Scopus İndeksli Açık & Kapalı Erişimli Yayınlar
Permanent URI for this communityhttps://hdl.handle.net/11727/10752
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Item Estimation of Concentration Values of Different Gases Based on Long Short-Term Memory by Using Electronic Nose(2021) Bakiler, Hande; Guney, Selda; 0000-0002-0573-1326An electronic nose (e-nose) is commonly used in different areas. In the e-nose studies, one of the most important subjects is the estimation of the different concentration values of different gases. An accurate estimation of gas concentrations plays a very important role in sensitive issues such as disease detection. This study has been carried out to increase the classification and regression successes of concentration values of four different gases detected by 4 metal oxide gas sensors. The different methods are used to compare the success of the classification of the concentration levels and the success of the estimation of concentration values of these all gases. In order to realize these classification and regression processes, first a preprocessing and a feature extraction steps were applied to the raw data. The focus of this study is to increase the success achieved in classification and regression by performing the feature extraction using the proposed method. In the proposed method, "Fully Connected Layer" of Long Short-Term Memory networks was used as a feature extraction. Then, these extracted features were used. The results of the proposed method are compared the other traditional methods. It was observed that there was an improvement in both the classification and regression results with the proposed method. The highest accuracy rate in the classification were obtained in the Support Vector Machine method with 90.8% and in the regression problem, the best mean square errors were obtained with Gaussian Process Regression by using the proposed method.Item Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure(2020) Guney, Selda; Atasoy, Ayten; 0000-0002-0573-1326The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a seals of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.