Hybrid Deep Learning Model for COVID-19 Prediction Using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (LSTM) Network-Reference-Cited by-同舟云学术

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Hybrid Deep Learning Model for COVID-19 Prediction Using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (LSTM) Network

Published:2023 Issue:3 Volume:15 Page:125-129
ISSN:1793-8201
Container-title:International Journal of Computer Theory and Engineering
language:
Short-container-title:IJCTE

Author:

Malhotra Deepti,Sodhi Gurinder Kaur

Publisher

IACSIT Press

Subject

Computational Theory and Mathematics,Computer Science Applications

Link

http://www.ijcte.org/vol15/IJCTE-V15N3-1341.pdf

Reference24 articles.

1. [1] Z. A. A. Alwaeli and A. A. Ibrahim, "Predicting COVID-19 trajectory using machine learning," in Proc. 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2020.

2. [2] S. Bodapati, H. Bandarupally, and M. Trupthi, "COVID-19 time series forecasting of daily cases, deaths caused and recovered cases using long short term memory networks," in Proc. IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 2020.

3. [3] H. Chao, X. Fang, and P. Yan, "Integrative analysis for COVID-19 patient outcome prediction," Medical Image Analysis, 2020.

4. [4] D. Rafiq, S. A. Suhail, and M. A. Bazaz, "Evaluation and prediction of COVID-19 in India: A case study of worst hit states," Chaos, Solitons & Fractals, 2020.

5. [5] S. F. Ardabili, A. Mosavi, P. Ghamisi, F. Ferdinand, A. R. Varkonyi-Koczy, U. Reuter, T. Rabczuk, and P. M. Atkinson, "COVID-19 outbreak prediction with machine learning," Algorithms, vol. 13, no. 249, 2020.

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