S
Selcuk Yildirim
Researcher at Siirt University
Publications - 14
Citations - 1198
Selcuk Yildirim is an academic researcher from Siirt University. The author has contributed to research in topics: Feature extraction & Wavelet. The author has an hindex of 9, co-authored 14 publications receiving 1087 citations. Previous affiliations of Selcuk Yildirim include Fırat University.
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An effective wavelet-based feature extraction method for classification of power quality disturbance signals
TL;DR: A wavelet norm entropy-based effective feature extraction method for power quality (PQ) disturbance classification problem and a classification algorithm composed of a wavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron are presented.
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1D-local binary pattern based feature extraction for classification of epileptic EEG signals
TL;DR: In this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented.
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Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition
TL;DR: The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.
1D-Local Binary Pattern Based Feature Extraction for Classification of Epileptic EEG Signals, Applied Mathematics and Computation243 (2014): 209-219.
TL;DR: An attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals, which could acquire high accuracy in classification of epileptic EEG signals.
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An expert system based on S-transform and neural network for automatic classification of power quality disturbances
TL;DR: The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier that can effectively classify different PQ disturbances.