Efficient and reliable storage, analysis, and transmission of medical images are imperative
for accurate diagnosis, treatment, and management of various diseases. Since quantum …

As an extension of fuzzy set, intuitionistic fuzzy set (IFS) considers the degrees of non-
membership and hesitancy along with the degree of membership, therefore, the knowledge …

Abstract The Fuzzy C-means (FCM) clustering algorithm is an effective method for image
segmentation. Non-local spatial information considers more redundant information of the …

Colors characterize each object around us. For this reason, the study of colors has played a
key role in Artificial Intelligence (think, for instance, of image classification, object recognition …

Automatic segmentation of salient objects in real-world images has gained increasing
interests owing to its popularity in diverse real-world applications, such as autonomous …

Soft clustering, a widely utilized method in data analysis, offers a versatile and flexible
strategy for grouping data points. Most soft clustering algorithms assume that all the features …

The possibilistic fuzzy c-means clustering (PFCM) algorithm is a hybridization of possibilistic
c-means clustering (PCM) and fuzzy c-means clustering (FCM) algorithms. However, there …

In existing deep clustering methods, as the model gets deeper, extracted representations
can be deteriorated due to a vanishing gradient, leading to reduced performance. Also …

The kernel fuzzy clustering algorithms can explore the non-linear relations of pixels in an
image. However, most of kernel-based methods are computationally expensive for color …

In existing deep clustering methods, it is assumed that all generated representations are
equally important during the clustering procedure. However, if the model can't learn proper …