Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

Identification of leaf disorder plays an important role in the economic prosperity of any
country. Many parts of a plant can be infected by a virus, fungal, bacteria, and other …

Twin support vector regression (TSVR) is a useful extension of traditional support vector
regression (SVR). As a new regression model, the basic idea of TSVR is generating a pair of …

Several projects and application development teams are spending their precious time and
energy in the field of classification and regression. So, the main target of the proposed …

Conventional regression methods often fail when encountering noise. The application of a
bounded loss function is an effective means to enhance regressor robustness. However …

The class imbalance learning (CIL) problem indicates when one class have very low
proportions of samples (minority class) compared to the other class (majority class). Even …

Data classification is a key domain of research in real-world applications. One of the big
challenges of real-world data classification is to tackle the presence of noise and outliers. In …

Random vector functional link (RVFL) is a widely used powerful model for solving real-life
problems in classification and regression. However, the RVFL is not able to reduce the …

Abstract The SOH (State of Health) of lithium-ion batteries is an important feature that
characterizes the reliable operation of energy storage systems. In this study, according to the …

Twin support vector regression (TSVR) is an important algorithm to handle regression
problems developed on the basis of support vector regression (SVR). However, TSVR …