The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Computer vision is the field of artificial intelligence concerned with enabling computers to interpret and extract information from visual data such as images and video. Core tasks include image classification, object detection and localization, semantic and instance segmentation, facial recognition, and optical character recognition. Early approaches relied on hand-crafted feature descriptors combined with classical machine learning classifiers; current computer vision is dominated by convolutional neural networks and, increasingly, vision transformer architectures trained on large annotated image datasets. A notable 2026 development is the shift toward foundation models that displace task-specific training for many commercial applications, alongside growing use of agentic vision systems moving from research into operational deployment. Computer vision supports applications including autonomous vehicle perception, medical image analysis, industrial quality inspection, surveillance and security systems, and augmented reality. As an open-access computer vision journal, IJACSA publishes research on computer vision algorithms, model architectures, and applied vision systems evaluated on standard and domain-specific image datasets.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
Sign language serves as a primary mode of communication for individuals who are deaf or speech impaired, using hand gestures to convey meaning visually. While it facilitates communication among the deaf community, it pre…
Plant diseases remain a major threat to crop productivity, especially where timely diagnosis is difficult. This paper introduces TomDetLeaf, a new annotated dataset designed for tomato leaf detection in diverse agricultu…
Falls are a significant health problem among older adults, leading to serious injuries and adversely affecting both quality of life and public health burdens. Although various fall detection systems have been developed u…
The rapid progress of deepfake technology, fueled by generative adversarial networks (GANs), has increased the challenge of verifying the authenticity of digital media. This study suggests a more powerful deepfake detect…
Ultra-high resolution bioimaging based on quantum optics offers high sensitivity at relatively low cost, yet conventional reconstruction algorithms face challenges of excessive sampling time, long computation, and artifa…
Content‑Based Image Retrieval (CBIR) systems have become increasingly crucial in healthcare as the volume of medical imaging data continues to grow exponentially. However, existing systems struggle to balance privacy pre…
Accurate object detection and classification are paramount in precision agriculture for assessing ripeness stages and optimizing yield, particularly for high-value crops like toma-toes. Traditional manual inspection meth…
The early detection of breast cancer is critically important for prompt treatment and rescuing lives. However, the accuracy of small-sized breast masses’ early detection in various algorithms remains unsatisfactory, as t…
In recent medical research, skin cancer has emerged as one of the most prevalent and fatal cancers globally. Previous studies have faced challenges in detecting skin cancer early due to the complexity of identifying spec…
Ensuring firefighter safety in high-risk environments requires strict adherence to Personal Protective Equipment (PPE) protocols. This study presents an automated real-time detection system for PPE using deep learning an…
