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
Non-invasive anemia screening from palpebral conjunctiva images may support preliminary triage when access to laboratory hemoglobin testing is limited. This study presents and evaluates a reproducible mobile-cloud framew…
Apple diseases cause great losses in fruit yield and quality, while manual diagnosis is time-consuming, subjective, and difficult to scale across orchards. Deep learning has thus become a staple in image-based disease re…
Transformer health index (THI) prediction supports condition-based maintenance by mapping dissolved-gas, oil-quality, and furan indicators to an interpretable asset-condition score. This study develops a supervised bench…
Distributed acoustic sensing (DAS), together with broader distributed fibre-optic sensing (DFOS), has emerged over the past decade as a candidate technology for large-scale infrastructure monitoring, while machine learni…
Digital media integrity is seriously threatened by deepfake technology, especially under real-world visual degradations such as poor resolution, noise, greyscale conversion, and compression artefacts. Unlike prior hybrid…
Diamond clarity grading aims to automatically determine the quality of diamonds by analyzing visible inclusions and structural flaws using intelligent analysis methods. By integrating diamond cost prediction with clarity…
Tumors of the lymphatic system can be either benign or malignant. However, because traditional diagnostic approaches rely on subjective assessment by individual pathologists and often lead to significant discrepancies an…
Receipts are vital documents that validate transactions by capturing key details such as dates, items purchased, prices, and seller information. However, accurately documenting receipts is often compromised by issues lik…
Sleep is essential for maintaining overall physical and mental health, yet analyzing sleep patterns manually is a com-plex and time-consuming process that requires expert knowledge and is often prone to subjectivity. In…
Land use scene classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable resource management. In recent years, deep learning methods have signifi…
