Impact of Mobile Technology and Use of Big Data in Physics Education During Coronavirus Lockdown

The speed of spread of Coronavirus Disease 2019 led to global lockdowns and disruptions in the academic sector. The study examined the impact of mobile technology on physics education during lockdowns. Data were collected through an online survey and later evaluated using regression tools, frequency, and an analysis of variance (ANOVA). The findings revealed that the usage of mobile technology had statistically significant effects on physics instructors' and students' academics during the coronavirus lockdown. Most of the participants admitted that the use of mobile technologies such as smartphones, laptops, PDAs, Zoom, mobile apps, etc. were very useful and helpful for continued education amid the pandemic restrictions. Online teaching is very effective during lock-down with smartphones and laptops on different platforms. The paper brings the limelight to the growing power of mobile technology solutions in physics education. © 2018 Tsinghua University Press.

COVID-19 Outbreak Prediction Using Machine Learning

The recent global outbreak of the coronavirus has thrown new challenges for the research community. First case was registered in China, and then, it got spread in most of the countries of the world. Initially, the speed of spread was slow but later on, its spread rate was very really high and on analysis it turned out to be exponential. Governments all across the world imposed lockdowns, and people were asked to practice social distancing in order to prevent the spread of the COVID-19 virus. Later on, it was announced as pandemic by World Health Organization (WHO). Machine learning-driven methods can prove to be really vital in predicting risks, effects and parameters of this pandemic. These predictions will help in making strategies to control its spread and understand its nature. More research is beginning to anticipate and a remarkable amount of machine learning models are being talked about to predict COVID-19 cases used by experts or researchers around the globe. In this research project, we have used univariate LSTM model to make predictions. The number of confirmed cases, number of recovered cases and the number of death cases in the coming days are being predicted. Mean absolute error (MAE) is used as the measure of performance metric of the predicted results. The results produced are quite accurate. These results prove that univariate LSTM is a promising model to make predictions for COVID-19 outbreak. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.