The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
IJACSA research on deep learning — covering neural network architectures such as CNNs, RNNs, and transformers, and their applications across computer science domains.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed August 2026
Structural variations (SVs) play a pivotal role in human genetics, influencing gene expression, disease mechanisms, and phenotypic diversity. Despite the advancements in short-read sequencing technologies, long-read sequ…
Image-based plant disease identification methods have demonstrated potential in enhancing crop protection through early detection. However, the development of this field faces several challenges, such as the scarcity of…
Malaria continues to be a life-threatening disease, especially in tropical and low-resource regions, where timely and accurate diagnosis remains a major challenge. Traditional diagnostic approaches like manual microscopy…
This study aims to provide a comprehensive biblio-metric analysis of research on transfer learning in breast cancer detection from 2016 to 2024. It highlights publication trends, influential contributors, collaborations,…
Ulcerative Colitis (UC), a chronic inflammatory bowel disease, presents significant diagnostic challenges due to its overlapping symptoms with other gastrointestinal disorders and the complex visual patterns in endoscopi…
Exchange rate volatility forecasting plays a vital role in guiding financial decisions and economic planning, particularly in China’s dynamic foreign exchange market. This study proposes a novel deep learning framework,…
Images from low-light frequently exhibit poor visibility, excessive noise, and color distortion, which substantially impair both computer vision systems and human visual perception. Although numerous enhancement techniqu…
Atmospheric fine particulate matter (PM2.5) poses a serious threat to public health, and its accurate prediction is crucial for environmental management and pollution control. However, existing prediction methods have di…
Complicated underwater environment, such as visibility limitations and illumination conditions pose significant challenges for underwater imaging and its object recognition performance. These issues are especially critic…
Recent years have seen a surge in the use of deep learning for human activity recognition (HAR) in various applications. However, running complex deep learning models on edge devices with limited resources, such as proce…
