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
This study introduces a privacy-preserving data preparation and feature engineering framework designed for machine learning-based cybersecurity vulnerability risk prediction, utilizing real-world enterprise scan data fro…
Modeling realistic tree structures remains a challenging problem in computer graphics due to the complex interaction between intrinsic growth patterns and environmental influences. Traditional procedural methods provide…
Container migration is one of the important techniques used to provide the continuity of services in Multi-access Edge Computing environments. Services are migrated from edge servers to follow the mobility of users among…
The use of machine learning in recruitment has raised growing concerns about fairness, as automated hiring systems can generate unequal outcomes across demographic groups. These disparities are influenced not only by imb…
Iterative Magnitude Pruning (IMP) is a widely used technique for compressing neural networks by progressively removing low-magnitude weights while maintaining predictive accuracy. Despite its widespread application and s…
The performance of machine learning (ML) systems often deteriorates over time owing to data drift, which is typically caused by changes in data quality or distribution. Such degradation in deployment environments can res…
Career transition into digital professions is a strategic lever for addressing youth unemployment in Sub-Saharan Africa. However, existing training programs lack objective career guidance tools. This study presents two c…
Stress is a psychological and physiological response to internal or external pressures or challenges that exceed an individual's ability to cope. In response to these conditions, the human body produces physiological sig…
Malware detection is a major difficulty in cybersecurity as malicious software continues to evolve in scale, diversity, and sophistication. While deep learning and highly complex architectures are becoming increasingly i…
This study compared the performance of Decision Tree, Random Forest, and Logistic Regression models in predicting business readiness for digital technology integration using survey data from 400 business respondents in P…
