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智能计算框架来分析COVID-19的传播风险:迈耶波纹人工神经网络

Kottakkaran Sooppy Nisar¹, Iqra Naz², Muhammad Asif Zahoor Raja³

¹Department of Mathematics, College of Science and Humanities in Al Kharj, Prince Sattam bin Abdulaziz University, 11942, Saudi Arabia; Saveetha School of Engineering, SIMATS, Chennai, India.

Computational biology and chemistry

|October 12, 2024

概括

本研究介绍了一种先进的智能计算方法,MWANNs-GA-SQP,用于优化COVID-19控制策略. 这种方法有效地减少了疾病传播的安全性和成本效益,通过模拟验证.

科学领域:

流行病学流行病学
计算智能是一种计算智能.
人工智能的人工智能

背景情况:

由于COVID-19的流行,需要有效的控制策略.
传统的流行病学模型需要先进的计算方法来优化.
智能计算为复杂疾病建模提供了新的方法.

研究的目的:

开发和验证一个先进的智能计算基础设施,以优化COVID-19控制.
确定最佳的控制方法,尽量减少疾病传播,成本和复杂性.
评估拟议的计算模型的可靠性,安全性和耐受性.

主要方法:

利用人工神经网络与无监督学习优化器集成:遗传算法 (GA) 和顺序二次编程 (SQP).
采用了使用GA和SQP (MWANNs-GA-SQP) 优化的人工神经网络 (MWANNs).
开发了一个客观优化函数,以使用前神经网络框架将平均平方误差最小化.

主要成果:

通过MWANNs-GA-SQP的方法,有效地减少了疾病的总传播.
提出的方法证明了安全性,专注医疗保健和外部灭菌的适用性.
图形模拟和分析评估标准验证了解决方案的有效性,稳定性和可靠性.

结论:

该MWANNs-GA-SQP解决方案为优化COVID-19控制策略提供了有效和强大的解决方案.
智能计算方法为流行病学建模提供了一种可靠,安全和宽容的方法.
这项研究强调了混合人工智能技术在管理公共卫生危机方面的潜力.

关键词:

人工神经网络 (ANN) 的人工神经网络在 COVID-19 疫情中,流行病学流行病学梅耶尔波段波段是一个波段.顺序二次编程的第二级编程.

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