COVID-19 Patient Count Prediction Using LSTM.

Iqbal, Muhammad; Al-Obeidat, Feras; Maqbool, Fahad; Razzaq, Saad; Anwar, Sajid; Tubaishat, Abdallah; Khan, Muhammad Shahrose; Shah, Babar

Iqbal, Muhammad; Al-Obeidat, Feras; Maqbool, Fahad; Razzaq, Saad; Anwar, Sajid; Tubaishat, Abdallah; Khan, Muhammad Shahrose; Shah, Babar.

Afiliación

Iqbal M; Department of Computer Science and Information TechnologyUniversity of Sargodha Sargodha 40100 Pakistan.
Al-Obeidat F; College of Technological InnovationZayed University Abu Dhabi United Arab Emirates.
Maqbool F; Department of Computer Science and Information TechnologyUniversity of Sargodha Sargodha 40100 Pakistan.
Razzaq S; Department of Computer Science and Information TechnologyUniversity of Sargodha Sargodha 40100 Pakistan.
Anwar S; Center of Excellence in Information TechnologyInstitute of Management Sciences Peshawar 25100 Pakistan.
Tubaishat A; College of Technological InnovationZayed University Abu Dhabi United Arab Emirates.
Khan MS; Department of Computer ScienceNational University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus Chiniot 35400 Pakistan.
Shah B; College of Information TechnologyZayed University Abu Dhabi United Arab Emirates.

IEEE Trans Comput Soc Syst ; 8(4): 974-981, 2021 Aug.

Article en En | MEDLINE | ID: mdl-37982037

Palabras clave

Covid-19; deep learning; forecasting; long short-term memory (LSTM); pandemics; risk estimation; short term predictio

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IEEE Trans Comput Soc Syst Año: 2021 Tipo del documento: Article