15EG FPGA

Federated learning model, collects data on engine temperature, vibration and sound to anomaly detection occurring to the engine

 

Contact

Location: Room 309, Floor 3, Dong A University, 33 Xo Viet Nghe Tinh street, Hai Chau district, Da Nang city, Viet Nam

Email:

    - Honorary head & Chair holder:

    Senior Associate Professor Kim Phuc Tran: kimphuc.tran@univ-lille.fr

    - Head of Chair & Scientific Director: 

    Assist. Prof. Kim Duc Tran: ductk@donga.edu.vn

    - Head of the international Unit of IAD, Coordinator & Researcher:

    MPH. Tho Nguyen:  thon@donga.edu.vn

 

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