## Mehdi Shirmaleki

I'm a data scientist. I received my M.S. degree in Mathematical Statistics at Iran University of Science & Technology (IUST). The title of my M.S. thesis was: "Fuzzy Entropy”. I have over four years of experience working as an expert of statistics and data collection at General Department of Justice. Also, I have been the instructor at Bu-Ali Sina University for more than three years. I have a lot of experience in tutoring university and high school students.

## MY LATEST RESEARCH

## Entropy of interval-valued fuzzy sets and intuitionistic fuzzy sets

Fuzzy entropy is a non-probabilistic quantity which investigate measure of fuzziness and vagueness of system. In this research, we introduce entropy of fuzzy sets as a criterion for uncertainty. After that, we introduce σ-entropy on fuzzy sets and distance measure for a fuzzy set and we investigate relationship between distance measure and fuzzy entropy.

The distance measure is a measure that describes the difference between fuzzy sets. Using the concept of σ-distance measure, we have studied some properties of fuzzy entropy. Then, we will study fuzzy relative entropy based on relative entropy. Next, we introduce inclusion measure of interval-valued fuzzy set and relationship among the normalized distance, the similarity measure, the inclusion measure, and the entropy of interval-valued fuzzy sets is investigated in detail.

## The survey of fuzzy sets theory and rough sets theory use to data mining technique

The combination of fuzzy sets theory and rough sets theory use to data mining technique due to transfer vague and imprecise data. From the previous literature, rough set theory can only operate effectively with dataset containing discrete value. As most dataset contain real-valued features, it is essential for employing by standard fuzzification techniques which are called modified minimization entropy principle algorithm (MMEPA) is proposed to construct membership function of fuzzy sets.