ABSTRACT
The COVID-19 pandemic demanded the closure of education institutions abruptly in the middle of the academic term, disrupting regular teaching and learning activities throughout the world. The teaching fraternity immediately moved to online teaching to minimize learning damage and continue academic activities. With the sudden shift from traditional practices to online teaching, the key question arises about effectiveness of online teaching in higher education and how the teaching fraternity pursues academic activities, grouped under pre, during and post online teaching. This study aimed at examining the faculty perspective of online teaching in higher education without much experience and preparation. Data was collected from 81 faculty members across the disciplines of Engineering, Technology and Science for technical courses and Management and Commerce for the School of Social Science. Opinion of respondents received in pre, during and post online teaching activities and effectiveness in comparison to traditional system were analyzed.While the results show that there is no substantial pedagogical change or difficulty in delivery through online teaching. However, concerns remain about classroom management and the evaluation process through online as compared to face to face teaching. There is no significant difference of opinion from the faculties of School of Engineering and School of Management in regard to pre and post, except during the online teaching activities.
ABSTRACT
This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios;93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios;95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods.
ABSTRACT
Dental clinics can play a crucial role in spreading the SARS-CoV-2 infections because of the nature of close contact with the patient’s oral cavity and aerosol generating essential dental procedures. Dental practices in regions affected with SARS-CoV-2 are needed to take utmost precautions and follow strict effective infection control protocols even when the movement control/lockdown is gradually lifted post pandemic. Thus, we aim to summarise the current research findings on SARS-CoV-2 in dental settings and relevant international guidelines and recommended management protocols for the dental clinica. The knowledge on the viral properties of SARS-CoV-2 and epidemiologic characteristics are essential to implement proper mitigation strategies against transmission of Covid-19 infections via dental clinics. © 2020, Institute of Medico-Legal Publications. All rights reserved.