Öz
In this study, clustering process has been performed using artificial neural network (ANN) approach on the pictures belonging to our dataset to determine if the banknotes are genuine or counterfeit. Four input parameters, one hidden layer with 10 neurons and one output has been used for the ANN. All of these parameters were real-valued continuous. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extractfeatures from images. Four input parameters are processed in the hidden layer with 10 neurons and the output realizes the clustering process. The classification process of 1372 unit data by using ANN approach is sure to be a success as much as the actual data set. The regression results of the clustering process is considerably well. It is determined that the training regression is 0,99914, testing regression is 0,99786 and the validation regression is 0,9953, respectively. Based on the results obtained, it is seen that classification process using ANN is capable of achieving outstanding success.
Anahtar Kelimeler
Kaynakça
- http://yzgrafik.ege.edu.tr/~tekrei/dosyalar/sunum/gi.pdf
- http://archive.ics.uci.edu/ml/datasets/banknote+authentication
- Yüksel Özbay, “EKG Aritmilerini Hızlı Tanıma”, Doktora Tezi, 1999
- Ömer KELEŞOĞLU, Cevdet Emin EKİNCİ, Adem FIRAT, The Using Of Artificial Neural Networks In Insulation Computations, Journal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi Sigma/2005-3
- Çolak,C , Çolak M.C, Atıcı M.A, “Ateroskleroz’un Tahmini İçin Bir Yapay Snir Ağı” 2005. http://dergiler.ankara.edu.tr/dergiler/36/204/1672.pdf
- Yao X. Evolving Artificial Networks, Proceeding of the Iee 1999;87:1423-44.
- Cinar, M., Engin, M., Engin, E.Z., & Ates, Y.Z. (2009). Early Prostate Cancer Diagnosis by Using Artificial Neural Networks. Expert Systems with Applications, 6357–6361.
- Lorenz, A., Blum, M., Ermert, H., & Senge, Th. (1997). Comparison of Different Neuro-Fuzzy Classification Systems for the Detection of Prostate Cancer in Ultrasonic Images. Ultrasonics Symposium, 2, 1201-1204.
- Ronco, A.L., & Fernandez, R. (1999). Improving Ultrasonographic Diagnosis of Prostate Cancer with Neural Networks. Ultrasound in Med. & Biol., vol. 25, no. 5, pp. 729–733.
Öz
Kaynakça
- http://yzgrafik.ege.edu.tr/~tekrei/dosyalar/sunum/gi.pdf
- http://archive.ics.uci.edu/ml/datasets/banknote+authentication
- Yüksel Özbay, “EKG Aritmilerini Hızlı Tanıma”, Doktora Tezi, 1999
- Ömer KELEŞOĞLU, Cevdet Emin EKİNCİ, Adem FIRAT, The Using Of Artificial Neural Networks In Insulation Computations, Journal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi Sigma/2005-3
- Çolak,C , Çolak M.C, Atıcı M.A, “Ateroskleroz’un Tahmini İçin Bir Yapay Snir Ağı” 2005. http://dergiler.ankara.edu.tr/dergiler/36/204/1672.pdf
- Yao X. Evolving Artificial Networks, Proceeding of the Iee 1999;87:1423-44.
- Cinar, M., Engin, M., Engin, E.Z., & Ates, Y.Z. (2009). Early Prostate Cancer Diagnosis by Using Artificial Neural Networks. Expert Systems with Applications, 6357–6361.
- Lorenz, A., Blum, M., Ermert, H., & Senge, Th. (1997). Comparison of Different Neuro-Fuzzy Classification Systems for the Detection of Prostate Cancer in Ultrasonic Images. Ultrasonics Symposium, 2, 1201-1204.
- Ronco, A.L., & Fernandez, R. (1999). Improving Ultrasonographic Diagnosis of Prostate Cancer with Neural Networks. Ultrasound in Med. & Biol., vol. 25, no. 5, pp. 729–733.
Ayrıntılar
| Bölüm | Research Article |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 31 Mart 2016 |
| Yayımlandığı Sayı | Yıl 2016 Cilt: 4 Sayı: 1 |