In this paper, rstly we have introduced to entropy of sequences of fuzzy sets and given sometheorems about it. Secondly, the waves P and T which appears in electrocardiograms weretransferred to fuzzy sets, by using denition of entropy for sequences of fuzzy sets, and somenumerical values were obtained for sequences of waves P and T. Thus any person can makea medical predictions for some cardiac...

Combined with weight of samples and kernel function, fuzzy clustering method with generalized entropy is studied. Objective function for fuzzy clustering with generalized entropy based on sample weighting is obtained. Following that, fuzzy clustering algorithm with generalized entropy based on sample weighting is presented. In addition, by introducing kernel into the presented objective functio...

Fuzzy clustering based on generalized entropy is studied. By introducing the generalized entropy into objective function of fuzzy clustering, a unified model is given for fuzzy clustering in this paper. Then fuzzy clustering algorithm based on the generalized entropy is presented. At the same time, by introducing the spatial information of image into the generalized entropy fuzzy clustering alg...

In this paper the notion of fuzzy topological r-entropy as an extension of the notion of topological r-entropy is studied. It is proved that fuzzy topological r-entropy is an invariant object under uniformly fuzzy equivalent relation and fuzzy topological equivalent relation.

This paper provides new hybrid medical image segmentation based on Global Minimization by Active Contour (GMAC) method and Spatial Fuzzy C Means Clustering method (SFCM) tailored to CT imaging applications. GMAC is the unification of image segmentation and image denoising, which is a combination of snake, Rudin-Osher denoising and the Mumford Shah model. Here globalization of contour is applied...

Comparison and data analysis to the similarity measures and entropy for fuzzy sets are studied. The distance proportional value between the fuzzy set and the corresponding crisp set is represented as fuzzy entropy. We also verified that the sum of the similarity measure and the entropy between fuzzy set and the corresponding crisp set constitutes the total information. Finally, we derive a simi...

Feature Selection (FS) methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the FS process with much success. More recently a method has been developed which uses fuzzy-entropy [9] to perform this task. Such use of fuzzy-entropy as an evaluation measure in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS a...