About the Journal

The Nile Journal of Computing (NJC) aims to serve as a high-quality, peer-reviewed forum for the publication of original, rigorous, and impactful research in theory, design, development, and application of computing and information technologies. The journal is committed to advancing knowledge that addresses both foundational challenges and emerging innovations in computing, with relevance to academia, industry, and society.

The journal seeks to encourage the dissemination of research that demonstrates technical depth, methodological soundness, and practical relevance, including interdisciplinary studies that bridge computing with real-world applications. NJC particularly welcomes contributions that promote innovative solutions, scalable systems, and data-driven intelligence, as well as research that reflects regional and global perspectives, especially within developing and emerging economies.

 

Scope and Thematic Areas

The Nile Journal of Computing publishes high-quality research articles, reviews, and technical papers in areas including, but not limited to, the following thematic domains:

  • Computer Science: algorithms, data structures, theory of computation, programming languages, operating systems, distributed systems, and high-performance computing.
  • Software Engineering: software design and architecture, requirements engineering, software testing and quality assurance, maintenance and evolution, DevOps, and agile methodologies.
  • Cybersecurity: network and system security, cryptography, privacy and trust, intrusion detection, digital forensics, risk assessment, and secure software systems.
  • Data Science: data analytics, big data systems, data mining, statistical learning, predictive modeling, and data-driven decision support systems.
  • Information Technology: IT infrastructure, cloud and edge computing, computer networks, enterprise systems, and emerging IT platforms.
  • Information Systems: database systems, knowledge management, decision support systems, enterprise information systems, and socio-technical perspectives of IS deployment.
  • Artificial Intelligence, Machine Learning, and Deep Learning: intelligent systems, neural networks, reinforcement learning, explainable AI, and real-world AI applications.
  • Natural Language and Image Processing: text and speech processing, information retrieval, sentiment analysis, language modeling, multilingual and low-resource language technologies.