The role of e-learning in higher education during COVID 19 pandemic

Over 60 per cent of pupils in the COVID 19 epidemic created significant disruption in the educational system, and educational institutions across the globe were closed. Essential elements of e-learning during COVID-19 have been technology management, management support, improved student knowledge about the usage of e-learning systems and the need for a high standard of information technology from instructors, students, and institutions. This article aims to find key success elements for E-learning during COVID-19 by utilising the MSP and TOPSIS methods to improve the educational process. The objective of the paper was to identify crucial success factors for e-learning during COVID-19. In COVID-19, 69 E-learning managers were interviewed based on specified evaluation and e-learning methods across various channels. Mixed learning was the best appropriate training method for practical use among the five learning systems. These findings showed the readiness of E-learning to carry out instruction throughout the COVID-19 epidemic, regardless of how exceptional the technology was at a school. © 2023 Author(s).

An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia

Current technological advances are paving the way for technologies based on deep learning to be utilized in the majority of life fields. The effectiveness of these technologies has led them to be utilized in the medical field to classify and detect different diseases. Recently, the pandemic of coronavirus disease (COVID-19) has imposed considerable press on the health infrastructures all over the world. The reliable and early diagnosis of COVID-19-infected patients is crucial to limit and prevent its outbreak. COVID-19 diagnosis is feasible by utilizing reverse transcript-polymerase chain reaction testing;however, diagnosis utilizing chest x-ray radiography is deemed safe, reliable, and precise in various cases. © 2022 IGI Global. All rights reserved.

Facemask Wearing Detection Based on Deep CNN to Control COVID-19 Transmission

Since the expansion of the COVID-19, almost all countries have advocated their residents to put on facemasks and adopt social distance and hand cleanliness. Due to the complicated attitudes in the settings of real life, besides several socio-behavioral and cultural factors, it is not easy to give a convincing situation for the general public that wearing facemasks is useful and effective. Therefore, facemasks wearing has not been widely embraced by many residents. However, the usage of facemasks has offered the considerable potential to filter or block the transmission of respiratory viruses including COVID-19. In this paper, a model of deep convolutional neural network (CNN) for facemask wearing detection is proposed to control covid-19 transmission. This proposed deep learning model includes two main processes;feature extraction and classification. The CNN classifier provides 99.57% of accuracy for the utilized Real-World Masked Face Dataset (RMFD). © 2022 IEEE.