TY - CHAP
T1 - A hybrid deep learning method with attention for COVID-19 spread forecasting
AU - Dairi, Abdelkader
AU - Harrou, Fouzi
AU - Sun, Ying
AU - Khadraoui, Sofiane
N1 - KAUST Repository Item: Exported on 2022-04-20
Acknowledged KAUST grant number(s): OSR-2019-CRG7–3800
Acknowledgements: This chapter is based on work supported by the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7–3800.
PY - 2022/4
Y1 - 2022/4
N2 - This chapter introduces a hybrid deep learning model for COVID-19 spread forecasting. Specifically, the proposed approach combines the desirable characteristics of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and an attention mechanism. Importantly, this combination, called BiLSTM-A-CNN, is intended to amalgamate the ability of LSTMs to model time dependencies, the capability of the attention mechanism to highlight relevant features, and the noted ability of CNNs to extract features from complex data. The use of the BiLSTM-A-CNN model is expected to improve the forecasting accuracy of future COVID-19 trends.
AB - This chapter introduces a hybrid deep learning model for COVID-19 spread forecasting. Specifically, the proposed approach combines the desirable characteristics of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and an attention mechanism. Importantly, this combination, called BiLSTM-A-CNN, is intended to amalgamate the ability of LSTMs to model time dependencies, the capability of the attention mechanism to highlight relevant features, and the noted ability of CNNs to extract features from complex data. The use of the BiLSTM-A-CNN model is expected to improve the forecasting accuracy of future COVID-19 trends.
UR - http://hdl.handle.net/10754/676293
UR - https://iopscience.iop.org/book/978-0-7503-3795-3/chapter/bk978-0-7503-3795-3ch5
U2 - 10.1088/978-0-7503-3795-3ch5
DO - 10.1088/978-0-7503-3795-3ch5
M3 - Chapter
BT - Artificial Intelligence Strategies for Analyzing COVID-19 Pneumonia Lung Imaging, Volume 1
PB - IOP Publishing
ER -