AIMC Topic: Support Vector Machine

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Possibilistic classification by support vector networks.

Neural networks : the official journal of the International Neural Network Society
In many real-world classification problems, the available information is often uncertain. In order to effectively describe the inherent vagueness and improve the classification performance, this paper proposes a novel possibilistic classification alg...

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.

BMC cardiovascular disorders
BACKGROUND: Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease de...

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.

Aging
Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resist...

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems.

Computational and mathematical methods in medicine
Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authe...

Abnormal Degree Centrality as a Potential Imaging Biomarker for Right Temporal Lobe Epilepsy: A Resting-state Functional Magnetic Resonance Imaging Study and Support Vector Machine Analysis.

Neuroscience
Previous studies have reported altered neuroimaging features in right temporal lobe epilepsy (rTLE). However, the alterations in degree centrality (DC) as a diagnostic method for rTLE have not been reported. Therefore, we aimed to explore abnormaliti...

Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud-Robot System.

Sensors (Basel, Switzerland)
Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communi...

Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.

Scientific reports
The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be prese...

Hybrid machine learning classification scheme for speaker identification.

Journal of forensic sciences
Motivated by the requirement to prepare for the next generation of "Automatic Spokesperson Recognition" (ASR) system, this paper applied the fused spectral features with hybrid machine learning (ML) strategy to the speech communication field. This st...

Using Support Vector Machines for Facet Partitioning in Multidimensional Scaling.

Multivariate behavioral research
In this article we focus on interpreting multidimensional scaling (MDS) configurations using facet theory. The facet theory approach is attempting to partition a representational space, facet by facet, into regions with certain simplifying constraint...