ABSTRACT
Coronavirus pandemic that has spread all over the world, is one of its kind in the recent past, that has mobilized researchers in areas such as (not limited to) pre-screening solutions, contact tracing, vaccine developments, and crowd estimation. Pre-screening using symptoms identification, cough classification, and contact tracing mobile applications gained significant popularity during the initial outbreak of the pandemic. Audio recordings of coughing individuals are one of the sources that can help in the pre-screening of COVID-19 patients. This research focuses on quantitative analysis of covid cough classification using audio recordings of coughing individuals. For analysis, we used three different publicly available datasets i.e., COUGHVID, NoCoCoDa, and a self-collected dataset through a web application. We observed that wet cough has more correlation with covid cough as opposed to dry cough. However, the classification model trained with wet and dry coughs, both, has similar test performance as that of the model trained with wet cough samples only. We conclude that audio-signal recordings of coughing individuals have the potential as a pre-screening test for COVID-19. © 2022 ACA.
ABSTRACT
Since the first confirmed case of COVID-19, in-formation was spreading in large amounts over social media platforms. Information spreading about the COVID-19 pandemic can strongly influence people's behavior. Therefore, identifying information superspreaders (or influencers) during the COVID-19 pandemic is an important step towards understanding public reactions and information dissemination. In this work, we present an analysis over a large Arabic tweets collected during the COVID-19 pandemic. The presented study construct a network from users' behaviors to identify information superspreaders during the month of March, 2020. black We employ several techniques including Centrality Metrics, HITS, PageRank, VoteRank algorithms, and the weighted correlated influence measure (WCI) to analyze the influence of information spreading, and compare the ranking of the users. The results show that the most of superspreaders were news and governments accounts © 2021 IEEE.
ABSTRACT
The outbreak of the new coronavirus disease (COVID-19) has affected human life to a great extent on a worldwide scale. During the coronavirus pandemic, public health professionals at the early outbreak faced an extraordinary challenge to track and quantify the spread of disease. To investigate whether a digital surveillance model using google trends (GT) is feasible to monitor the outbreak of coronavirus in the Kingdom of Saudi Arabia. We retrieve GT data using ten common COVID-19 symptoms related keywords from March 2, 2020, to October 31, 2020. Spearman correlation were performed to determine the correlation between COVID-19 cases and the Google search terms. GT data related to Cough and Sore Throat were the most searched symptoms by the Internet users in Saudi Arabia. The highest daily correlation found with the Loss of Smell followed by Loss of Taste and Diarrhea. Strong correlation as well was found between the weekly confirmed cases and the same symptoms: Loss of Smell, Loss of Taste and Diarrhea. We conducted an investigation study utilizing Internet searches related to COVID-19 symptoms for surveillance of the pandemic spread. This study documents that google searches can be used as a supplementary surveillance tool in COVID-19 monitoring in Saudi Arabia. © 2021 IEEE.
ABSTRACT
The new coronavirus outbreak (COVID-19) has swept the world since December 2019 posing a global threat to all countries and communities on the planet. Information about the outbreak has been rapidly spreading on different social media platforms in unprecedented level. As it continues to spread in different countries, people tend to increasingly share information and stay up-to-date with the latest news. It is crucial to capture the discussions and conversations happening on social media to better understand human behavior during pandemics and alter possible strategies to combat the pandemic. In this work, we analyze the Arabic content of Twitter to capture the main discussed topics among Arabic users. We utilize Non-negative Matrix Factorization (NMF) to discover main issues and topics based on a dataset of Arabic tweets from early January to the end of April, and identify the most frequent unigrams, bigrams, and trigrams of the tweets. Eventually, the discovered topics are then presented and discussed which can be roughly classified into COVID-19 origin topics, prevention measures in different Arabic countries, prayers and supplications, news and reports, and finally topics related to preventing the spread of the disease such as curfew and quarantine. To our best knowledge, this is the first work addressing the issue of detecting COVID-19 related topics from Arabic tweets. © 2020, Springer Nature Switzerland AG.
ABSTRACT
In epidemic situations such as the novel coronavirus (COVID-19) pandemic, face masks have become an essential part of daily routine life. The face mask is considered as a protective and preventive essential of everyday life against the coronavirus. Many organizations using a fingerprint or card-based attendance system had to switch towards a face-based attendance system to avoid direct contact with the attendance system. However, face mask adaptation brought a new challenge to already existing commercial biometric facial recognition techniques in applications such as facial recognition access control and facial security checks at public places. In this paper, we present a methodology that can enhance existing facial recognition technology capabilities with masked faces. We used a supervised learning approach to recognize masked faces together with in-depth neural network-based facial features. A dataset of masked faces was collected to train the Support Vector Machine classifier on state-of-the-art Facial Recognition Feature vector. Our proposed methodology gives recognition accuracy of up to 97% with masked faces. It performs better than exiting devices not trained to handle masked faces. © 2020 IEEE.