The world is becoming increasingly complex with a major drive to incorporate increasingly intricate coupling relationships and interactions between entities, behaviors, subsystems, and systems and their dynamics and evolution of any kinds and in any domains. Effective modeling in complex couplings and interactions in complex data, behaviors, and systems directly addresses the core nature and challenges of complex problems and is critical for building next-generation intelligent theories and systems to improve machine intelligence and understand real-life complex systems. This Special Issue on learning complex couplings and interactions will collect and report the latest advancements in artificial intelligence and data science theories, models, and applications of modeling complex couplings and interactions in big data, complex behaviors, and complex systems.
Scope of Interest:
This special issue will solicit the recent theoretical and practical advancements in learning complex couplings and interactions in areas relevant but not limited to the following:
Submission Guidelines:
All submissions must comply with the submission guidelines of IEEE Intelligent Systems and will be reviewed by research peers. Submit manuscripts here. The schedule is as follows:
• Paper Submission Date: 30 May 2020
• Revision Date: 30 July 2020
• Final Accept/Reject Date: 2 October 2020
• Camera Ready Copy Date: 30 October 2020
• Publication Date: Jan./Feb. 2021
Guest Editors:
• Dr. Can Wang (Griffith University, Australia)
• Prof. Fosca Giannotti (University of Pisa, Italy)
• Prof. Longbing Cao (University of Technology Sydney, Australia)
Inquiries:
Inquiries about this special issue can be sent to is1-21@computer.org.
