Smart waste classification in IoT-enabled smart cities using VGG16 and Cat Swarm Optimized random forest
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.
Authors
- Razaz Waheeb Attar (ORCID: https://orcid.org/0000-0001-9819-7945)
- Akshat Gaurav (ORCID: https://orcid.org/0000-0002-5796-9424)
- Brij B. Gupta (ORCID: https://orcid.org/0000-0003-4929-4698)
- Kwok Tai Chui (ORCID: https://orcid.org/0000-0001-7992-9901)
- Varsha Arya (ORCID: https://orcid.org/0000-0001-7549-4429)
- Shavi Bansal
- Ahmed Alhomoud (ORCID: https://orcid.org/0000-0003-2782-3106)
Institutions
- Chandigarh University (IN)
- Princess Nourah bint Abdulrahman University (SA)
- Northern Border University (SA)
- Asia University (TW)
- China Medical University (TW)
- Korea University (KR)
- Symbiosis International University (IN)
- Kyung Hee University (KR)
- China Medical University Hospital (TW)
- Ronin Institute (US)
- University of Business and Technology (SA)
- University of Petroleum and Energy Studies (IN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1371/journal.pone.0316930
- Citations
- 6
- Primary Topic
- Municipal Solid Waste Management
- Type
- article
- Field-Weighted Citation Impact
- 5.98