Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Study of Fish Species Discrimination Via Electronic Nose(2015) Guney, Selda; Atasoy, Ayten; 0000-0003-1188-2902; 0000-0002-0573-1326; HJH-3630-2023; AAC-7404-2020Fish freshness is a critical issue in determining fish quality. Since fish freshness changes according to the fish species, fish species has to be identified before examining the freshness. So far, fish species have been distinguished through different methods such as image processing. In this paper, an electronic nose has been used to distinguish between different species of fish. Thus, both freshness and species of fish will be determined just using a single, low cost device. The aim of this study is to distinguish between three different species of fish - horse mackerel, anchovy and whiting - by using an electronic nose composed of 8 different metal oxide gas sensors. In order to distinguish between the species of fish, a whole new method, which is not applied to this kind of data previously, is used and proposed for use in the pattern recognition unit of the electronic nose. It is examined in three parts such as signal pre-processing, feature extraction and classification. In the pre-processing stage, to reduce the negative effect of sensor drift, a new method is applied to the raw signal in addition to the well-known baseline manipulation method. In the feature extraction part, the sub-sampling method which is not frequently used is applied to the pre-processed signal. The extracted features are used in the classification part. The structure of the proposed classification algorithm is based on binary decision tree structure. The binary decision tree structure is composed of nodes. In every node of the decision tree structure, the feature spaces or classification algorithm can be changed according to the problem. Classification results demonstrate the effectiveness of the presented models. The overall accuracy of the identification of fish species achieved with the proposed methods is 96.18%. The performance of the proposed method is also compared to conventional methods such as Naive Bayes, k-Nearest Neighbor and Linear Discriminant Analysis. The successes of these classifiers are 84.73, 80 and 82.4, respectively. (C) 2015 Elsevier B.V. All rights reserved.Item 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-7404-2020In 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.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.