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
At present, research on landscape preferences mostly uses traditional questionnaire surveys to obtain public aesthetic attitudes, and the analysis method still relies on manual coding with small sample sizes. However, th…
This paper presents a novel deep learning and computer vision-based system for detecting and separating abnormal bags within automatic bagging machines, addressing a key challenge in industrial quality control. The core…
This study explores the application of the VGG19 convolutional neural network (CNN) model, pre-trained on ImageNet, for the classification of rice crop diseases using image segmentation techniques. The research aims to e…
Mangroves are a collection of plants that inhabit the intertidal zone, namely the area between the lowest and highest points reached by the tide. Overall, mangroves provide a range of advantages, including the prevention…
This paper focuses on using Convolutional Neural Networks (CNNs) for tasks such as image classification. It covers both pre-trained models and those that are built from scratch. The paper begins by demonstrating how to u…
An object hierarchy in images refers to the structured relationship between objects, where parent objects have one or more child objects. This hierarchical structure is useful in various computer vision applications, suc…
In this research, using dynamic analysis ten critical features were extracted from malware samples operating in isolated virtual machines. These features included process ID, name, user, CPU usage, network connections, m…
This paper research introduces a cutting-edge approach to enhancing urban infrastructure safety through the integration of modern technologies. Leveraging state of the art deep learning techniques, specifically the recen…
Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solu…
People in areas affected by natural disasters and use social media websites such as Facebook, Twitter (also known as “X”) and Instagram tend to post images of damage to their surroundings. These social media sites have b…
