A Hybrid CNN Approach in Waste Classification

Serap Çakar Kaman; Gamze Ağırtaş

doi:10.35377/saucis...1774531

EN

A Hybrid CNN Approach in Waste Classification

Abstract

Recycling plays a vital role in environmental sustainability and the conservation of natural resources. However, increasing population, industrialization, and waste have increased the need for automation in waste sorting processes. This study investigated the performance of deep learning-based classification models on waste classification using two different datasets (Garbage Classification and Garbage-Dataset). Experiments were conducted on four Convolutional Neural Networks (DenseNet121, InceptionV3, MobileNetV2, and VGG16), utilizing data augmentation and transfer learning techniques. A hybrid model was created by aggregating the features of these four models. Performance evaluation was performed with the 5-fold cross-validation method. In both datasets analyzed according to the experimental results, the hybrid model yielded the highest performance metrics, including Accuracy, Precision, Recall, F1-score, and ROC-AUC. A test accuracy rate of 85.88% was obtained in the Garbage Classification dataset and 91.26% in the Garbage-Dataset. The study highlights the critical impact of dataset size and model architecture choices on classification performance, providing an essential foundation for developing automation solutions in waste management.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Serap Çakar Kaman ^*
0000-0002-3682-0831
Türkiye

Gamze Ağırtaş
0000-0003-3931-1976
Türkiye

Early Pub Date

March 16, 2026

Publication Date

March 16, 2026

Submission Date

August 30, 2025

Acceptance Date

November 27, 2025

Published in Issue

Year 2026 Volume: 9 Number: 1

DOI

https://doi.org/10.35377/saucis...1774531

IZ

https://izlik.org/JA78YL52WE

Cite

RIS / Bibtex

APA

Çakar Kaman, S., & Ağırtaş, G. (2026). A Hybrid CNN Approach in Waste Classification. Sakarya University Journal of Computer and Information Sciences, 9(1), 157-166. https://doi.org/10.35377/saucis...1774531

AMA

1.Çakar Kaman S, Ağırtaş G. A Hybrid CNN Approach in Waste Classification. SAUCIS. 2026;9(1):157-166. doi:10.35377/saucis.1774531

Chicago

Çakar Kaman, Serap, and Gamze Ağırtaş. 2026. “A Hybrid CNN Approach in Waste Classification”. Sakarya University Journal of Computer and Information Sciences 9 (1): 157-66. https://doi.org/10.35377/saucis. 1774531.

EndNote

Çakar Kaman S, Ağırtaş G (March 1, 2026) A Hybrid CNN Approach in Waste Classification. Sakarya University Journal of Computer and Information Sciences 9 1 157–166.

IEEE

[1]S. Çakar Kaman and G. Ağırtaş, “A Hybrid CNN Approach in Waste Classification”, SAUCIS, vol. 9, no. 1, pp. 157–166, Mar. 2026, doi: 10.35377/saucis...1774531.

ISNAD

Çakar Kaman, Serap - Ağırtaş, Gamze. “A Hybrid CNN Approach in Waste Classification”. Sakarya University Journal of Computer and Information Sciences 9/1 (March 1, 2026): 157-166. https://doi.org/10.35377/saucis. 1774531.

JAMA

1.Çakar Kaman S, Ağırtaş G. A Hybrid CNN Approach in Waste Classification. SAUCIS. 2026;9:157–166.

MLA

Çakar Kaman, Serap, and Gamze Ağırtaş. “A Hybrid CNN Approach in Waste Classification”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 1, Mar. 2026, pp. 157-66, doi:10.35377/saucis. 1774531.

Vancouver

1.Serap Çakar Kaman, Gamze Ağırtaş. A Hybrid CNN Approach in Waste Classification. SAUCIS. 2026 Mar. 1;9(1):157-66. doi:10.35377/saucis. 1774531

Öz

A Hybrid CNN Approach in Waste Classification

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite