The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Artificial intelligence (AI) is the branch of computer science concerned with building systems that perform tasks normally requiring human intelligence, such as reasoning, planning, perception, and language understanding. The field spans symbolic approaches (rule-based expert systems, knowledge graphs, automated planning) and statistical approaches (machine learning, deep neural networks, probabilistic reasoning), increasingly combined to improve interpretability and reliability. AI research addresses search and optimization, multi-agent coordination, knowledge representation, and generalization from limited data. Applications range from recommendation engines and autonomous vehicles to fraud detection, scientific discovery, and decision-support systems in medicine, finance, and manufacturing. Agentic AI, systems that plan and act across multi-step tasks, has become a leading research focus; Stanford's 2026 AI Index Report finds most organizations are still experimenting with AI agents rather than deploying them at scale. As a peer-reviewed, open-access artificial intelligence journal, IJACSA (an AI journal indexed in Scopus) publishes research spanning foundational algorithms to applied AI systems evaluated on real-world datasets.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
Artificial intelligence (AI) is increasingly embedded in STEM problem-solving activities, yet existing reviews have generally organized the literature by technology type or learning outcome. This review shifts the analyt…
Cardiovascular disease remains a major public health burden and is associated with demographic, behavioral, and chronic-health characteristics. This study evaluates explainable machine learning for cross-sectional classi…
Diabetic Foot Ulcers (DFUs) are one of the most serious but preventable complications of diabetes mellitus that often develop without clinical signs until the late stages. This means that neuropathic and vascular abnorma…
Generative artificial intelligence (Gen AI) and large language models (LLMs) offer substantial potential to improve how organisations capture, organise, retrieve and reuse knowledge. Existing knowledge management (KM) fr…
Smart cities are becoming more interconnected, data-driven, and increasingly autonomous, with 6th-generation communication, edge-cloud computing, Internet of Things infrastructures, digital twins, federated learning, and…
This study aims to improve the automatic recognition of geological host rocks from mining data and to make the prediction process easier to understand for geologists, mining engineers, and data analysts. The study uses a…
Predicting Mohs hardness from mineralogical information is still a challenging task because of the complex and nonlinear relationship between mineral composition and hardness properties. Traditional machine learning (ML)…
Machine learning can triage digital evidence at scale, but two obstacles limit its forensic adoption: opaque decisions, and point predictions without a valid statement of confidence. We present ForensiQ, a hierarchical a…
In the medical field, AI has made its mark, and almost every medical profession has experienced the development of a chatbot to check symptoms and provide instructions to the owners at an early stage. However, determinin…
Schema matching remains a fundamental challenge for achieving data interoperability across heterogeneous information systems. Existing deep learning-based approaches often suffer from semantic drift, overlook the structu…
