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2020 National Conference on Advances in Applied Sciences and Mathematics, NCASM 2020 ; 2357, 2022.
Article in English | Scopus | ID: covidwho-1873612


From the past several year's educational institutions at every level was practicing conventional teaching-learning process. It seems to be the only way of imparting education to the learners. However, due to the onset of Coronavirus, i.e., Covid19, everything came to a halt for a while all over the world. The complete ecosystem of human society gets affected by this. Educational institutions also hit hard by this pandemic, and the complete layout of the teaching-learning process over the globe changed. It brings challenges for both students and teachers to withstand in this tough situation and to restore the backbone of learning. E-learning activities slowly-slowly gripped the complete educational ecosystem and the way of imparting education also changed. The primary purpose of this study is to examine the level of cognition faced by e-learners as compared to traditional learners at higher education level during Covid19. For this study, 120 students from Chitkara University have voluntarily participated and submitted their responses through the Google Form. It deduces from this study that learners using E-learning mode feel more cognitive load as compared to traditional learners. The cohen's d-value which represents the effect size comes to be 0.97 which means that the effect of cognitive load is significantly large on e-learners than the traditional learners. In the future, the reasons behind the rise of the cognition level of e-learners need to evaluate and improve the learning gain of e-learners. © 2022 Author(s).

Asian Journal of Pharmaceutics ; 15(3):392-398, 2021.
Article in English | Web of Science | ID: covidwho-1519425


Purpose: The objectives of present study were to evaluate the properties of a behavior modification of people in India associated with coronavirus outbreak. The behavior modification is measure of coronavirus related psychopathology, which was validated through a large sample study on adults who reported significant change in behavior due to watching and reading about coronavirus pandemic. Methods: Door to door and online survey conducted in Morena and Gwalior District, Madhya Pradesh, India, for collection of data of 1050 adults'. The partakers were contacted individually, through online and complete information were taken out through a questionnaire of different parameters. Because the study focused on the effect of thinking and/or watching about coronavirus disease 2019 (COVID-19) on physical activity of body, behavior, mental stress, anxiety, faith in God, and appetite. Results: A total of 16 symptoms of behavior modification due to coronavirus outbreak were statistically defined through a principal component analysis with Varimax rotation. The results were confirmed by a two-component structure and the total variance explained 55.23% for first component accounting. The six prime loadings on the first component were selected for the behavior changes because these loadings well exceeded the criteria for psychometrically prime items. Especially, communalities extraction coefficients (CEC) ranged from 0.717 to 0.853, coefficients of structure/pattern ranged from 0.71 to 0.90, and cross-loadings ranged from 0.17 to 0.22. These symptoms were used for the determination of different parameters such as decreased physical activity, psychological disturbances, mental stress, anxiety, faith in God, and appetite before arising COVID-19 infection and were highly reliable (alpha = 0.83) as a cluster. Conclusion: For the coronavirus outbreak, models will be required for control the negative behavior modification of peoples so that they can use their skill for positive outcomes. The clear cut updated policy of should be implemented to reduce these types of modifications. The prime symptoms of behavior modification were validated and stabilized through statistical tools such as CEC, coefficients of structure/pattern, and analysis of variance. Hence, we can say that if opinions of some experts are correct, then world's most of population need special care to avoid behavior modification due to coronavirus outbreak.

3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021 ; : 1048-1053, 2021.
Article in English | Scopus | ID: covidwho-1476057


The COVID-19 (previously known as '2019 novel coronavirus') took the big form and outspread rapidly around the world becoming a pandemic. Artificial intelligence tools come out to be one of the fastest solutions to detect the disease and in another way helping to control the spread. This paper signifies how chest X-ray images use deep learning techniques which are very useful for analyzing images to detect the virus and spotting high-risk patients for controlling the spread. Further, it shows how the Convolutional Neural Network (CNN) technology of deep learning helps to detect the virus quickly. A CNN is a type of artificial neural network that is used for image pre-processing and consists of many layers that aid in detection. A sequential CNN model is proposed with different kernel sizes, filters, and having different parameters using a dataset of 2159 images. The output shows that a model with an adequate amount of filters, max-pooling layers, dropout layers and dense layers imparts the highest accuracy of 99.53% in detecting the coronavirus. © 2021 IEEE.