ABSTRACT
The epidemic of COVID-19 has turned out to be a huge fear for the world. There is currently no advisable drug or cure available to treat this condition. According to WHO statistics, COVID-19 has become a progressive lung illness that is spread by respiratory droplets and other forms of contact. According to WHO, there is still no treatment or defensive plan that has risen till the period to encounter the COVID- 19 pandemic that was arisen in China in late 2019. The purpose of our study is to predict the COVID-19 situation by analyzing the death rate, recovery rate, and susceptibility rate with the help of the regression model and SEIR model. Two analytical models (SEIR and Regression) have been used. Our analysis has shown the prediction of the COVID-19 death rate in Bangladesh with the help of a Regression and SEIR model. We have analyzed the instances per million, number of death rates per million from the SEIR and Regression results and compared them with the real-time result. We have used a valid data set of Bangladesh, collected from the Institute of Epidemiology, Disease Control and Research (ICR) from 18 March 2020 to July 18, 2021. Our experimental result shows promising performance. Examples and descriptions are provided to explain the technique. © 2021 IEEE.
ABSTRACT
This study describes the deployment of an image processing approach for finding COVID-19 affected lungs. Medical scans are useful in diagnosing illnesses and determining if organs are working normally. Medical image processing is an ongoing research subject in where numerous ways are used to help diagnosis, as well as different image processing techniques that may be used. Picture processing was used in this work, which includes image pretreatment, histogram leveling, smothering, eroding, and dilation. The usage of 2-bit picture is selected since this characteristic is well-known and there are several resources accessible. The Open CV library, which includes a plethora of image processing functions, is likewise free to use. Our experiment has shown how COVID-19 affected lung disorders can easily be identified with the help of a 2-bit image segmentation technique. The plan comprises (1) using a deep robust acquisition access to portion proper regions of interest from bleak medical examination image sizes of 903 total, (2) using a propagative neural network to improve contrast, sharpness, and illuminance of image contents, and (3) from the beginning to the conclusion, a regression strategy plan was used to accomplish medical picture categorization by material design in deep neural networks.
ABSTRACT
Scientists from the whole world have been working their heart and soul to invent the COVID-19 vaccine. When they are succeed to make the vaccine, various rumors are spread. COVID-19 situation has made our world standstill. When the vaccine came out for the first time, people were enthusiastic to take a shot. But the myth, rumors about vaccination also followed the success. In this paper, we have tried to validate the COVID-19 related vaccine myth and rumors with the help of the LDA algorithm. We have used data mining, text mining and sentiment analysis for the experiment. The outcome of our experiment has shown that most people are positive about vaccination but the negative impact is also there. Our experiment has found that most of the people are talking about "vaccine", "people","moron"and "ever". We have proposed a technique to validate this kind of vaccine myth. LDA algorithms have been able to predict and validate the myth up to 70% compared to other frameworks out there. Promising efficiency is exhibited by our experimental result. © 2022 ACM.