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Analysis on novel coronavirus (COVID-19) using machine learning methods
In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health O...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324348/ https://www.ncbi.nlm.nih.gov/pubmed/32834604 http://dx.doi.org/10.1016/j.chaos.2020.110050 |
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author | Yadav, Milind Perumal, Murukessan Srinivas, M |
author_facet | Yadav, Milind Perumal, Murukessan Srinivas, M |
author_sort | Yadav, Milind |
collection | PubMed |
description | In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy. |
format | Online Article Text |
id | pubmed-7324348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73243482020-06-30 Analysis on novel coronavirus (COVID-19) using machine learning methods Yadav, Milind Perumal, Murukessan Srinivas, M Chaos Solitons Fractals Article In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy. Elsevier Ltd. 2020-10 2020-06-30 /pmc/articles/PMC7324348/ /pubmed/32834604 http://dx.doi.org/10.1016/j.chaos.2020.110050 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Yadav, Milind Perumal, Murukessan Srinivas, M Analysis on novel coronavirus (COVID-19) using machine learning methods |
title | Analysis on novel coronavirus (COVID-19) using machine learning methods |
title_full | Analysis on novel coronavirus (COVID-19) using machine learning methods |
title_fullStr | Analysis on novel coronavirus (COVID-19) using machine learning methods |
title_full_unstemmed | Analysis on novel coronavirus (COVID-19) using machine learning methods |
title_short | Analysis on novel coronavirus (COVID-19) using machine learning methods |
title_sort | analysis on novel coronavirus (covid-19) using machine learning methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324348/ https://www.ncbi.nlm.nih.gov/pubmed/32834604 http://dx.doi.org/10.1016/j.chaos.2020.110050 |
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