In medical image processing, brain tumor detection and segmentation is a challenging and
time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in …

Brain tumor segmentation and classification is the interesting area for differentiating the
tumerous and the non-tumerous cells in the brain and to classify the tumerous cells for …

In a dynamic environment, the data collected from real applications varies not only with the
amount of objects but also with the number of features, which will result in continuous …

In this paper, a novel construction algorithm called imbalanced Takagi-Sugeno-Kang fuzzy
classifier (IB-TSK-FC) for the TSK fuzzy classifier is presented to improve the classification …

Least-squares regression (LSR)-based classifiers are effective in multiclassification tasks.
However, most existing methods use limited projections, resulting in loss of much …

An increasing number of crystal structures available on one side, and the boost of
computational power available for computer-aided drug design tasks on the other, have …

Pedestrian detection in infrared images is always a challenging task. Segmentation is an
important step of pedestrian detection. An accurate segmentation could provide more …

In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy classifier is casted into the Bayesian
inference framework and a new fuzzy classifier called Bayesian TSK fuzzy classifier (B-TSK …

Topic models [eg, probabilistic latent semantic analysis, latent Dirichlet allocation (LDA), and
supervised LDA] have been widely used for segmenting imagery. However, these models …

The fuzzy c-means clustering algorithm is the most widely used soft clustering algorithm. In
contrast to hard clustering, the cluster membership of data generated using the fuzzy c …