Network intrusion detection system is an essential part of network security research. It
detects intrusion behaviors through active defense technology and takes emergency …

This research aims to study and assess state-of-the-art physics-informed neural networks
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

The effectiveness of machine learning models can be significantly averse to redundant and
irrelevant features present in the large dataset which can cause drastic performance …

Abstract Magnetic Induction (MI) based Wireless underground sensor networks (WUSNs)
consist of magnetic-antenna sensors that are buried in and communicate through soil …

Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition
(SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers …

The rapid evolution of modern automobiles into intelligent and interconnected entities
presents new challenges in cybersecurity, particularly in Intrusion Detection Systems (IDS) …

The growing evolution of cyber-attacks imposes a risk in network services. The search of
new techniques is essential to detect and classify dangerous attacks. In that regard, deep …

Anomaly detection has been used to detect and analyze anomalous elements from data for
years. Various techniques have been developed to detect anomalies. However, the most …

Breast cancer (BC) is a primary reason for death among the female population around the
world. Early identification can aid in decreasing the mortality rates associated with this …