Impact of Social Media Like WhatsApp for Education Domain

The pandemic situation is affected in various ways in the education domain. The sudden transformation from offline to online teaching-learning process made students and teachers use different tools like WhatsApp for communication. The reason for this consideration is to investigate the impacts of WhatsApp utilized for instruction and decide the suppositions of understudies towards the method. The study is designed, keeping in mind the current COVID-19 situation and how it affected the education system turning it into online mode. On different questionnaires, regression and heatmap analysis is performed. The investigation showed that both learning situations have diverse impacts on the victory of understudies while supporting the conventional environment by utilizing WhatsApp is more successful for the increment of victory. The assessment moreover showed that students had superior pleasant reviews closer to the usage of WhatsApp in their courses. They requested the same workout in their one-of-a-kind courses as well. They expressed that picking up information can moreover take out unwittingly and the messages with pics were more prominent and viable for their picking up information. Be that as it may, some college understudies have communicated harming audits approximately the timing of a few posts and the repetitive posts within the bunch. At long last, it is supported that the utilization of WhatsApp within the preparing framework is to be energized as a steady innovation. . © 2023 IEEE.

Sentiment Analysis using COVID-19 Twitter Data

The COVID-19 pandemic has essentially transformed the way millions of people across the world live their life. As offices remained closed for months, employees expressed conflicting sentiments on the work from home culture. People worldwide now use social media platforms such as Twitter to talk about their daily lives. This study aims to gage the public’s sentiment on working from home/remote locations during the COVID-19 pandemic by tracking their opinions on Twitter. It is essential to study these trends at this point in the pandemic as organizations should decide whether to continue remote work indefinitely or reopen offices and workspaces, depending on productivity, and employee satisfaction. Tweets posted in the live Twitter timeline is used to generate the set of data and accessed through Tweepy API. About 2 lakh tweets relevant to the remote work during the pandemic were tokenized and then passed to Naive Bayes classifier that classifies the sentiments positive, negative, neutral to every tweet. Our findings emphasize on population sentiment which is the effects of the COVID-19 pandemic, especially resulting from the work from home policy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Computer Vision-based Social Distancing Surveillance with Automated Camera Calibration for Large-scale Deployment

Social distancing has been suggested as one of the effective measures to break the chain of viral transmission in the ongoing COVID-19 pandemic. We herein describe a computer vision-based AI-assisted solution to aid compliance with social distancing norms. The solution consists of modules to detect and track people, and to identify distance violations. It provides the flexibility to choose between a tool-based mode requiring user input or a fully automated mode of camera calibration (devised in-house), making the latter suitable for large-scale deployments. We also outline a strategy to estimate the number of video feeds which can be supported in parallel for scalability. Finally, we discuss different metrics to assess the risk associated with social distancing violations, including the use of 'violation clusters', and how we can differentiate between transient or persistent violations. Our proposed solution performs satisfactorily under different test scenarios, processes video feed at real-time speed, as well as addresses data privacy regulations by blurring faces of detected people, making it ideal for deployments. © 2021 IEEE.

Employee Sentiment Analysis Towards Remote Work during COVID-19 Using Twitter Data

The COVID-19 pandemic has essentially transformed the way of leading a life for millions of people across the world. As offices remained closed for months, employees expressed conflicting sentimental analysis on the workfrom-home culture. People worldwide use social media platforms such as Twitter to talk about their daily lives madea trend in the online platform. This research study aims to gauge the public's sentiment on working from home/ remotelocations during the COVID-19 pandemic by tracking their opinions on Twitter. The existing random forest modeltrained the data faster but failed to predict the results faster. Therefore, an ensemble model is proposed to predict anoutcome using a distinct modeling algorithm. An ensemble classifier has been used for enhancing the performancesusing the base learning classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM),Logistic Regression (LR) form an Ensemble classifier. The proposed ensemble model aggregates each base model forthe prediction and results for the unseen data. These tokens are then passed to the Ensemble classifier that classifiesthe sentiments and assigns a polarity (positive, negative, neutral) to every tweet. The proposed Ensemble methodimprove the average prediction performance over any contributing member in the ensemble. The results obtained bythe proposed Ensemble model reached accuracy of 97.47 % when compared to the existing models such as DeepLSTM, SVM model that obtained accuracy of 83 %, 84.46 % © 2022,International Journal of Intelligent Engineering and Systems. All Rights Reserved.

Impact of the COVID-19 pandemic on radiologists

COVID-19 pandemic is one of the biggest crises faced by health-care systems in the recent times. The aim of this study was to assess the impact of the COVID-19 pandemic on radiology workflow, working pattern, training and continuing professional development (CPD) activities, as well as personal well-being of the radiologists during the pandemic. Material and Methods: Questionnaire designed to gather the opinions regarding the impact of the COVID-19 pandemic was distributed to radiologists throughout the world in electronic format. Anonymized responses were obtained and analyzed. Two hundred radiologists, working in 17 different countries, responded to our questionnaire. Majority of the respondents were from India (72.8%) and 70% of the them were in the age group of 25-45 years. About 80% of respondents felt that they were well protected or moderately well protected in terms of the personal protective equipment (PPE), however, most of them felt that the use of PPE had affected their ability to work. Similar number of radiologists felt that there was significant reduction in the radiology workload. More than half of the respondents felt that their working patterns were altered by the pandemic with drastic impact on teaching, CPD activities, and personal well-being. COVID-19 pandemic has had profound impact on the radiologists all over the world. Learning from the experiences of the first wave should be used to provide innovative solutions to some of the challenges posed to provide better radiology services, training, and improve the well-being of radiologists if we encounter a similar situation in the future. COVID-19 pandemic had significant impact on radiologists. Radiologists felt that they were well or moderately well protected with PPEs;however, PPEs affected their ability to work. Radiology workflow was significantly reduced in the pandemic with more radiologists working from home. COVID-19 pandemic had deleterious effect on radiologist's well-being, education, and CPD activities.