Journal Publications
Ahmed, A. A. M., Jui, S. J. J., Sharma, E., Ahmed, M. H., Raj, N., & Bose, A. (2024). An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables. Science of The Total Environment, 906, 167234.Â
Hossain, M. K., Rubel, M. H. K., Akbar, M. A., Ahmed, M. H., Haque, N., Rahman, M. F., ... & Hossain, K. M. (2022). A review on recent applications and future prospects of rare earth oxides in corrosion and thermal barrier coatings, catalysts, tribological, and environmental sectors. Ceramics International.
Ahmed, M.H., Seats, P., Tou, J., & Lin, L. (2022). Multi-objective planning for food production in a mountainous region: Strategic land utilization for meeting food demand and economic revitalization. Cleaner And Circular Bioeconomy, 3, 100023. doi: 10.1016/j.clcb.2022.100023.
Hossain, M. K., Raihan, G. A., Akbar, M. A., Kabir Rubel, M. H., Ahmed, M. H., Khan, M. I., ... & El-Denglawey, A. (2022). Current Applications and Future Potential of Rare Earth Oxides in Sustainable Nuclear, Radiation, and Energy Devices: A Review. ACS Applied Electronic Materials, 4(7), 3327-3353.
Ahmed, A. A., Ahmed, M. H., Saha, S. K., Ahmed, O., & Sutradhar, A. (2022). Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model. Stochastic Environmental Research and Risk Assessment, 1-29.
Hossain, M. K., Ahmed, M. H., Khan, M. I., Miah, M. S., & Hossain, S. (2021). Recent Progress of Rare Earth Oxides for Sensor, Detector, and Electronic Device Applications: A Review. ACS Applied Electronic Materials, 3(10), 4255-4283.
Hossain, M. K., Hossain, S., Ahmed, M. H., Khan, M. I., Haque, N., & Raihan, G. A. (2021). A Review on Optical Applications, Prospects, and Challenges of Rare-Earth Oxides. ACS Applied Electronic Materials, 3(9), 3715-3746.
Ahmed, M. H., & Lin, L. S. (2021). Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique. Journal of Hydrology, 597, 126213.