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一个双重犹的模糊-TOPSIS框架用于医疗电子学习系统的多标准评估
Nabilah Abughazalah1, Majid Khan2
1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|November 19, 2025
概括
本研究引入了一种新的双犹模糊集 (DHFS) 多标准决策 (MCDM) 模型,用于选择牙科电子学习平台. DHFS-Entropy-TOPSIS方法有效地处理专家评级中的模两可,以优化数字学习.
科学领域:
- 牙科教育技术 牙科教育技术
- 决策科学 决策科学 决策科学
- 模糊的集合理论 模糊的集合理论
背景情况:
- 在牙科教育中,电子学习平台正在迅速发展.
- 在这个领域的决策涉及复杂的,多标准的问题与固有的模两可.
- 现有的模糊系统在捕捉专家评级中的双层模糊性方面存在局限性.
研究的目的:
- 提出一个创新的多标准决策 (MCDM) 模型,使用双犹模糊集 (DHFS).
- 在牙科电子学习平台选择的背景下,解决专家评级中的双层模糊性.
- 通过结合权和改进的TOPSIS算法来增强决策过程.
主要方法:
- 基于双犹模糊集 (DHFS) 的新型MCDM模型的开发.
- 整合权重来客观地确定标准的重要性.
- 应用一个增强的TOPSIS算法来对牙科电子学习平台进行排名.
- 评估使用一个示例案例研究与五个平台和七个替代方案.
主要成果:
- 拟议的DHFS-Entropy-TOPSIS模型在标准模糊决策技术上表现出优越性.
- 灵敏度分析证实了该程序对重量变化的稳定性.
- 该模型有效地管理了取决于专家评级的双层模糊性,保留了成员身份和价值犹.
结论:
- DHFS-MCDM方法被验证为优化牙科教育中的数字学习网站的有效和可扩展的解决方案.
- 与现有的模糊系统相比,该方法提供了更大的语义深度.
- 未来的研究方向包括探索用于动态,自适应性决策的机器学习.
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