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 …

Rapid advances that improve flood management have facilitated the disaster response by
providing first aid services, finding safe routes, maintaining communication and develo** …

Support vector machine (SVM) is a widely used pattern classification method that its
classification accuracy is greatly influenced by both kernel parameter setting and feature …

In this paper, a new support vector machines (SVM) parameter tuning scheme that uses the
fruit fly optimization algorithm (FOA) is proposed. Termed as FOA-SVM, the scheme is …

Abstract Background and Objective: In countries with high prevalence of tuberculosis (TB),
clinicians often diagnose tuberculous pleural effusion (TPE) by using diagnostic tests, which …

Support vector machine (SVM) has been recently considered as one of the most efficient
classifiers. However, the time complexity of kernel SVM, which is quadratic in the number of …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

Neutrophil extracellular traps (NETs) is an important process involved in the pathogenesis of
systemic lupus erythematosus (SLE), but the potential mechanisms of NETs contributing to …

The problem of model selection for support vector machines (SVMs) is considered. We
propose an evolutionary approach to determine multiple SVM hyperparameters: The …

Introduction Alzheimer's disease is the most common dementia with clinical and
pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which …