Chi-Square Similarity Measure for Interval Valued Neutrosophic Set

The present paper deals with a similarity measure known as chi-square similarity measure which is applied to interval valued neutrosophic sets. Although many similarity measures are available in the neutrosophic set theory, we have proposed a new one and compared it with some existing similarity measures and found the results are in good agreement with the already defined measures. In this paper, we have applied the new chi-square similarity measure on the real-life problems based on the recognition of pattern and diagnosis of illness in medical field. Further, the compared results with different similarity measures are shown in the form of table. The importance of pattern recognition problem in various application areas such as in image processing and medical diagnosis is shown for single-valued neutrosophic sets (SVNSs) but not for interval valued neutrosophic sets (IVNSs). Results related to IVNSs ensure that chi-square similarity measure is indeed effective and in some cases gives better results than the existing similarity measure.