通过线性二氧化多模糊集增强决策:在医学和工程领域应用新型信息措施
Jeevitha Kannan1, Vimala Jayakumar1, Nasreen Kausar2
1Department of Mathematics, Alagappa University, Karaikudi, Tamilnadu, India.
Scientific reports
|November 18, 2024
概括
本研究引入了对线性二多模糊集 (LDMFS) 的新信息指标,增强了它们的实际使用. 这些指标在医学诊断,材料科学和模式识别方面表现有前途.
科学领域:
- 模糊的集合理论 模糊的集合理论
- 信息指标信息指标
- 应用数学 应用数学 应用数学
背景情况:
- 线性二多模糊集 (LDMFS) 缺乏用于实际应用的既定信息指标.
- 现有的模糊集合理论不能完全解决多维模糊数据的复杂性.
- 需要强有力的定量措施来有效评估和利用LDMFS.
研究的目的:
- 引入和分析线性二定多模糊集合的新型信息指标.
- 在LDMFS中建立相似性,,包含和距离测量的理论框架.
- 证明这些指标在各种现实场景中的实际适用性.
主要方法:
- 为LDMFS开发Cosine,Jaccard和指数相似度的发展.
- 用支持定理制定,包容和距离的测量方法.
- 在医学诊断,材料制造和模式识别的案例研究中应用开发的指标.
主要成果:
- 对于LDMFS,成功开发并理论验证了新的相似性,,包容性和距离指标.
- 这些指标在预测妊娠前,优化手术机器人制造和模式识别任务方面表现出有效性.
- 对比分析证实了拟议信息指标的优越性和有效性.
结论:
- 信息指标的实施显著提高了线性二氧化多模糊集的实用性和适用性.
- 这些进步为使用复杂模糊数据的领域提供了改进决策和解决问题的基础.
- 该研究为进一步创新和在各种科学和工业领域实践整合LDMFS铺平了道路.
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