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The Fourier transform was utilized for feature extraction, and a fuzzy C-means clustering was applied to build a spatiotemporal model for each expression in[15].
In this work, the integro-differential operator is applied for iris segmentation and the 2D Gabor filters are utilized for feature extraction while the Hamming distance scores serve as a comparator.
The entropy principle together with fast discrete orthogonal S-transform (FDOST) represented by window dependent bases is utilized for feature extraction and the support vector machine (SVM) classifier model & support vector regression (SVR) model are employed for pattern recognitions to predict the types and locations of faults.
SNPs that pass the detection filter are further utilized for feature extraction.
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In this section, performance of the proposed method is investigated considering classification accuracy obtained under different conditions, such as varying the feature dimension, utilizing different spectral bands for feature extraction and employing various types of kernel function in SVM classifier.
Yang et al. [ 8] employed the word segmentation method for feature extraction on the protein sequence and then utilized the SVM for classification.
For feature extraction, five different well known techniques are investigated: Local binary patterns [16] (MRLBP): describes a texture by utilizing the joint distribution of pixel intensity differences represented by binary patterns.
Audio features used for feature extraction.
Both LPC and MFCC are used for feature extraction.
Forty-four fareors are considered for feature extraction.
DWT method was used for feature extraction from EEG signals.
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