Journal of Data Intelligence and AI Systems is an international peer-reviewed academic journal dedicated to the study and application of data intelligence, artificial intelligence, and intelligent system technologies. The journal aims to provide a scholarly platform for researchers, academics, engineers, and industry practitioners to publish high-quality research that advances the development, evaluation, and real-world deployment of intelligent data-driven systems.
The journal welcomes original research articles, review papers, and applied studies covering topics such as machine learning, deep learning, data mining, natural language processing, computer vision, intelligent decision support, knowledge discovery, data analytics, optimization, automation, and AI-enabled systems. Interdisciplinary research that applies data intelligence and artificial intelligence to fields such as healthcare, finance, education, manufacturing, cybersecurity, smart cities, engineering, and business analytics is also encouraged.
The scope of the journal emphasizes both theoretical innovation and practical implementation. Submissions should demonstrate clear methodological rigor, technical contribution, and relevance to intelligent computing or data-driven decision-making. The journal particularly values research that supports reliable, scalable, interpretable, and ethically responsible AI systems.
Journal of Data Intelligence and AI Systems is committed to maintaining academic integrity, transparent editorial procedures, and a rigorous peer-review process. The journal seeks to promote the exchange of knowledge and contribute to the continued advancement of artificial intelligence, data science, and intelligent systems research worldwide.
Journal Name: Journal of Data Intelligence and AI Systems
Languages: English
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ISSN Online:
Publication Frequency: Quarterly
Audience: Researchers, academics, engineers, data scientists, and industry practitioners in data intelligence, artificial intelligence, machine learning, intelligent systems, computer science, and related interdisciplinary fields.
Review Type: Double Blind Peer Review
