The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
IJACSA research on deep learning — covering neural network architectures such as CNNs, RNNs, and transformers, and their applications across computer science domains.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed August 2026
This study aims to address the insufficient model recognition accuracy and limitations of authentication techniques in current IoT authentication methods. The research presents a more accurate face video image authentica…
In the English translation task, the semantics of context play an important role in correctly understanding the subtle differences between keywords. The bidirectional LSTM includes a positive LSTM and a reverse LSTM. Whe…
In industrial production, timely and accurate detection and identification of surface defects in steel materials were crucial for ensuring product quality, enhancing production efficiency, and reducing production costs.…
Deep learning has been widely used in various scenarios such as image classification, natural language processing, and speech recognition. However, deep neural networks are vulnerable to adversarial attacks, resulting in…
Alzheimer's disease (AD) poses a significant challenge to modern healthcare, as effective treatment remains elusive. Drugs may slow down the progress of the disease, but there is currently no cure for it. Early AD identi…
The growing challenge of increasing traffic volumes presents a real challenge for road safety, emergency response and overall transport efficiency. Intelligent transportation systems play a fundamental role in solving th…
The paper explores the evolving landscape of network security, in Software Defined Networking (SDN) highlighting the challenges faced by security measures as networks transition to software-based control. SDN revolutioni…
This research paper addresses the critical issue of cyberbullying detection within the realm of social networks, employing a comprehensive examination of various machine learning and deep learning techniques. The study i…
This research paper investigates the application of deep learning techniques, specifically convolutional neural networks (CNNs), for crack detection in historical buildings. The study addresses the pressing need for non-…
Aiming at addressing the challenges of large model parameters, high computational cost, and low accuracy of the traditional tea disease identification model, an improved MobileNet model integrated spatial and channel att…
