Optimizing solid waste classification using deep learning and grey wolf optimizer for recycling efficiency
Yazarlar (3)
Y. Eryeşil Selçuk Üniversitesi, Türkiye
H. Kahramanli Örnek Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Environmental Science and Technology (Q3) | ||
| Dergi ISSN | 1735-1472 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2026 |
| Kabul Tarihi | 29-10-2025 | Yayınlanma Tarihi | 27-11-2025 |
| Cilt / Sayı / Sayfa | 23 / 1 / 44–0 | DOI | 10.1007/s13762-025-06834-2 |
| Makale Linki | https://link.springer.com/article/10.1007/s13762-025-06834-2 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Solid waste management is important for environmental sustainability. Correct classification of recyclable materials plays a critical role in increasing the efficiency of recycling processes. In our research, hyperparameters of the EfficientNet model were optimized with Grey Wolf Optimizer (GWO) to increase this efficiency. In addition to hyperparameter optimization, the performance of machine learning algorithms such as Naive Bayes, Logistic Regression, and Multilayer Perceptron (MLP) was evaluated. Furthermore, the effect of these algorithms on the classification of features extracted from different deep learning models, including EfficientNet, MobileNet, and VGG, was investigated. The EfficientNet model optimized with GWO achieved the best performance with a 95.43% accuracy rate. These results showed that hyperparameter optimization applied to deep learning models increased the success in the solid waste … |
| Anahtar Kelimeler |
| Deep learning | Feature extraction | GWO | Machine learning hyperparameter optimization | Solid waste management |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 3 |
| Scopus | 3 |
| Google Scholar | 5 |