Improved cosine similarity measures of simplified intuitionistic sets for medicine diagnoses

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 neutrosophic cosine similarity measures. Then, the weighted cosine similarity measures of SNSs are introduced by considering the importance of each element. Moreover, compared with existing cosine similarity measures of SNSs by numerical examples, the improved cosine similarity measures of SNSs demonstrate their effectiveness and rationality and can overcome some disadvantages of existing cosine similarity measures of SNSs in some cases. Finally, the medical diagnosis problems are given to show the applications and effectiveness of the improved cosine similarity measures.