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An SVM classifier is utilized to classify the feature vectors in order for aggressiveness decision.
Classification: Nearest neighbor classifier (NNC) [10] is utilized to classify extracted feature based on this algorithm.
In this paper, we choose μ = 5. 3) Classification: Nearest neighbor classifier (NNC) [10] is utilized to classify extracted feature based on this algorithm.
Therefore, MARTE hardware resource modeling (HRM) package is utilized to classify different types of HW elements.
Next, the information of the corresponding color-class map is utilized to classify the pixels.
As for clustering method, discriminant analysis using regression formula is utilized to classify the data with 2 classes.
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Finally, a radial basis function neural network was utilized to classify gearbox faults.
PCA and OPLS-DA were utilized to classify the metabolic phenotypes and identify the differentiating metabolites.
The calculated DDTW results can then be utilized to classify the bearing conditions since different conditions have different DDTW bands.
Furthermore, a support vector machine and different kernel functions are utilized to classify the samples into different keybeds.
To evaluate and compare the performance of analysis methods, four linear and nonlinear methods were utilized to classify the extracted features as a seizure or a Nonseizure event.
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