The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Deep learning is a subfield of machine learning built on artificial neural networks with multiple layers that automatically learn hierarchical representations of data, reducing the need for manual feature engineering. Architectures include convolutional neural networks for image and spatial data, recurrent neural networks and long short-term memory networks for sequential data, and transformer models, which now underpin most state-of-the-art natural language processing and increasingly computer vision systems. Training deep networks typically relies on large labeled datasets, backpropagation, and gradient-based optimization, along with regularization techniques and specialized hardware such as GPUs and TPUs. A notable 2026 shift in the field favors smaller, specialized models over ever-larger ones, prioritizing reliability, transparency, and efficient inference over raw parameter count. Deep learning drives advances in image recognition, speech processing, machine translation, medical image diagnosis, and generative models for text, images, and audio. As an open-access deep learning journal, IJACSA covers novel deep learning architectures and their evaluation across vision, language, and applied domains.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed October 2026
The rice nursery stage is a very critical stage of the rice growing cycle, as intrusion of herbivorous animals can lead to substantial economic losses and yield losses. Manual guarding and physical fencing are inefficien…
Social media has become a significant channel for communication and interaction, significantly influencing how organizations are perceived in the digital era. Online reputation management (ORM), as an addition to public…
Mental health disorders are among the biggest global public health challenges, affecting over 1 billion people worldwide and placing a burden on healthcare systems, economies, and societies. Despite progress in clinical…
Glioblastoma Multiforme (GBM) is the most invasive form of primary brain tumor. The average survival time after diagnosis is only 15 months. Accurately and automatically analyzing GBM MRI scans remains a major clinical c…
Autonomous driving requires robust, accurate and real-time environmental perception. Although cameras, LiDAR, and radar provide complementary sensing capabilities, individual modalities remain vulnerable to limitations s…
Reliable Alzheimer’s disease (AD) magnetic resonance imaging (MRI) classification requires evaluation beyond accuracy alone. This study investigates a four-class image-level framework based on ImageNet-pretrained ResNet-…
Social media platforms are key spaces for sharing opinions, experiences, and daily events. However, the rapid rise in hate speech, especially implicit hate, has become a significant concern. Implicit hate is subtle, cont…
Convolutional Neural Networks (CNNs) are the most common deep learning architecture used for video pro-cessing enhancement. Particularly, the Multi-Deep Convolutional Neural Network (MD-CNN) model, embedded into the 3D-H…
As the realism of DeepFakes has increased significantly, there is an emerging necessity to develop efficient, reliable, and robust automatic detection frameworks that are feasible to apply in the wild. This study propose…
As intelligent traffic systems evolve to manage complex urban mobility, conventional congestion estimation techniques, such as the time-windowed Volume-to-Capacity (V/C) ratio, fail to capture capture the real-time traff…
