support vector machine

Information technology for gender recognition by voice

Gender recognition from voice is a challenging problem in speech processing. This task involves extracting meaningful features from speech signals and classifying them into male or female categories. In this article, was implemented a gender recognition system using Python programming. I first recorded voice samples from both male and female speakers and extracted Mel-frequency cepstral coefficients (MFCC) as features. Then trained, a Support Vector Machine (SVM) classifier was on these features and evaluated its performance using accuracy, precision, recall, and F1-score metrics.

Density based fuzzy support vector machine: application to diabetes dataset

In this work, we propose a deep prediction diabetes system based on a new version of the support vector machine optimization model.  First, we determine three types of patients (noisy, cord, and interior) basing on specific parameters. Second, we equilibrate the clinical data sets by suppressing noisy and cord patients.  Third, we determine the support vectors by solving an optimization program with a reasonable size.

Regression analysis of the performance of asynchronous electric motors on the basis of support vector machine (SVM)

The subject of the article is relevant, since the proposed method for performing a regression analysis of the operation of asynchronous motors does not have special requirements to the accuracy of measuring the quantities used in the regression analysis and to the volume of a training sample, so it can be used in modern embedded diagnostic systems.