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 October 2026
Skin cancer is a considerable health issue worldwide, occurring when pigment cells turn malignant. However, diagnosing skin lesions is difficult for dermatologists because most lesions have similar characteristics. Initi…
Automated road damage detection has become a critical component of intelligent transportation systems, enabling timely infrastructure maintenance and enhanced traffic safety. However, detecting pavement defects such as c…
Chili peppers are a high-value horticultural crop that is highly susceptible to foliar diseases, which can significantly reduce yield and market quality. This study proposes and evaluates a Convolutional Neural Network (…
Estimating crowd density is a cornerstone of modern urban management and public safety, particularly in the aftermath of catastrophic incidents, such as the 2015 Mina stampede. With the rapid advancement of artificial in…
Pangasius farming in the Mekong Delta is a major component of Vietnam’s aquaculture industry, characterized by large-scale production, intensive farming practices, and significant contributions to export revenue. However…
Urban centers in Indonesia are facing increasing pressure in managing municipal solid waste as a result of rapid population growth, rising labor costs, and stricter demands for high-purity recyclable materials. Manual so…
The underwater investigations and research remain challenging due to various underwater distortion factors, scattering, and low wavelength absorption. Hyperspectral imaging helps in obtaining detailed information on each…
This study evaluates three classification scenarios: image-based only, acoustic-based using Mel Frequency Cepstral Coefficients (MFCC), and a combined multimodal CNN architecture integrating both modalities. The experime…
Accurate crowd counting in real-world scenes re-mains challenging due to severe occlusions, perspective distortion, and large intra-scene density variation. Recent deep learning based approaches typically address these c…
As intelligent manufacturing advances toward precision and automation, cutting tool condition critically impacts product quality, equipment safety, and production efficiency. Anomalies like wear, chipping, or fracture ca…
