Guest Editors
Prof. Liang Zhao
Email: liangzhao@dlut.edu.cn
Affiliation: School of Software Technology, Dalian University of Technology, Dalian, 16000, China
Homepage:
Research Interests: big data and AI
Prof. Qingchen Zhang
Email: zhangqingchen@hainanu.edu.cn
Affiliation: School of Computer Science and Technology, Hainan University, Haikou, 570000, China
Homepage:
Research Interests: artificial intelligence and smart medicine
Prof. Boxiang Dong
Email: dongb@montclair.edu
Affiliation: Computer Science Department, Montclair State University, New Jersey, USA
Homepage:
Research Interests: verifiable computing, data mining, anomaly detection, data security, and privacy
Summary
In the big data era, with the enrichment of data collection and description measures, a wide array of data in various formats are collected much easier than before. It is significant to discover the knowledge hidden in the mass by comprehensive understanding and multimodal learning to realize the data intelligence, which can help human in various dimensions, such as intelligent decisions and predictive services. However, the high-dimensional, heterogeneous, real-time, and low-quality characteristics of the collected data pose great challenges to the design of knowledge discovery methods. If we can effectively perform multimodal learning on massive high-dimensional, heterogeneous, real-time, and low-quality big data to discover the hidden knowledge and rules, the potential values and insights can be identified. Thus, it will provide a comprehensive understanding and a favourable decision-making framework based on the massive data to realize the real big data intelligence.
This special issue aims to seek the high-quality papers from academics and industry-related researchers in the areas of big data, data mining, multimodal learning, artificial intelligence, and multimedia analysis to present the most recently advanced methods and applications for realizing big data intelligence.
Proposed submissions should be original, unpublished, and novel for in-depth research. Topics include but not limited to:
· Big Data Theory and Methods
· Artificial Intelligence Theory and Methods
· Multimodal Learning
· Domain Adaption and Transfer Learning
· Deep Learning and Reinforcement Learning
· Multimodal Uncertainty Data Analysis
· Multimodal Data Reliability Analysis
· Multimodal Medical Big Data Analysis and Applications
· Multimodal Industrial Big Data Analysis and Applications
· Multimodal Data Analysis and Application in Other Fields
Keywords
Big data analysis, artificial intelligence, multimodal learning, big data applications
Published Papers
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