The single-valued neutrosophic set (SVNS) is a well-known model for handling uncertain and indeterminate information. Information measures such as distance measures, similarity measures and entropy measures are very useful tools to be used in many applications such as multi-criteria decision making (MCDM), medical diagnosis, pattern recognition and clustering problems. A lot of such information measures have been proposed for the SVNS model. However, many of these measures have inherent problems that prevent them from producing reasonable or consistent results to the decision makers. In this paper, we propose several new distance and similarity measures for the SVNS model. The proposed measures have been verified and proven to comply with the axiomatic definition of the distance and similarity measure for the SVNS model. A detailed and comprehensive comparative analysis between the proposed similarity measures and other well-known existing similarity measures has been done. Based on the comparison results, it is clearly proven that the proposed similarity measures are able to overcome the shortcomings that are inherent in existing similarity measures. Finally, an extensive set of numerical examples, related to pattern recognition and medical diagnosis, is given to demonstrate the practical applicability of the proposed similarity measures. In all numerical examples, it is proven that the proposed similarity measures are able to produce accurate and reasonable results. To further verify the superiority of the suggested similarity measures, the Spearman’s rank correlation coefficient test is performed on the ranking results that were obtained from the numerical examples, and it was again proven that the proposed similarity measures produced the most consistent ranking results compared to other existing similarity measures.

单值中智集（SVNS）是用于处理不确定和不确定信息的众所周知的模型。信息度量（例如距离度量，相似性度量和熵度量）是非常有用的工具，可用于许多应用程序中，例如多标准决策（MCDM），医学诊断，模式识别和聚类问题。对于SVNS模型，已经提出了许多这样的信息措施。但是，这些措施中有许多存在固有的问题，使它们无法为决策者提供合理或一致的结果。在本文中，我们为SVNS模型提出了几种新的距离和相似性度量。所提出的措施已经过验证并证明符合SVNS模型的距离和相似性措施的公理定义。已对拟议的相似性度量与其他已知的现有相似性度量进行了详细而全面的比较分析。基于比较结果，可以清楚地证明所提出的相似性度量能够克服现有相似性度量固有的缺点。最后，给出了与模式识别和医学诊断有关的大量数值示例，以证明所提出的相似性度量的实际适用性。在所有数值示例中，都证明了所提出的相似性度量能够产生准确而合理的结果。为了进一步验证建议的相似性度量的优越性，