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Yıl 2023, Cilt: 5 Sayı: 2, 161 - 169, 30.06.2023

Kıyas Kayaalp Fehmi Özkaner

https://doi.org/10.47933/ijeir.1269680

Öz

Kaynakça

  • [1] A. I. Khan, S. M. K. Quadri, S. Banday, and J. L. Shah, “Deep diagnosis: A real-time apple leaf disease detection system based on deep learning,” Comput. Electron. Agric., vol. 198, p. 107093, 2022.
  • [2] F. Şenel, “A Hyperparameter Optimization for Galaxy Classification,” Comput. Mater. Contin., vol. 74, no. 2, 2023.
  • [3] H. Temiz, B. Gökberk, and L. Akarun, “TurCoins: Turkish republic coin dataset,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1–4.
  • [4] S. Aslan, S. Vascon, and M. Pelillo, “Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games,” Pattern Recognit. Lett., vol. 131, pp. 158–165, 2020. [5] A. G. YILMAZ, Ç. SAYGILI, and V. NABİYEV, “Roma Dönemi Madeni Paralarının Derin Öğrenme Tabanlı Sınıflandırılması.”
  • [6] H. Anwar, S. Sabetghadam, and P. Bell, “An Image-Based Class Retrieval System for Roman Republican Coins,” Entropy, vol. 22, no. 8, p. 799, 2020.
  • [7] Y. Ma and O. Arandjelović, “Classification of ancient roman coins by denomination using colour, a forgotten feature in automatic ancient coin analysis,” Sci, vol. 2, no. 2, p. 37, 2020.
  • [8] H. Anwar, S. Anwar, S. Zambanini, and F. Porikli, “Deep ancient Roman Republican coin classification via feature fusion and attention,” Pattern Recognit., vol. 114, p. 107871, 2021.
  • [9] G. Ciaburro and B. Venkateswaran, Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing Ltd, 2017.
  • [10] M. Crawford, “Coinage of the Roman Republic Online,” 2021. http://numismatics.org/crro/.
  • [11] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning, 2019, pp. 6105–6114.
  • [12] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700–4708.
  • [13] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,” J. Biomol. Struct. Dyn., vol. 39, no. 15, pp. 5682–5689, 2021.
  • [14] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251–1258.
  • A. Howard et al., “Searching for mobilenetv3,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 1314–1324.

BI-DIRECTIONAL CLASSIFICATION OF ROMAN PERIOD COINS BY DEEP LEARNING METHODS

Yıl 2023, Cilt: 5 Sayı: 2, 161 - 169, 30.06.2023

Kıyas Kayaalp Fehmi Özkaner

https://doi.org/10.47933/ijeir.1269680

Öz

In this study, the problem of classification of coins, which have historical importance and can only be distinguished by experts, is discussed with pre-learning deep learning algorithms. In the solution of the problem, the RRC-60 dataset, which consists of the images of the coins used in the Roman Republic period, was used. In this study, pre-learning Xception, MobileNetV3-L, EfficientNetB0 and DenseNet201 models were trained using the images on both sides of the coins in the data set. As a result of the training, the best values, Precision, Recall and F1-Score metrics in the MobileNetV3-L model were 98.2%, 96.8%, 97.5%, respectively, and the test accuracy was 95.2%

Anahtar Kelimeler

classification CNN coin deep learning

Kaynakça

  • [1] A. I. Khan, S. M. K. Quadri, S. Banday, and J. L. Shah, “Deep diagnosis: A real-time apple leaf disease detection system based on deep learning,” Comput. Electron. Agric., vol. 198, p. 107093, 2022.
  • [2] F. Şenel, “A Hyperparameter Optimization for Galaxy Classification,” Comput. Mater. Contin., vol. 74, no. 2, 2023.
  • [3] H. Temiz, B. Gökberk, and L. Akarun, “TurCoins: Turkish republic coin dataset,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1–4.
  • [4] S. Aslan, S. Vascon, and M. Pelillo, “Two sides of the same coin: Improved ancient coin classification using Graph Transduction Games,” Pattern Recognit. Lett., vol. 131, pp. 158–165, 2020. [5] A. G. YILMAZ, Ç. SAYGILI, and V. NABİYEV, “Roma Dönemi Madeni Paralarının Derin Öğrenme Tabanlı Sınıflandırılması.”
  • [6] H. Anwar, S. Sabetghadam, and P. Bell, “An Image-Based Class Retrieval System for Roman Republican Coins,” Entropy, vol. 22, no. 8, p. 799, 2020.
  • [7] Y. Ma and O. Arandjelović, “Classification of ancient roman coins by denomination using colour, a forgotten feature in automatic ancient coin analysis,” Sci, vol. 2, no. 2, p. 37, 2020.
  • [8] H. Anwar, S. Anwar, S. Zambanini, and F. Porikli, “Deep ancient Roman Republican coin classification via feature fusion and attention,” Pattern Recognit., vol. 114, p. 107871, 2021.
  • [9] G. Ciaburro and B. Venkateswaran, Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing Ltd, 2017.
  • [10] M. Crawford, “Coinage of the Roman Republic Online,” 2021. http://numismatics.org/crro/.
  • [11] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning, 2019, pp. 6105–6114.
  • [12] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700–4708.
  • [13] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,” J. Biomol. Struct. Dyn., vol. 39, no. 15, pp. 5682–5689, 2021.
  • [14] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251–1258.
  • A. Howard et al., “Searching for mobilenetv3,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 1314–1324.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Kıyas Kayaalp 0000-0002-6483-1124

Fehmi Özkaner 0000-0003-4652-5155

Erken Görünüm Tarihi 6 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Kabul Tarihi 7 Mayıs 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 5 Sayı: 2

Kaynak Göster

APA Kayaalp, K., & Özkaner, F. (2023). BI-DIRECTIONAL CLASSIFICATION OF ROMAN PERIOD COINS BY DEEP LEARNING METHODS. International Journal of Engineering and Innovative Research, 5(2), 161-169. https://doi.org/10.47933/ijeir.1269680
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