Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models

The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models. © 2023 IEEE.

Usage of ML and IoT in Healthcare Diagnose During Pandemic

The pandemic crisis has obliterated human existence as we know it, as well as regional, social, and commercial action, as well as compelled human civilization in living inside the defined perimeter. Uses of IoT with ML in health care applications is described in this article. The created ML with IoT dependent observation prototype assists for tracing COVID-19 positive detected persons using prior information and isolates them from non-infected individuals. By anticipating as well as analyzing information with AI, proposed ML-IoT system employs parallel computing to track pandemic sickness and also to avoid pandemic disease. The use of machine learning-dependent IoT for COVID in health conditions diagnose likely to be demonstrated the effectiveness for detection and prevention of CORONAVIRUS transmission. It still effects in better way on lowering preventive expenditures also leds to better treatment for infected individuals. In terms of monitoring and tracking, the recommended technique is 95% accurate. The findings will aid for stopping the pandemic's spread and providing assistance to the healthcare sector. © 2022 IEEE.

Data Analytics for Quality-of-Life Monitoring and Control in the Era of Covid-19 Pandemic

Quality-of-life (QoL) is a multidimensional and complex issue that helps to develop an improved human civilization. In the era of COVID-19, a heightened negative impact has been observed in human lifestyle-related behaviors. A detailed analysis is required for the understanding of the mixed effect on human QoL in the ongoing pandemic outbreak. This study aims to establish an interrelationship between life evaluation factors and their effects on human QoL. Additionally, the role of data analytics has been discussed for monitoring and control of human QoL with the association of AI and statistical tools. However, AI-based analysis has provided a way to understand the mental state during this pandemic. In a similar fashion, the statistical-based analysis has helped to identify the most effective parameters that can improve overall human QoL. © 2021 ACM.