Browsing by Author "Ozsandikcioglu, Umit"

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    Classification of Different Objects with Artificial Neural Networks Using Electronic Nose
    (2015) Ozsandikcioglu, Umit; Atasoy, Ayten; Guney, Selda; 0000-0002-5397-6301; 0000-0003-1188-2902; 0000-0002-0573-1326; AAR-4368-2020; HJH-3630-2023; AAC-7404-2020
    In this paper; an e-nose with low cost which consisting of 8 different gas sensors was used and with this e-nose 9 different odors ((mint, lemon, egg, rotten egg, angelica root, nail polish, naphthalene, rose water, and acetone) was classified. This 9 different odor are classified with Artificial Neural Networks and by using different activation functions, and then the successes of the classification were compared with each other. The maximum success of the testing data is obtained with 100% accuracy rate by using logsig activation function in hidden layer and tansig activation function in output layer. In conclusion; using the chemical database containing the odor of the different objects, distinct odors were shown to be classified correctly.
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    Fish Freshness Testing with Artificial Neural Networks
    (2015) Atasoy, Ayten; Ozsandikcioglu, Umit; Guney, Selda; 0000-0002-0573-1326; AAC-7404-2020
    In this work, with the use of an electronic nose which has 8 metal oxide gas sensors and was set up at Karadeniz Technical University, a fish freshness system was designed. There are 7 classes (1, 3, 5, 7, 9, 11, 13 day for fish storage) for classification and to perform classification process, Artificial Neural Networks was used in this work. To increase the classification success, Artificial Neural Network architecture, activation functions and input data obtained from different feature extraction method was changed, the storage condition is very important factor for fish freshness and fishes used in this study were stored at fish market conditions. In this study to determine the classification success, 5-Fold Cross Validation method was used and the maximum success rate was obtained as 98.94 %.