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
Sickle Cell Anemia (SCA) is a hereditary condition causing abnormal red blood cells, leading to severe health complications. Traditional treatment approaches for SCD often involve reactive management, which can delay app…
Road accidents and delayed emergency responses remain a major concern in urban environments, contributing to over 1.4 million fatalities globally each year. With rapid urbanization and increasing vehicle density, timely…
One of the most serious malignancies that affects women’s health worldwide is ovarian cancer. As a result, prompt accurate diagnosis and treatment are necessary. This study’s primary objective is to determine whether or…
Early-warning dashboards in higher education typically stop at tagging students as “at-risk,” offering no concrete guidance for remedial action; this limitation contributes to the loss of thousands of learners each year.…
The emerging confluence between artificial intelligence and ecology has generated a new research frontier, which we refer to as habitat intelligence, aiming to unveil species environment relationships through data-driven…
Recent years have seen a dramatic increase in the number of and severity of natural disasters, driven in part by climate change and urbanization. Artificial Intelligence (AI) appears to be a promising new technology that…
Diabetes mellitus presents a growing prevalence at the global level, representing a significant public health challenge. Despite the availability of specific treatments, it is imperative to develop innovative strategies…
Focusing events are sudden, impactful occurrences that spark widespread discussions. Analyzing fake news during such events is challenging due to limited and short-lived datasets. Online fact checkers are slow in identif…
Federated learning (FL) is a cutting-edge method of collaborative machine learning that lets organizations or companies train models without exchanging personal information. Adversarial attacks such as data poisoning, mo…
This study presents an automated and data-driven framework for analysing glucose response patterns in individuals with Type 1 diabetes by integrating machine learning and computer vision methodologies. The system leverag…
