The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Traditional networking is hardware-based, having the control plane coupled with the data
plane. Software-Defined Networking (SDN), which has a logically centralized control plane …

In recent years, a forward-looking subfield of machine learning has emerged with important
applications in a variety of scientific fields. Semi-supervised learning is increasingly being …

Nowadays, the application of data mining is widely prevalent in the education system. The
ability of data mining to obtain meaningful information from meaningless data makes it very …

During the last decades, intensive efforts have been devoted to the extraction of useful
knowledge from large volumes of medical data employing advanced machine learning and …

Self-training is a commonly semi-supervised learning Algorithm framework. How to select
the high-confidence samples is a crucial step for algorithms based on self-training …

Heart disease is a leading global cause of mortality, demanding early detection for effective
and timely medical intervention. In this study, we propose a machine learning-based model …

Identifying Photovoltaic (PV) array faults is crucial for improving the service life and
consolidating system performance overall. The strategies based on the supervised Machine …

Beta regression models are a class of supervised learning tools for regression problems
with univariate and limited response. Current fitting procedures for beta regression require …

Credit scoring is generally recognized as one of the most significant operational research
techniques used in banking and finance, aiming to identify whether a credit consumer …