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
On a global scale, traffic problems are an essential factor affecting urban operations, particularly challenging the frequent occurrence of traffic congestion and accidents. The solution to the problem requires real-time…
The paper explores the application of Convolutional Neural Networks (CNNs), specifically ResNet-18, in revolutionizing the identification of diseases in tomato crops. Facing threats from pathogens like Phytophthora infes…
Amid global health crises, such as the COVID-19 pandemic, the heightened prevalence of mental health disorders like stress and anxiety has underscored the importance of understanding and predicting human emotions. Introd…
In the wake of the COVID-19 pandemic, the use of medical imaging, particularly X-ray radiography, has become integral to the rapid and accurate diagnosis of pneumonia induced by the virus. This research paper introduces…
In the contemporary landscape, social media has emerged as a dominant medium via which individuals are able to articulate a wide range of emotions, encompassing both positive and negative sentiments, therefore offering s…
In the contemporary landscape of smart transportation systems, the imperative role of intelligent traffic monitoring in bolstering efficiency, safety, and sustainability cannot be overstated. Leveraging recent strides in…
In recent times, many fake faces have been created using deep learning and machine learning. Most fake faces made with deep learning are referred to as “deepfake photos.” Our study’s primary goal is to propose a useful f…
Time series data analysis is vital in numerous fields, driven by advancements in deep learning and machine learning. This paper presents a comprehensive overview of data augmentation techniques in time series analysis, w…
Interest in autonomous robots has grown significantly in recent years, motivated by the many advances in computational power and artificial intelligence. Space probes landing on extra-terrestrial celestial bodies, as wel…
Malware analysis is a critical component of cyber-security due to the increasing sophistication and the widespread of malicious software. Machine learning is highly significant in malware analysis because it can process…
