Abstract Machine learning (ML) has been increasingly used within cardiology, particularly in
the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML …

The need to manufacture more competitive equipment, together with the emergence of the
digital technologies from the so-called Industry 4.0, have changed many paradigms of the …

Modern vehicles, including connected vehicles and autonomous vehicles, nowadays
involve many electronic control units connected through intravehicle networks (IVNs) to …

With the increase in popularity of Internet of Things (IoT) and the rise in interconnected
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …

Background: It is important to be able to predict, for each individual patient, the likelihood of
later metastatic occurrence, because the prediction can guide treatment plans tailored to a …

Intrusion detection is a critical aspect of network security to protect computer systems from
unauthorized access and attacks. The capacity of traditional intrusion detection systems …

The rapid growth of e-commerce has significantly increased the demand for advanced
techniques to address specific tasks in the e-commerce field. In this paper, we present a …

Terahertz imaging and time-domain spectroscopy have been widely used to characterize
the properties of test samples in various biomedical and engineering fields. Many of these …

This study explores the utilization of ensemble-based soft computing techniques for
predicting the liquefaction potential of fine-grained soils. Generally, deterministic methods …

Novel and high-performance medical image classification pipelines are heavily utilizing
ensemble learning strategies. The idea of ensemble learning is to assemble diverse models …