The Journal of Big Data and Artificial Intelligence (JBDAI) (ISSN 2692-7977) is an open access peer-reviewed scholarly journal devoted to the publication of high-quality research on Artificial Intelligence, big data, informatics, data science, machine learning and related topics. The goal of this journal is to publish the latest contributions from academia, practitioners and industry to advance these fields. Original research papers, state-of-the-art reviews, innovative case studies and tutorials are invited for publication.

Areas of interest include (but not restricted to):
• Artificial Intelligence - theory, applications, human interaction and impacts

• Data - Theory and Foundational Issues
• Data Mining Methods, Visualization
• Algorithms - Machine, Deep, Reinforcement Learning 
• Informatics, Knowledge Discovery Processes
• Intelligent Applications & Information Systems
• Ethical, policy and economic aspects of big data, machine learning and AI
• Domain Applications of AI & big data (e.g. Finance, Policy, Health, GIS, Business, Physics)
• Big data analytics, data science and decision-making

Publication Frequency
The Journal of Big Data: Theory and Practice (JBDTP) publishes one volume per year, and accepted articles will be published online on a continuous basis.