基于MCGDM技术的梯形嗜中性粒细胞数的对数运算律和聚集算子确定最有害病毒。

Tipu Sultan Haque, Avishek Chakraborty, Sankar Prasad Mondal, Shariful Alam
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引用次数: 9

摘要

在当今时代,模糊理论和多准则群体决策(MCGDM)技术被研究者广泛应用于招聘政策、金融投资、复杂电路设计、疾病临床诊断、物资管理等分离性领域。近年来,梯形嗜中性数(TNN)因其在把握日常生活问题的模糊性和不确定性方面发挥着重要作用而引起了研究人员的广泛关注。本文重点推导并建立了对数底数μ为正实数的梯形嗜中性数(TNN)的对数运算规律。本文利用对数运算律引入对数梯形中性加权算术聚集算子(l&a m)和对数梯形中性加权几何聚集算子(lgg o)。此外,还利用对数运算律和聚合算子证明了一种新的MCGDM方法,该方法已成功地应用于数值问题的求解。通过灵敏度分析,证明了该方法的稳定性和可靠性。最后,通过与现有方法的对比分析,验证了所提方法的合理性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A novel logarithmic operational law and aggregation operators for trapezoidal neutrosophic number with MCGDM skill to determine most harmful virus.

A novel logarithmic operational law and aggregation operators for trapezoidal neutrosophic number with MCGDM skill to determine most harmful virus.

A novel logarithmic operational law and aggregation operators for trapezoidal neutrosophic number with MCGDM skill to determine most harmful virus.

A novel logarithmic operational law and aggregation operators for trapezoidal neutrosophic number with MCGDM skill to determine most harmful virus.

In the current era, the theory of vagueness and multi-criteria group decision making (MCGDM) techniques are extensively applied by the researchers in disjunctive fields like recruitment policies, financial investment, design of the complex circuit, clinical diagnosis of disease, material management, etc. Recently, trapezoidal neutrosophic number (TNN) draws a major awareness to the researchers as it plays an essential role to grab the vagueness and uncertainty of daily life problems. In this article, we have focused, derived and established new logarithmic operational laws of trapezoidal neutrosophic number (TNN) where the logarithmic base μ is a positive real number. Here, logarithmic trapezoidal neutrosophic weighted arithmetic aggregation (L a r m ) operator and logarithmic trapezoidal neutrosophic weighted geometric aggregation (L g e o ) operator have been introduced using the logarithmic operational law. Furthermore, a new MCGDM approach is being demonstrated with the help of logarithmic operational law and aggregation operators, which has been successfully deployed to solve numerical problems. We have shown the stability and reliability of the proposed technique through sensitivity analysis. Finally, a comparative analysis has been presented to legitimize the rationality and efficiency of our proposed technique with the existing methods.

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