A network of actual physical objects or “IoT components” linked to the internet and equipped
with sensors, electronics, software, and network connectivity is known as the Internet of …

Learning from imbalanced datasets is a nontrivial task for supervised learning community.
Traditional classifiers may have difficulties to learn the concept related to the minority class …

In their study, Stavropoulos et al.(2023) capitalized on supervised machine learning and a
longitudinal design and reported that the User-Avatar Bond could be accurately employed to …

The aim of this informative review was to investigate the application of radiomics in cancer
imaging and to summarize the results of recent studies to support oncological imaging with …

Objective Data imbalance is a pervasive issue in medical data mining, often leading to
biased and unreliable predictive models. This study aims to address the urgent need for …

Neonatal mortality is a concerning issue for many families worldwide. With advances in
perinatal and neonatal care, neonatal mortality has been markedly reduced. While the …

Medical data typically encompass a multitude of features, which contain vast hidden
knowledge and also exhibit deep uncertainties. How to search the valuable features is a …

Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for
early clinical interventions, prevention of secondary complications such as nosocomial …

Imbalanced classification is a major issue that degrades the performance of conventional
classifiers in machine learning. As a result, predecessors have proposed methods to …

Abstract Inflammatory Bowel Diseases (IBDs) are chronic inflammatory disorders of the
gastrointestinal tract and colon affecting approximately 7 million individuals worldwide. The …