Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR)
networks based on machine learning techniques which are used for pattern classification. In …

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious
challenges to the human society in China and across the world. COVID-19 induced …

Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

With the growing number of aging infrastructure across the world, there is a high demand for
a more effective inspection method to assess its conditions. Routine assessment of structural …

Ageing influences gait patterns causing constant threats to control of locomotor balance.
Automated recognition of gait changes has many advantages including, early identification …

Support vector machines (SVMs) are theoretically well-justified machine learning
techniques, which have also been successfully applied to many real-world domains. The …

Since roller bearings are the key components in rotating machinery, detecting incipient
failure occurring in bearings is an essential attempt to assure machinery operational safety …

In this paper, a computationally efficient framework, referred to as ensemble of subset online
sequential extreme learning machine (ESOS-ELM), is proposed for class imbalance …