Texture-based convolutional neural networks (CNNs) have shown great promise in
predicting various types of cancer, including lower grade glioma (LGG) through radiomics …

The two-dimensional principal component analysis (2DPCA) has been one of the basic
methods of developing artificial intelligent algorithms. To increase the feasibility, we propose …

Linear regression classification (LRC) has proven to be a successful recognition tool in
recent years. LRC depends on using the least square algorithm to get the solution of the …

Two-dimensional quaternion principal component analysis (2D-QPCA) is one of the
successful dimensionality reduction methods for color face recognition. However, 2D-QPCA …

Face recognition has been deeply studied and widely used in recent years. A novel method,
called local gradient number pattern (LGNP), is firstly presented in the paper for face …

A new data-driven bilateral generalized two-dimensional quaternion principal component
analysis (BiG2DQPCA) is presented to extract the features of matrix samples from both row …

Linear regression is a simple and widely used machine learning algorithm. It is a statistical
approach for modeling the relationship between a scalar variable and one or more …

This paper provides an in-depth discussion on the application of quaternion orthogonal
Fourier-Mellin Moments (QOFMM) in the field of digital image processing, and proposes a …

Linear regression is one of the simplest and widely used machine learning algorithms that
has received a lot of attention in many fields. Linear Regression Classification (LRC) …

One of the most powerful methods of color image recognition is the two-dimensional
principle component analysis (2DQPCA) approach, which is based on quaternion …