Institutional Depot of BBA University

Classification du son contre la toux fondée sur des méthodes explicables : étude de cas Covid 19

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dc.contributor.author Hamza, Abdessamed
dc.contributor.author Yaiche, Imad Eddine
dc.date.accessioned 2024-09-30T09:56:17Z
dc.date.available 2024-09-30T09:56:17Z
dc.date.issued 2024
dc.identifier.issn MM/835
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/5516
dc.description.abstract This project aims to develop a computer-aided diagnostic tool for the early detection of COVID-19 through voice analysis. The proposed system operates in two main stages: sound feature extraction and classification. For feature extraction, we utilized Mel-Frequency Cepstral Coefficients (MFCCs), a common technique in voice-based disease detection. The disease classification task employs three supervised machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT). We will evaluate our proposed system using a publicly available dataset (TOS). The performance of the system will be measured using metrics such as accuracy, sensitivity, specificity, F1 score, and Receiver Operating Characteristic (ROC) curves. These metrics provide insights into the system's ability to correctly identify positive and negative cases. en_US
dc.language.iso fr en_US
dc.subject voice,COVID-19,features extraction, classification. en_US
dc.subject voix,COVID-19,extraction de fonctionnalités, classificateurs,simulation. iii en_US
dc.title Classification du son contre la toux fondée sur des méthodes explicables : étude de cas Covid 19 en_US
dc.type Thesis en_US


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