Neurodegenerative disorders like Alzheimer's disease (AD) are irreversible and show
atrophies in the area of the cerebral cortex of brain. AD leads to loss of memory and other …

In the domain of machine learning, the significance of the loss function is paramount,
especially in supervised learning tasks. It serves as a fundamental pillar that profoundly …

For classification problems, twin support vector machine with pinball loss (Pin-GTSVM) is
noise insensitive and has better performance than twin support vector machine (TWSVM) …

The initial concept of negative hesitation fuzzy sets (NHFSs) has been introduced recently.
NHFSs are applied to decision-making problems accompanied by soft set theory. In this …

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

In an educational setting, a teacher plays a crucial role in various classroom teaching
patterns. Similarly, mirroring this aspect of human learning, the learning using privileged …

Effective management of EEG artifacts is pivotal for accurate neurological diagnostics,
particularly in detecting early stages of Alzheimer's disease. This review delves into the …

The plane-based classifiers, support vector machine (SVM) and twin support vector machine
(TWSVM), are susceptible to the negative impact of noise, outliers, and class imbalance …

The data mining community has a major challenge in classifying datasets with noise,
outliers, and imbalanced classes. Twin support vector machine (TSVM) is a well-known …

Biomedical data exhibit high-dimensional complexity in its internal structure and are
susceptible to noise interference, making classification tasks in biomedical data highly …