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INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS

Year 2025, Volume: 28 Issue: 2, 933 - 956, 03.06.2025

Mehmet Umut Salur , Nermin Elmas

Abstract

Increasing waste production and inadequate waste management have further complicated global environmental problems. The limited natural resources and the damage caused by waste to the environment necessitate the improvement of waste management systems. Accurate and effective classification of waste provides both economic benefits and reduces environmental impacts. In this study, a hybrid approach is presented by combining deep learning, machine learning, and ensemble learning techniques to classify environmental waste. ResNet50, InceptionResNet-V2, and DenseNet169 models were used, and these models were fine-tuned using pre-trained weights. We created an ensemble model by combining the feature maps obtained from each model. Among the features extracted by the ensemble deep learning model, the most effective features were determined with ANOVA, Variance Threshold, Mutual Information, Random Forests, Lasso, RFE, PCA, and Ridge Regression feature selection methods. The selected features were classified with SVM, MLP and Random Forest, XGBoost, hard voting, and soft voting methods. This study presents the contributions of both individual and ensemble models for environmental waste classification. The effectiveness of the proposed method was tested on two different datasets, and its effectiveness was verified. The results show that the proposed method can make a significant contribution to waste management and recycling processes.

Keywords

Waste classification ensemble learning machine learning deep learning features selection

Project Number

2024-GAP-Mühe-0008

Thanks

This study was supported by Gaziantep Islamic Science and Technology University Scientific Research Projects Coordination Unit under Project No: 2024-GAP-Mühe-0008.

References

  • Abdu, H., & Noor, M. H. M. (2022). A survey on waste detection and classification using deep learning. IEEE Access, 10, 128151–128165. https://doi.org/10.1109/ACCESS.2022.3226682
  • Al Duhayyim, M., Alotaibi, S. S., Al-Otaibi, S., Al-Wesabi, F. N., Othman, M., Yaseen, I., Rizwanullah, M., & Motwakel, A. (2023). An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques. Computers, Materials & Continua, 74(2). https://doi.org/10.32604/cmc.2023.033250
  • Alsubaei, F. S., Al-Wesabi, F. N., & Hilal, A. M. (2022). Deep Learning-Based Small Object Detection and Classification Model for Garbage Waste Management in Smart Cities and IoT Environment. Applied Sciences (Switzerland), 12(5). https://doi.org/10.3390/app12052281
  • Chatterjee, S., Hazra, D., & Byun, Y. C. (2022). IncepX-Ensemble: Performance Enhancement Based on Data Augmentation and Hybrid Learning for Recycling Transparent PET Bottles. IEEE Access, 10, 52280–52293. https://doi.org/10.1109/ACCESS.2022.3174076
  • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
  • Chen, W., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. (2024). A survey on imbalanced learning: latest research, applications and future directions. Artificial Intelligence Review, 57(6), 1–51. https://doi.org/10.1007/s10462-024-10759-6
  • Dietterich, T. G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, 1–15. https://doi.org/10.1007/3-540-45014-9_1
  • Filtreleme yöntemleri· Miuul. (2021). https://miuul.com/blog/filtreleme-yontemleri-ile-makine-ogrenmesinde-degisken-secimi Accessed 11.01.2025.
  • Garbage Classification. (2021). https://www.kaggle.com/datasets/asdasdasasdas/garbage-classification Accessed 10.01.2025. https://doi.org/10.34740/kaggle/ds/81794
  • Genuer, R., Poggi, J.-M., Genuer, R., & Poggi, J.-M. (2020). Random forests. Springer. https://doi.org/10.1007/978-3-030-56485-8_3
  • Greenacre, M., Groenen, P. J. F., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100. https://doi.org/10.1038/s43586-022-00184-w
  • Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar), 1157–1182.
  • Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389–422. https://doi.org/10.1023/A:1012487302797
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
  • Hutchinson, M. L., Antono, E., Gibbons, B. M., Paradiso, S., Ling, J., & Meredig, B. (2017). Overcoming data scarcity with transfer learning. ArXiv Preprint ArXiv:1711.05099. https://doi.org/10.48550/arXiv.1711.05099
  • Kanevski, M., Pozdnukhov, A., Canu, S., & Maignan, M. (2002). Advanced spatial data analysis and modeling with Support Vector Machines.
  • Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: a global snapshot of solid waste management to 2050. World Bank Publications.
  • Ma, X., Chen, L., Deng, Z., Xu, P., Yan, Q., Choi, K.-S., & Wang, S. (2023). Deep image feature learning with fuzzy rules. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 724–737. https://doi.org/10.1109/TETCI.2023.3259447
  • McDonald, G. C. (2009). Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1), 93–100. https://doi.org/10.1002/wics.14
  • Meyen, S., Göppert, F., Alber, H., von Luxburg, U., & Franz, V. H. (2021). Specialists Outperform Generalists in Ensemble Classification. ArXiv Preprint ArXiv:2107.04381. https://doi.org/10.48550/arXiv.2107.04381
  • Ouedraogo, A. S., Kumar, A., & Wang, N. (2023). Landfill Waste Segregation Using Transfer and Ensemble Machine Learning: A Convolutional Neural Network Approach. Energies, 16(16), 5980. https://doi.org/10.3390/en16165980
  • Pinkus, A. (1999). Approximation theory of the MLP model in neural networks. Acta Numerica, 8, 143–195. https://doi.org/10.1017/S0962492900002919
  • Poudel, S., & Poudyal, P. (2022). Classification of waste materials using CNN based on transfer learning. Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation, 29–33. https://doi.org/10.1145/3574318.3574345
  • Santoso, H., Hanif, I., Magdalena, H., & Afiyati, A. (2024). A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction. JOIV: International Journal on Informatics Visualization, 8(2), 623-634. https://dx.doi.org/10.62527/joiv.8.2.1943
  • Salur, M. U., Elmas, N., Koçak, A. N., & Kaymaz, M. (2024). Derin Öğrenme ile Çevresel Atıkların Sınıflandırılmasına Dayalı Akıllı Çöp Konteyneri Tasarımı ve Prototipinin Geliştirilmesi. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 11(24), 547–563. https://doi.org/10.54365/adyumbd.1557588
  • Shahab, S., Anjum, M., & Umar, M. S. (2022). Deep learning applications in solid waste management: A deep literature review. International Journal of Advanced Computer Science and Applications, 13(3). https://doi.org/10.14569/ijacsa.2022.0130347
  • Single, S., Iranmanesh, S., & Raad, R. (2023). RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning. Information (Switzerland), 14(12). https://doi.org/10.3390/info14120633
  • Sürücü, S., & Ecemiş, İ. N. (2022). Garbage classification using pre-trained models. Avrupa Bilim ve Teknoloji Dergisi, 36, 73–77. https://doi.org/10.31590/ejosat.1103628
  • Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11231
  • Şirin, E. (2017). Ensemble Yöntemler: Basit Teorik Anlatım ve Python Uygulama - Veri Bilimi Okulu - Veri Bilimi Okulu. https://www.veribilimiokulu.com/ensemble-yontemler-topluluk-ogrenmesi-basit-teorik-anlatim-ve-python-uygulama/ Accessed 10.01.2025
  • Tatke, A., Patil, M., Khot, A., & Karad’s, P. J. V. (2021). Hybrid approach of garbage classification using computer vision and deep learning. International Journal of Engineering Applied Sciences and Technology, 5(10). https://doi.org/10.33564/IJEAST.2021.v05i10.032
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
  • Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement: Journal of the International Measurement Confederation, 153. https://doi.org/10.1016/j.measurement.2019.107459
  • Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural computing and applications, 24, 175-186. https://doi.org/10.1007/s00521-013-1368-0
  • Wu, N., Wang, G., & Jia, D. (2024). A Hybrid Model for Household Waste Sorting (HWS) Based on an Ensemble of Convolutional Neural Networks. Sustainability, 16(15), 6500. https://doi.org/10.3390/su16156500
  • Yıldız, E. N., Bingöl, H., & Yıldırım, M. (2023). Önerilen Derin Öğrenme ve Makine Öğrenmesi Tabanlı Hibrit Model ile Çevresel Atıkların Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(1), 353–361. https://doi.org/10.35234/fumbd.1230982
  • Younis, H., & Obaid, M. (2024). Performance Comparison of Pretrained Deep Learning Models for Landfill Waste Classification. International Journal of Advanced Computer Science & Applications, 15(11). https://dx.doi.org/10.14569/IJACSA.2024.0151166
  • Yulita, I. N., Ardiansyah, F., Sholahuddin, A., Rosadi, R., Trisanto, A., & Ramdhani, M. R. (2024, January). Garbage Classification Using Inception V3 as Image Embedding and Extreme Gradient Boosting. International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 1394-1398). IEEE. https://doi.org/10.1109/ICETSIS61505.2024.10459560
  • Zheng, H., & Gu, Y. (2021). Encnn-upmws: Waste classification by a CNN ensemble using the UPM weighting strategy. Electronics (Switzerland), 10(4), 1–21. https://doi.org/10.3390/electronics10040427

TOPLULUK ÖĞRENME MODELLERİ İLE ÇEVRESEL ATIK SINIFLANDIRMADA YENİLİKÇİ YAKLAŞIM

Year 2025, Volume: 28 Issue: 2, 933 - 956, 03.06.2025

Mehmet Umut Salur , Nermin Elmas

Abstract

Artan atık üretimi ve yetersiz atık yönetimi, küresel çevre sorunlarını daha da karmaşık hale gelmiştir. Doğal kaynakların sınırlılığı ve atıkların çevreye verdiği zararlar atık yönetim sistemlerinin iyileştirilmesini zorunlu kılmaktadır. Atıkların doğru ve etkili sınıflandırılması hem ekonomik fayda sağlamakta hem de çevresel etkileri azaltmaktadır. Bu çalışmada, çevresel atıkların sınıflandırmak için derin öğrenme, makine öğrenmesi ve topluluk öğrenme knikleri birleştirilerek hibrit bir yaklaşım sunulmuştur. ResNet50, InceptionResNet-V2 ve DenseNet169 modelleri kullanılmış ve bu modeller önceden eğitilmiş ağırlıklar kullanılarak fine-tuning yapılmıştır. Her bir modelden elde edilen özellik haritaları birleştirilerek ensemble bir model oluşturulmuştur. Ensemble deep learning modeli tarafından çıkarılan öznitelikler arasından ANOVA, Variance Threshold, Mutual Information, Random Forests, Lasso, RFE, PCA ve Ridge Regresyon özellik seçim yöntemleri ile en etkili öznitelikler belirlenmiştir. Seçilen öznitelikler SVM, MLP ve Random Forest, XGBoost, çoğunluk oylama ve yumuşak oylama yöntemleriyle sınıflandırılmıştır. Bu çalışma çevresel atık sınıflandırmak için hem bireysel modellerin hem de ensemble modellerin sağladığı katkıları ortaya koymaktadır. Önerilen yöntemin etkinliği iki farklı veri kümesinde test edilmiş ve etkinliği doğrulanmıştır. Elde edilen sonuçlar, önerilen yöntemin atık yönetimi ve geri dönüşüm süreçlerine anlamlı bir katkı sağlayabileceğini göstermiştir.

Keywords

Atık sınıflandırma Topluluk öğrenme Makine öğrenmesi Derin öğrenme Öznitelik seçimi

Project Number

2024-GAP-Mühe-0008

Thanks

Bu çalışma Gaziantep İslam Bilim ve Teknoloji Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından 2024-GAP-Mühe-0008 nolu proje kapsamında desteklenmiştir.

References

  • Abdu, H., & Noor, M. H. M. (2022). A survey on waste detection and classification using deep learning. IEEE Access, 10, 128151–128165. https://doi.org/10.1109/ACCESS.2022.3226682
  • Al Duhayyim, M., Alotaibi, S. S., Al-Otaibi, S., Al-Wesabi, F. N., Othman, M., Yaseen, I., Rizwanullah, M., & Motwakel, A. (2023). An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques. Computers, Materials & Continua, 74(2). https://doi.org/10.32604/cmc.2023.033250
  • Alsubaei, F. S., Al-Wesabi, F. N., & Hilal, A. M. (2022). Deep Learning-Based Small Object Detection and Classification Model for Garbage Waste Management in Smart Cities and IoT Environment. Applied Sciences (Switzerland), 12(5). https://doi.org/10.3390/app12052281
  • Chatterjee, S., Hazra, D., & Byun, Y. C. (2022). IncepX-Ensemble: Performance Enhancement Based on Data Augmentation and Hybrid Learning for Recycling Transparent PET Bottles. IEEE Access, 10, 52280–52293. https://doi.org/10.1109/ACCESS.2022.3174076
  • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
  • Chen, W., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. (2024). A survey on imbalanced learning: latest research, applications and future directions. Artificial Intelligence Review, 57(6), 1–51. https://doi.org/10.1007/s10462-024-10759-6
  • Dietterich, T. G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, 1–15. https://doi.org/10.1007/3-540-45014-9_1
  • Filtreleme yöntemleri· Miuul. (2021). https://miuul.com/blog/filtreleme-yontemleri-ile-makine-ogrenmesinde-degisken-secimi Accessed 11.01.2025.
  • Garbage Classification. (2021). https://www.kaggle.com/datasets/asdasdasasdas/garbage-classification Accessed 10.01.2025. https://doi.org/10.34740/kaggle/ds/81794
  • Genuer, R., Poggi, J.-M., Genuer, R., & Poggi, J.-M. (2020). Random forests. Springer. https://doi.org/10.1007/978-3-030-56485-8_3
  • Greenacre, M., Groenen, P. J. F., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100. https://doi.org/10.1038/s43586-022-00184-w
  • Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar), 1157–1182.
  • Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389–422. https://doi.org/10.1023/A:1012487302797
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
  • Hutchinson, M. L., Antono, E., Gibbons, B. M., Paradiso, S., Ling, J., & Meredig, B. (2017). Overcoming data scarcity with transfer learning. ArXiv Preprint ArXiv:1711.05099. https://doi.org/10.48550/arXiv.1711.05099
  • Kanevski, M., Pozdnukhov, A., Canu, S., & Maignan, M. (2002). Advanced spatial data analysis and modeling with Support Vector Machines.
  • Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: a global snapshot of solid waste management to 2050. World Bank Publications.
  • Ma, X., Chen, L., Deng, Z., Xu, P., Yan, Q., Choi, K.-S., & Wang, S. (2023). Deep image feature learning with fuzzy rules. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 724–737. https://doi.org/10.1109/TETCI.2023.3259447
  • McDonald, G. C. (2009). Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1), 93–100. https://doi.org/10.1002/wics.14
  • Meyen, S., Göppert, F., Alber, H., von Luxburg, U., & Franz, V. H. (2021). Specialists Outperform Generalists in Ensemble Classification. ArXiv Preprint ArXiv:2107.04381. https://doi.org/10.48550/arXiv.2107.04381
  • Ouedraogo, A. S., Kumar, A., & Wang, N. (2023). Landfill Waste Segregation Using Transfer and Ensemble Machine Learning: A Convolutional Neural Network Approach. Energies, 16(16), 5980. https://doi.org/10.3390/en16165980
  • Pinkus, A. (1999). Approximation theory of the MLP model in neural networks. Acta Numerica, 8, 143–195. https://doi.org/10.1017/S0962492900002919
  • Poudel, S., & Poudyal, P. (2022). Classification of waste materials using CNN based on transfer learning. Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation, 29–33. https://doi.org/10.1145/3574318.3574345
  • Santoso, H., Hanif, I., Magdalena, H., & Afiyati, A. (2024). A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction. JOIV: International Journal on Informatics Visualization, 8(2), 623-634. https://dx.doi.org/10.62527/joiv.8.2.1943
  • Salur, M. U., Elmas, N., Koçak, A. N., & Kaymaz, M. (2024). Derin Öğrenme ile Çevresel Atıkların Sınıflandırılmasına Dayalı Akıllı Çöp Konteyneri Tasarımı ve Prototipinin Geliştirilmesi. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 11(24), 547–563. https://doi.org/10.54365/adyumbd.1557588
  • Shahab, S., Anjum, M., & Umar, M. S. (2022). Deep learning applications in solid waste management: A deep literature review. International Journal of Advanced Computer Science and Applications, 13(3). https://doi.org/10.14569/ijacsa.2022.0130347
  • Single, S., Iranmanesh, S., & Raad, R. (2023). RealWaste: A Novel Real-Life Data Set for Landfill Waste Classification Using Deep Learning. Information (Switzerland), 14(12). https://doi.org/10.3390/info14120633
  • Sürücü, S., & Ecemiş, İ. N. (2022). Garbage classification using pre-trained models. Avrupa Bilim ve Teknoloji Dergisi, 36, 73–77. https://doi.org/10.31590/ejosat.1103628
  • Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11231
  • Şirin, E. (2017). Ensemble Yöntemler: Basit Teorik Anlatım ve Python Uygulama - Veri Bilimi Okulu - Veri Bilimi Okulu. https://www.veribilimiokulu.com/ensemble-yontemler-topluluk-ogrenmesi-basit-teorik-anlatim-ve-python-uygulama/ Accessed 10.01.2025
  • Tatke, A., Patil, M., Khot, A., & Karad’s, P. J. V. (2021). Hybrid approach of garbage classification using computer vision and deep learning. International Journal of Engineering Applied Sciences and Technology, 5(10). https://doi.org/10.33564/IJEAST.2021.v05i10.032
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
  • Toğaçar, M., Ergen, B., & Cömert, Z. (2020). Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement: Journal of the International Measurement Confederation, 153. https://doi.org/10.1016/j.measurement.2019.107459
  • Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural computing and applications, 24, 175-186. https://doi.org/10.1007/s00521-013-1368-0
  • Wu, N., Wang, G., & Jia, D. (2024). A Hybrid Model for Household Waste Sorting (HWS) Based on an Ensemble of Convolutional Neural Networks. Sustainability, 16(15), 6500. https://doi.org/10.3390/su16156500
  • Yıldız, E. N., Bingöl, H., & Yıldırım, M. (2023). Önerilen Derin Öğrenme ve Makine Öğrenmesi Tabanlı Hibrit Model ile Çevresel Atıkların Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(1), 353–361. https://doi.org/10.35234/fumbd.1230982
  • Younis, H., & Obaid, M. (2024). Performance Comparison of Pretrained Deep Learning Models for Landfill Waste Classification. International Journal of Advanced Computer Science & Applications, 15(11). https://dx.doi.org/10.14569/IJACSA.2024.0151166
  • Yulita, I. N., Ardiansyah, F., Sholahuddin, A., Rosadi, R., Trisanto, A., & Ramdhani, M. R. (2024, January). Garbage Classification Using Inception V3 as Image Embedding and Extreme Gradient Boosting. International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 1394-1398). IEEE. https://doi.org/10.1109/ICETSIS61505.2024.10459560
  • Zheng, H., & Gu, Y. (2021). Encnn-upmws: Waste classification by a CNN ensemble using the UPM weighting strategy. Electronics (Switzerland), 10(4), 1–21. https://doi.org/10.3390/electronics10040427
There are 40 citations in total.

Details

Primary Language English
Subjects Image Processing, Deep Learning, Waste Management, Reduction, Reuse and Recycling
Journal Section Computer Engineering
Authors

Mehmet Umut Salur 0000-0003-0296-6266

Nermin Elmas 0009-0004-5617-6344

Project Number 2024-GAP-Mühe-0008
Publication Date June 3, 2025
Submission Date February 18, 2025
Acceptance Date April 21, 2025
Published in Issue Year 2025Volume: 28 Issue: 2

Cite

APA Salur, M. U., & Elmas, N. (2025). INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 933-956.
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