Similarity measure for complex non-linear Diophantine fuzzy hypersoft set and its application in pattern recognition

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2024-10-28 DOI:10.1016/j.ins.2024.121591

AN. Surya, J. Vimala

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引用次数: 0

Abstract

As a hybrid fuzzy extension of the complex non-linear Diophantine fuzzy set, the complex non-linear Diophantine fuzzy hypersoft set was developed by fusing it with the hypersoft set. To address multi-sub-attributed real-world similarity problems within complex non-linear Diophantine fuzzy ambiance, this study proposes distance measures and five innovative similarity measures such as Jaccard similarity measure, exponential similarity measure, cosine similarity measure, similarity measure based on cos function, and similarity measure based on cot function for complex non-linear Diophantine fuzzy hypersoft set. Furthermore, based on proposed similarity measures, a highly effective algorithm is provided for handling decision-making issues exquisitely in the pattern recognition field, along with an illustrative example of mineral identification. Then, to demonstrate the validity, reliability, robustness, and superiority of the proposed notion and algorithm, a detailed comparative study with proper discussion has been presented in the study.

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复杂非线性 Diophantine 模糊超软集的相似度量及其在模式识别中的应用

作为复非线性 Diophantine 模糊集的混合模糊扩展，复非线性 Diophantine 模糊超软集与超软集相融合，发展出了复非线性 Diophantine 模糊超软集。为了解决复杂非线性 Diophantine 模糊环境中的多子属性真实世界相似性问题，本研究针对复杂非线性 Diophantine 模糊超软集提出了距离度量和五种创新的相似性度量，如 Jaccard 相似性度量、指数相似性度量、余弦相似性度量、基于 cos 函数的相似性度量和基于 cot 函数的相似性度量。此外，基于所提出的相似性度量，还提供了一种高效的算法，用于在模式识别领域精细地处理决策问题，并以矿物识别为例进行说明。然后，为了证明所提出的概念和算法的有效性、可靠性、稳健性和优越性，研究还进行了详细的比较研究和适当的讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.