A comparative analysis of machine learning algorithms for waste classification: inceptionv3 and chi-square features
Yazarlar (2)
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
M. Koklu
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Environmental Science and Technology (Q3) | ||
| Dergi ISSN | 1735-1472 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2025 |
| Kabul Tarihi | 21-11-2024 | Yayınlanma Tarihi | 20-12-2024 |
| Cilt / Sayı / Sayfa | 22 / 10 / 9415–9428 | DOI | 10.1007/s13762-024-06233-z |
| Makale Linki | https://doi.org/10.1007/s13762-024-06233-z | ||
| UAK Araştırma Alanları |
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| Özet |
| Effective waste management requires the correct categorization of recyclables. It is possible to classify organic waste and recyclable waste using machine learning techniques. Accurately sorting waste is important for improving recycling processes, however separating organic waste from recyclables remains a challenge. This study aimed to provide the importance of machine learning in the field of waste management and automate classification of solid waste. We compared the accuracy of three machine learning classifiers based on the Chi2 feature selection method. Feature extraction was performed using the InceptionV3 deep convolutional neural network. The training of three machine-learning classifiers was performed using the extracted features. Based on a labeled waste classification image dataset, the performance of the classifiers was evaluated. Despite using any of the feature’s selections, SVM attained … |
| Anahtar Kelimeler |
| Chi2 | Classification | Feature extraction | InceptionV3 | Machine learning | Solid waste | Waste management |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 8 |
| Scopus | 9 |
| Google Scholar | 21 |