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SEFWaM–deep learning based smart ensembled framework for waste management

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Abstract

Waste generation has seen a significant surge in the last decade, presenting an urgent need for efficient and sustainable waste management strategies. The mounting piles of landfill waste underscore the criticality of segregating recyclable from non-recyclable waste, a measure that could alleviate numerous global environmental challenges. The increasing necessity to differentiate waste into specific categories such as paper, plastic, and metal is becoming evident; these categories demand distinct disposal and recycling methods. Addressing this automatic detection and segregation issue calls for an automated garbage classification system, achievable through computer vision. This paper introduces a deep learning and computer vision-based Smart Ensembled Framework for Waste Management (SEFWaM) to categorize garbage into various classes for improved waste management. This innovative model employs a fusion of transfer learning—a deep learning technique and boosting to devise an ensembled approach for efficient waste classification. Our proposed approach, trained on the Trashnet 2.0 dataset, has demonstrated superior performance over competing algorithms in terms of Weighted Macro Precision (WMP), Weighted Macro Recall (WMR), Weighted Macro F1-Score (WMF), and test accuracy. The proposed SEFWaM model achieves an accuracy of 94.2% for the considered dataset, proving its superior efficiency over other deep learning-based models. This research thereby contributes to a pressing environmental issue by offering an automated, efficient solution for waste management.

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Data availability

The datasets generated during and/or analysed during the current study are available in the Trashnet repository, https://github.com/garythung/trashnet.

References

  • Abdel-Shafy, H. I., & Mansour, M. S. M. (2018). Solid waste issue: Sources, composition, disposal, recycling, and valorization. Egyptian Journal of Petroleum, 27(4), 1275–1290. https://doi.org/10.1016/j.ejpe.2018.07.003

    Article  Google Scholar 

  • Acar, Z. Y., Başçiftçi, F., & Ekmekci, A. H. (2022). A Convolutional Neural Network model for identifying Multiple Sclerosis on brain FLAIR MRI. Sustainable Computing: Informatics and Systems, 35, 100706.

    Google Scholar 

  • Adedeji, O., & Wang, Z. (2019). Intelligent waste classification system using deep learning convolutional neural network. Procedia Manufacturing., 35, 607–612.

    Article  Google Scholar 

  • Agarap, A. F. (2018) ‘Deep Learning using Rectified Linear Units (ReLU)’, Ar**v, abs/1803.08375.

  • Ahlawat, S., & Choudhary, A. K. (2020). Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 167, 2554–2560.

    Article  Google Scholar 

  • Ali, P. J. M., Faraj, R. H., Koya, E., Ali, P. J. M., & Faraj, R. H. (2014). Data normalization and standardization: A technical report. Mach Learn Tech Rep, 1(1), 1–6.

    Google Scholar 

  • Al-Sabaawi, A. et al. (2020) ‘Amended Convolutional Neural Network with Global Average Pooling for Image Classification’, in International Conference on Intelligent Systems Design and Applications.

  • Álvarez, J. M. and Salzmann, M. (2016) ‘Learning the Number of Neurons in Deep Networks’, Ar**v, abs/1611.06321.

  • Ao, Y., et al. (2022). ‘Identifying the driving factors of rural residents’ household waste classification behavior: Evidence from Sichuan. China’, Resources, Conservation and Recycling, 180, 106159.

    Article  Google Scholar 

  • Awe, O., Mengistu, R., & Sreedhar, V. Final Report Smart Trash Net Waste Localization and Classification. ar**v 2017. preprint.

  • Ba, J., Kiros, J. R. & Hinton, G. E. (2016) ‘Layer Normalization’, Ar**v, abs/1607.06450.

  • Balakrishnan, K., Rosmi, T. B., Krishna, K. S., Sreejith, S., & Subha, T. D. (2016). Automatic Waste Segregator and Monitoring System. Journal of Microcontroller Engineering and Applications, 3(2), 1–7.

    Google Scholar 

  • Benjeddou, O., Ravindran, G., & Abdelzaher, M. A. (2023). Thermal and acoustic features of lightweight concrete based on marble wastes and expanded perlite aggregate. Buildings, 13(4), 992.

    Article  Google Scholar 

  • Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.

    Google Scholar 

  • Bhatia, V., & Rani, R. (2018). DFuzzy: A deep learning-based fuzzy clustering model for large graphs. Knowledge and Information Systems, 57(1), 159–181. https://doi.org/10.1007/s10115-018-1156-3

    Article  Google Scholar 

  • Bjorck, N., Gomes, C. P., Selman, B., & Weinberger, K. Q. (2018). Understanding batch normalization. Advances in neural information processing systems, 31.

  • Chen, T. and Guestrin, C. (2016) ‘XGBoost: A Scalable Tree Boosting System’, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

  • Chu, J. L., & Krzyżak, A. (2014). Analysis of Feature Maps Selection in Supervised Learning Using Convolutional Neural Networks. In M. Sokolova & P. van Beek (Eds.), Advances in Artificial Intelligence 27th Canadian Conference on Artificial Intelligence Canadian AI 2014 Montréal QC Canada May 6–9 2014 Proceedings. Cham: Springer.

    Google Scholar 

  • Debicha, I. et al. (2021) ‘Adversarial Training for Deep Learning-based Intrusion Detection Systems’, Ar**v, abs/2104.09852.

  • Dell’Aversana, P. (2019) ‘Deep Neural Networks With Keras Testing different Deep Neural Network parameters for classification of rock data samples’.

  • Donovan, J. (2016) ‘Auto-Trash sorts garbage automatically at the TechCrunch Disrupt Hackathon’.

  • Elkhouly, H. I., Abdelzaher, M. A., & El-Kattan, I. M. (2021). Experimental and modeling investigation of physicomechanical properties and firing resistivity of cement pastes incorporation of micro-date seed waste. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46, 1–13.

    Google Scholar 

  • Gutberlet, J., & Uddin, S. M. N. (2017). Household waste and health risks affecting waste pickers and the environment in low- and middle-income countries. International Journal of Occupational and Environmental Health, 23(4), 299–310. https://doi.org/10.1080/10773525.2018.1484996

    Article  Google Scholar 

  • Hanbal, I. F., et al. (2020). ‘Classifying wastes using random forests, gaussian naïve bayes, support vector machine and multilayer perceptron’, in IOP conference series: materials science and engineering. Institute of Physics Publishing. https://doi.org/10.1088/1757-899X/803/1/012017

    Article  Google Scholar 

  • Huang, G. et al. (2017) ‘Densely Connected Convolutional Networks’, In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269. doi: https://doi.org/10.1109/CVPR.2017.243.

  • Huang, G. L., He, J., Xu, Z., & Huang, G. (2020). A combination model based on transfer learning for waste classification. Concurrency and Computation: Practice and Experience, 32(19), e5751.

    Article  Google Scholar 

  • Huang, M. L., Chuang, T. C., & Liao, Y. C. (2022). Application of transfer learning and image augmentation technology for tomato pest identification. Sustainable Computing: Informatics and Systems, 33, 100646.

    Google Scholar 

  • Iwana, B. K., Kuroki, R. and Uchida, S. (2019) ‘Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation’,In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 4176–4185.

  • Jiao, W., Hao, X., & Qin, C. (2021). The image classification method with CNN-XGBoost model based on adaptive particle swarm optimization. Information, 12(4), 156.

    Article  Google Scholar 

  • Josephine, V. L. H., Nirmala, A. P., & Alluri, V. L. (2021). Impact of Hidden Dense Layers in Convolutional Neural Network to enhance Performance of Classification Model. IOP Conference Series: Materials Science and Engineering, 1131(1), 012007.

    Article  Google Scholar 

  • Kamal, A. H. M., Alam, M. G. R., Hassan, M. R., Apon, T. S., & Hassan, M. M. (2023). Explainable indoor localization of BLE devices through RSSI using recursive continuous wavelet transformation and XGBoost classifier. Future Generation Computer Systems, 141, 230–242.

    Article  Google Scholar 

  • Kang, Z., et al. (2020). An automatic garbage classification system based on deep learning. IEEE Access, 8, 140019–140029.

    Article  Google Scholar 

  • Kiela, D. et al. (2019) ‘Supervised multimodal bitransformers for classifying images and text’, Ar**v, abs/1909.02950.

  • Kingma, D. P. and Ba, J. (2014) ‘Adam: A Method for Stochastic Optimization’, CoRR, abs/1412.6980.

  • Lin, K., et al. (2022). Applying machine learning to fine classify construction and demolition waste based on deep residual network and knowledge transfer. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-022-02740-6

    Article  Google Scholar 

  • Lugosch, L. et al. (2019) ‘Speech model pre-training for end-to-end spoken language understanding’, Ar**v, abs/1904.03670.

  • Majchrowska, S., et al. (2022). Deep learning-based waste detection in natural and urban environments. Waste Management, 138, 274–284.

    Article  CAS  Google Scholar 

  • Malik, M., et al. (2022). Waste classification for sustainable development using image recognition with deep learning neural network models. Sustainability, 14(12), 7222.

    Article  CAS  Google Scholar 

  • Mikami, K. et al. (2018) ‘DeepCounter: Using Deep Learning to Count Garbage Bags’, 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pp. 1–10.

  • Mittal, G. et al. (2016) ‘SpotGarbage: smartphone app to detect garbage using deep learning’, Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing.

  • Muraina, I. (2022). Ideal dataset splitting ratios in machine learning algorithms: general concerns for data scientists and data analysts. In 7th International Mardin Artuklu Scientific Research Conference.

  • Nakerst, G., Brennan, J. and Haque, M. (2020) ‘Gradient descent with momentum - to accelerate or to super-accelerate?’, Ar**v, abs/2001.06472.

  • Narayana, T. (2009). Municipal solid waste management in India: From waste disposal to recovery of resources? Waste Management, 29(3), 1163–1166.

    Article  CAS  Google Scholar 

  • Peng, C., et al. (2022). Research of image recognition method based on enhanced inception-ResNet-V2. Multimedia Tools and Applications, 81, 34345–34365.

    Article  Google Scholar 

  • Rahman, M. W., et al. (2022). Intelligent waste management system using deep learning with IoT. Journal of King Saud University-Computer and Information Sciences, 34(5), 2072–2087.

    Article  Google Scholar 

  • Ren, X., et al. (2017). A novel image classification method with CNN-XGBoost model. In C. Kraetzer, Y.-Q. Shi, J. Dittmann, & H. J. Kim (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer.

    Google Scholar 

  • Schilling, F. (2016) ‘The Effect of Batch Normalization on Deep Convolutional Neural Networks’

  • Seredkin, A., et al. (2019). Development of a method of detection and classification of waste objects on a conveyor for a robotic sorting system. Journal of Physics: Conference Series, 1359, 012127.

    CAS  Google Scholar 

  • Shahab, S., Anjum, M., & Umar, M. S. (2022). Deep learning applications in solid waste management: A deep literature review. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2022.0130347

    Article  Google Scholar 

  • Shaikh, F. V. et al. (2020) ‘Waste Profiling and Analysis using Machine Learning’. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 488–492.

  • Shi, C., et al. (2021). A waste classification method based on a multilayer hybrid convolution neural network. Applied Sciences., 11(18), 8572.

    Article  CAS  Google Scholar 

  • Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 1–48.

    Article  Google Scholar 

  • Singh, A. (2019). Managing the uncertainty problems of municipal solid waste disposal. Journal of Environmental Management, 240, 259–265.

    Article  Google Scholar 

  • Singh, A. (2022). Indicators and ICTs application for municipal waste management. Waste Management & Research, 40(1), 24–33.

    Article  CAS  Google Scholar 

  • Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45, 427–437.

    Article  Google Scholar 

  • Srivastava, N., et al. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.

    Google Scholar 

  • Sun, Z., Ozay, M., & Okatani, T. (2015). Design of Kernels in Convolutional Neural Networks for Image Classification. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision–ECCV 2016 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016 Proceedings, Part VII. Cham: Springer.

    Google Scholar 

  • Tammina, S. (2019). Transfer learning using VGG-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications (IJSRP)., 9(10), 9420.

    Article  Google Scholar 

  • Thongsuwan, S., et al. (2020). ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost. Nuclear Engineering and Technology., 53(2), 522–531.

    Article  Google Scholar 

  • Vo, A. H., et al. (2019). A Novel Framework for Trash Classification Using Deep Transfer Learning. IEEE Access, 7, 178631–178639.

    Article  Google Scholar 

  • Wu, Y., Shen, X., Liu, Q., **ao, F., & Li, C. (2021). A garbage detection and classification method based on visual scene understanding in the home environment. Complexity, 2021, 1–14.

    CAS  Google Scholar 

  • **e, S. et al. (2016) ‘Aggregated Residual Transformations for Deep Neural Networks’ In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987–5995.

  • Yang, J., et al. (2021). GarbageNet: a unified learning framework for robust garbage classification. IEEE Transactions on Artificial Intelligence, 2, 372–380.

    Article  Google Scholar 

  • Yang, M., & Thung, G. (2016). Classification of trash for recyclability status. CS229 Project Report, 2016(1), 3.

    Google Scholar 

  • Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series, 1168, 022022.

    Google Scholar 

  • Yusoff, S. H., et al. (2019). Classification of different types of metal from recyclable household waste for automatic waste separation system. Bulletin of Electrical Engineering and Informatics., 8(2), 488–498.

    Article  Google Scholar 

  • Zaheer, R. and Shaziya, H. (2019) ‘A Study of the Optimization Algorithms in Deep Learning’In: 2019 Third International Conference on Inventive Systems and Control (ICISC), pp. 536–539.

  • Zhang, Z. and Sabuncu, M. R. (2018) ‘Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels’, Ar**v, abs/1805.07836.

  • Zhao, D.-E., et al. (2019). Research on garbage classification and recognition based on hyperspectral imaging technology. Guang Pu Xue Yu Guang Pu Fen **/spectroscopy and Spectral Analysis, 39, 917–922. https://doi.org/10.3964/j.issn.1000-0593(2019)03-0917-06

    Article  CAS  Google Scholar 

  • Zhou, H., et al. (2022). A deep learning approach for medical waste classification. Scientific Reports, 12(1), 2159.

    Article  CAS  Google Scholar 

  • Zhuang, F., et al. (2019). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109, 43–76.

    Article  Google Scholar 

  • Ziouzios, D., & Dasygenis, M. (2019). A smart recycling bin for waste classification. In 2019 Panhellenic Conference on Electronics & Telecommunications (PACET) (pp. 1–4). IEEE. https://doi.org/10.1109/PACET48583.2019.8956270

    Book  Google Scholar 

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  1. Department of Computer Science & Engineering, Netaji Subhas University of Technology, Delhi, India

    Sujal Goel, Anannya Mishra, Garima Dua & Vandana Bhatia

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  1. Sujal Goel
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Goel, S., Mishra, A., Dua, G. et al. SEFWaM–deep learning based smart ensembled framework for waste management. Environ Dev Sustain (2023). https://doi.org/10.1007/s10668-023-03568-4

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