Manuscript received November 30, 2022; revised January 11, 2023; accepted March 27, 2023.
Abstract—In a very short interval, COVID-19 had spread all over the world and affected the medical and economic condition of all the developed, developing and underdeveloped countries, badly. Though it has been more than three years since its first onset, but even today its fear grips the entire world. Time and again, its various mutants have been detected. Early-stage diagnosis, reporting and isolation of the patient are the only ways to restrict the spread of virus of this nature. In addition, prediction models to help in this regard. This paper presents a novel Hybrid Model based on Deep Learning techniques. In this work, an authentic dataset has been collected, pre-processed and finally classified for the prediction of the disease. The model has been implemented using Python programming code. It’s accuracy, precision and recall has been calculated, and compared with that of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model.
Index Terms—COVID-19, deep learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), bidirectional LSTM, hybrid model
Deepti Malhotra and Gurinder Kaur Sodhi are with Desh Bhagat University, Punjab, India.
*Correspondence: deeptimalhotra1981@gmail.com (D.M.)
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Cite:Deepti Malhotra and Gurinder Kaur Sodhi, "Hybrid Deep Learning Model for COVID-19 Prediction Using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (LSTM) Network," International Journal of Computer Theory and Engineering vol. 15, no. 3, pp. 125-129, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
