International Journal of Computer Networks & Communications (IJCNC)

AIRCC PUBLISHING CORPORATION

IJCNC 04

Design and Develop Machine Learning Based Frameworks for Combating the Covid-19

Ashenafi Tulu, Jemal Abate2, Tilahun Shiferaw3, Matiyos Alemayehu4

College of Computing and Informatics, P.O.Box 138, Haramaya University, Dire Dawa, Ethiopia

Abstract

In this worldwide health crisis, the medical industry is looking for new technologies to monitor and controls the spread of the COVID-19 (Coronavirus) pandemic. An AI-based framework is one such technology that can easily predict mortality risk by adequately analyzing the previous data of the patients. The objective of this study will be to design and develop artificial intelligence-based frameworks to predict the outbreak from large-scale data analytics. The study will have significant importance to plan effective disease control strategies and tracking COVID-19 spread, which is of paramount importance for healthcare organizations and governments in controlling successfully the coronavirus pandemic. In essence, the study proposed an artificial intelligence-based framework to combat the coronavirus spread.

Keywords

Artificial Intelligence, Machine Learning, Framework, Covid-19.

Introduction

Respiratory tract infections are so widespread in Ethiopia, the doctor pointed out that confirming the impact of COVID-19 will be challenging [2]. Ethiopia’s surveillance system is insensitive to changes in disease trends, and that a lack of testing techniques and facilities may have impeded the country’s ability to discover infections quickly and accurately track index cases [2]. According to Ethiopia’s health minister, political motivations to suppress information about the virus’ transmission in Ethiopia reinforce the theory that COVID-19 was prevalent in the country before the first case was announced on March, 2019[3].

Teshome[4] attempted to design and construct Exponential Smoothing Model, and stated Double Exponential Smoothing method was appropriate in forecasting the future number of COVID-19 cases in Ethiopia. Figa[2] try to  apply the Autoregressive Integrated Moving Average (ARIMA) modeling approach for projecting COVID-19 prevalence in selected East African countries. Argawu[5] developed a OLS model to predict Number of new laboratory tests and number of new cases from Addis Ababa city. Artificial Intelligence (AI) is likely to play a key role in the worldwide fight against the COVID-19 pandemic[6]. In Ethiopia there is no analytical methods established to estimate the outbreak possibility which is significant importance to plan effective disease control strategies using AI.

To the best of researcher’s knowledge, no enough research has been done which was used to combat covid19 using machine learning approaches using data collected from ten regional state and two administrative cities of Ethiopia. Therefore, the aim of this research is to apply the Autoregressive (AR), Autoregressive Moving Average (ARMA), ARIMA, and Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling approach to reach the best predicted result of COVID-19 using data collected from ten regional state and two administrative cities of Ethiopia.

 

 

Figure 1 The design architecture of the system

AUTHORS

Min Yao received the B.Sc, M.Sc, and Ph.D degrees from the Nanjing University of Aeronautics and Astronautics, China, in 1997, 2002, and 2008, respectively. She is currently an Associate Professor with the Nanjing University of Aeronautics and Astronautics. Her research interests include computer measurement, control and UAVs task assignment, data and signal processing, and algorithm optimization.

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This entry was posted on December 23, 2022 by .
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