International Journal of Applied Science

Generative AI (GAI) Use for Cybersecurity Resilience: A Scoping Literature Review

Jessica Parker — Sun, 09 Mar 2025 00:00:00 +0800

With cyberattacks increasing in volume and number, organizations are increasingly at risk of adverse financial and reputational impacts. Cyber attackers are quick to implement technologies like Generative Artificial Intelligence (GAI) to enhance attacks, while organizations have yet to fully benefit from GAI to improve cybersecurity defenses. This scoping literature review analyzes current research and identifies gaps in the literature about how Generative Artificial Intelligence (GAI) can be used to enhance cybersecurity resilience. The analysis includes an overview of GAI, ethical considerations and challenges, future directions and research opportunities, and a discussion of how this GAI research can be applied.

Application of Machine Vision in Structural Deformation and Health Monitoring

Zhonglai Qin — Sun, 06 Apr 2025 00:00:00 +0800

There are two main methods of structural health monitoring. Among them, the traditional structure health monitoring technology is mainly artificial, which has some problems such as inflexibility, large error, high cost and difficult to adapt to the change of natural environment. The structural health monitoring method based on machine vision has the characteristics of flexible measuring point, high accuracy, fast speed and no contact. A variety of machine vision technologies, such as image acquisition, image processing, three-dimensional vision and deep learning technologies, have made great progress, and their application scenarios are constantly expanding. This paper is based on infrared image acquisition technology, 3D vision acquisition technology, image stitching technology, stereo vision technology four perspectives. The improvement of machine vision technology in the field of structural health monitoring is described in detail. The development trend of machine vision acquisition technology and image processing technology and the huge growth space of machine vision technology are analyzed. The application range of these key technologies is also introduced. The application results in building crack detection, seepage detection and fire detection are summarized, and the future development of this technology is prospected from the aspects of algorithm robustness.

An Enhanced Framework for Urban Water Consumption Analysis: Feature Clustering with Ensemble Methods

Faye F.F. Jiang — Sun, 06 Apr 2025 00:00:00 +0800

Urban water consumption analysis presents significant challenges due to the complex interplay of socioeconomic, demographic, and built environment factors. This paper introduces a novel Feature Clustering Framework of TopK and Threshold with Ensemble Method (FCTTE) specifically designed to address high-dimensional urban datasets. We evaluate this framework using a comprehensive dataset of 1,120 features across eight domains related to New York City's urban environment. Our experiments demonstrate that FCTTE significantly outperforms conventional feature selection methods, improving LightGBM classification accuracy by 4.6% compared to baseline, while traditional methods achieved only 1% improvement. The framework identified median family income, energy usage intensity, adult male population, greenhouse gas emissions, and commercial building characteristics as the most influential factors affecting water consumption. By effectively managing feature redundancy through hierarchical clustering and strategic selection, FCTTE provides urban planners with interpretable insights for water resource management while maintaining superior predictive performance. This integrated approach bridges the gap between fragmented analyses of individual urban factors and the need for holistic understanding of water consumption patterns in complex urban environments.