Analysis of Biodegradable and Non-Biodegradable Materials Using Selected Deep Learning Algorithms
Keywords:Deep Learning, convolutional neural Networks, biodegradable, non-biodegradable, classification
It is possible to divide the materials used in the world into recyclable and nonrecyclable. Biodegradable materials contain elements naturally degraded by microorganisms such as foods, plants, fruits, etc. Waste from this material can be processed into compost. non-biodegradable materials include materials that do not naturally decompose, such as plastics, metals, inorganic elements, etc. Waste from this material can only be reused by converting it into new materials. In this study, the classification of biodegradable and non-biodegradable materials was done using deep learning methods. Convolutional Neural Network (CNN) performs steps such as preprocessing and feature extraction in classification. 5430 images were used for the dataset. 70% of this dataset was used as training data, 15% as validation data, and 15% as test data. Of the Deep Learning methods, the pre-trained neural networks AlexNet, ShuffleNet, SqueezeNet, and GoogleNet were used. For each algorithm, the performances were evaluated by classifying them as biodegradable and non-biodegradable. With this study, we can identify, track, sort, and process waste materials by classifying materials.
E. Chiellini and R. Solaro, “Biodegradable polymeric materials,” Adv. Mater., vol. 8, no. 4, pp. 305–313, 1996.
Y. F. Zheng, X. N. Gu, and F. Witte, “Biodegradable metals,” Mater. Sci. Eng. R Rep., vol. 77, pp. 1–34, 2014.
R. Smith, Biodegradable polymers for industrial applications. CRC Press, 2005.
L. S. Nair and C. T. Laurencin, “Biodegradable polymers as biomaterials,” Prog. Polym. Sci., vol. 32, no. 8–9, pp. 762–798, 2007.
A. D. DİKER, “Sıtma Hastalığının Sınıflandırılmasında Evrişimsel Sinir Ağlarının Performanslarının Karşılaştırılması,” Bitlis Eren Üniversitesi Fen Bilim. Derg., vol. 9, no. 4, pp. 1825–1835.
D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, High Performance Convolutional Neural Networks for Image Classification,” IJCAI, 2011.
M. Sarıgül, B. M. Ozyildirim, and M. Avci, “Differential convolutional neural network,” Neural Netw., vol. 116, pp. 279–287, 2019.
B. B. Traore, B. Kamsu-Foguem, and F. Tangara, “Deep convolution neural network for image recognition,” Ecol. Inform., vol. 48, pp. 257–268, 2018.
Y. Seo and K. Shin, “Hierarchical convolutional neural networks for fashion image classification,” Expert Syst. Appl., vol. 116, pp. 328–339, 2019.
E. Cetinic, T. Lipic, and S. Grgic, “Fine-tuning Convolutional Neural Networks for fine art classification,” Expert Syst. Appl., vol. 114, pp. 107–118, 2018.
H. Han, Y. Li, and X. Zhu, “Convolutional neural network learning for generic data classification,” Inf. Sci., vol. 477, pp. 448–465, 2019.
Y. Park and H. S. Yang, “Convolutional neural network based on an extreme learning machine for image classification,” Neurocomputing, vol. 339, pp. 66–76, 2019.
M. M. dos Santos, A. G. da S. Filho, and W. P. dos Santos, “Deep convolutional extreme learning machines: Filters combination and error model validation,” Neurocomputing, vol. 329, pp. 359–369, 2019.
L. F. S. Coletta, M. Ponti, E. R. Hruschka, A. Acharya, and J. Ghosh, “Combining clustering and active learning for the detection and learning of new image classes,” Neurocomputing, vol. 358, pp. 150–165, 2019.
J. Yuan, X. Hou, Y. Xiao, D. Cao, W. Guan, and L. Nie, “Multi-criteria active deep learning for image classification,” Knowl.-Based Syst., vol. 172, pp. 86–94, 2019.
S. Matiz and K. E. Barner, “Inductive conformal predictor for convolutional neural networks: Applications to active learning for image classification,” Pattern Recognit., vol. 90, pp. 172–182, 2019.
Kaggle, “Kaggle,” Kaggle data set, 06-Dec-2021. [Online]. Available: https://www.kaggle.com/datasets.
A. G. Dastider, F. Sadik, and S. A. Fattah, “An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound,” Comput. Biol. Med., vol. 132, p. 104296, May 2021.
F. Demir, D. A. Abdullah, and A. Sengur, “A New Deep CNN Model for Environmental Sound Classification,” IEEE Access, no. 8, pp. 66529–66537, 2020.
I. Atik, “A HYBRID PREDICTION APPROACH BASED ON ANN AND NAR NEURAL NETWORKS FOR ANNUAL ELECTRIC ENERGY DEMAND IN TURKEY.”
H. Naeem and A. A. Bin-Salem, “A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images,” Appl. Soft Comput., vol. 113, p. 107918, Dec. 2021.
M.,Alhussein, A. K., and S. I. Haider, “Hybrid CNN-LSTM model for short-term individual household load forecasting,” IEEE Access, vol. 8, pp. 180544–180557, 2020.
G. Altan, “DeepGraphNet: Grafiklerin Sınıflandırılmasında Derin Öğrenme Modelleri,” Avrupa Bilim Ve Teknol. Derg., pp. 319–327, Oct. 2019.
A. Altan and S. Karasu, “Ayrıştırma Yöntemlerinin Derin Öğrenme Algoritması ile Tanımlanan Rüzgâr Hızı Tahmin Modeli Başarımına Etkisinin İncelenmesi,” Avrupa Bilim Ve Teknol. Derg., no. 20, pp. 844–853, Dec. 2020.
İ. Ati̇k, “Comparison of Short-Term Electricity Load Forecasting Using Different Deep Learning Methods,” Avrupa Bilim Ve Teknol. Derg., no. 31, pp. 616–623, Dec. 2021.
J. Kim, J. K. Lee, and K. M. Lee, “Deeply-recursive convolutional network for image super-resolution,” presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1637–1645.
C. Guan, “Realtime multi-person 2d pose estimation using shufflenet,” presented at the 2019 14th International Conference on Computer Science & Education (ICCSE), 2019, pp. 17–21.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst., vol. 25, pp. 1097–1105, 2012.
M. Toğaçar, B. Ergen, and Z. Cömert, “A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models,” IRBM, Nov. 2019.
H. Durmuş, E. O. Güneş, and M. Kırcı, “Disease detection on the leaves of the tomato plants by using deep learning,” in 2017 6th International Conference on Agro-Geoinformatics, 2017, pp. 1–5.
C. A. Ronao and S.-B. Cho, “Human activity recognition with smartphone sensors using deep learning neural networks,” Expert Syst. Appl., vol. 59, pp. 235–244, 2016.
B. N. Narayanan, R. Ali, and R. C. Hardie, “Performance analysis of machine learning and deep learning architectures for malaria detection on cell images,” presented at the Applications of Machine Learning, 2019, vol. 11139, p. 111390W.
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