Mehedi, Md Humaion KabirArafin, IrfanaHasan, MuradRahman, FarhinTasin, RufaidaRasel, Annajiat Alim2026-07-262026-07-262023-01-01M. H. K. Mehedi, I. Arafin, M. Hasan, F. Rahman, R. Tasin and A. A. Rasel, "A Transfer Learning Approach For Efficient Classification of Waste Materials," 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2023, pp. 0636-0640, doi: 10.1109/CCWC57344.2023.10099127.97983503328652-s2.0-85156250932https://hdl.handle.net/10361/28640The authors of this study have used the Waste Classification Dataset to build a highly accurate model that classified rubbish into two distinct groups in an effort to address the problem of waste classification for various classes of discarded material. VGG16, MobileNetV2, and a baseline 6 layer CNN model are used in the experiments. The VGG16 model have achieved 96.00% accuracy, while the MobileNetV2 model achieved 95.51 %, and the baseline CNN model achieved 90.61 % accuracy. The garbage in the input picture can be correctly classified by the neural network model. The experimental findings are compared to other studies in the same area. In addition, LIME is also implemented to make our models's prediction more explainable. This investigation's experimental applications are geared on facilitating more precise trash classification.636-640en-USCNNDeep learningMobileNetV2Transfer learningVGG16Waste classificationRefuse and refuse disposal.Waste management.Deep learning (Machine learning).Conference Proceeding10.1109/CCWC57344.2023.10099127