The similarity measures are essential concepts to discuss the closeness between sets. Fuzzy similarity measures and intuitionistic fuzzy similarity measures dealt with the incomplete and inconsistent data more efficiently. With time in decision-making theory, a complex frame of the environment that occurs cannot be specified entirely by these sets. A generalization like the Pythagorean fuzzy set can handle such a situation more efficiently. The applicability of this set attracted the researchers to generalize it into N-Pythagorean, interval-valued N-Pythagorean, and N-cubic Pythagorean sets. For this purpose, first, we define the overlapping ratios of N-interval valued Pythagorean and N-Pythagorean fuzzy sets. In addition, we define similarity measures in these sets. We applied this proposed measure for comparison analysis of plagiarism software.

相似性度量是讨论集合之间接近性的基本概念。模糊相似度度量和直觉模糊相似度度量更有效地处理不完整和不一致的数据。随着决策理论的发展，发生的复杂环境框架不能完全由这些集合指定。像毕达哥拉斯模糊集这样的泛化可以更有效地处理这种情况。该集合的适用性吸引了研究人员将其推广为 N-Pythagorean、区间值 N-Pythagorean 和 N-cubic Pythagorean 集合。为此，首先，我们定义了 N 区间值勾股和 N 勾股模糊集的重叠比率。此外，我们在这些集合中定义了相似性度量。我们将这一提议的措施应用于剽窃软件的比较分析。