A Hybrid CNN- GRU Approach with Transfer Learning for Advanced Waste Classification in Support of Environmental Sustainability

Waste classification remains pivotal to environmental sustainability along with proper waste management. Traditional approaches such as CNNs and LSTM networks prove to be inadequate in properly capturing the spatial and temporal correlations in waste images. To cover for this, the study puts forward a new waste classification strategy that leverages CNNs, GRUs and transfer learning for increased classification performance. It is worth mentioning that proposed approach relies on CNNs for the spatial feature extraction, GRUs for temporal sequence learning and transfer learning for utilizing pre-trained models for both feature extraction and sequential learning. The proposed framework is developed in Python and tested on the waste classification dataset with accuracy of 97% which is superior to the traditional CNN (89%) and LSTM ($\mathbf{9 2 \%}$). This result underlines the capability of applying the transfer learning of convolution neural network (CNN-GRU) that has the capability to develop an effective framework for waste classification efficiently. The research finds that use of such techniques enhances development of AI based solutions towards efficient waste management and environment protection.