Öz
In this study, the types of wheat seeds are classified using present data with artificial neural network (ANN) approach. Seven inputs, one hidden layer with 10 neurons and one output has been used for the ANN in our system. All of these parameters were real-valued continuous. The wheat varieties, Kama, Rosa and Canadian, characterized by measurement of main grain geometric features obtained by X-ray technique, have been analyzed. Results indicate that the proposed method is expected to be an effective method for recognizing wheat varieties. These seven input parameters reaches the 10-neurons hidden layer of the network and they are processed and then classified with an output. The classification process of 210 units of data using ANN is determined to make a successful classification as much as the actual data set. The regression results of the classification process is quite high. It is determined that the training regression R is 0,9999, testing regression is 0,99785 and the validation regression is 0,9947, respectively. Based on these results, classification process using ANN has been seen to achieve outstanding success.
Anahtar Kelimeler
ANN Seed Classification Artificial Neural Network Kama Rosa Canadian Machine Learning Database
Kaynakça
- UCI Machine Learning Repository ( Center for Machine Learning and Intelligent Systems http://archive.ics.uci. edu/ml/datasets.html).
- Civalek, Ö., “Nöro-Fuzzy Tekniği ile Dikdörtgen Plakların Analizi”, III. Ulusal Hesaplamalı Mekanik Konferansı, 16-18 Kasım, İstanbul, 1998, 518-524.
- 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
- 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
- Dias W.P.S., Pooliyadda S.P. “Neural Networks for Predicting Properties of Concrete with Admixtures”, 2001
- Özsoy İ., Fırat M. “Kirişsiz Döşemeli Betonarme Bir Binada Oluşan Yatay Deplasmanın Yapay Sinir Ağları İle Tahmini” 2004
- M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, 'A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images', in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 1524
Öz
Kaynakça
- UCI Machine Learning Repository ( Center for Machine Learning and Intelligent Systems http://archive.ics.uci. edu/ml/datasets.html).
- Civalek, Ö., “Nöro-Fuzzy Tekniği ile Dikdörtgen Plakların Analizi”, III. Ulusal Hesaplamalı Mekanik Konferansı, 16-18 Kasım, İstanbul, 1998, 518-524.
- 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
- 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
- Dias W.P.S., Pooliyadda S.P. “Neural Networks for Predicting Properties of Concrete with Admixtures”, 2001
- Özsoy İ., Fırat M. “Kirişsiz Döşemeli Betonarme Bir Binada Oluşan Yatay Deplasmanın Yapay Sinir Ağları İle Tahmini” 2004
- M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, 'A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images', in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 1524
Ayrıntılar
| Bölüm | Research Article |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 31 Mart 2016 |
| Yayımlandığı Sayı | Yıl 2016 Cilt: 4 Sayı: 1 |