The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Natural language processing (NLP) is the field of computer science focused on enabling computers to understand, interpret, and generate human language. Core tasks include tokenization, part-of-speech tagging, syntactic parsing, named entity recognition, machine translation, text summarization, question answering, and sentiment analysis. Early NLP systems relied on rule-based grammars and statistical language models; current approaches are dominated by transformer-based architectures and large language models pretrained on extensive text corpora and fine-tuned for specific tasks. Active research increasingly targets efficient attention mechanisms, including linear and sparse attention, to reduce the heavy compute and memory costs of standard transformers, alongside work on multilingual and low-resource languages, model bias, and factual reliability in generated text. Applications include chatbots and virtual assistants, automated document analysis, information extraction from unstructured text, and cross-lingual translation systems. As an open-access natural language processing journal (an NLP journal), IJACSA publishes research on language models and applied systems evaluated across multiple languages and domain-specific text corpora.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
An Artificial Intelligence-driven child learning system with a Machine Learning and Natural Language Processing-based approach to dynamically personalize educational experiences for children is proposed in this study. Us…
Cross-site scripting (XSS) attacks remained among the most persistent threats in web-based systems, often bypassing traditional input validation techniques through obfuscated or embedded scripting payloads. Existing dete…
The integration of Big Data and Artificial Intelligence (AI) is fundamentally transforming how labor markets are analyzed, predicted and managed. Despite significant advances in using these technologies for workforce ana…
Large Language Models (LLMs) have demon-strated remarkable capabilities in generating human-like text; however, their effectiveness in abstractive summarization across diverse domains remains underexplored. This study co…
XPath is a widely used language for navigating and extracting data from XML documents due to its simple syntax and powerful querying capabilities. However, non-technical users often struggle to retrieve the needed inform…
To that end, this study presents the Hierarchical Context-Aware Transformer (HCAT), a new model to perform analysis on unstructured healthcare data that resolves significant problems related to medical text. In the propo…
Accurate disease prediction from symptom descriptions is vital for improving early detection and enabling remote healthcare services, especially in the evolving landscape of digital health. Traditional diagnosis methods…
Safety and security are essential to social stability since their absence disrupts economic, social, and political structures and weakens basic human needs. A secure environment promotes development, social cohesion, and…
Automatic detection of hate speech in low-resource languages presents a persistent challenge in natural language processing, particularly with the rise of toxic discourse on social media platforms. Arabic, characterized…
CodifiedCant is a new idea that employs Natural Language Processing to simplify company guidelines and legal documents. Legal texts are extensive, complicated and hard for non-experts to understand. To tackle the above p…
