In this paper, we present a new method for fuzzy risk analysis based on ranking generalized fuzzy numbers with different heights and different spreads. First, we present a new method for ranking generalized fuzzy numbers. The proposed method considers the defuzzified values, the heights and the spreads for ranking generalized fuzzy numbers. Based on the proposed method for ranking generalized f...

We have presented trapezoidal intuitionistic fuzzy number. Arithmetic operations of trapezoidal intuitionistic fuzzy number is presented based on (α, β)-cuts. We have presented intuitionistic fuzzy fault tree analysis using trapezoidal intuitionistic fuzzy number. In application of intuitionistic fuzzy fault tree analysis, we have computed failure of fire protection system taking failure of eac...

In this paper, we present a new method for ranking generalized fuzzy numbers for dealing with fuzzy risk analysis problems. The proposed method considers the defuzzified values, the heights and the spreads of generalized fuzzy numbers, simultaneously, for ranking generalized fuzzy numbers. It gets better ranking results to rank generalized fuzzy numbers than the existing methods. We also apply ...

In this paper, a fuzzy shape annotation approach for automatic image labeling is presented. A fuzzy clustering process guided by partial supervision is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes representative of a number of semantic categories. Next, prototypes are manually annotated by attaching textual labels related to semantic categori...

Similarity and inclusion measures between type-2 fuzzy sets have a wide range of applications. New similarity and inclusion measures between type-2 fuzzy sets are respectively defined in this paper. The properties of the measures are discussed. Some examples are used to compare the proposed measures with the existing results. Numerical results show that the proposed measures are more reasonable...

Methods like DBSCAN are widely used in the analysis of spatial data. These methods are based on the neighborhood relations which use distance between points. However, these neighborhood relations consider to have at least a certain number of neighbors within a definite boundary. In this proposed work such a neighborhood analysis is done by using the benefits of fuzzy sets theory. Usage of fuzzy...

This paper presents fuzzy clustering algorithms for mixed features of symbolic and fuzzy data. El-Sonbaty and Ismail proposed fuzzy c-means (FCM) clustering for symbolic data and Hathaway et al. proposed FCM for fuzzy data. In this paper we give a modi3ed dissimilarity measure for symbolic and fuzzy data and then give FCM clustering algorithms for these mixed data types. Numerical examples and ...