使用进化智能计算方法分析皮肤病的框架
Muhammad Shoaib1, Rafia Tabassum2, Kottakkaran Sooppy Nisar3
1AI Center, Yuan Ze University, Taoyuan, Taiwan.
Computer methods in biomechanics and biomedical engineering
|March 12, 2024
概括
一个新的神经进化算法有效地模拟和解决非线性皮肤的疾病动态. 这种方法为了解和管理儿童的冲提供了高准确度.
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:
- ,一种传染性细菌性皮肤疾病,通常会影响婴儿和幼儿,表现为,主要在脸上.
- 了解impetigo的动态对于开发有效的治疗和预防策略至关重要.
研究的目的:
- 引入一种神经进化全球算法,用于解决非线性皮肤病症模型 (SSDM).
- 开发一种基于人工神经网络的方法来建模impetigo动态.
主要方法:
- 将全球遗传算法与局部顺序二次编程 (GA-LSQP) 集成在一起,以解决SSDM.
- 使用基于激活函数的神经网络模型,通过平均平方误差训练Impetigo动态.
- 通过与亚当策略的比较和绝对错误分析进行验证.
主要成果:
- 拟议的GA-LSQP算法实现了高精度,通过绝对错误分析证明了这一点.
- 统计调查证实了综合技术的一致性,稳定性和趋同性.
- 解决者的准确性进一步由小的统计值 (最小值,中位数,最大值,平均值等) 证明. ) 的情况.
结论:
- 神经进化算法为解决像impetigo.com这样的非线性疾病模型提供了强大而准确的方法.
- 该研究强调了将人工智能与流行病学研究的数学建模相结合的有效性.
- 开发的方法为分析和潜在控制皮肤病的传播提供了可靠的工具.
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