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Has files: falseSubject: neural network

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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Neural Network Based Estimation of Resonant Frequency of an Equilateral Triangular Microstrip Patch Antenna
    (Univ Osijek, Tech Fac, 2013) Kapusuz, Kamil Yavuz; Tora, Hakan; Can, Sultan; Airframe and Powerplant Maintenance; Department of Electrical & Electronics Engineering
    This study proposes an artificial neural network (ANN) model in order to approximate the resonant frequencies of equilateral triangular patch antennas. The neural network structure applied here is trained and tested for both single-layer and double-layer antennas. It is shown upon experiment that the resonant frequencies obtained from the neural network are both more accurate than the calculated frequencies by formula and satisfactorily close to the measured frequencies. Results appear to be promising as per the available literature. This paper also may offer more efficient approach to developing antennas of such nature. While the total absolute error of 7 MHz and the average error of 0,09 % are achieved for single-layer antenna, the total absolute and average errors are 49 MHz and 0,07 % for the double-layered antenna, respectively.
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    Conference Object
    Citation - Scopus: 11
    Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2017) Akan,E.; Tora,H.; Uslu,B.
    In this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor. © 2017 IEEE.
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    Article
    Citation - Scopus: 1
    Two-Stage Feature Generator for Handwritten Digit Classification
    (Mdpi, 2023) Pirim, M. Altinay Gunler; Tora, Hakan; Oztoprak, Kasim; Butun, Ismail
    In this paper, a novel feature generator framework is proposed for handwritten digit classification. The proposed framework includes a two-stage cascaded feature generator. The first stage is based on principal component analysis (PCA), which generates projected data on principal components as features. The second one is constructed by a partially trained neural network (PTNN), which uses projected data as inputs and generates hidden layer outputs as features. The features obtained from the PCA and PTNN-based feature generator are tested on the MNIST and USPS datasets designed for handwritten digit sets. Minimum distance classifier (MDC) and support vector machine (SVM) methods are exploited as classifiers for the obtained features in association with this framework. The performance evaluation results show that the proposed framework outperforms the state-of-the-art techniques and achieves accuracies of 99.9815% and 99.9863% on the MNIST and USPS datasets, respectively. The results also show that the proposed framework achieves almost perfect accuracies, even with significantly small training data sizes.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 8
    Hand Gesture Classification Using Inertial Based Sensors Via a Neural Network
    (Ieee, 2017) Akan, Erhan; Tora, Hakan; Uslu, Baran
    In this study, a mobile phone equipped with four types of sensors namely, accelerometer, gyroscope, magnetometer and orientation, is used for gesture classification. Without feature selection, the raw data from the sensor outputs are processed and fed into a Multi-Layer Perceptron classifier for recognition. The user independent, single user dependent and multiple user dependent cases are all examined. Accuracy values of 91.66% for single user dependent case, 87.48% for multiple user dependent case and 60% for the user independent case are obtained. In addition, performance of each sensor is assessed separately and the highest performance is achieved with the orientation sensor.