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 October 2026
The rapid transition to digital education in the Philippines, accelerated by the COVID-19 pandemic, has highlighted significant integration challenges for public school teachers in rural provinces like Bukidnon. While di…
Deep learning (DL) is currently considered one of the most powerful tools for environmental monitoring. Many environmental variables, such as air quality, climate, water, and energy, are monitored using Internet of Thing…
This study presents an investigation of the HiTar-2024 dataset performed in terms of the distribution of label attack types and the distribution of attacks by protocol, normal, and Denial of Service (DoS) connections ove…
Accurate inflation forecasting is essential for supporting forward-looking monetary policy, maintaining price stability, and preserving economic welfare. This study proposes an interpretable machine learning framework fo…
The surge in e-commerce has seen an increase in mobile-based credit card transactions, resulting in a sharp escalation of fraud that inflicts substantial financial losses on both consumers and corporations. Because these…
This study examined the predictive relationship of work ethics and work values with employee task performance using a hybrid PCA–Random Forest model. Data were obtained from 231 PSU employee respondents and 81 questionna…
With the rapid increase in the number of vehicles on roads, traffic management, and safety enforcement have become significant challenges worldwide. Traditional speed violation detection systems either employ high-end ha…
Ransomware is one of the most dangerous cyber threats today, as it can disrupt systems and cause serious financial losses. Traditional detection methods often fail to catch newer attacks because they can hide within norm…
Effective marketing plays a pivotal role in modern businesses, where strategic allocation of resources is essential for maximizing return on investment (ROI). Customer segmentation involves dividing users into distinct s…
The increasing use of large language models (LLMs) in domains requiring interpretation and judgment has raised critical questions about trust, reliability, and account-ability, particularly in contexts where decisions ca…
