TY  - JOUR
T1  - Efficient Hyperparameter-Tuned Convolutional Neural Network for Waste Classification
AU  - Saraçoğlu, Yaren Didenaz
AU  - Çetin Kaya, Yasemin
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.54287/gujsa.1721478
JF  - Gazi University Journal of Science Part A: Engineering and Innovation
JO  - GU J Sci, Part A
PB  - Gazi University
WT  - DergiPark
SN  - 2147-9542
SP  - 815
EP  - 835
VL  - 12
IS  - 3
LA  - en
AB  - Today, increasing consumption habits and industrial activities have made waste management a major environmental issue on a global scale. Traditional waste classification methods cannot provide sufficient efficiency due to high labor costs and human errors. Our primary goal in this study is to design a new custom convolutional neural network (CNN) model that yields the highest accuracy and to find the optimal hyperparameters for this purpose. Using the TrashNet dataset, classification was performed in six classes: cardboard, glass, metal, paper, plastic, plastic and other wastes. We also compared the proposed custom CNN model with the seven pre-trained CNN models in the literature. According to the experimental results, the proposed custom CNN model showed the highest success with 95.03% accuracy and 95% F1 score. The results show that deep learning methods can work with high accuracy in waste classification problems and provide more reliable results compared to traditional methods.
KW  - Waste Classification
KW  - Deep Learning
KW  - CNN
KW  - TrashNet
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UR  - https://doi.org/10.54287/gujsa.1721478
L1  - https://dergipark.org.tr/en/download/article-file/4967216
ER  -