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 October 2026
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…
Recent developments in Large Language Models (LLMs) have created new opportunities to automate educational assessment and reduce workload for instructors. However, concerns regarding grading consistency, transparency, an…
The rapid growth of artificial intelligence (AI) technologies has transformed educational environments and created new opportunities for enhancing digital literacy in science education. However, variations in technologic…
Breast cancer is one of the major causes of death among women worldwide, and timely and accurate diagnosis has proven to play a critical role in increasing breast cancer patient survival. Traditional single-modality diag…
