Online learning during Covid-19 pandemic: issues and challenges

The Covid-19 pandemic appears to have led us towards a new change in education systems around the world. Digital learning is the modus operandi of professionals looking to improve their skills in an increasingly automated world. Online learning has found a place in the curriculum of schools and universities to ensure academic continuity. Access to online learning is highly dependent on the subject and the tools the students are familiar with. This research report looks at different aspects of the challenges in the form of an online learning questionnaire. The study shows that most of the younger generations are very familiar with the use of online platforms, but use them as part of their daily academic activities, but are faced with many attitude problems that drive them to behave differently, which can be further elaborated in the findings of this study. Copyright © 2023 Inderscience Enterprises Ltd.

An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction

Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier (ERNN-EC) is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model's modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error (RAE). As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%.