This research introduces a theoretical framework for network anomaly detection in
cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble …

The resistance of concrete to chloride penetration determines the durability and service life
of reinforced concrete building structures in coastal or chloride-laden environments. This …

Recently, there has been a significant advancement in the water quality index (WQI) models
utilizing data-driven approaches, especially those integrating machine learning and artificial …

This study aims to achieve the swift and precise classification of ductile and brittle failure
modes in flexural reinforced concrete (RC) members, specifically those with tension sides …

Cyber Security plays a significant role in almost all the applications in the networks including
host protection, network protection and cloud infrastructure protection. Designing an …

Network Security is an extremely arising field that secures frameworks, organizations, and
information from advanced attacks. With the evolution of the Internet and the development of …

Due to the popularity of Internet of Things devices, the exponential progress of computer
networks, and a plethora of associated applications, cybersecurity has recently attracted …

Accurate prediction of concrete aging factor is pivotal for performance-based durability
reinforced concrete design. This study introduces an innovative method leveraging machine …

Machine learning-based Network Intrusion Detection Systems (NIDS) are designed to
protect networks by identifying anomalous behaviors or improper uses. In recent years …

Within the integration and development of data-driven process models, the underlying
process is digitally mapped in a model through sensory data acquisition and subsequent …