模糊APPSS:在三角球形模糊环境下,一种用于量化印度COVID-19影响的新方法
Aicevarya Devi Sakthivel1, Felix Augustin2
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, 600127, Chennai, India.
Scientific reports
|December 27, 2024
概括
一种新的模糊决策方法APPSS使用三角形球形模糊数字 (TSFN) 识别COVID-19.
科学领域:
- 决策科学 决策科学
- 模糊的数学 模糊的数学
- 公共卫生 公共卫生
背景情况:
- 决策模型对于现实世界问题分析至关重要.
- 模糊的决策模型解决了动态问题的复杂性.
- 需要先进的技术来实时评估不确定性.
研究的目的:
- 介绍偏好与绩效与满意得分之间的关联 (APPSS) 方法.
- 开发一种新的模糊决策方法,使用三角形球形模糊数 (TSFN).
- 分析COVID-19对印度不同年龄组的影响.
主要方法:
- 开发了APPSS方法,整合了偏好和绩效组件.
- 使用三角形球形模糊数字 (TSFN) 进行增强的不确定性处理.
- 应用了基于分级平均值整合方法的TSFN新分数函数.
主要成果:
- APPSS 方法有效地分析复杂的决策场景.
- 确定60-69岁的年龄组在印度对COVID-19非常脆弱.
- 敏感性和比较分析证实了这些发现的可靠性.
结论:
- APPSS方法为模糊的决策提供了一个可靠的框架.
- 研究结果提供了关于COVID-19在不同年龄组的影响差异的见解.
- 结果支持决策者评估未来的健康风险和并发症.
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