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
As a result of an increase in the human popula-tion globally, traffic congestion in the urban area is becoming worse, which leads to time-consuming, waste of fuel, and, most importantly, the emission of pollutants. There…
With the increase relay on the internet, and the shift of most business to provide remote services, the burdens of protecting the network and detecting any attack quickly become more significant, as the attack surface an…
Big Data is an extremely massive amount of hetero-geneous and multisource data which often requires fast processing and real time analysis. Solving big data analytics problems needs powerful platforms to handle this enor…
For many years, the area of health care has evolved, mainly using medical images to detect and evaluate diseases. Nowadays, the world is going through a pandemic due to COVID- 19, causing a severe effect on the health sy…
The most common disorder affecting millions of population worldwide due to insufficient release of insulin by pancreas is diabetes. Early detection or precaution of diabetes is necessary, otherwise leads to many complica…
In most conditions, it is a problematic mission for a machine-learning model with a data record, which has various attributes, to be trained. There is always a proportional relationship between the increase of model feat…
With the advent of the Big Data explosion due to the Information Technology (IT) revolution during the last few decades, the need for processing and analyzing the data at low cost in minimum time has become immensely cha…
Usually, time series data suffers from high percentage of missing values which is related to its nature and its collection process. This paper proposes a data imputation technique for imputing the missing values in time…
Deep learning frameworks have progressed beyond human recognition capabilities and, now it’s the perfect opportunity to optimize them for implementation on the embedded platforms. The present deep learning architectures…
The techniques of data mining are used widely in the healthcare sector to predict and diagnose various diseases. Diagnosis of heart disease is considered as one of the very important applications of these systems. Data i…
