The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
IJACSA research on machine learning — spanning algorithms, applications, and techniques across computer science domains. One of the journal's largest and most active research areas.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed August 2026
Ensemble learning in machine learning applications is crucial because it leverages the collective wisdom of multiple models to enhance predictive performance and generalization. Ensemble learning is a method to provide a…
This research paper investigates the development of deep learning models for traffic sign recognition in autonomous vehicles. Leveraging convolutional neural networks (CNNs), the study explores various architectural conf…
Bipolar disorder and Attention-deficit/Hyperactivity disorder (ADHD) are two prevalent disorders whose symptoms are similar. In order to reduce the misdiagnosis between bipolar disorder and ADHD, a machine learning-based…
Necrotizing enterocolitis (NEC) is a severe gastrointestinal emergency in neonates, marked by its complex etiology, ambiguous clinical manifestations, and significant morbidity and mortality, profoundly affecting long-te…
This study addresses the challenge of forecasting fuel consumption for various categories of construction equipment, with a specific focus on Backhoe Loaders (BL). Accurate predictions of fuel usage are crucial for optim…
Diabetes is increasing gradually due to the inability to effectively use the human body’s insulin, which threatens public health. People with diabetes who go undiagnosed at early stages or who have diabetes have a high r…
As cyber threats continue to evolve in complexity, the need for robust intrusion detection systems (IDS) becomes increasingly critical. Machine learning (ML) models have demon-strated their effectiveness in detecting ano…
In the volatile and uncertain financial markets of the post-COVID-19 era, our study conducts a comparative analysis of traditional econometric models—specifically, the AutoRegressive Integrated Moving Average (ARIMA) and…
In the contemporary business landscape, organizational trustworthiness is of utmost importance. Employee behavior, a pivotal aspect of trustworthiness, undergoes analysis and prediction through data science methodologies…
The research tackles the complex problem of accurately predicting cooling loads in the context of energy efficiency and building management. It presents a novel approach that increases the precision of cooling load forec…
