Alzheimer's disease (AD) is the leading neurodegenerative disorder and primary cause of
dementia. Researchers are increasingly drawn to automated diagnosis of AD using …

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

Twin support vector machine (TSVM) is an emerging machine learning model with versatile
applicability in classification and regression endeavors. Nevertheless, TSVM confronts …

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

Recent advancements in intelligent diagnosis rely heavily on data-driven methods.
However, these methods often encounter challenges in adequately addressing class …

The identification of DNA-binding proteins (DBPs) is a critical task due to their significant
impact on various biological activities. Understanding the mechanisms underlying protein …

In the realm of data classification, broad learning system (BLS) has proven to be a potent
tool that utilizes a layer-by-layer feed-forward neural network. However, the traditional BLS …

The random vector functional link (RVFL) network is well-regarded for its strong
generalization capabilities in the field of machine learning. However, its inherent …

The ensemble deep random vector functional link (edRVFL) neural network has
demonstrated the ability to address the limitations of conventional artificial neural networks …

Multilabel learning deals with datasets where each sample is associated with multiple
labels. It is commonly assumed that label correlations should be well exploited to build an …