Kumud Verma, Ankur Chaudhary

DOI: https://doi.org/10.5281/zenodo.23810565

Waste management requires the effective prediction and classification of waste in order to aid in effective planning and sustainable decision making. As the waste streams can be heterogeneous in nature and can exhibit different kinds of data patterns, there cannot always be an optimal model to predict or classify every kind of waste stream. In this study, we have performed a comparative analysis between various statistical and machine learning models used in waste management. Traditional time-series models like Error-Trend-Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) have been compared with modern machine learning techniques like Random Forest (RF), XGBoost, LSTM networks, and a CNN VGG16 transfer learning technique used for image classification of wastes. The effectiveness of the different models has been measured using common error measurement metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Area Under the Curve (AUC). Although in cases where temporal patterns are well defined, ARIMA outperforms ETS, it is observed that machine learning models perform much better due to their ability to capture non-linear relationships in data. Hence, it is clear that the choice of models needs to be made depending on the nature of data itself.

Management of waste, Waste management models, Machine learning, Predicting and forecasting, Waste classification, Sustainable development, Statistical models, Deep learning, Transfer learning, Image recognition, Forecasting waste generation, Environmental sustainability, Management decision support systems, Intelligent waste management, Data driven modeling, Waste management in cities.

This study introduced an efficient deep learning-based waste classification system using a customized and optimized VGG-16 convolutional neural network. This model leverages transfer learning to use the learned features from large-scale datasets for effective feature extraction and classification performance. As it can be seen from the analysis of experimental results, the model demonstrates a consistent learning process characterized by an increase in training accuracy and decreasing losses. Additionally, taking into account the rather balanced nature of the selected training dataset, the model works effectively, providing appropriate prediction accuracy and consistency. These results confirm that the optimized architecture of the neural network is effective for processing multi-dimensional images and making accurate classifications. On the one hand, this experiment proves that the created waste classification algorithm demonstrates good technical properties and achieves consistent results throughout the process. On the other hand, the experiment under discussion reveals the significance and potential applications of artificial intelligence to environmental issues. As a matter of fact, automatic classification systems may minimize the need to perform such tedious, laborious and sometimes dangerous processes as manual sorting. Therefore, it is possible to use such models to improve the recycling process and introduce sustainable environmental practices to this area. In addition, it can be argued that machine learning techniques have a great potential in terms of solving various image classification problems. Specifically, in the context of the current research, it has become possible to see that the algorithm used to work with images in the discussed way is very flexible and adaptive. Since the current system is highly modular and customizable, it can be updated, expanded, and improved in the future, such as adding more classes to the training dataset. Therefore, the studies in this area can be continued further by improving existing solutions in terms of performance, efficiency, and capabilities. In conclusion, the performed experiments prove that optimized deep convolutional neural network architectures such as VGG-16 serve as the base for effective and efficient waste classification models. Overall, , the current research serves as the basis for innovative developments in the field.

[1] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” International Conference on Learning Representations (ICLR), 2015.
[2] Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
[3] C. Szegedy et al., “Going Deeper with Convolutions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, 2015.
[4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
[5] J. Deng et al., “ImageNet: A Large-Scale Hierarchical Image Database,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, 2009.
[6] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.
[7]
[8] F. Chollet, “Keras: The Python Deep Learning Library,” Astrophysics Source Code Library, 2015. M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, pp. 265–283, 2016.
[9] S. Thung and M. Yang, “Classification of Trash for Recyclability Status,” Stanford University Project Report, 2016.
[10] G. Yang and T. Thung, “Automatic Classification of Waste Materials Using Deep Learning,” Journal of Environmental Informatics, vol. 34, no. 2, pp. 1–10, 2019.
[11] M. R. Abdallah et al., “Waste Management Using Machine Learning Algorithms: A Review,” Environmental Science and Pollution Research, vol. 28, pp. 1–15, 2021.
[12] S. Sultana, M. S. Rahman, and M. S. Islam, “Waste Classification Using Convolutional Neural Networks,” International Journal of Computer Applications, vol. 178, no. 7, pp. 22–27, 2019.
[13] H. Zhang et al., “Deep Learning-Based Waste Classification for Smart Waste Management,” Sensors, vol. 21, no. 3, pp. 1–15, 2021.
[14] N. O. O. A. Alqahtani and M. M. H. Rahman, “Smart Waste Classification Using Deep Learning Techniques,” IEEE Access, vol. 9, pp. 1–10, 2021.
[15] R. Girshick, “Fast R-CNN,” IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448, 2015.
[16] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, 2016.
[17] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference on Learning Representations (ICLR), 2015.

Kumud Verma, Ankur Chaudhary (2026). Scalable Multi-Class Recognition: Optimizing Deep Convolutional Architecture (VGG-16) for High Dimensional Classification. International Journal of Computer Techniques, 13(5), 605–611. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.23810565