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Articles (4)

Research Article

Published: 08 Aug 2025

An Enhanced Puma Optimized Reinforcement Learning Model for Detection of Results Anomalies in Higher Education

Volume 2

Journal: Computing&AI Connect
Review Article

Published: 12 May 2025

Artificial Intelligence at the Crossroads of Engineering and Innovation

Volume 2

The field of Artificial Intelligence (AI) is progressively transforming various advanced engineering disciplines, including mechanical, civil, electrical, aerospace, environmental, and biomedical engineering, through improved design, manufacturing, maintenance, and optimization methodologies. Yet, the disjointed and specialized state of the art too frequently prevents the cross-disciplinary application of AI solutions due to disparate performance measures, which result in reduced knowledge transfer and exaggerated performance in segregated domains. This study overcomes these issues..

Journal: Computing&AI Connect
Systematic Review

Published: 07 Feb 2025

A Systematic Review on the Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach

Volume 2

Modern electrical systems rely on sensors and relays for fault detection in three-phase transmission lines and distribution transformers, but these devices often face time complexity issues and false alarms. In this study, the fault detection accuracy is compared in models studied in 2023 and 2024 following PRISMA guidelines. The objectives were to identify fault types, utilize machine learning models to assess their predictive efficacy, and establish accuracy levels. To explore..

Journal: Computing&AI Connect
Research Article

Published: 29 Jul 2024

Mirror, Mirror on the Wall: Automating Dental Smile Analysis with AI in Smart Mirrors

Volume 1

This paper presents a smart diagnostic framework for dental smile analysis. To accurately and efficiently identify esthetic issues from a single image of a smile, a convolutional neural network (CNN) was trained. To overcome the limitations of scarce data, a diffusion model was employed to generate dental smile images in addition to manually curated data. The CNN was trained and evaluated on three datasets: all real images, all generated images,..

Journal: Computing&AI Connect
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