Affordable and Reliable Power and Communication Device for Continuous Online Learning for African Students

The COVID-19 pandemic that has ravaged the world since December 2019 caused disruptions in the engineering education sector as students in African universities were unable to learn virtually. From two recent multi-language, multi-cultural pan-African online surveys to assess the impact on students, over 6,000 responses showed the twin constraints of irregular electric power supply and poor internet connectivity to effectively participate in virtual learning. This project is aimed at developing an affordable and reliable power and communication device for continuous online learning for engineering students, showcasing the strength in Africa's diversity through a collaborative, multinational, multicultural, multi-lingual and gender-sensitive platform to solve this identified global African Engineering Education challenge. A collapsible 100-watt solar photovoltaic module charging a set of lithium batteries via the charge controller was used to power a laptop computer, a mobile phone and a 5-watts bulb simultaneously through a Direct Current/Direct Current (DC/DC) converter. An embedded modem in the device provided the wireless network for internet connectivity. The initial prototypes produced weighed less than 7 kg, and preliminary performance tests showed that the gadget was able to charge up a laptop and two smartphones totaling 45.5WH from 0% to 100% while the remaining backpack state of charge remains 12.8V at 88% (that is 12% depth of discharge). The power supply and communication device for continuous online learning for African engineering students will not only bring engineering solution collaboration among hundreds of engineers, technologists, and technicians from the entire African continent, but will also boost entrepreneurial skills for many African engineering practitioners when fully commercialised. © 2021 IEEE.

COVID-19 Prediction model using Machine Learning

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r(2)) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.

New Detection Cheating Method of Online-Exams during COVID-19 Pandemic

A novel approach for the detection of cheating during e-Exams is presented here using convolutional neural networks (CNN) based systems. This system will help the proctors to identify any kind of uncertain event at the time of online exams, for which most of the government's across the globe are recommending due to the Covid-19 pandemic. Most of the institutions and students across the globe are badly affected by their academic programs and it is a challenging task for universities to conduct examinations using the traditional methods. Therefore, the students are attending most of their classes using different types of third party applications that are available online. However, to conduct online exams the universities cannot rely on these service providers for a long time. Therefore, in this work, a complete setup of the software tools is provided for the students, which can be used by students at their respective laptops/personal computers with strict guidelines from the university. The proposed approach helps most of the universities in Saudi Arabia to maintain their database of different events/activities of students at the time of E-Exams. This method proved to be more accurate and CNN based detection proved to be more sensitive with an accuracy of 97% to detect any kind of uncertain activity of the students at the time of e-Exam.