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Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19’s impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students’ health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student’s remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

The Zone and Energy Aware protocol based on the Ad hoc On demand Distance Vector (AODV) protocol (ZEA-AODV) provides a superior performance compared to the classic AODV protocol when number of nodes is less than 70 [1]. ZEA-AODV protocol is using location and energy information to reduce energy consumption and routing overhead. It is a combination of the two proposed protocols, Zone-aware AODV (Z-AODV) protocol and Energy-Balanced AODV (EB-AODV) protocol. In [1], we assumed that each host knows its current location precisely. In this paper, Global Positioning System (GPS) error is considered, therefore, the standard GPS error is modelled by generating two samples for movement of a terminal by simulation. One of the movements is as a simulation of the actual movement, and the other one as a simulation of the error. Then both movements are provided to the terminal. This GPS error modelling is considered in the case of ZEA-AODV protocol to investigate the effect of such error on its performance. Using the simulation, it is found that, although the overall performance of ZEA-AODV protocol is degraded when GPS error is considered, but energy conservation is noticeably still better than that of normal AODV protocol.

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