Waste Classification Using Random Forest Classifier with DenseNet201 Deep Features
Yazarlar (2)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
Doç. Dr. Abdulkadir Karacı Samsun University, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1007/978-3-031-36118-0_36 | ||
| Kongre Adı | ICCSEEA2023: The 6th International Conference on Computer Science, Engineering and Education Applications (Advances in Computer Science for Engineering and Education VI) | ||
| Kongre Tarihi | 17-03-2023 / 19-03-2023 | ||
| Basıldığı Ülke | Polonya | Basıldığı Şehir | Warsaw |
| Bildiri Linki | http://dx.doi.org/10.1007/978-3-031-36118-0 | ||
| Özet |
| The successful management of solid waste, which has become a major issue in urban life, reduces environmental pollution. To address this issue, numerous studies using various deep learning models have been conducted. This study focuses on traditional convolutional neural networks and transformer-based deep learning architectures and provides a detailed examination summary. In this context, many experimental studies dealing with the PoolFormer and ResNet transformers and the DenseNet201 and ResNet50 pre-trained CNN models were run on the TrashNet dataset containing waste images. The Random Forest (RF) classifier was trained on different deep features extracted from 80% of this dataset, and the remaining 20% was reserved as the test dataset. Test dataset was used to validate the performance of the RF. According to the results, RF with DenseNet201 deep features presents 96.4 … |
| Anahtar Kelimeler |
| DenseNet201 deep features | Random Forest | Recycling | Waste classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 3 |