Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …

Support vector machines (SVMs) are widely used classifiers for detecting physiological
patterns in human–computer interaction (HCI). Their success is due to their versatility …

Human behavior pattern recognition (BPR) from accelerometer signals is a challenging
problem due to variations in signal durations of different behaviors. Analysis of human …

This article presents an innovative research methodology that enables the efficient
classification of cardiac disorders (17 classes) based on ECG signal analysis and an …

Emerging machine learning (ML)-based approaches provide powerful and novel tools to
study a variety of physical and chemical problems. In this contribution, we outline a universal …

Accurate apple recognition is a vital step in the operation of robotic fruit picking. To improve
robot recognition ability and perception in three-dimensional (3D) space, an automatic …

Due to the constantly increasing demand for the automatic localization of landmarks in hand
gesture recognition, there is a need for a more sustainable, intelligent, and reliable system …

This article presents an innovative genetic ensembles of classifiers applied to classification
of cardiac disorders (17 classes) based on electrocardiography (ECG) signal analysis. From …

Support vector machine (SVM) is a classification method based on the structured risk
minimization principle. Penalize, C; and kernel, σ parameters of SVM must be carefully …

The diagnosis of Parkinson's disease (PD) is important in neurological pathology for
appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been …