Predicting the Death Rate Around the World Due to COVID-19 Using Regression Analysis

Nowadays, COVID-19 is considered to be the biggest disaster that the world is facing. It has created a lot of destruction in the whole world. Due to this COVID-19, analysis has been done to predict the death rate and infected rate from the total population. To perform the analysis on COVID-19, regression analysis has been implemented by applying the differential equation and ordinary differential equation (ODE) on the parameters. The parameters taken for analysis are the number of susceptible individuals, the number of infected individuals, and the number of recovered individuals. This work will predict the total cases, death cases, and infected cases in the near future based on different reproductive rate values. This work has shown the comparison based on four different productive rates (i.e., 2.45, 2.55, 2.65, and 2.75). The analysis is done on two different datasets;the first dataset is related to China, and the second dataset is associated with the world's data. The work has predicted that by 2020-08-12 there will be 59,450,123 new cases, 432,499,003 total cases, and 10,928,383 deaths.

COVID-19 prediction through X-ray images using various layers in convolutional neural network

Purpose: The purpose of this paper is to focus on the prediction of Coronavirus 2019 (COVID-19) using X-ray image. Design/methodology/approach: This study proposed convolutional neural network (CNN) approach to predict COVID-19. Findings: Prediction of COVID-19 using CNN. Originality/value: The work has implemented multiple CNN models to classify chest X-ray of affected patients by using their chest scans. According to three models, the ResNet-50 is advantageous because of its high service reliability. © 2021, Emerald Publishing Limited.

Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

Purpose: The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images. Design/methodology/approach: We used machine learning techniques with convolutional neural network. Findings: Detecting COVID-19 symptoms from patient CT scan images. Originality/value: This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images. © 2021, Emerald Publishing Limited.