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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021 ; : 586-591, 2021.
Article in English | Scopus | ID: covidwho-1730949


The pandemic (COVID-19), which emerged in late December 2019 in Wuhan, China, is still continuing to ravage every country in the world. As a result of the outbreak, the world has experienced tough times. To date, only 24% of the world's population has been vaccinated yet, and over 18 million cases are still active. In many places, the hospital is running short of beds and oxygen cylinders. With many undetected cases and casual approaches by people, this third wave is predicted to be a big hit. Conventional methods of diagnosis, such as antigen analysis, serological tests, and polymerase chain reactions, although widely used, are time-consuming. The use of Deep Learning (DL) and a convolutional neural network (CNN) to examine chest CT (computerized tomography) or chest x-ray images has been shown to be a promising technique for early diagnosis of COVID-19. In this study, a multi-level analysis method is proposed to detect COVID-19 from chest radiographs of a human. Through the model, a fast and accurate diagnosis of coronavirus can be made at the reach of fingertips. Based on patient clinical data, an ANN is used to calculate the likelihood of the patient becoming infected with COVID-19. The proposed model makes use of 8 layers of Convolutional Neural Network which are trained on the dataset (80-10-10 train-validate-test split) which gives an accuracy of 97% on the training data and 98.7% on the validation data. © 2021 IEEE.

Information Discovery and Delivery ; ahead-of-print(ahead-of-print):16, 2021.
Article in English | WHO COVID | ID: covidwho-1052258


Purpose The study aims to develop a theoretical model that highlights the determinants of the adoption of online teaching at the time of the outbreak of COVID-19. This study adopted a time-series analysis to understand the factors leading to the adoption of online teaching. Design/methodology/approach Empirical data were gathered from 222 university faculty members by using an online survey. In the first phase, data were collected from those faculty members who had no experience of conducting online classes but were supposed to adopt online teaching as a result of the COVID-19 pandemic and subsequent lockdown. After two weeks, a slightly modified questionnaire was forwarded to the same group of faculty members, who were conducting online classes to know their perception regarding the adoption and conduct of online teaching. Findings Both the proposed conceptual frameworks were investigated empirically through confirmatory factor analysis and structural equation modeling. Significant differences were found in the perceptions of faculty members regarding before and after conducting classes through online teaching. Originality/value This study contributes to the literature by presenting and validating a theory-driven framework that accentuates the factors influencing online teaching during the outbreak of a pandemic. This research further extends the unified theory of acceptance and use of technology by introducing and validating three new constructs, namely: facilitative leadership, regulatory support and project team capability. Based on the findings, practical insights are provided to universities to facilitate adoption, acceptance and use of online teaching during a health-care emergency leading to campus lockdowns or the imposition of restrictions on the physical movement of people.

15th International Conference on Availability, Reliability and Security, ARES 2020 ; 2020.
Article in English | Scopus | ID: covidwho-1017160


The current COVID-19 pandemic highlights the utility of contact tracing, when combined with case isolation and social distancing, as an important tool for mitigating the spread of a disease [1]. Contact tracing provides a mechanism of identifying individuals with a high likelihood of previous exposure to a contagious disease, allowing additional precautions to be put in place to prevent continued transmission. Here we consider a cryptographic approach to contact tracing based on secure two-party computation (2PC). We begin by considering the problem of comparing a set of location histories held by two parties to determine whether they have come within some threshold distance while at the same time maintaining the privacy of the location histories. We propose a solution to this problem using pre-shared keys, adapted from an equality testing protocol due to Ishai et al [2]. We discuss how this protocol can be used to maintain privacy within practical contact tracing scenarios, including both app-based approaches and approaches which leverage location history held by telecoms and internet service providers. We examine the efficiency of this approach and show that existing infrastructure is sufficient to support anonymised contact tracing at a national level. © 2020 ACM.