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
Machine learning-based network intrusion detection systems (NIDS) increasingly operate in environments where adversaries can adapt their behavior in response to deployed defenses. However, most empirical IDS studies eval…
Credit score classification is a vital component of risk management in the financial sector. It conventionally relies on obsolete models that fail to capture dynamic patterns. This study utilizes K-Nearest Neighbor (KNN)…
Credit default risk prediction is important for financial institutions in geographically diverse, collateral-based lending, where early identification of risky borrowers supports preventive monitoring. Most credit scorin…
Digital payments, fintech, and online banking are now part of our daily lives. Making a transfer, paying for a service, or shopping online is faster, but this progress has also given rise to new forms of fraud. Rule-base…
Adopting cloud computing within the tertiary education sector is still problematic, especially in developing countries, because of the low level of readiness and the absence of predictive decision support tools. In this…
Distributed Denial of Service (DDoS) attacks are among the most significant cybersecurity threats today. Traditional DDoS detection systems struggle to cope with emerging attack vectors, while Deep Learning (DL)-based ap…
In Pakistan, wheat is one of the important crops, and the production of wheat is highly reliant upon the use of pure and certified seed varieties. Hence, wheat seeds should be properly identified to ensure the quality of…
Transportability limitations can affect discrimination, probability calibration, and uncertainty estimates even when machine-learning pipelines use the same input variables. This study evaluated 5-year breast cancer surv…
The increasing complexity of embedded computing systems has made it difficult to achieve efficient code execution while effectively utilizing limited hardware resources. Conventional optimization methods mainly rely on c…
With the emergence of smart homes based on the Internet of Things, network-based attacks have become more prevalent and require effective and lightweight intrusion detection (ID). In this study, a Correlation-Aware Hybri…
