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
Water Distribution Networks (WDNs) frequently experience significant water losses due to pipeline leakages. These losses not only create economic challenges for water utilities but also intensify global concerns regardin…
Predictive maintenance classification is widely used to support industrial maintenance planning; however, reported model performance is often influenced by evaluation practices that allow unintended information leakage b…
Injection attacks persist as dominant threats in modern web systems due to obfuscation, polymorphism, and multi-vector exploitation across SQLi, XSS, LDAP Injection, and Command Injection. Existing defenses often rely on…
Artificial intelligence is driving digital transformation across multiple sectors, including healthcare, pharmaceuticals, industrial production, and the automotive industry. In healthcare specifically, AI-powered predict…
Integrating heterogeneous exogenous data into financial prediction models is challenging due to scale mismatches and semantic ambiguity. We propose a trend-encoding framework that transforms raw exogenous time-series int…
Accurate opponent modeling is critical for effective automated negotiation, enabling agents to adapt their strategies based on the type of opponent. This study investigates machine learning approaches for classifying neg…
The rapid proliferation of fake news across digital platforms has intensified the need for reliable and computationally efficient automated detection systems. While deep learning models have demonstrated strong performan…
This study proposes a hybrid modeling and control framework for intelligent wheelchair systems that integrates formal methods with adaptive artificial intelligence to ensure safety, robustness, and real-time performance.…
The rising demand for real-time, data-driven decision support in retail platforms has underscored the need for intelligent systems capable of modeling both behavioral sequences and product relationships. This study intro…
Media budget allocation remains a persistent challenge in the advertising industry. Inefficient spending and biased planning decisions often reduce campaign effectiveness. Advertisers struggle to balance investments acro…
