The single-valued neutrosophic set (SVNS) is considered as an attractive tool for handling highly uncertain and vague information. With this regard, different from the most current distance-based technique for order preference by similarity to ideal solution (TOPSIS) methods, this study proposes a correlation-based TOPSIS model for addressing the single-valued neutrosophic (SVN) multiple attribute decision making (MADM) problems. To achieve this aim, we first develop a novel conception of SVN correlation coefficient, whose significant feature is that it lies in the interval [[Formula: see text],1], which is in accordance with the classical correlation coefficient in statistics, whereas all the existing SVN correlation coefficients in the literature are within unit interval [0,1]. Afterwards, a weighted SVN correlation coefficient is also introduced to infuse the importance of attributes. Moreover, a correlation-based comprehensive index is further proposed to establish the central structure of TOPSIS model, called the SVN correlation-based TOPSIS approach. Finally, a numerical example and relevant comparative analysis are implemented to explain the applicability and effectiveness of the mentioned methodology.

中文翻译：

---

基于相关性的单值中智信息多属性决策TOPSIS方法

单值中智集（SVNS）被认为是处理高度不确定和模糊信息的有吸引力的工具。在这方面，与当前基于距离的排序偏好技术与理想解相似度（TOPSIS）方法不同，本研究提出了一种基于相关性的TOPSIS模型来解决单值中智（SVN）多属性决策问题。 MADM) 问题。为了达到这个目的，我们首先提出了一个新的SVN相关系数概念，其显着特点是它位于区间[[公式：见正文]，1]，符合统计学中的经典相关系数，而文献中现有的所有SVN相关系数都在单位区间[0,1]内。然后，还引入了加权 SVN 相关系数来注入属性的重要性。此外，进一步提出了一种基于相关性的综合指标来建立TOPSIS模型的中心结构，称为SVN基于相关性的TOPSIS方法。最后，通过数值例子和相关的比较分析来说明上述方法的适用性和有效性。