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
Traditional on-demand laundry operations suffer from inefficient manual coordination, a lack of real-time delivery visibility, and delayed customer support handling. To address these operational challenges, this study pr…
One of the challenges in assessing earthquake damage risk in areas with high seismic activity and limited data is the lack of a detailed building inventory and the absence of available data. Therefore, a remote sensing a…
Semantic interoperability is a major challenge in the integration of heterogeneous information systems, where differences in data structures, terminologies, and representations complicate the automatic identification of…
Distributed acoustic sensing (DAS), together with broader distributed fibre-optic sensing (DFOS), has emerged over the past decade as a candidate technology for large-scale infrastructure monitoring, while machine learni…
The increasing availability of fragmented public business and procurement data creates opportunities for company-level empirical analysis, but limited evidence exists on how integrated procurement and administrative reco…
Electrical Submersible Pumps (ESPs) are critical components of oil well production that can be evaluated for their longevity through daily performance predictions. Traditional predictive methodologies have failed to hand…
Comparative studies of feature selection (FS) and feature extraction (FE) for IoT intrusion detection rely almost universally on random train/test splits, which let temporally adjacent, highly similar records appear on b…
In the aviation industry, flight delays represent a major challenge due to their economic impact, operational disruptions, and adverse effects on passenger satisfaction and transportation efficiency. This study proposes…
Machine learning can triage digital evidence at scale, but two obstacles limit its forensic adoption: opaque decisions, and point predictions without a valid statement of confidence. We present ForensiQ, a hierarchical a…
The swift deployment of IoT-based smart home appliances has increased the attack surface for the smart environment and exposed it to attacks like botnet command-and-control communications, brute force attacks, denial-of-…
