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84 Birds Classification Using Transfer Learning and EfficientNetB2

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1688))

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Abstract

Nowadays, global warming, wildfires, and air pollution have caused significant effects and seriously threatened the life and diversity of animals in common and birds in particular. Therefore, the conservation of birds is very urgent. Identify and classify them for statistics on the number of species, and distribution, and as a way to come up with reasonable conservation measures from scientists who study the environment and animals. In this research, we propose a new approach to classify birds using the EfficientNetB2 model, transfer learning techniques, and customizing the model’s hyperparameters. Model trained on the original dataset with the highest accuracy of 93% on both the validation and the testing set.

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References

  1. Khan, S., Singh, S., Almas, S., Razzaque, A.: Animal classification using CNN with VGG-16 architecture. Int. J. Adv. Res. Sci. Commun. Technol. 85–192 (2022). https://doi.org/10.48175/IJARSCT-3240

  2. Vithakshana, L.G.C., Samankula, W.G.D.M.: IoT based animal classification system using convolutional neural network. In: 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, pp. 90–95, September 2020. https://doi.org/10.1109/SCSE49731.2020.9313018

  3. Suryawanshi, S., Jogdande, V., Mane, A.: Animal classification using deep learning. Int. J. Eng. Appl. Sci. Technol 04(11), 305–307 (2020). https://doi.org/10.33564/IJEAST.2020.v04i11.055

    Article  Google Scholar 

  4. Jiang, B., Huang, W., Tu, W., Yang, C.: An animal classification based on light convolutional network neural network. In: 2019 International Conference on Intelligent Computing and its Emerging Applications (ICEA), Tainan, Taiwan, pp. 45–50, August 2019. https://doi.org/10.1109/ICEA.2019.8858309

  5. Zeng, P.: Research on similar animal classification based on CNN algorithm. J. Phys. Conf. Ser. 2132(1), 012001 (2021). https://doi.org/10.1088/1742-6596/2132/1/012001

    Article  Google Scholar 

  6. Hossain, M.A., Alam Sajib, M.S.: Classification of image using convolutional neural network (CNN). Glob. J. Comput. Sci. Technol. 13–18 (2019). https://doi.org/10.34257/GJCSTDVOL19IS2PG13

  7. Bhatt, D., et al.: CNN variants for computer vision: history, architecture, application, challenges and future scope. Electronics 10(20), 2470 (2021). https://doi.org/10.3390/electronics10202470

    Article  Google Scholar 

  8. Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. arXiv, 11 September 2020. http://arxiv.org/abs/1905.11946

  9. Kallipolitis, A., Revelos, K., Maglogiannis, I.: Ensembling EfficientNets for the classification and interpretation of histopathology images. Algorithms 14(10), 278 (2021). https://doi.org/10.3390/a14100278

    Article  Google Scholar 

  10. Jie, Y., et al.: Combined multi-layer feature fusion and edge detection method for distributed photovoltaic power station identification. Energies 13(24), 6742 (2020). https://doi.org/10.3390/en13246742

    Article  Google Scholar 

  11. Hong, K.V., Minh, T.T., Duc, H.L., Nhat, N.T., Hoang, H.L.: 104 fruits classification using transfer learning and DenseNet201 fine-tuning. In: Barolli, L. (eds.) Complex, Intelligent and Software Intensive Systems, vol. 497, pp. 160–170. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08812-4_16

  12. Animal Dataset "Animal Dataset." https://www.kaggle.com/datasets/goelyash/animal-dataset

  13. Sancho, C.R., Ahlberg, J., Markuš, N., Salembier, P.: Pedestrian detection using a boosted cascade of histogram of oriented gradients, p. 67 (2014)

    Google Scholar 

  14. Kelly, C., Siddiqui, F.M., Bardak, B., Woods, R.: Histogram of oriented gradients front end processing: an FPGA based processor approach. In: 2014 IEEE Workshop on Signal Processing Systems (SiPS), Belfast, United Kingdom, pp. 1–6, October 2014. https://doi.org/10.1109/SiPS.2014.6986093

  15. Cheon, M.-K., Lee, W.-J., Hyun, C.-H., Park, M.: Rotation Invariant Histogram of Oriented Gradients. Int. J. Fuzzy Log. Intell. Syst. 11(4), 293–298 (2011). https://doi.org/10.5391/IJFIS.2011.11.4.293

    Article  Google Scholar 

  16. Dandil, E., Polattimur, R.: PCA-based animal classification system. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, pp. 1–5, October 2018. https://doi.org/10.1109/ISMSIT.2018.8567256

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Authors and Affiliations

  1. Information Technology Department, FPT University, Can Tho, Vietnam

    Hoa Le Duc, Tin Tang Minh, Khanh Vo Hong & Huong Luong Hoang

Authors
  1. Hoa Le Duc
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  2. Tin Tang Minh
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  3. Khanh Vo Hong
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  4. Huong Luong Hoang
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Corresponding author

Correspondence to Hoa Le Duc .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Seoul, Korea (Republic of)

    Tai M. Chung

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Duc, H.L., Minh, T.T., Hong, K.V., Hoang, H.L. (2022). 84 Birds Classification Using Transfer Learning and EfficientNetB2. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_50

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  • DOI: https://doi.org/10.1007/978-981-19-8069-5_50

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8068-8

  • Online ISBN: 978-981-19-8069-5

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