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Title	Smart Waste Classification In Iot-Enabled Smart Cities Using Vgg16 And Cat Swarm Optimized Random Forest
ID_Doc	51714
Authors	Gaurav A.; Gupta B.B.; Arya V.; Attar R.W.; Bansal S.; Alhomoud A.; Chui K.T.
Year	2025
Published	PLoS ONE, 20, 2 February
DOI	http://dx.doi.org/10.1371/journal.pone.0316930
Abstract	Effective waste management is becoming a crucial component of sustainable urban development as smart technologies are used by smart cities more and more. Smart trash categorization systems provided by IoT may greatly enhance garbage sorting and recycling mechanisms. In this context, this work presents a waste categorization model based on transfer learning using the VGG16 model for feature extraction and a Random Forest classifier tuned by Cat Swarm Optimization (CSO). On a Kaggle garbage categorization dataset, the model outperformed conventional models like SVM, XGBoost, and logistic regression. With an accuracy of 85% and a high AUC of 0.85 the Random Forest model shows better performance in precision, recall, and F1-score as compared to standard machine learning models. © 2025 Gaurav et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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