RMD Open

Development and assessment of an assisted diagnosis model using machine learning for identifying adult-onset Still’s disease in fever of unknown origin: a retrospective …

Background Adult-onset Still’s disease (AOSD) is a systemic autoinflammatory disorder lacking a gold-standard diagnostic ...
News-Medical.Net

AI model identifies where snoring starts in the airway

A new study developed a snore-source classification model that uses STFT spectrograms, pretrained CNN features, and an L2-regularized SVM to identify where snoring originates in the upper airway.
IEEE

Density-Convoluted Support Vector Machines for High-Dimensional Classification

Abstract: The support vector machine (SVM) is a popular classification method which enjoys good performance in many real applications. The SVM can be viewed as a penalized minimization problem in ...
TechRepublic

Detecting Credit Card Fraud by Decision Trees and Support Vector Machines

With the developments in the Information Technology and improvements in the communication channels, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention ...
Scientific Research Publishing

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review ()

1 Department of Basic Sciences, School of Basic and Biomedical Sciences, University of Health and Allied Sciences, Ho, Ghana. 2 Department of Statistics and Actuarial Science, University of Ghana, ...
Visual Studio Magazine

Support Vector Machines Using Accord.NET

A support vector machine (SVM) is a software system that can perform binary classification. For example, you can use an SVM to create a model that predicts the sex of a person (male, female) based on ...
C&EN

Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction

In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among the most widely used machine learning methods for the identification of new active compounds. In ...