jismar Journal of Information Systems and Management Research 2717-9931 M. Hanefi CALP 10.59940/jismar.1746258 Software Engineering (Other) Yazılım Mühendisliği (Diğer) Açıklanabilir Yapay Zekâ Araçları ve Transfer Öğrenme ile Görüntü Tabanlı Atık Sınıflandırması Image-Based Waste Classification With Explainable Artificial Intelligence Tools and Transfer Learning Kışlıoğlu İkra ÇANKIRI KARATEKİN ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ https://orcid.org/0000-0003-3272-5973 Güler Osman ÇANKIRI KARATEKİN ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ 12 31 2025 7 2 175 191 07 19 2025 11 07 2025 Copyright © 2019, Journal of Information Systems and Management Research 2019 Journal of Information Systems and Management Research

Sürdürülebilir çevre yönetimi ve verimli geri dönüşüm sistemleri için etkili atık sınıflandırması esastır. Bu çalışma atıkların otomatik olarak dokuz kategoriye sınıflandırmak için RealWaste veri setinden alınan görüntü verilerini kullanan derin öğrenmeye dayalı bir yaklaşım sunmaktadır. VGG16, ResNet50, DenseNet201, EfficientNetB3 ve Xception dâhil olmak üzere on beş evrişimli sinir ağı (CNN) mimarisi, transfer öğrenmesi ve ince ayar kullanılarak değerlendirildi. Sınıf dengesizliğini azaltmak için veri artırma ve sınıf ağırlıklandırma stratejileri kullanıldı. Modeller doğruluk, kesinlik, geri çağırma, F1 puanı ve karışılık matrisleri kullanılarak değerlendirildi. Ek olarak modellerin yorumlanabilirliğini artırmak için Grad-CAM görselleştirmeleri kullanıldı. DenseNet201 ve EfficientNetB2, güçlü genelleme ve yüksek sınıf bazında performans göstererek en iyi performansı gösterenler olarak ortaya çıktı. Bu araştırma mobil veya gömülü platformlara uyarlanabilen atık sınıflandırma sistemleri oluşturmada derin öğrenmenin ve açıklanabilir yapay zekanın potansiyelini vurgulamaktadır.

Effective waste classification is essential for sustainable environmental management and efficient recycling systems. This study presents a deep learning-based approach using image data from the RealWaste dataset to automatically classify waste into nine categories. Fifteen convolutional neural network (CNN) architectures, including VGG16, ResNet50, DenseNet201, EfficientNetB3, and Xception, were evaluated using transfer learning and fine-tuning. Data augmentation and class weighting strategies were employed to mitigate class imbalance. The models were assessed using accuracy, precision, recall, F1-score, and confusion matrices. Additionally, Grad-CAM visualizations were utilized to enhance the interpretability of the models. DenseNet201 and EfficientNetB2 emerged as top performers, demonstrating strong generalization and high class-wise performance. This research highlights the potential of deep learning and explainable AI in building robust waste classification systems adaptable to mobile or embedded platforms

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