The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Machine learning is a branch of artificial intelligence in which systems improve their performance on a task by learning patterns from data rather than following explicitly programmed rules. It is broadly divided into supervised learning for classification and regression from labeled examples, unsupervised learning for clustering and dimensionality reduction on unlabeled data, and reinforcement learning for learning optimal actions through trial-and-error interaction with an environment. Common algorithms include decision trees, support vector machines, ensemble methods such as random forests and gradient boosting, and neural networks, chosen based on data characteristics, interpretability needs, and computational constraints. Industry surveys suggest most organizations remain in experimentation or pilot phases with machine learning, with only about a third reporting they have begun scaling programs organization-wide. Machine learning underlies applications across nearly every domain, including predictive maintenance, credit scoring, medical diagnosis support, and recommendation systems. As an open-access machine learning journal (an ML journal), IJACSA publishes comparative studies and applied research spanning these algorithm families.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
The rapid expansion of internet services and cloud-based platforms has increased cybersecurity threats, particularly phishing attacks that deceive users into disclosing sensitive information. Traditional phishing detecti…
Recent advances in 3D mesh acquisition and the development of interactive modeling tools have significantly increased both the quantity and diversity of available 3D model databases. Therefore, the task of searching, que…
Epileptic seizure recognition is a critical task in clinical decision support systems, where both accuracy and reliability of predictions directly affect patient outcomes. While deep learning architectures such as CNNs a…
Federated learning (FL) makes it possible to train models across distributed data sources without collecting raw data in one place. However, even in federated settings, trained models may still leak sensitive information…
Intelligent tutoring systems generate a large volume of data, which becomes particularly valuable when effectively leveraged for learner performance prediction in adaptive learning environments. In this context, the spee…
Diabetes mellitus is a major chronic metabolic disorder that often leads to serious long-term vascular complications. Traditional monitoring methods focus mainly on metabolic indicators and often miss early vascular chan…
Mobile Ad Hoc Networks (MANETs) are decentralized in nature and, therefore, they have no centralized control, and consequently, they are highly susceptible to routing attacks like black hole attacks and gray hole attacks…
Early and accurate diagnosis of Monkeypox is essential to limit transmission and support effective treatment. This study aims to compare the performance of Random Forest and Gradient Boosting models for classifying Monke…
This study presents a user behaviour analysis approach for detecting insider threats in an enterprise web application environment. The approach applies machine learning techniques to analyze patterns of user activity. Us…
The rapid increase in population and the ongoing expansion of urban regions have resulted in a substantial growth in municipal solid waste generation, creating serious challenges for environmental protection and urban ma…
