Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 14 Sayı: 1, 83 - 87, 30.06.2024
https://doi.org/10.36222/ejt.1336397

Öz

Kaynakça

  • [1] Türe A, Savaşçın MY. Birth of jewelery Goldaş publications 2000.
  • [2] Öztemel E. Artificial neural networks Papatya publications April 2012.
  • [3] Deng L, Yu D. Deep Learning: Methods and Applications, vol. 7. 2013.
  • [4] LeCun Y, Bengio Y, Hinton G. Deep learning Nature İnternational journel of science pages 436–444 (28 May 2015)
  • [5] Goodfellow I. “Chapter06 Deep Feedforward Networks,” Deep Learning Book, no. 1, pp. 169–229, 2015.
  • [6] Buduma N, Locascio N. Fundamentals of Deep Learning, vol. 521. 2015.
  • [7] Ahmetoğlu H, Daş R. Classification of Attack Types from Big Data Sets with Deep Learning 2019 International Artificial Intelligence and Data Processing Symposium (IDAP)

Classification of filigree silver with Artificial Neural Networks according to production methods

Yıl 2024, Cilt: 14 Sayı: 1, 83 - 87, 30.06.2024
https://doi.org/10.36222/ejt.1336397

Öz

The jewelry industry uses precious stones and metals in various ways while ornaments and jewelry are made. One of the methods used is the filigree method. The most critical factor in the filigree method is human and craftsmanship. However, rapid technological developments make the machine use in filigree mandatory. As a result, filigree products produced by handwork can be created using serial molds in the factory environment. This study aims to classify the molded product filigree silver using artificial neural networks. Filigree products produced by filigree masters and as mold products were compared to distinguish the filigree products. The color of the silver jewelry, the state of the jewelry, the silver setting status, the brass metal used in the silver jewelry, the form of the inner filling motif, the shape of the roof wire, the smoothness of the structure, the proper placement of the inner filling, the symmetrical status of the motifs on the jewelry are trained in the system using Deep Learning, which is an artificial neural networks method through thehe data collected from features such as the use of valuable or worthless stones. The success of classifying filigree jewelry handcrafts or mold products using Deep Learning through artificial neural network methods was evaluated. As a result of the study, the classification with deep learning was conducted successfully.

Kaynakça

  • [1] Türe A, Savaşçın MY. Birth of jewelery Goldaş publications 2000.
  • [2] Öztemel E. Artificial neural networks Papatya publications April 2012.
  • [3] Deng L, Yu D. Deep Learning: Methods and Applications, vol. 7. 2013.
  • [4] LeCun Y, Bengio Y, Hinton G. Deep learning Nature İnternational journel of science pages 436–444 (28 May 2015)
  • [5] Goodfellow I. “Chapter06 Deep Feedforward Networks,” Deep Learning Book, no. 1, pp. 169–229, 2015.
  • [6] Buduma N, Locascio N. Fundamentals of Deep Learning, vol. 521. 2015.
  • [7] Ahmetoğlu H, Daş R. Classification of Attack Types from Big Data Sets with Deep Learning 2019 International Artificial Intelligence and Data Processing Symposium (IDAP)
Toplam 7 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Sabahattin Akgül 0000-0001-9088-0993

Hamit Adin 0000-0003-2455-967X

Hüseyin Ahmetoğlu 0000-0002-4320-0198

Erken Görünüm Tarihi 23 Ağustos 2024
Yayımlanma Tarihi 30 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 14 Sayı: 1

Kaynak Göster

APA Akgül, S., Adin, H., & Ahmetoğlu, H. (2024). Classification of filigree silver with Artificial Neural Networks according to production methods. European Journal of Technique (EJT), 14(1), 83-87. https://doi.org/10.36222/ejt.1336397

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