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Articles (4)

Research Article

Published: 14 Jul 2025

Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets

Volume 2

COVID-19 has created a major public health problem worldwide and other issues such as economic crisis, unemployment, mental distress, etc. The pandemic has affected people not only through infection but also by causing stress, worry, fear, resentment, and even hatred. Twitter is a highly influential social media platform and a major source of health-related information, news, opinions, and public sentiment, with content shared by both individuals and official government sources...

Journal: Computing&AI Connect
Research Article

Published: 20 Mar 2025

Prediction of Cognitive Impairment Using a Deep Learning Autoencoder Algorithm from a Singapore Study

Volume 2

Dementia is a decline in cognitive function, typically diagnosed when the acquired impairment becomes severe enough to impact social or occupational functioning. Between no cognitive impairment (NCI) and dementia, there are many intermediate states. Predictive cognitive impairment can be useful for initiating treatment to prevent further brain damage. Several deep learning-based approaches have been proposed for the classification of Magnetic Resonance Imaging (MRI) to diagnose Alzheimer’s disease (AD) or dementia...

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
Review Article

Published: 13 Jun 2024

Hierarchical Autoencoder-Based Lossy Compression for Large-Scale High-Resolution Scientific Data

Volume 1

Lossy compression has become an essential technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size ranges up to several petabytes. Although Autoencoder-based models have been successfully leveraged to compress images and videos, such neural networks have not widely gained attention in the scientific data domain. Our work presents a neural network that not only significantly compresses large-scale scientific..

Journal: Computing&AI Connect
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