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Author: search.filters.author.Guney, SeldaSubject: search.filters.subject.Classification

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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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    Applications of Deep Learning Techniques to Wood Anomaly Detection
    (2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPG
    Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. Some difficulty and complexity of production processes result in high variability of raw materials such as a wide range of visible structural defects that must be checked by specialists on line or of line. These issues are not only difficult and biased in manual processes, but also less effective and misleading. To overcome the drawbacks of the manual quality control processes, machine vision-based inspection systems are in great of interest recently for quality control applications. In this study, the wood anomaly has been detected by using deep learning. As it will be a distinction-based method on image processing, the Convolution Neural Network (CNN), which is one of the most suitable methods, has been used for anomaly detection. In addition, it will be tried to obtain the most suitable one among different CNN architectures such as ShuffleNet, AlexNet, GoogleNet for the problem. MobileNet, SqueezeNet, GoogleNet, ShuffleNet among considered methods show promising results in classifying normal and abnormal wood products.
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    Obesity Level Estimation based on Machine Learning Methods and Artificial Neural Networks
    (2021) Celik, Yaren; Guney, Selda; Dengiz, Berna
    Obesity is a growing societal and public health problem starting from 1980 that needs more attention. For this reason, new studies are emerging day by day, including those looking for obesity in children, especially the impact factors, and how to predict the emergence of the situation under these factors. In this study, different classification methods were applied for the estimation of obesity levels. Based on the evaluation criteria, the results were compared for different machine learning methods. When the Cubic SVM method was applied by selecting the appropriate features specific to the problem, 97.8% accuracy was obtained.
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    Wi-Fi Based Indoor Positioning System with Using Deep Neural Network
    (2020) Guney, Selda; Erdogan, Alperen; Aktas, Melih; Ergun, Mert
    Indoor positioning is one of the major challenges for the future large-scale technologies. Nowadays, it has become an attractive research subject due to growing demands on it. Several algorithms and techniques have been developed over the decades. One of the most cost-effective technique is Wi-Fi-based positioning systems. This technique is infrastructure-free and able to use existing wireless access points in public or private areas. These systems aim to classify user's location according to pre-defined set of grids. However, Wi-Fi signals could be affected by interference, blockage of walls and multipath effect which increases error of classification. In this study Deep Neural Networks and conventional machine learning classifiers are utilized to classify 22 squared grids which represent locations. Five primary Wireless Access Points (WAPs) were mounted indoor environment and 177 secondary WAPs are observed by Wi-Fi module. Dataset was created with using five primary and 177 secondary WAPs. The performance of proposed method was tested using Deep Neural Networks and machine learning classifiers. The results show that Deep Neural Network present the best performance as compared to machine learning classifiers. 95.45% accuracy was achieved by using five primary WAPs and 97.27% accuracy was achieved by using five primary and 177 secondary WAPs together for Deep Neural Network.