Dr. Kabir received the Ph.D in Computer Science from the City University of Hong Kong under Dr. Jacky Keung. He was a faculty member in the Department of Software Engineering at Daffodil International University prior to joining AIUB. Currently, he is a postdoctoral research fellow in the Artificial Intelligence and Intelligent Systems research group at Mälardalen University, Sweden. He is an active software engineering researcher, and his research lies at the intersection of empirical software engineering and AI. Recently, he has been trying to employ the knowledge of data stream mining, statistical modeling, and empirical investigation to produce more reliable defect prediction models. His research work has been published in prestigious journals, including Information and Software Technology, Applied Soft Computing, and other leading conferences. His Ph.D research publications have been supported by software companies and Hong Kong Government research funds. He has received a prestigious postgraduate studentship for his Ph.D studies at the City University of Hong Kong from 2018 to 2021. Research Interests/ Areas: Trustworthiness of Deep Learning Models Empirical Software Engineering Software Analytics I've previously crafted innovative methods and remedies for: 1. Concept drift issue in software defect for the development of reliable models. 2. Class imbalance issue in software defect prediction 3. Oversampling technique to alleviate class imbalance issue Dr. Kabir's mission is to help the community towards engineering high-quality and secure software systems for social good. If you share the same value, please reach out for collaborations. For updates, please check - https://makabir4.github.io/ Journal Publications: 1. Elahe, M. F., Kabir, M. A., Mahmud, S. M. H., & Azim, R. (2023). Factors impacting short-term load forecasting of charging station to electric vehicle. Electronics, 12(1). (Impact Factor: 2.690) 2. Faruqui, N., Kabir, M. A., Yousuf, M. A., Whaiduzzaman, M., Barros, A., & Mahmud, I. (2023). Trackez: An IoT-based 3D-Object Tracking from 2D Pixel Matrix using Mez and FSL Algorithm. IEEE Access. (Impact Factor: 3.9) 3. Ur Rehman, A., Belhaouari, S. B., Kabir, M. A., & Khan, A. (2023). On the Use of Deep Learning for Video Classification. Applied Sciences, 13(3), 2007. (Impact Factor: 2.7) 4. Kabir, M. A., Begum, S., Ahmed, M. U., & Rehman, A. U. (2022). CODE: A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect Prediction. Symmetry, 14(12), 2508. (Impact Factor: 2.940) 5. Mahmud, M. H., Nayan, M. T. H., Ashir, D. M. N. A., & Kabir, M. A. (2022). Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques. Applied Sciences, 12(22), 11694. (Impact Factor: 2.838) 6. Kabir, M. A., Keung, J., Turhan, B., & Bennin, K. E. (2021). Inter-release defect prediction with feature selection using temporal chunk-based learning: An empirical study. Applied Soft Computing, 113, 107870. (Impact Factor: 8.263) 7. Yang, Z., Keung, J., Kabir, M. A., Yu, X., Tang, Y., Zhang, M., & Feng, S. (2021). AComNN: Attention enhanced Compound Neural Network for financial time-series forecasting with cross-regional features. Applied Soft Computing, 111, 107649. (Impact Factor: 8.263) 8. Zhang, M., Keung, J. W., Xiao, Y., & Kabir, M. A. (2021). Evaluating the effects of similar-class combination on class integration test order generation. Information and Software Technology, 129, 106438. (Impact Factor: 3.862) 9. Feng, S., Keung, J., Yu, X., Xiao, Y., Bennin, K. E., Kabir, M. A., & Zhang, M. (2021). COSTE: Complexity-based OverSampling TEchnique to alleviate the class imbalance problem in software defect prediction. Information and Software Technology, 129, 106432. (Impact Factor: 3.862) 10. Kabir, M. A., & Han, B. (2016). An improved usability evaluation model for point-of-sale systems. International Journal of Smart Home, 10(7), 269-282.