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

The purpose is to explore the feature recognition, diagnosis, and forecasting performances
of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins …

At present, the excellent performance of support vector machine (SVM) has made it
successfully applied in many fields. However, when SVM is used for two-dimensional matrix …

In most of the real world datasets, there is an imbalance in the number of samples belonging
to different classes. Various pattern classification problems such as fault or disease …

Alzheimer's disease (AD) is the most pervasive form of dementia, resulting in severe
psychosocial effects such as affecting personality, reasoning, emotions, and memory …

Alzheimer's disease (AD) and Schizophrenia (SCZ) are prominent neurodegenerative
conditions and leading causes of dementia, resulting in progressive cognitive decline and …

Imbalanced datasets are prominent in real-world problems. In such problems, the data
samples in one class are significantly higher than in the other classes, even though the other …

Multiview learning (MVL) seeks to leverage the benefits of diverse perspectives to
complement each other, effectively extracting and utilizing the latent information within the …

In real world problems, imbalance of data samples poses major challenge for the
classification problems as the data samples of a particular class are dominating. Problems …

Generalized eigenvalue proximal support vector machine (GEPSVM) has attracted
widespread attention due to its simple architecture, rapid execution, and commendable …