Fuzzy Mathematics theory has been studied extensively in this study. Most early interest in fuzzy set theory pertained to representing uncertainty in human cognitive processes. Fuzziness can be found in many areas of daily life, such as engineering, business, medical and related health sciences, and the natural sciences. It is particularly frequent, however, in all areas in which human judgment, evaluation and decisions are important. However, in many real-life cases, the decision data of human judgments with preferences are often vague, so the traditional ways of using crisp values are inadequate. Due to a lack of information, the future state of the real system might not be known completely. Fuzzy mathematical modelling represents a real-world problem in a mathematical form to find solutions to the problem that correspond closely to how humans perceive it. Also, fuzzy modelling increases the validity of ill-structured systems by offering a deeper understanding of the complexities of decision parameters. In this paper, we analyze the problem of the transgender using FCMs and Combined Fuzzy Cognitive Maps (CFCM). This paper is divided into five sections. Section one introduces the basic introduction of transgender and the previous research of Fuzzy Cognitive Maps (FCM) and Combined FCM. Section two introduces FCM and combined FCM and the basic operations. Section three shows the basic definitions of FCM and Combined CFCM. The fourth section provides methods to analyze transgender problems using combined FCM. The final section gives the conclusions and some suggestions based on our study.

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered Unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientation) values. The output image of this step clarified different textures and then used low pass Gaussian filter for smoothing the image. These two filters were used as preprocessing stage of texture images. In this research, we used K-means algorithm for initial segmentation. In this study, we used Expectation Maximization (EM) algorithm to estimate parameters, too. Finally, the segmentation was done by Iterated Conditional Modes (ICM) algorithm updating the labels and minimizing the energy function. In order to test the segmentation performance, some of the standard images of Brodatz database are used. The experimental results show the effectiveness of the proposed method.