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1st International Conference on Advances in Computing and Future Communication Technologies, ICACFCT 2021 ; : 33-38, 2021.
Article in English | Scopus | ID: covidwho-2018770


With the periodic rise and fall of COVID-19 and countries being inflicted by its waves, an efficient, economic, and effortless diagnosis procedure for the virus has been the utmost need of the hour. Amongst the infected subjects, the asymptomatic ones need not be entirely free of symptoms caused by the virus. They might not show any observable symptoms like the symptomatic subjects, but they may differ from uninfected ones in the way they cough. These differences in the coughing sounds are minute and indiscernible to the human ear, however, these can be captured using machine learning models. In this paper, we present a deep learning approach to analyze the acoustic dataset provided in Track 1 of the DiCOVA 2021 Challenge containing cough sound recordings belonging to both COVID-19 positive and negative examples. To perform the classification we propose a ConvNet model. It achieved an AUC score percentage of 72.23 on a blind test set provided in the challenge for an unbiased evaluation of the models. Moreover, the ConvNet model incorporated with Data Augmentation further increased the AUC score percentage from 72.23 to 87.07. It also outperformed the DiCOVA 2021 Challenge's baseline model by 23% thus, claiming the top position on the DiCOVA 2021 Challenge leaderboard. This paper proposes the use of Mel Frequency Cepstral Coefficients as the input features to the proposed model. © 2021 IEEE.

Kathmandu University Medical Journal ; 20(77):12-18, 2022.
Article in English | EMBASE | ID: covidwho-1925450


Background Patients are hesitant to enter a dental hospital because of the significant danger of cross infection and illness transmission due to rapid spread of corona virus. Objective To assess knowledge regarding Covid-19, oral health practices and circumstances on dental treatment during a pandemic. Method Cross sectional study was conducted among patients visiting dental department of Dhulikhel hospital from September to October 2020. Questionnaires were interviewed following safety protocols regarding the pandemic and descriptive analysis was performed. Both verbal and written consent as well as ethical approval was taken before the study. Result A total 411 patients aged 14 to 75 years old from 14 different districts across Nepal participated in the study. All of the patient were free of Covid-19 symptoms and had strong knowledge and awareness about disease transmission. During the crisis 96% of the people maintain good oral hygiene while 25.8% acquire new dental problems where majority experienced oral discomfort and swelling, 93.2% of them did not attend a dental clinic or hospital in the interim owing to fear and inaccessibility. Majority of the participants were impressed by the safety precautions and preparations during treatment and 99.3% strongly suggest or pledge to visit dental department if necessary during the pandemic. Conclusion Dental patient visiting Dhulikhel hospital is highly aware of current health crisis, possible transmission and preventive measures. Proper safe hospital setup can encourage them to seek dental treatment during crisis. Dental pain and swelling in Endodontic department recorded most common dental emergency during this pandemic.