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Öğe Banknote classification using artificial neural network approach(2016) Kaya, Esra; Yasar, Ali; Saritas, IsmailIn 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 successÖğe Classification of leaf type using artificial neural networks(2015) Yasar, Ali; Saritas, Ismail; Sahman, M. Akif; Dundar, A. OktayA number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. Then Silva et al in 2014 have presented database comprises 40 different plant species. We performed in our study a classification process using dataset and artificial neural networks which have been prepared by Silva and et al. It has been determined that classification accuracy is over 92%.Öğe Classification of Wheat Types by Artificial Neural Network(2016) Yasar, Ali; Kaya, Esra; Sarıtas, IsmailIn 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 successÖğe Evaluation of The Classification Performance of Methods Developed in ANN Training(Selçuk Üniversitesi, 2021) Yasar, Ali; Saritas, IsmailIn this study, the performance and success of some models used in the training of artificial neural networks are compared. The UCI machine learning database was used for the performance evaluation and comparison process and for the IRIS dataset), which we often encounter in performance evaluations. In the study, the classification performances of our data sets were calculated using Feedforward Neural Network Cross-Validation (FNN-CV), Probabilistic Neural Network Cross-Validation (PNN-CV) and Recurrent Neural Network Cross-Validation (RNNCV) learning techniques. Our data has been classified using a 10-fold cross validation technique. The RNN-CV method for the IRIS data set proved to be a good classification method by achieving 100% accuracy success which shows that it should be used in classification problems.