Brain tissue segmentation is an important component of the clinical diagnosis of brain
diseases using multi-modal magnetic resonance imaging (MR). Brain tissue segmentation …

The dissimilarity measures between fuzzy sets/intuitionistic fuzzy sets/picture fuzzy sets are
studied and applied in various matters. In this paper, we propose some new dissimilarity …

Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant
algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy …

This paper presents an efficient Meta-cognitive Fuzzy C-Means (McFCM) clustering
approach inspired by human meta-cognitive learning principles. Unlike other clustering …

PYTHAGOREAN PICTURE FUZZY SETS, PART 1- BASIC NOTIONS Page 1 Journal of
Computer Science and Cybernetics, V.35, N.4 (2019), 293–304 DOI 10.15625/1813-9663/35/4/13898 …

Clustering is a crucial method for deciphering data structure and producing new information.
Due to its significance in revealing fundamental connections between the human brain and …

The study of biotic nomenclature has resulted from gene expression profiling using
integrated image analysis techniques. The study emphasized in this paper confers Modified …

In image processing, one of the most important steps is image segmentation. The objects in
remote sensing images often have to be detected in order to perform next steps in image …

Maximum margin clustering (MMC) is an effective clustering algorithm, which first extends a
large margin principle into unsupervised learning. This paper revisits the MMC problem and …

In 2013, BC Cuong and V. Kreinovich introduced the concept of picture fuzzy set [1], which is
a directly generalization concept of the Zadeh's fuzzy sets and Atanassov's intuitionistic …