Brain image segmentation is one of the most time-consuming and challenging procedures in
a clinical environment. Recently, a drastic increase in the number of brain disorders has …

This study proposed a novel framework for plant leaf disease identification. The proposed
model consists of four steps including pre-processing, segmentation, feature extraction, and …

The automatic segmentation of blood cells for detecting hematological disorders is a crucial
job. It has a vital role in diagnosis, treatment planning, and output evaluation. The existing …

Traditional fuzzy clustering algorithms suffer from two problems in image segmentations.
One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy …

Since a noisy image has inferior characteristics, the direct use of Fuzzy-Means (FCM) to
segment it often produces poor image segmentation results. Intuitively, using its ideal value …

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 …

Melanoma is the kind of skin cancer that poses the greatest risk to one's life and has the
maximum mortality rate within the group of skin cancer disorders. Even so, the automated …

As the core mechanism of intelligent systems, decision-making has received widespread
attention in recent years. As decision-making environments become more complex, large …

Tumor detection and diagnosis have become topical subjects in the current age. In this
paper, an innovative technique for segmenting brain tumor is furnished. The proposed …

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for
disseminating truthful event messages. False messages may cause vehicles to behave in …