E-learning among 2-year MBBS students in a Government Medical College in South India - A KAP study

Background: E-learning or electronic learning is a popular modality to address the educational needs of a population. In the context of medical education, E-learning is useful but has its limitations. Aim and Objectives: This study was conducted among 2-year MBBS students of a Government Medical College in South India to know their knowledge, attitude, and practice of E-learning and also to learn from their experiences during the COVID pandemic. Material(s) and Method(s): After obtaining informed consent, students were asked to fill up a questionnaire containing 15 questions in Google Forms and submit it. Result(s): This study shows that more than 70% of students consider themselves capable of using computers for everyday activities. They also reported using search engines and online animations for updated medical information. However, they preferred their course content to be delivered through blended learning, a combination of classroom and E-learning. The students reported poor internet connectivity as a major limitation in E-learning. They also suggested having a separate website for each college where the teaching material can be uploaded by the faculty and can be accessed by all the students of the institution. Conclusion(s): From this study, it can be concluded that a majority of students have good knowledge and are already using E-learning modalities. They are also open to the idea of blended learning for clinical cases.Copyright © 2023 Jeyasudha J, et al.

Fake Information Analysis and Detection on Pandemic in Twitter.

Twitter has become a popular platform to receive daily updates. The more the people rely on it, the more critical it becomes to get genuine information out. False information can easily be shared on Twitter, which influences people's feelings, especially if fake information is linked to COVID-19. Therefore, it is of utmost importance to detect fake information before it becomes uncontrollable. Real-time tweets were used as part of this study. A few features like tweet's text, sentiment etc., were extracted and analyzed. The project returns a set of statistics determining the tweet's veracity. In this study, various classifiers have been used to see which of them works best with the proposed model in classifying the used dataset. The proposed model achieved the best accuracy of 84.54% and the highest F1-score of 0.842 with Random Forest. With careful analysis while feature selection and using few features, the model developed is equivalent in performance to the other models that use a lot of features. This confirms that the model developed is less complex and highly dependable.

Topic Modeling and Sentimental Analysis of Tweets on Covid19 to Find the Weightage of the Popular Hashtag

The Hashtags plays a vital role in the social media and it is easily highlighted by each and every people when they tag it for their own views. Marketing and advertisement is booming so that to make their products work through the views of the normal or common people. Sometimes they use the false content for their publicity and misleading the people. In this paper, the covid19 tweets are taken for finding out the popular hashtags using the correlation techniques like pearson, spearman and kendall rank correlation. The Covid19 hashtag is more popular with the correlation coefficient and sentimental analysis of the tweet than coronovirus tag. To justify the popularity, the weightages of the hashtag is found out by applying the topic modeling. In that the coronovirus tag is having more weightage than Covid19 tag.

An Intelligent Centrality Measures for Influential Node Detection in COVID-19 Environment.

With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure's (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.