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 September 2026
The integration of IoT technologies with smart logistics operations has opened unprecedented avenues for optimizing energy consumption in warehouse facilities. Accurate forecasting of electricity load is a key factor in…
Fine-Grained Image Classification focuses on unique features between visually similar subclasses within a wider category, which remains a challenging task due to low inter-class variations and high intra-class similarity…
Breast ultrasound images can be classified as benign, malignant, and normal. Due to the imbalanced distribution of classes in breast ultrasound images, intra-class heterogeneity of lesions, and ultrasound artifacts like…
Breast cancer is the most frequently diagnosed cancer in women worldwide, with approximately 2.3 million new cases annually. Accurate molecular subtyping is essential for guiding treatment decisions; however, existing PA…
In this study, researchers propose a novel solution for efficient enhancement of vulnerability detection in several IoT environments. Efficient Vulnerability Classification has been introduced as the presented technique…
Accurate and real-time assessment of road infrastructure is critical for smart city maintenance and transportation safety. However, conventional object detection models often struggle with complex environmental factors,…
Recommender systems are widely used as an information filtering technology to automatically predict and identify a set of interesting items for users based on their needs and preferences. They are widely applied in many…
Intelligent transportation systems aim to improve traffic management and road safety, manage traffic effectively, and reduce roadway system congestion. This optimally requires estimating future traffic congestion. Unfort…
With the fast enhancement of deep learning, research on automatic detection of breast tumors is becoming increasingly in-depth. However, traditional CNNs’ linear kernel has difficulty not only in capturing the nonlinear…
Accurate and intelligent detection of rice pests is critical for ensuring food security and advancing precision agriculture. However, due to the small size, irregular morphology, dense distribution, and complex backgroun…
