The Journal of Big Data: Theory and Practice (JBDTP) (ISSN 2692-7977) is an open access peer-reviewed journal devoted to the publication of high-quality papers on theoretical and practical aspects of big data, AI and machine learning. The JBDTP is the flagship journal of the New Jersey Big Data Alliance (NJBDA). 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:
• Big Data Theory and Foundational Issues
• Artificial Intelligence (AI)
• Data Mining Methods
• Machine Learning Algorithms
• Knowledge Discovery Processes
• Application Issues
• Ethical, policy and economic aspects of big data, machine learning and AI
• Big data analytics and decision-making
• Natural Language Processing, Understanding and Generation
Special Issue - Enabling Technologies in Intelligent Healthcare (Call for Manuscripts)
Call for manuscripts for the JBDTP special issue - Enabling Technologies in Intelligent Healthcare: From the Internet of Things (IoT), To Artificial Intelligence, Big Data, and Blockchain.
CALL FOR MANUSCRIPTS
It is our great pleasure to announce the call for papers for the Journal of Big Data: Theory and Practice (JBDTP).
Volume 1, No. 1JBDTP Inaugural Issue
Inaugural issue of the Journal of Big Data: Theory and Practice (JBDTP), the flagship journal of the New Jersey Big Data Alliance (NJBDA), a premier open access peer-reviewed publication of papers on theoretical and practical aspects of big data, AI and machine learning.