Covid safety Measures Using Machine Learning
Gayatri Navnath Dighe
, Akash Dilip Ghuge , Ashwini Babasaheb Kote , Jagruti Arvind Wagh , Prof. Patil P.A.
Covid-19, Machine learning, personification, privacy, prediction, symptoms.
The Covid-19 outbreak has taken the world completely unawares, exposing the vulnerability of public health systems in coping with infectious pandemic. The current death toll of the pandemic is staggering, and it is the need of an hour to eradicate the virus at the earliest and prepare a system that stands tall to armor the world in case the future holds any unpredictable biological or health crisis of this scale. Since December 2019, Novel coronavirus disease has been shown an extensive impact on social, mental, personal, and economic fields throughout the world. In this pandemic situation, people are worried and interested to know what is going on in the upcoming days. Therefore, it is very important to provide relevant information about how many people are affected and will infect in near future. Moreover, they need to know how to spread different symptoms and prevention steps of this disease. This research work proposes a complete COVID-19 safety measures which helps people to defend against it. This is first of its kind application that uses machine learning to combat the need. Machine learning model to be built to deal with various safety measures. By using the technology, it alerts the people who are in need of it. The proposed approach will provide an intuitive way to understand the risk of being getting affected based on the immunization of respiratory system of an individual. The risk factor will provide a basis for personification and to take safety measures in this long-lasting pandemic situation.
"Covid safety Measures Using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 5, page no.197 - 201, May-2022, Available :https://ijsdr.org/papers/IJSDR2205038.pdf
Volume 7
Issue 5,
May-2022
Pages : 197 - 201
Paper Reg. ID: IJSDR_200369
Published Paper Id: IJSDR2205038
Downloads: 000347259
Research Area: Engineering
Country: City- Kopargaon, Dist- Ahmadnagar, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
