Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

This research studies a support vector machine (SVM)-based ensemble learning algorithm
for breast cancer diagnosis. Illness diagnosis plays a critical role in designating treatment …

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function
(RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm …

This study aims to provide an effective and accurate machine learning approach to predict
the compressive strength (CS) and flexural strength (FS) of 3D printed fiber reinforced …

Corrosion is an important factor leading to the failure of subsea process operations
especially subsea crude oil pipelines. Developing a data-driven corrosion prediction model …

The detection of water pipeline leakage is important to ensure that water supply networks
can operate safely and conserve water resources. To address the lack of intelligent and the …

Addressing the issue of SVMs parameters optimization, this study proposes an efficient
memetic algorithm based on particle swarm optimization algorithm (PSO) and pattern search …

Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality
reduction and has been applied to image processing, pose estimation and other fields …

The selection of hyper-parameters plays an important role to the performance of least-
squares support vector machines (LS-SVMs). In this paper, a novel hyper-parameter …

The support vector machine (SVM) is a powerful classifier which has been used successfully
in many pattern recognition problems. It has also been shown to perform well in the …