COVID-19 Pandemic: A Comparative Prediction Using Machine Learning. Sadik, R., Reza, M. L., Noman, A. A., Mamun, S. A., Kaiser, M. S., & Rahman, M. A. International Journal of Automation, Artificial Intelligence and Machine Learning, 1(1):1–16, October, 2020. Number: 1![COVID-19 Pandemic: A Comparative Prediction Using Machine Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 4 downloads Coronavirus Disease 2019 or COVID-19 is an infectious disease which is declared as a pandemic by the World Health Organization (WHO) have a noxious effect on the entire human civilization. Each and every day the number of infected people is going higher and higher and so the death toll. Many of country Italy, UK, USA was affected badly, yet since the identification of the first case, after a certain number of days, the scenario of infection rate has been reduced significantly. However, a country like Bangladesh couldn't keep the infection rate down. A number of algorithms have been proposed to forecast the scenario in terms of the number of infection, recovery and death toll. Here, in this work, we present a comprehensive comparison based on Machine Learning to predict the outbreak of COVID-19 in Bangladesh. Among Several Machine Learning algorithms, here we used Polynomial Regression (PR) and Multilayer Perception (MLP) and Long Short Term Memory (LSTM) algorithm and epidemiological model Susceptible, Infected and Recovered (SIR), projected comparative outcomes.
@article{sadik_covid-19_2020,
title = {{COVID}-19 {Pandemic}: {A} {Comparative} {Prediction} {Using} {Machine} {Learning}},
volume = {1},
copyright = {Copyright (c) 2020 Rifat Sadik, Md Latifur Reza, Abdullah Al Noman, Shamim Al Mamun , M Shamim Kaiser, Muhammad Arifur Rahman},
issn = {2563-7568},
shorttitle = {{COVID}-19 {Pandemic}},
url = {https://www.researchlakejournals.com/index.php/AAIML/article/view/44},
abstract = {Coronavirus Disease 2019 or COVID-19 is an infectious disease which is declared as a pandemic by the World Health Organization (WHO) have a noxious effect on the entire human civilization. Each and every day the number of infected people is going higher and higher and so the death toll. Many of country Italy, UK, USA was affected badly, yet since the identification of the first case, after a certain number of days, the scenario of infection rate has been reduced significantly. However, a country like Bangladesh couldn't keep the infection rate down. A number of algorithms have been proposed to forecast the scenario in terms of the number of infection, recovery and death toll. Here, in this work, we present a comprehensive comparison based on Machine Learning to predict the outbreak of COVID-19 in Bangladesh. Among Several Machine Learning algorithms, here we used Polynomial Regression (PR) and Multilayer Perception (MLP) and Long Short Term Memory (LSTM) algorithm and epidemiological model Susceptible, Infected and Recovered (SIR), projected comparative outcomes.},
language = {en},
number = {1},
urldate = {2022-09-13},
journal = {International Journal of Automation, Artificial Intelligence and Machine Learning},
author = {Sadik, Rifat and Reza, Md Latifur and Noman, Abdullah Al and Mamun, Shamim Al and Kaiser, M. Shamim and Rahman, Muhammad Arifur},
month = oct,
year = {2020},
note = {Number: 1},
keywords = {COVID-19, LSTM, MLP, Machine learning, PR, Pandemic, SIR},
pages = {1--16},
}
Downloads: 4
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