Loss function is a critical component of machine learning. Some robust loss functions are
proposed to mitigate the adverse effects caused by noise. However, they still face many …

Emotion recognition based on electroencephalogram (EEG) signals is helpful in various
fields, including medical healthcare. One possible medical application is to diagnose …

Deep neural networks have shown their promise in recent years with their state-of-the-art
results. Yet, backpropagation-based methods may suffer from time-consuming training …

Alzheimer's disease (AD) is a progressive brain disorder. Machine learning models have
been proposed for the diagnosis of AD at early stage. Recently, deep learning architectures …

Twin support vector machine (TSVM) is a practical machine learning algorithm, whereas
traditional TSVM can be limited for data with outliers or noises. To address this problem, we …

Objective Diabetes mellitus is a serious disease where the body of affected patients are
failed to produce enough insulin that causes an abnormality of blood sugar. This disease …

Due to rapid climate change and man-made activities, the types of leaf diseases are
gradually increasing. As a result, taking the essential measures to recognize and diagnose …

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML)
models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the …

Support vector machine (SVM), being considered one of the most efficient tools for
classification, has received widespread attention in various fields. However, its performance …

Linear and quadratic discriminant analysis are two fundamental classification methods used
in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids …