Open AccessA Regression Model Frame with IoT to Predict COVID Zone
Author(s) -
Subhajit Pati,
Sourav Mallick,
Snehashis Chakraborty,
Rajbinder Kaur,
Moloy Dhar,
Amrut Ranjan Jena
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1797/1/012029
Subject(s) - set (abstract data type) , computer science , covid-19 , frame (networking) , data set , regression analysis , population , regression , data mining , statistics , geography , artificial intelligence , machine learning , mathematics , telecommunications , demography , medicine , programming language , disease , pathology , infectious disease (medical specialty) , sociology
In this paper, we are planning to build an application through which victims of Covid-19 can put their data. Once an effective data set is built, the application is capable to predict a COVID-19 zone based on these data by using a regression model. The parameters of the data set will be: 1. How many times a person visited outside. 2. City of the person, who visited abroad recently if yes then which country (take the ratio of victims to their population density as a parameter), if no then we will put a zero. 3. The cases in their home town by simply accessing their location (take the ratio of victims to the population density of that area). 4. Once data set is built, then train a regression model to predict the chances of a person being a victim of Covid - 19 and once that information is available we can simply observe in which area the no of suspected no of people is greater through which we can tell the chance of an area being affected by this situation. 5. Although parameters of the data set are limited now but we are planning to add more so that more accurate models can be built.