The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Data mining is the process of discovering patterns, correlations, and anomalies in large datasets using methods drawn from statistics, machine learning, and database systems. Core techniques include classification, clustering, association rule mining, regression, and anomaly detection, applied to structured data in relational databases, semi-structured data such as logs and JSON, and increasingly unstructured data such as text and images. A typical workflow involves data cleaning and preprocessing, feature selection or extraction, model application, and validation against held-out data to avoid overfitting. With global data volume estimated to approach 221 zettabytes by 2026 according to industry forecasts, scaling mining algorithms to high-volume, high-dimensional data has become a central research concern. Data mining underlies applications including market basket analysis, customer segmentation, credit risk scoring, predictive maintenance, and scientific data analysis in genomics and astronomy. As an open-access data mining journal, IJACSA covers novel algorithms, comparative performance studies, and domain-specific applications across business, healthcare, and engineering datasets.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
Cardiovascular disease remains a major public health burden and is associated with demographic, behavioral, and chronic-health characteristics. This study evaluates explainable machine learning for cross-sectional classi…
Rapid urbanization and increasing pressure on natural resources demand efficient, transparent, and sustainable management systems. However, existing booking systems in government agencies often operate in silos and lack…
A student stuck in a failing compile loop looks productive from across the room. The instructor sees keystrokes; the blockage is invisible until the student raises a hand or falls silent. Existing detection methods work…
The rapid growth of artificial intelligence (AI) technologies has transformed educational environments and created new opportunities for enhancing digital literacy in science education. However, variations in technologic…
Students' opinions play a pivotal role in the formulation of successful educational policies. However, the computational analysis of the Moroccan Arabic Dialect remains widely viewed as problematic due to a focus on comp…
Graduate curriculum planning is critical to ensuring that teacher education programs remain responsive to professional standards, workplace demands, and emerging educational priorities. This study applies Educational Dat…
The rapid evolution of AI-enhanced learning environments has created an urgent need for intelligent educational systems capable of delivering early and personalized interventions under severe data sparsity conditions. Th…
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…
Accurate student performance prediction is critical for data-driven educational decision-making; however, it is often hindered by data heterogeneity, privacy constraints, and domain shift across academic contexts. This s…
Predicting student academic performance constitutes a strategic priority for higher education institutions seeking to reduce attainment gaps and provide timely, targeted support. Existing approaches predominantly generat…
