Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/31680
Title: | Machine learning to detect covid-19 using cough sounds |
Authors: | BENSID, Khaled GHOUAR, Brahim Elkhalil SENOUSSI, Mohammed Larbi |
Keywords: | voice COVID-19 features extraction classification |
Issue Date: | 2022 |
Publisher: | UNIVERSITY OF OUARGLA |
Abstract: | The objective of this project is to design a diagnostic aid system for the early COVID- 19 Detection disease from the voice. Mainly, the proposed system is based on two main steps: feature extraction of sound and classification. in this case we have chosen the Mel-Frequency Cepstral Coefficient (MFCC). The classification process is based on three machine learning supervised classifiers: -Support Vector Machine (SVM) -Knearest neighbors (KNN) - Decision tree (DT). Our proposed system evaluated using TOS. The performance used of our system are the accuracy, sensitivity, and specificity, F1 score, and Receiver Operating Characteristics (ROC). |
Description: | System of Telecommunication |
URI: | https://dspace.univ-ouargla.dz/jspui/handle/123456789/31680 |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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GHOUAR-SENOUSSI.pdf | System of Telecommunication | 7,8 MB | Adobe PDF | View/Open |
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