1. Parvin, K., Shuvo, E. A., Khan, W. A., Adib, S. A., Eiti, T. A., Shovon, M., & Nafiz, S. A. (2026). Computationally efficient explainable AI framework for skin cancer detection. Advances in Science, Technology and Engineering Systems Journal, 11(1), 11–24.

2. Parvin, K., Polas, M. R. H., & Irikefe, O. B. (2025). State-of-the-Art Review and Future Directions in Artificial Intelligence-Based Detection of Brain Tumors Using MRI Datasets. Journal of Applied Technology and Innovation, 9(4), 53-73.

3. Islam, K., Polas, M. R. H., Parvin, K., & Akter, T. (2023). Decoding Demographics on Generation Z’s Post-Pandemic Shopping Trends: E-Commerce Evolution 4.0, the Digital Shopper’s Dilemma, and Tailoring Strategies. International Journal of Business, Management and Economics, 4(4), 360-380.

4. Shafkat, A., Parvin, K., Yasar, S., Rahman, M. M., Mallik, B., & Mumenin, N. (2025, August). Assessing the Effectiveness of Supervised Learning Models in Predicting Risks of Diabetes and Cardiovascular Disease with Explainability Analysis. In International Conference on Data Science, AI and Applications (pp. 267-282). Cham: Springer Nature Switzerland.

5. Monalisa, N. T., Himi, S. T., Parvin, K., & Uddin, M. S. (2023). Shanketik: Revolutionizing Communication for the Deaf and Mute - A Reinforcement Learning-Enhanced Bangla AAC Application. 26th International Conference on Computer and Information Technology (ICCIT), pp. 1-5. IEEE.

6. Suity, R. A., Samin, N. H., Mallik, B., Shafkat, A., Rahman, M. M., & Parvin, K. (2025). ARMEN: Adversarially Resilient Network for Emotion Recognition from Music. IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON), pp. 271-276.