Abstract Background and Motivation: Cardiovascular disease (CVD) causes the highest
mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment …

Majority of the learning algorithms used for the training of feedforward neural networks
(FNNs), such as backpropagation (BP), conjugate gradient method, etc. rely on the …

Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …

Thoracic disease, like many other diseases, can lead to complications. Existing multi-label
medical image learning problems typically include rich pathological information, such as …

An accurate estimation of local scour depth around piles group is inevitably essential to
provide stability of marine structures. Over the past decades, many investigations have been …

Motivation Machine learning (ML) algorithms can provide better cardiovascular event (CVE)
prediction. However, ML algorithms are mostly explored for predicting a single CVE at a …

With advancements in computer and communication technologies, the prevalence of high-
dimensional multi-label data has significantly increased across various domains. Feature …

With the rise of the fruit processing industry, machine learning and image processing have
become necessary for quality control and monitoring of fruits. Recently, strong vision-based …

Abstract This study utilizes Geographic Information System (GIS) and remote sensing
techniques to predict water quality metrics in the Noyyal River. Satellite data is employed to …

This study aims at the limitations of traditional methods in the evaluation of stroke sequelae
and rehabilitation effect monitoring, especially for the accurate identification and tracking of …