Optimizing Broad Learning System Hyper-parameters through Particle Swarm Optimization for Predicting COVID-19 in 184 Countries-Reference-Cited by-同舟云学术

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Optimizing Broad Learning System Hyper-parameters through Particle Swarm Optimization for Predicting COVID-19 in 184 Countries

Published:2021-03-01 Issue: Volume: Page:
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Container-title:2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM)
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Author:

Zhan Choujun,Wu Zhengdong,Wen Quansi,Gao Ying,Zhang Haijun

Funder

National Science Foundation of China

Science and Technology Program of Guangzhou, China

Natural Science Foundation of Guangdong Province, China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9398959/9398911/09399020.pdf?arnumber=9399020

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Nonstationary time series forecasting using optimized-EVDHM-ARIMA for COVID-19;Frontiers in Big Data;2023-06-14

2. Preventing the Early Spread of Infectious Diseases Using Particle Swarm Optimization;Nature-Inspired Optimization Methodologies in Biomedical and Healthcare;2022-11-15

3. Estimating Effective Reproduction Number for SIR Compartmental Model: A Stochastic Evolutionary Approach;Journal of Social Computing;2022-06

4. A particle swarm optimization approach for predicting the number of COVID-19 deaths;Scientific Reports;2021-08-16

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