PUBLICATION

A. Journal

1. Huynh, T. N. T., Bui, H. D., Nguyen, T. N. T., & Trinh, T. D. Enhancing prediction of ride-hailing fares using advanced deep learning techniques. New Trends in Computer Sciences, 3(1), 64–82 (2025). https://doi.org/10.3846/ntcs.2025.23932 

2. Vuong, P.H., Phu, L.H., Van Nguyen, T.H. et al. A comparative study of deep learning approaches for stock price prediction. Digital Finance (2025). https://doi.org/10.1007/s42521-025-00149-0 (Scopus)

3. Vu, P.C.L.T., Bao, P.T. & Trinh, T.D. Block-recurrent visual transformer for enhanced human detection in thermal imaging. Machine Vision and Applications 36, 88 (2025). https://doi.org/10.1007/s00138-025-01711-x (ISI/Scopus)

4. Trinh, D., Minh, T. N. T., Tuan, L. N., Ai, N. L., Quoc, P. T., & Khanh, N. N. H. (2025). Detecting Referee Whistle Sounds in Soccer Videos Using Machine Learning Models. Journal of Data Science and Artificial Intelligence, 4(1). 

5. Nguyen, T. N. T., Trinh, T. D., Vu, P. C. L. T., & Bao, P. T. (2024). Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam. Journal of Environmental Engineering and Landscape Management, 32(4), 292–304. https://doi.org/10.3846/jeelm.2024.22361 (ISI/ Scopus)

6. Pham, H., Lam, H., Duy, L., Pham, T., & Trinh, T. (2024). An improved convolutional recurrent neural network for stock price forecasting. IAES International Journal of Artificial Intelligence (IJ-AI), 13(3), 3381-3394. (Scopus)

7. T. D. Trinh, P. C. L. T. Vu, and P. T. Bao, "Comparing Mask R-CNN backbone architectures for human detection using thermal imaging," International Journal of Electrical and Computer Engineering, Article vol. 14, no. 4, pp. 3962-3970, 2024, doi: 10.11591/ijece.v14i4.pp3962-3970. (Scopus)

8. Dang, L. T. A., Ngoc, V. D., Thien Vu, P. C. L., Truong, N. N., & Trinh, T. D. (2024). Vietnam Vehicle Number Recognition Based on an Improved CRNN with Attention Mechanism. International Journal of Intelligent Transportation Systems Research, 1-16. (ISI/Scopus)

9. Vuong, P. H., Phu, L. H., Van Nguyen, T. H., Duy, L. N., Bao, P. T., & Trinh, T. D. (2024). A bibliometric literature review of stock price forecasting: from statistical model to deep learning approach. Science Progress, 107(1), 00368504241236557. (ISI/Scopus)

10. Tam, N. D., Truong, N. N., Vu, P. C. L. T., & Dat, T. T. (2023). An application of Vietnamese handwriting text recognition for information extraction from high school admission form. IAES International Journal of Artificial Intelligence, 12(2), 568. (Scopus)

11. Dat, T. T., Truong, N. N., Vu, P. C. L. T., Sang, V. N. T., & Vuong, P. T. (2022). An improved CRNN for Vietnamese Identity Card Information Recognition. Computer Systems Science & Engineering, 40(2). (ISI/Scopus)

12. Vuong, P. H., Dat, T. T., Mai, T. K., & Uyen, P. H. (2022). Stock-price forecasting based on XGBoost and LSTM. Computer Systems Science & Engineering, 40(1). (ISI/Scopus)

13. Dat, T. T., Le Thien Vu, P. C., Truong, N. N., Anh Dang, L. T., Thanh Sang, V. N., & Bao, P. T. (2021). Leaf recognition based on joint learning multiloss of multimodel convolutional neural networks: a testing for Vietnamese herb. Computational Intelligence and Neuroscience, 2021(1), 5032359. (ISI/Scopus)

14. Dat, T. T., Dang, L. T. A., Sang, V. N. T., Thuy, L. N. L., & Bao, P. T. (2021). Convolutional recurrent neural network with attention for Vietnamese speech to text problem in the operating room. International Journal of Intelligent Information and Database Systems, 14(3), 294-314. (Scopus)

15. Thuy, L. N. L., Trinh, T. D., Anh, L. H., Kim, J. Y., Hieu, H. T., & Bao, P. T. (2021). Coronary Vessel Segmentation by Coarse‐to‐Fine Strategy Using U‐nets. BioMed Research International, 2021(1), 5548517. (ISI/Scopus)

16. Tran, H. P., Tran, L. N. H., Dang, H. T., Vu, T. D., Trinh, D. T., Pham, B. T., & Sang, V. N. T. (2020). A SWOT analysis of human-and machine learning-based embryo assessment. IEEE Access, 8, 227466-227481. (ISI/Scopus)

17. Trinh, T. D., Tran, T. B., Thuy, L. N. L., Shimizu, I., & Kim, J. Y. (2019). Coronary vessel segmentation by coarse-to-fine strategy using Otsu algorithm and decimation-free directional filter bank. Journal of IKEEE, 23(2), 557-570. (KCI)

18. Shahid, N., Yu, G. H., Trinh, T. D., Sin, D. S., & Kim, J. Y. (2019). Real-time implementation of human detection in thermal imagery based on CNN. Journal of the Korean Institute of Information Technology, Vol. 17, No. 1 (JKIIT, Vol.17, No.1), 107-121. (KCI)

19. Trinh, T. D., & Kim, J. Y. (2018). Multi-scale Pedestrian Detection in Thermal Imaging Using Deep Convolutional Neural Network and Adaptive NMS. Journal of the Korean Institute of Information Technology, Vol. 16, No. 9 (JKIIT, Vol.16, No.9), 85-94. (KCI)

20. Trinh, T. D., Ma, X., Kim, J. Y., & Kim, H. G. (2017). Enhanced speaker verification using an adaptive multiple low-rank representation based on the modified adaptive Gaussian mixture model framework. Cluster Computing, 20, 2333-2347. (ISI/Scopus)

21. Ma, X., Trinh, T. D., & Kim, J. Y. (2017). Comparison of Observation Confidence Estimators for Robust Speaker Verification. Journal of the Korean Institute of Information Technology, Vol. 15, No. 7 (JKIIT, Vol.15, No.7), 119-130. (KCI)

22. Trinh, T. D., Ma, X., & Kim, J. Y. (2017). Improved Running Gaussian Average for Background Subtraction in Thermal Imagery. Journal of the Korean Institute of Information Technology, Vol. 15, No. 7 (JKIIT, Vol.15, No.7), 101-117. (KCI)

23. Trinh, T. D., Ma, X., Lee, H. J., Kim, J. Y., & Choi, S. H. (2016). Cheering Event Detection in Basketball Audio Stream Using Adaptive GMM Model and Low Rank Matrix Recovery. Journal of the Korean Institute of Information Technology, Vol. 14, No. 10 (JKIIT, Vol.14, No.10), 87-96. (KCI)

24. Bui, N. N., Trinh, T. D., Park, M. K., & Kim, J. Y. (2016). Histogram of multi-directional Gabor filter bank for motion trajectory feature extraction. International Journal of Computers, 1.

25. Trinh, T. D., Kim, J. Y., Choi, S. H., & Cho, K. (2016). Robust speaker verification using low-rank matrix recovery and weighted sparse representation under total variability space. Journal of the Korean Institute of Information Technology, Vol. 14, No. 3 (JKIIT, Vol.14, No.3), 59-69. (KCI)

26. Trinh, T. D., Park, M. K., Kim, J. Y., Lee, K. R., Choi, S. H., & Cho, K. (2015). A modified adaptive GMM approach based GMM supervector and i-vector using NMF decomposition for robust speaker verification. Journal of the Korean Institute of Information Technology, Vol. 13, No. 7 (JKIIT, Vol.13, No.7), 117-125. (KCI)

27. Bui, N. N., Kim, J. Y., & Trinh, T. D. (2014). A non-linear GMM KL and GUMI kernel for SVM using GMM-UBM supervector in home acoustic event classification. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 97(8), 1791-1794. (ISI/Scopus)

28. Tan Dat Trinh , Jin Young Kim , Pham The Bao (2014) "Enhanced Face Recognition by Fusion of Global and Local Features under Varying Illumination," Journal of the Korea Institute of Information Technology, Vol. 12, No. 12 (JKIIT, Vol.12, No.12),57 - 67. ( KCI)

29. Trinh, T. D., Bui, N. N., Min, S. H., & Kim, J. Y. (2013). Audio event classification using SVM with GMM-UBM supervectors. Journal of the Korea Institute of Information Technology, Vol. 11 No. 11 (JKIIT, Vol.11, No.11), 91-98. (KCI)

30. Đạt, T. T. (2013). Dùng đặc trưng Gabor kết hợp Adaboost và k-means trong bài toán nhận dạng mặt người. Tạp chí Khoa học, (43), 77. 

B. Conference paper

1. Tan-Dat Trinh, Thanh-Sang Vu-Ngoc, Lam-Thuy Le-Nhi, Duy-Dong Le, Thai-Binh Nguyen, and The-Bao Pham. 2024. Violence Detection in Videos based on CNN feature for ConvLSTM2D. In Proceedings of the 5th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR '24). Association for Computing Machinery, New York, NY, USA, 33–36. https://doi.org/10.1145/3643488.3660306

2. Thuy, L. N. L., Sang, V. N. T., Bao, P. T., & Trinh, T. D. (2023, November). Invasive Ductal Carcinoma Classification from Whole Slide Image Based on BRISQUE and Convolutional Neural Networks. In International Conference on Future Data and Security Engineering (pp. 580-589). Singapore: Springer Nature Singapore. (Scopus)

3. Ma, X., Trinh, T. D., Kim, J. Y., & Kim, H. G. (2016). Speaker verification using a modified adaptive GMM approach based on low rank matrix recovery. In Mobile and Wireless Technologies 2016 (pp. 109-116). Springer Singapore. (Scopus)

4. Dat, T. T., Kim, J. Y., Kim, H. G., & Lee, K. R. (2015, August). Robust speaker verification using low-rank recovery under total variability space. In 2015 5th International Conference on IT Convergence and Security (ICITCS) (pp. 1-4). IEEE.

5. Trinh, T. D., Park, M. K., Kim, J. Y., Lee, K. R., & Cho, K. (2015). Enhanced speaker verification using GMM-supervector based modified adaptive GMM training. In Mobile and Wireless Technology 2015 (pp. 147-153). Springer Berlin Heidelberg. (Scopus)

6. Trinh, T. D., Kim, J. Y., & Na, S. Y. (2014, October). Enhanced face recognition by fusion of global and local features under varying illumination. In 2014 International Conference on IT Convergence and Security (ICITCS) (pp. 1-4). IEEE. (Scopus)

7. Dat, T. T., & Kim, J. Y. (2013). Audio Event Recognition Using GMM Supervector Based SVM. 2013 Electronic Engineering Society of Korea Summer Symposium, 666-669.