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Predicting COVID-19 Outbreak in Algeria Using Long Short-Term Memory Networks
7th International Conference on Image and Signal Processing and their Applications, ISPA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1922721
ABSTRACT
From late November 2019 until today, the Coronavirus (COVID19) has been circulating worldwide on a broad scale. The World Health Organization agrees that this is the world's most serious outbreak in the last twenty years. Algeria is no exception;although the relatively early precautions taken by the Algerian government, especially the national lockdown, the spread of Coronavirus has attained all cities. This study aims at clarifying how COVID-19 has spread in Algeria so fast in a short time and perform an intelligent system to predict confirmed, deaths, recovered, and active cases. This paper presents firstly a comprehensive analytics study on the spread of the pandemic to understand the COVID19 evolution in Algeria. Secondly, a predictive model based on a Long Short Term Memory Network is proposed and studied to estimate the future number of confirmed, deaths, recovered, and Active cases. © 2022 IEEE.
Keywords

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Topics: Long Covid Language: English Journal: 7th International Conference on Image and Signal Processing and their Applications, ISPA 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Topics: Long Covid Language: English Journal: 7th International Conference on Image and Signal Processing and their Applications, ISPA 2022 Year: 2022 Document Type: Article