Comparative Analysis of Convolutional Neural Network Models for Solid Waste Categorization

References

[1] Ike, C. C., Ezeibe, C. C., Anijiofor, S. C., & Daud, N. N. N. (2018). Solid Waste Management in Nigeria: Problems, Prospects, and Policies. The Journal of Solid Waste Technology and Management, 44(2), 163–172.

[2] Saleh, H. M., & Hassan, A. I. (2021). Introduction Chapter: Solid Waste. In H. M. Saleh (Ed.), Strategies of Sustainable Solid Waste Management (pp. 1 – 4). IntechOpen.

[3] Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 1–74.

[4] Yang, X. S. (2019). Neural networks and deep learning. Introduction to Algorithms for Data Mining and Machine Learning, 139–161.

[5] Stancic, A., Vyroubal, V., & Slijepcevic, V. (2022). Classification Efficiency of Pre-Trained Deep CNN Models on Camera Trap Images. Jounal of Imaging, 8, 20.

[6] Yang, M., & Thung, G. (2016). Classification of trash for recyclability status (CS229 project report). Stanford University. http://cs229.stanford.edu/proj2016/report/ThungYang-ClassificationOfTrashForRecyclabilityStatus-report.pdf

[7] Rahman, M. W., Islam, R., Hasan, A., Bithi, N. I., Hasan, M. M., & Rahman, M. M. (2020). Intelligent waste management system using deep learning with IoT. Journal of King Saud University - Computer and Information Sciences, 1–16.

[8] Gyawali, D., Regmi, A., Shakya, A., Gautam, A., & Shrestha, S. (2020). Comparative analysis of multiple deep CNN models for waste classification. ArXiv preprint.

[9] Bhandari, S. (2020). Automatic Waste Sorting in Industrial Environments Via Machine Learning Approaches, [master’s thesis, Tampere University]

[10] Wang, C., Qin, J., Qu, C., Ran, X., Liu, C., & Chen, B. (2021). A smart municipal waste management system based on deep-learning and Internet of Things. Waste Management, 135, 20-29.

[11] Radhika, D. A. (2021). Real Life Smart Waste Management System [Dry, Wet, Recycle, Electronic & Medical]. International Journal of Scientific Research in Science and Technology, 7(4), 631–640.

[12] Geetha, S., Saha, J., Dasgupta, I., Bera, R., Lawal, I. A., & Kadry, S. (2022). Design of Waste Management System Using Ensemble Neural Networks. Designs, 6(2), 27.

[13] Ravichandiran, S. (Ed.). (2019). Introduction to Deep Learning. In Hands-On Deep Learning Algorithms with Python (pp. 7–39). Van Haren Publishing.

[14] Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.

[15] Islam, M., Chen, G., & Jin, S. (2019). An Overview of Neural Network. American Journal of Neural Networks and Applications, 5(1), 7–11.

[16] Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9(4), 611–629.

[17] Gavali, P., & Banu, J. S. (2019). Deep Convolutional Neural Network for Image Classification on CUDA Platform. Elsevier eBooks, 99-122.

[18] Xia, W., Jiang, Y., Chen, X., & Zhao, R. (2022). Application of machine learning algorithms in municipal solid waste management: A mini review. Waste Management & Research, 40(6), 609 – 624

[19] Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021). Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sensing, 13(22), 4712.

[20] Zheng, X., & Cloutier, R. S. (2022). A review of image classification algorithms in IoT. EAI Endorsed Transactions on Internet of Things, 7(28), 1-11

[21] Dhillon, A., & Verma, G. K. (2019). Convolutional neural network: a review of models, methodologies, and applications to object detection. Progress in Artificial Intelligence, 9(2), 85–112.

[22] Imane, C. (2022, November 12). EfficientNet [model architecture]. OpenGenus IQ: Computing Expertise & Legacy. Retrieved January 20, 2023, from https://iq.opengenus.org/efficientnet/.

[23] Phiphiphatphaisit, S., & Surinta, O. (2020). Food Image Classification with Improved MobileNet Architecture and Data Augmentation. Proceedings of the 2020 3rd International Conference on Information Science and Systems (pp. 51-56).

@article{1704821,
      author = {Tamuno-omie J. Alalibo, Nkolika O. Nwazor},
      title = {Comparative Analysis of Convolutional Neural Network Models for Solid Waste Categorization},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {12},
      pages = {1396-1405},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704821.pdf},
      abstract = {This paper is on the comparative analysis of various convolutional neural network (CNN) models that could be used in the classification of solid waste into different categories for recycling. Effective solid waste classification is a crucial aspect of waste disposal and recycling processes to maintain a sustainable environment. In recent years, CNNs have emerged as a powerful tool for solid waste classification, thanks to their ability to learn features from images and classify objects accurately. In this paper, the MATLAB platform was used to train eight pre-trained CNN models for solid waste classification. The models are AlexNet, GoogleNet, ResNet18, MobileNetV2, ResNet50, ResNet101, EfficientNetB0 and InceptionV3. Each model was trained for 5 epochs, 7 epochs, and 10 epochs respectively. The TrashNet dataset was used.  The dataset was split into 75% and 25% for the training set and validation set respectively. After training for 5 epochs, ResNet50 achieved validation accuracy of 90.95%, ResNet101 had 90.48%, while the rest achieved accuracies ranging from 64% to 89%. After training for 7 epochs, ResNet50 got an accuracy of 92.06%, ResNet101 and InceptionV3 both had an accuracy of 91.59%, while EfficientNetB0 improved to 90.63%. After 10 epochs, ResNet101 achieved an accuracy of 92.38%, ResNet50 got an accuracy of 92.02%, while EfficientNetB0 and InceptionV3 achieved an accuracy of 91.43%. From the results, ResNet-50 and ResNet-101 achieved higher validation accuracy. In real-world applications, these models can be used in smart bin waste collection, smart waste recycling, and waste management in general.},
      keywords = {Convolutional Neural Network, Waste Image Classification, TrashNet, ResNet50, Recycling},
      month = {June},
  }