Epidemic prediction based on entropy-improved factor analysis and WOA-optimized BP network algorithm

Jianan Zhang; Hongyi Duan; Bingsong Tong

doi:10.1117/12.2681323

23 May 2023 Epidemic prediction based on entropy-improved factor analysis and WOA-optimized BP network algorithm

Jianan Zhang, Hongyi Duan, Bingsong Tong

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126453R (2023) https://doi.org/10.1117/12.2681323
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

The new Coronavirus epidemic has had a huge impact on the economy, politics, and culture worldwide. However, it is very difficult to obtain accurate data on the new crown epidemic due to various uncertainties, such as the difficulty of detection. In this paper, we use objective and real Baidu search indexes as the basic data set and use factor analysis with the improvement of entropy method to reduce the dimensionality of Baidu search index data to solve the problem of fixed parameters caused by its excessive dimensionality. After that, the WOA algorithm is used to optimize the parameters of the conventional BP neural network, thus making the fit and accuracy greatly improved, which is of great practical significance for the prediction of epidemic data.

Citation Download Citation

Jianan Zhang, Hongyi Duan, and Bingsong Tong "Epidemic prediction based on entropy-improved factor analysis and WOA-optimized BP network algorithm", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126453R (23 May 2023); https://doi.org/10.1117/12.2681323

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