Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values.

Prevalence of online cheating during the COVID-19 pandemic

The COVID-19 pandemic has been a disruptive force throughout the world, especially in the education sector where millions of students worldwide are unable to attend schools and universities. This has resulted in many face-to-face higher education institutions making a transition to online teaching, learning, and assessments. Since this change happened suddenly and unexpectedly, many of these institutions were not prepared for this change and did not have time to implement proper measures to protect the quality and integrity of their online offering. Academic dishonesty has become an alarming ongoing challenge all over the world and this trend has been magnified with online assessments during COVID-19. Therefore, it has become imperative to investigate the prevalence of e-cheating and the common methods of students used to cheat during the COVID-19 pandemic. Using a quantitative approach, an online survey was sent to Information Systems and Technology students at a South African University. Results indicate that students perceived that e-cheating was occurring through impersonation, accessing forbidden aids, peer collaboration and outside assistance. Technologies such as WhatsApp messaging, Google, online lecture notes, and WhatsApp calls or telephone calls were used to e-cheat. © 2022 Elsevier Ltd. All rights reserved.

Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Probing Risk of Default in the Market-Leading Islamic Banking Industry: During the Covid-19 Pandemic

This study aims to highlight the bankruptcy-related challenges and the reasons behind bank failures for the global Islamic banking industry, and to understand the expected impact of Covid-19 on the financial health of the Islamic banking industry. Moreover, the study shows that going bankrupt is a costly process that affects all stakeholders. Evidence shows that in the case of Islamic banking, a Shariah-based bankruptcy prediction model for apprehending the true bankruptcy prediction is over-sighted. It may further sour the existing uncertain situation for the Islamic banking industry with the additional unfavorable impact of Covid-19. As the subjected pandemic has badly affected the flow of the financial system worldwide, Islamic banking is not isolated. Therefore, efficient bankruptcy prediction models equipped with the latest micro and macro-economic factors are compulsory for the sustainability of the Islamic banking industry. © 2022, Institute of Technology PETRONAS Sdn Bhd.

Virtual health assistance system

In the fast-paced world, Pandemic like Covid-19, people must take care of themselves and others. Personal care has been ignored in the world and mainly in India. “Virtual Health Assistance System” tries to become a health assistant in these times when people can contract disease very easily. It is also found out that obesity has increased during the lockdown. This can be a potential threat in the future as “too much of anything is good for nothing”. This is also amplified by the fact that people have stopped being afraid of the Covid-19. This must be resolved immediately. People should lead a healthy and disease-free life. This can be controlled by a device that is used the most – Smartphone. People using smartphones can be notified about proper diet and hydration, proper hygiene and exercise and a new way to lead a healthy lifestyle.