MSW-Net: A hierarchical stacking model for automated municipal solid waste classification

References

• Adedeji, O., and Z. Wang. 2019. Intelligent waste classification system using deep learning convolutional neural network. Procedia Manuf. 35:607–12. doi:10.1016/j.promfg.2019.05.086.
   Google Scholar
• Ahmad, K., K. Khan, and A. Al-Fuqaha. 2020. Intelligent fusion of deep features for improved waste classification. IEEE Access 8:96495–504. doi:10.1109/ACCESS.2020.2995681.
   Web of Science ®Google Scholar
• Ahuja, A. Solid waste management in India: The great garbage challenge | Waste management. NDTV-Dettol Banega Swasth Swachh India. Last Modified August 1, 2022. Accessed February 23, 2024. https://swachhindia.ndtv.com/solid-waste-management-in-india-the-great-garbage-challenge-69667/.
   Google Scholar
• Appalanaidu, M. V., and G. KumaraVelan. 2023. Rice plant nutrient deficiency classification using modified MOBILENET convolutional neural network. Int. J. Model. Simul. Sci. Comput. 14 (1):2243003. doi:10.1142/S1793962322430036.
   Google Scholar
• Aral, R. A., Ş. R. Keskin, M. Kaya, and M. Hacıömeroğlu. 2018. Classification of trashnet dataset based on deep learning models. 2018 IEEE International Conference on Big Data (Big Data), 2058–62, IEEE, December. doi:10.1109/BigData.2018.8622212.
   Google Scholar
• Bchir, O., S. Alghannam, N. Alsadhan, R. Alsumairy, R. Albelahid, and M. Almotlaq. 2021. Computer vision based Polyethylene Terephthalate (PET) sorting for waste recycling. Int. J. Adv. Comput. Sci. Appl. 12 (10). doi:10.14569/IJACSA.2021.0121069.
   Google Scholar
• Biswas, A., and S. Parida. 2021. Waste-wise cities: Best practices in municipal solid waste management. In Centre for science and environment and NITI Aayog, ed. S. Banerjee, A. Shankar, A. A. Parrey and A. Jain, 1–246. New Delhi: Centre for Science and Environment.
   Google Scholar
• Bobulski, J., and M. Kubanek. 2021. Deep learning for plastic waste classification system. Appl. Comput. Intell. Soft Comput. 2021:1–7. doi:10.1155/2021/6626948.
   Web of Science ®Google Scholar
• Centre for Science and Environment. 2023. The State of India’s environment report 2023, vol. 1, 1st ed. New Delhi: Down to Earth.
   Google Scholar
• Elnadi, B., and A. Rifaat. 2023. Interview with Jacques-Yves Cousteau. The UNESCO Courier. Accessed February 23, 2024. Last Modified. 20 April 2023. https://courier.unesco.org/en/articles/interview-jacques-yves-cousteau-0.
   Google Scholar
• Emami, S., and G. Martínez-Muñoz. 2023a. A gradient boosting approach for training convolutional and deep neural networks. IEEE Open Journal of Signal Processing. doi:10.1109/OJSP.2023.3279011.
   Google Scholar
• Emami, S., and G. Martínez-Muñoz. 2023b. Sequential training of neural networks with gradient boosting. IEEE Access. doi:10.1109/ACCESS.2023.3271515.
   Google Scholar
• Gupta, Y. S., and S. Mukherjee. 2022. An ensembling approach for efficient waste classification. 2022 IEEE Silchar Subsection Conference (SILCON), 1–6, IEEE, November. doi:10.1109/SILCON55242.2022.10028950.
   Google Scholar
• Gyawali, D., A. Regmi, A. Shakya, A. Gautam, and S. Shrestha. 2020. Comparative analysis of multiple deep CNN models for waste classification. arXiv preprint arXiv:2004.02168.
   Google Scholar
• Huang, G. L., J. He, Z. Xu, and G. Huang. 2020. A combination model based on transfer learning for waste classification. Concurr. Comput. Pract. Exper. 32 (19):e5751. doi:10.1002/cpe.5751.
   Web of Science ®Google Scholar
• Kaya, Y., and E. Gürsoy. 2023. A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection. Soft Comput. 27 (9):5521–35. doi:10.1007/s00500-022-07798-y.
   PubMed Web of Science ®Google Scholar
• Kaza, S., L. Yao, P. Bhada-Tata, and F. Van Woerden. 2018. What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank Pub. 1329–30. doi:10.1596/978-1-4648-.
   Google Scholar
• Liang, S., and Y. Gu. 2021. A deep convolutional neural network to simultaneously localize and recognize waste types in images. Waste Manage. 126:247–57. doi:10.1016/j.wasman.2021.03.017.
   PubMed Web of Science ®Google Scholar
• Liu, L., X. Wang, Q. Bao, and X. Li. 2023. Behavior detection and evaluation based on multi-frame MobileNet. Multimedia Tools Appl. 1–18. doi:10.1007/s11042-023-16150-x.
   Web of Science ®Google Scholar
• Loganayagi, S., and D. Usha. 2023. An automated approach to waste classification using deep learning. 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), 01–10, IEEE, February. doi:10.1109/ICECCT56650.2023.10179743.
   Google Scholar
• Majchrowska, S., A. Mikołajczyk, M. Ferlin, Z. Klawikowska, M. A. Plantykow, A. Kwasigroch, and K. Majek. 2022. Deep learning-based waste detection in natural and urban environments. Waste Manage. 138:274–84. doi:10.1016/j.wasman.2021.12.001.
   PubMed Web of Science ®Google Scholar
• Malik, M., S. Sharma, M. Uddin, C. L. Chen, C. M. Wu, P. Soni, and S. Chaudhary. 2022. Waste classification for sustainable development using image recognition with deep learning neural network models. Sustainability 14 (12):7222. doi:10.3390/su14127222.
   Web of Science ®Google Scholar
• MoHUA conducts SBM-U 2.0 planning & implementation review-cum-workshop. Accessed February 23, 2024. Last modified 2023. https://pib.gov.in/PressReleasePage.aspx?PRID=1938520.
   Google Scholar
• Mookkaiah, S. S., G. Thangavelu, R. Hebbar, N. Haldar, and H. Singh. 2022. Design and development of smart internet of things–based solid waste management system using computer vision. Environ. Sci. Pollut. Res. 29 (43):64871–85. doi:10.1007/s11356-022-20428-2.
   PubMed Web of Science ®Google Scholar
• Municipal solid waste | Global trash solutions. Global Trash Solutions. Accessed February 23, 2024. Last Modified July 14, 2022. https://globaltrashsolutions.com/waste-types/municipal-solid-waste/.
   Google Scholar
• Nurahmadan, I. F., R. M. Arjuna, H.D. Prasetyo, P. A. Hogantara, I. N. Isnainiyah, and R. Wirawan. 2021. A mobile based waste classification using mobilenets-v1 architecture. 2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 279–84, IEEE, October. doi:10.1109/ICIMCIS53775.2021.9699161.
   Google Scholar
• Rava Zolnikov, T. 2023. Stop treating waste pickers like garbage: An autoethnography on informal waste picking in Brazil. Glob. Public Health 18 (1):2201328. doi:10.1080/17441692.2023.2201328.
   PubMed Web of Science ®Google Scholar
• Ruiz, V., Á. Sánchez, J. F. Vélez, and B. Raducanu. 2019. Automatic image-based waste classification. In From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II 8, 422–31. Springer International Publishing. doi:10.1007/978-3-030-19651-6_41.
   Google Scholar
• Sajdl, D. Green issues - What are the benefits of environmental management. Grin. Accessed February 23, 2024. Last Modified October 21, 2005. https://www.grin.com/document/45690.
   Google Scholar
• Shi, C., C. Tan, T. Wang, and L. Wang. 2021. A waste classification method based on a multilayer hybrid convolution neural network. Appl. Sci. 11 (18):8572. doi:10.3390/app11188572.
   Google Scholar
• Thanawala, D., A. Sarin, and P. Verma. 2020. An approach to waste segregation and management using convolutional neural networks. In Advances in Computing and Data Sciences: 4th International Conference, ICACDS 2020, Valletta, Malta, April 24–25, 2020, Revised Selected Papers 4, 139–50. Springer Singapore. doi:10.1007/978-981-15-6634-914.
   Google Scholar
• Vo, A. H., M. T. Vo, T. Le, and T. Le. 2019. A novel framework for trash classification using deep transfer learning. IEEE Access 7:178631–39. doi:10.1109/ACCESS.2019.2959033.
   Google Scholar
• Wang, C., J. Qin, C. Qu, X. Ran, C. Liu, and B. Chen. 2021. A smart municipal waste management system based on deep-learning and internet of things. Waste Manag. 135:20–29. doi:10.1016/j.wasman.2021.08.028.
   PubMed Web of Science ®Google Scholar
• Wang, J. Z., N. H. Lu, W. C. Du, K. Y. Liu, S. Y. Hsu, C. Y. Wang, and Y. H. Huang, L. -C. Chang, W. -H. Twan, T. -B. Chen, Y. -H. Huang. 2023. Classification of color fundus photographs using fusion extracted features and customized CNN models. Healthcare 11 (15):2228). MDPI. August. doi:10.3390/healthcare11152228.
   Web of Science ®Google Scholar
• Waste Classification Data v2. Kaggle. Accessed February 23, 2024. Last Modified November 29, 2019. https://www.kaggle.com/datasets/sapal6/waste-classification-data-v2/.
   Google Scholar
• World Bank Group. Global waste to grow by 70 percent by 2050 unless urgent action is taken: World Bank report. World Bank. Accessed February 23, 2024. Last Modified September 24, 2018. https://www.worldbank.org/en/news/press-release/2018/09/20/global-waste-to-grow-by-70-percent-by-2050-unless-urgent-action-is-taken-world-bank-report.
   Google Scholar
• Yang, Z., Y. Bao, Y. Liu, Q. Zhao, H. Zheng, and Y. Bao. 2023. Research on deep learning garbage classification system based on fusion of image classification and object detection classification. Math. Biosci. Eng. 20 (3):4741–59. doi:10.3934/mbe.2023219.
   PubMed Web of Science ®Google Scholar
• Zhang, Q., Q. Yang, X. Zhang, Q. Bao, J. Su, and X. Liu. 2021. Waste image classification based on transfer learning and convolutional neural network. Waste Manage. 135:150–57. doi:10.1016/j.wasman.2021.08.038.
   PubMed Web of Science ®Google Scholar
• Zheng, H., and Y. Gu. 2021. Encnn-upmws: Waste classification by a CNN ensemble using the UPM weighting strategy. Electronics 10 (4):427. doi:10.3390/electronics10040427.
   Web of Science ®Google Scholar