Predictive analytics on Covid using recurrent neural network

In this paper, we develop a risk based predictive analytics model utilizing the available dataset to train the model. To predict the likelihood of a new strain of Covid-19 syndrome in patients, the researchers used a deep learning classifier called Recurrent Neural Network (RNN). The study considers diabetes patients as the respondents and the data is collected from the diabetes patients. The risk of covid-19 effects on diabetes patients are deeply analyzed using RNN. The collected datasets are initially pre-processed and then the features are extracted with final classification using RNN. The experimental analysis is conducted to validate the efficacy of the predictive analytics using RNN. The findings indicate that the suggested RNN outperforms other approaches for forecasting covid-19 risk in diabetic patients. © 2022 Author(s).

Online learning: ICT-based Tools for Interaction and Effectiveness

COVID 19 has been a challenge in all sectors and education in particular. During the pandemic, there was an immediate and compulsory shift in conducting classes via online mode. Several colleges and schools have asked their teachers to hold classes online, yet most of them are neither equipped nor in the mindset to adapt to this new teaching methodology. Despite these limitations, Google Meet, Zoom, Microsoft Teams, and other platforms have become integral parts of lecturing and learning. Subjects have been taught using these platforms. Despite the challenges, classes are still held online and teachers are still able to reach students. It is important to note, however, that there are some notable challenges across all sessions that remain unnoticed and unresolved. In an online environment, controlling students' absences, engaging all students in discussions, monitoring their presence, keeping them active, conducting assessments, and nurturing creativity are all questionable. Research is needed to initiate and incorporate different ICT (Information Communication Technology) tools, to facilitate effective teaching and learning. The purpose of this paper is to identify the various ICT tools and resources that can be used to support the above-discussed problems and make online teaching and learning more effective. © The Electrochemical Society

Predictive Analytics on Covid-19 Prediction using ResNets

Corona virus acute disease, a life-threatening condition, emerged in 2019. In December 2019, the virus was discovered for the first time in Wuhan, China, and has since spread throughout the world. This paper proposes using Residual Neural Networks (ResNets) to predict COVID-19, where the input is collected from Internet of Things (IoT) network. Using a system designed to combat a newly emerging infection in its early stages, this paper tackles the problem. In addition to tracking confirmed and reported cases, the system also keeps tabs on cures and deaths daily. This was done so that all parties involved could see the devastation that the lethal virus would cause as soon as possible. Using RNN and GRU in an ensemble, the RMSE value has been computed for various cases such as infected, cured, and dead. The results of simulation shows that the proposed ResNets for classification is effective in predicting the covid-19 cases than the other existing deep learning models. © 2022 IEEE.