ABSTRACT
Introduction: Coronavirus Disease-2019 (COVID-19) pandemic has made a huge impact on medical education resulting in adoption of online methods of teaching in order to continue with the process of teaching and learning so as to complete the syllabus. In addition, online education methods also helped to provide psychological support to the students in this time of crisis. Aim: To evaluate the perception of students for online learning and to compare it with conventional classroom teaching. Materials and Methods: This cross-sectional study was conducted in the Department of Pharmacology at Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India, on fifth semester 148 medical undergraduates, in April, 2021. All the participants were randomly divided into two groups, with each group consisting of 74 students. Group I was taught antitubercular drugs through online method while, Group II was taught the same topic in classroom and then a cross-over was done among the 2 groups for antimalarial drugs on the second day. The perception of students towards online learning and the preferred method of teaching were evaluated using a predesigned standardised questionnaire. The collected data was analysed using Z-Test and p-value 0.05 was considered significant. Results: Out of 148 students, 62.84% were females. Mobile phones (86.48%) were the most commonly used gadgets for online learning. Majority of students preferred conventional classroom teaching with regard to convenience (75.67%), acquisition of knowledge (83.78%), practical skills (89.86%), interactive (84.45%), doubt clearance and the preferred method of teaching (81.08%). However, most of the students agreed that online learning saves time (62.83%) and was better suited for submission of assignments (68.9%). A statistically significant difference (p-value 0.05) was observed when online learning was compared with classroom teaching. Conclusion: The study concluded that online learning is not a substitute for classroom teaching but might supplement conventional classroom teaching. However, it helps in completion of syllabus and boosts the morale of the students during this pandemic.
ABSTRACT
A global pandemic has reduced mobility, impacting the healthcare assessment, and diagnostics on co-morbid patients. Various challenges for location finding and indoor signalling issues give rise to the necessity for compact and low-cost ultra-wideband sensor technology. Lockdown has played a vital role during crisis in engaging with specific conditions like position estimation in areas under LOS and NLOS caused by diffraction and reflection of UWB signals across obstacles. UWB-based localisation system with edge/fog computing is used to analyse the indoor/outdoor usage of quarantined patients reducing the transmission, which becomes a threat for society and healthcare personals. The mathematical models to symbolise various patterns utilising UWB sensors with anchors and tags were used to determine the flow of all rapid and random movements within the specified clustered locations. The proposed system achieves high classification accuracy with SVM to classify the patterns and assist frontline medical workers in containing pandemics through human interaction. Copyright © 2021 Inderscience Enterprises Ltd.
ABSTRACT
COVID-19 virus started to outbreak in China in the year January 2020. Contact tracing is an open-minded measure of control that applies to an extensive range of transmissible diseases. It is being used to fight infections like SARS, tuberculosis, smallpox, and many sexually transmitted diseases (STDs). From the moment of the lockdown, there have been a great many talks of applications helping to combat the coronavirus. Technical developers bring a solution to this problem by providing tools that help to contain the coronavirus. This kind of application is helpful, but it lacks in accuracy and privacy concerns. COVID-19 virus, irrespective of causes, solution, treatments, clinical signs, and symptoms is discussed in this paper. The main aim of this paper proposes a contact tracing using k-nearest neighbour, which shows the correct prediction of an affected person of COVID-19 based on the distance and also reduces the transmission of disease. It was tested on the WHO dataset obtained the prediction accuracy of which was carried out on clinical and quarantine data. The evaluation result shows that the contact tracing technique's accuracy has been improved using the proposed algorithm. © 2021 River Publishers