相关实验视频
Updated: Jun 28, 2025

07:42
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
168
基于球形模糊信息的COVID-19紧急决策支持建模
Shahzaib Ashraf1, Saleem Abdullah1
1Department of Mathematics Abdul Wali Khan University Mardan Pakistan.
概括
这项研究引入了新的球形模糊集方法,以改善COVID-19等危机期间的紧急决策. 这些方法有效地处理不确定性和风险,为全球挑战提供准确的措施.
科学领域:
- 决策科学 决策科学
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 紧急情况的特点是时间限制,有限的信息和固有的不确定性.
- 在紧急情况下有效的决策至关重要,但由于模两可和风险而受到挑战.
- 现有的模糊结构可能无法完全捕捉紧急决策问题的复杂性.
研究的目的:
- 通过使用球形模糊集 (FS) 来引入紧急决策的新方法.
- 在紧急情况下解决决策问题 (DMP) 中的不确定性和模糊性.
- 开发和应用COVID-19紧急情况的方法.
主要方法:
- 为球形模糊集合开发了新的代数运算定律 (AOLs).
- 引入了爱因斯坦聚合运算符 (AgOp),用于不确定的信息.
- 扩展TOPSIS和灰色关系分析,使用未知重量标准.
- 在球形模糊环境下为紧急DMP设计了三种算法.
主要成果:
- 在球形模糊设置下提出的新型AOLs得到了验证.
- 爱因斯坦聚合操作员有效地聚合了不确定的信息.
- 扩展决策方法和算法在处理不确定性方面表现出有效性.
- 这些方法为COVID-19情况提供了准确的紧急措施.
结论:
- 球形模糊集为应急DMP处理不确定性提供了通用框架.
- 提出的新方法和算法对于紧急决策是有效的.
- 可以获得准确的紧急措施,以应对COVID-19等全球不确定性.
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