Ripening classification of bananas (Musa acuminate) using electrical impedance spectroscopy and support vector machine

Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.Objective: In this study, we aimed to evaluate the efficiency of discrete wavelet transfo...

Support Vector Machine Based Facies Classification Using Seismic Attributes in an Oil Field of Iran

Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...

Classification of Arrhythmias Using Support Vector Machine

Detection of ventricular fibrillation (VF) and hasty ventricular tachycardia (VT) is crucial for the success of the defibrillation analysis. The algorithm that combines ECG parameters with Multicast SVM to categorize VF/shockable arrhythmias has been offered. The multicast SVM learning algorithms can increase the effectiveness for the exposure of lifethreatening arrhythmias. FS methods might he...

Classification of Breast Tissue Using Electrical Impedance Spectroscopy

Pattern Classification Using Support Vector Machine Ensemble

While the support vector machine (SVM) can provide a good generalization performance, the classification result of the SVM is often far from the theoretically expected level in practical implementation because they are based on approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use an SVM ensembl...