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dc.contributor.advisorBarkat, Enfal
dc.contributor.advisorQaisar, Saeed
dc.contributor.authorAlsaedi, Nouf
dc.contributor.authorHamid, Razan
dc.date.accessioned2025-01-27T06:53:55Z
dc.date.available2025-01-27T06:53:55Z
dc.date.submitted2023-12-06
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1984
dc.description.abstractIn early 2020 the World Health association (WHO) declared the rapid transmission of the modern coronavirus (COVID-19) a pandemic, the current pandemic associated with the modern coronavirus since then researchers around the world have been working to aid in the diagnosis. Cough is an important symptom in many diseases and at times is the only major symptom to diagnose some ailments and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. Some of the coughs are different from each other which could define negative or positive for COVID detection. COVID-19 detection via cough sounds digital storage devices and sound sensors make it portable and accurate to record cough sounds, it would make a change in the computer technology and the availability of portable digital sound recording devices. In this context, this work focuses on the study, design, and development of an effective approach for COVID-19 detection via cough sounds-based features mining and machine learning. The objective is to achieve an effective solution with an improved level of precision compared to the existing counterparts. It can be done by smartly employing the hybrid features extraction and the robust classification techniques. The incoming audio segment will be enhanced by applying the appropriate preprocessing. The Mel-Frequency Cepstral Coefficients (MFCCs) and the discrete wavelet transform (DWT) will be extracted from the enhanced audio segment. Later appropriate lead based robust classifiers will be utilized to lead these extracted features with the reference database. The comparison outcomes will be used to make the classification decision. The classification decision will be transformed into a systematic signal wave sign. The system functionality will be tested with the help of a proposed system. The primary results and findings will be presented and discussed. The proposed approach has a potential to be helpful for the medical field to become a restful application for the majority of the peopleen_US
dc.language.isoenen_US
dc.titleCOVID-19 detection via cough sounds using a hybrid MFCC-DWT based features mining and machine learningen_US
dc.typeCapstoneen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US


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