使用WOA-BILSTM模型预测COVID-19
1School of Computer Science, South China Normal University, Guangzhou 510631, China.
Bioengineering (Basel, Switzerland)
|August 26, 2023
概括
这项研究介绍了一种新的鱼优化算法-双向长期短期记忆 (WOA-BILSTM) 模型,用于准确的COVID-19病例预测. 与传统方法相比,WOA-BILSTM模型在复杂的场景中表现出优越的性能.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 由于COVID-19的流行,因此需要准确地预测感染人数.
- SARS-CoV-2 传播受时间和空间因素的影响.
- 神经网络越来越多地用于流行病学预测.
研究的目的:
- 提出和评估一种新的鱼优化算法-双向长期短期记忆 (WOA-BILSTM) 模型,用于预测累积确认的COVID-19病例.
- 为了比较WOA-BILSTM模型与各种既定的机器学习和统计模型的性能.
主要方法:
- 输入区域流行病数据,包括累计,治愈,死亡和每日病例.
- 使用双向长短期内存 (BILSTM) 网络作为基础模型.
- 整合鱼优化算法 (WOA) 来优化BILSTM参数.
主要成果:
- 与LSTM,BILSTM,GRU,CNN,CNN-LSTM,RNN-GRU,DES,ARIMA,线性,拉索和SVM模型相比,WOA-BILSTM模型在预测累计确诊的COVID-19病例方面表现出卓越的准确性.
- 使用来自中国北京,广东和重庆的数据进行的实验证实了该模型在复杂场景中的有效性.
- 与贝叶斯式和网格搜索算法相比,WOA优化算法显示出快速的融合,并且很容易找到最佳解决方案.
结论:
- 拟议的WOA-BILSTM模型为COVID-19病例预测提供了一个高度准确和高效的方法.
- 该模型的表现表明,它有助于政府制定有效的公共卫生控制措施.
- 该研究强调了混合人工智能模型的潜力,将优化算法与深度学习相结合,用于流行病学预测.
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