Entropy (H) is the main subject of this article, concisely written to serve as a tutorial
introducing two feature extraction (FE) methods for usage in digital signal processing (DSP) …

Latent class analysis is used to group categorical data into classes via a probability model.
Model selection criteria then judge how well the model fits the data. When addressing …

Unsupervised image segmentation is one of the most important and fundamental tasks in
many computer vision systems. Mixture model is a compelling framework for unsupervised …

Dempster-Shafer evidence theory (DS theory) is widely used in brain magnetic resonance
imaging (MRI) segmentation, due to its efficient combination of the evidence from different …

Medical image semantic segmentation is essential in computer-aided diagnosis systems. It
can separate tissues and lesions in the image and provide valuable information to …

The ASA's statement on p-values: context, process, and purpose (Wasserstein and Lazar
2016) makes several reasonable practical points on the use of p-values in empirical …

Neutrosophic set (NS) is a formal framework proposed recently. NS can not only describe
the incomplete information in the decision-making system but also depict the uncertainty and …

This paper proposes a new multi-level entropy-based image thresholding method. The key
principle of the proposed method depends on the minimum of the variance entropy. The …

On the basis of the efficiency function introduced by Kittaneh and Beltagy [18], we compare
the performance of censored samples from lognormal and Weibull distributions as two …

The purpose of the paper is to introduce the Jensen–inaccuracy measure and examine its
properties. Furthermore, some results on the connections between the inaccuracy and …