Support vector machines (SVMs) are well-known machine learning algorithms for
classification and regression applications. In the healthcare domain, they have been used …

Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …

Loss function plays a vital role in supervised learning frameworks. The selection of the
appropriate loss function holds the potential to have a substantial impact on the proficiency …

The random vector functional link (RVFL) neural network has shown the potential to
overcome traditional artificial neural networks' limitations, such as substantial time …

The timely identification of significant memory concern (SMC) is crucial for proactive
cognitive health management, especially in an aging population. Detecting SMC early …

This work investigated the use of an ensemble deep random vector functional link (edRVFL)
network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low …

Breast cancer is a common disease that affects feminine health, making it an active area of
research. Also, support vector machine with pinball loss (pin-SVM) is an efficient …

Conventional functional connectivity network (FCN) based on resting-state fMRI (rs-fMRI)
can only reflect the relationship between pairwise brain regions. Thus, the hyper …

The random vector functional link (RVFL) network is a prominent classification model with
strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether …