The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Support vector machines (SVMs) is a very popular machine learning technique. An SVM
classifier trained on an imbalanced dataset can produce suboptimal models that are biased …

The computer-aided diagnosis (CAD) method plays a considerable role in the automated
recognition of medical images, considering the increasing numbers of lung cancer patients …

Nonsmall cell lung cancer is a prevalent disease. It is diagnosed and treated with the help of
computed tomography (CT) scans. In this paper, we apply radiomics to select 3-D features …

In recent years, the number of proposed fall-detection systems that have been developed
has increased dramatically. A threshold-based algorithm utilizing an accelerometer has …

Among all electrocardiogram (ECG) components, the QRS complex is the most significant
feature. This paper presents a new algorithm for recognition of QRS complexes in the …

Imbalanced dataset learning is an important practical issue in machine learning, even in
support vector machines (SVMs). In this study, a well known reference model for solving the …

This paper presents a review on methods for class-imbalanced learning with the Support
Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants …

Background and Objectives Using lasers instead of mechanical tools for bone cutting holds
many advantages, including functional cuts, contactless interaction, and faster wound …

A CT-scan is a vital tool for the diagnosis of lung cancer via tumor detection. Developing a
classifier to make use of the information in CT-scan images could provide a non-invasive …