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
The rise of cryptocurrency has dramatically changed. Cryptocurrencies have dramatically reshaped the landscape of financial transactions, enabling seamless cross-border exchanges without centralized oversight. This revol…
This research paper delves into the intricate domain of cyberbullying detection on social media, addressing the pressing issue of online harassment and its implications. The study encompasses a comprehensive exploration…
In industrial engineering, efficiency is paramount. Convolutional Neural Networks (CNNs) are commonly used to identify and detect labour activity in industrial environments. Accurate fault detection is crucial for identi…
In this paper, we propose the FEC-IGE framework includes data preprocessing, data augmentation, transfer learning, and fine-tuning of the pre-trained model of convolutional neural network (CNN) architecture for the probl…
The assessment of bone trabecular quality degrada-tion is important for the detection of diseases such as osteoporosis. The gold standard for its diagnosis is the Dual Energy X-ray Absorptiometry (DXA) image modality. Th…
Glaucoma and cataracts are leading causes of blindness worldwide, resulting in significant vision loss and quality of life impairment. Early detection and diagnosis are crucial for effective treatment and prevention of f…
Deep learning has revolutionized industries such as natural language processing and computer vision. This study explores the fusion of these domains by proposing a novel approach for text extraction and translation using…
The advancements in deep learning has generated a large-scale interest in development of black-box models for various use cases in different domains such as healthcare, in both at-home and critical setting for diagnosis…
The classification of motor imagery holds significant importance within brain-computer interface (BCI) research as it allows for the identification of a person's intention, such as controlling a prosthesis. Motor imagery…
This study aims to address the insufficient model recognition accuracy and limitations of authentication techniques in current IoT authentication methods. The research presents a more accurate face video image authentica…
