使用循环神经网络与贝叶斯规范化的循环神经网络建模伤寒动态.
Zulqurnain Sabir1, M A Abdelkawy2, Muhammad Athar Mehmood3
1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
Computational biology and chemistry
|November 23, 2025
概括
这项研究使用人工智能对台风流行病模型进行了数值研究. 贝叶斯规范化神经网络准确预测疾病传播,为流行病学建模提供了一种新的方法.
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:
- 在全球范围内,伤寒热仍然是一个重大的公共卫生问题.
- 数学模型对于理解疾病传播动态至关重要.
- 需要准确的数值方法来模拟和预测流行病的行为.
研究的目的:
- 为了数值地研究一种流行性伤寒模型.
- 应用随机人工智能,特别是贝叶斯规范化神经网络,用于伤寒模型.
- 评估模型在模拟疾病动态方面的精度和效率.
主要方法:
- 开发了一种五个部位的伤寒模型 (易感,携带者,感染者,恢复者,细菌).
- 该模型使用Runge-Kutta程序进行数值解决,以生成数据集.
- 贝叶斯规范化神经网络用于模型的训练,测试 (15%) 和验证 (10%),75%的数据用于训练.
- 使用实现与参考结果和错误指标来评估绩效.
主要成果:
- 该模型实现了高精度,绝对误差范围从10^-06到10^-07.
- 平均平方误差显著减少,在10^-08和10^-10之间.
- 通过测试证实了效率,包括组图错误,状态转换和相关性索引.
结论:
- 基于随机人工智能的循环神经网络,特别是使用贝叶斯规范化,为流行性伤寒模型的数值调查提供了精确有效的方法.
- 开发的模型在模拟伤寒传播动态方面表现出强大的能力.
- 这种方法为流行病学研究和公共卫生干预提供了一个有前途的工具.
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