In this work, we shall present some novel process to measure the similarity between picture fuzzy sets. Firstly, we adopt the concept of intuitionistic fuzzy sets, interval-valued intuitionistic fuzzy sets and picture fuzzy sets. Secondly, we develop some similarity measures between picture fuzzy sets, such as, cosine similarity measure, weighted cosine similarity measure, set-theoretic similar...

OBJECTIVE In pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved cosine similarity measures of SNSs based on cosine function, including single valued neutrosophic cosine similarity measures and int...

Similarity measures are an important tool in pattern recognition and medical diagnosis. To overcome some disadvantages of existing cosine similarity measures for simplified neutrosophic sets (SNSs) in vector space, this paper proposes improved cosine similarity measures for SNSs based on the cosine function, including single valued neutrosophic cosine similarity measures and interval neutrosoph...

Objective: In pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved cosine similarity measures of SNSs based on cosine function, including single valued neutrosophic cosine similarity measures and in...

In this paper, the cosine similarity measure of neutrosophic refined (multi-) sets is proposed and its properties are studied. The concept of this cosine similarity measure of neutrosophic refined sets is the extension of improved cosine similarity measure of single valued neutrosophic. Finally, using this cosine similarity measure of neutrosophic refined set, the application of medical diagnos...

Similarity search is a fundamental problem for many data analysis techniques. Many efficient techniques rely on the triangle inequality of metrics, which allows pruning parts space based transitive bounds distances. Recently, cosine similarity has become popular alternative choice to standard Euclidean metric, in particular context textual and neural network embeddings. Unfortunately, not metri...

Cosine similarity is a term-vector-based measure of similarity that has been used widely in information retrieval research. In this study, we collect user judgments of web document similarity in order to investigate the correlation between cosine similarity and users’ perception of similarity on web documents. Experimental results demonstrate that it is hard to deduce that cosine similarity cor...

When dealing with document similarity many methods exist today, like cosine similarity. More complex methods are also available based on the semantic analysis of textual information, which are computationally expensive and rarely used in the real time feeding of content as in enterprisewide search environments. To address these real-time constraints, we developed a new measure of document simil...

Cosine similarity is a widely implemented metric in information retrieval and related studies. This metric models a text as a vector of terms and the similarity between two texts is derived from cosine value between two texts' term vectors. Cosine similarity however still can't handle the semantic meaning of the text perfectly. This paper proposes an enhancement of cosine similarity measurement...