SPRINGER, Multimedia Tools and Applications

SPECIAL ISSUE ON “Soft computing Techniques and Applications for Intelligent Multimedia Systems”


AIMS & SCOPE:
In the recent times, the rapid development of social networks and multimedia technologies has delivered high Quality of Experience to the users globally. However, with the massive increase in the number of social network users, the demands for the sharing and exchange of multimedia content between users are ever-increasing. Therefore, the huge need for the research on multimedia analytics and development of efficient approaches to handle online multimedia content is high. Multimedia is increasingly becoming the most important and valuable source of insights and information from social networks, and it covers from everyone’s experiences to everything happening in the world. The huge volume of multimedia big data from social networks makes the traditional multimedia handling systems inefficient and spurs towards the development of new technologies for handling multimedia big data.
Soft computing techniques are delivering promising solutions to the complex research problems and make innovations at a rapid pace. As soft computing differs from traditional computing approaches, it is tolerant towards uncertainty, imprecision, and approximation. Inspired by the human mind model, soft computing is applied to the various research problems and has been proven to be proficient with the experimental performances. This special Issue will report the recent increasing interests in the design and development of Soft computing techniques for various applications of intelligent multimedia systems. Moreover, the authors are expected to investigate state-of-art research issues, architectures, applications and achievements in the field of multimedia big data. Unpublished innovative papers which are not currently under review to another journal or conference are solicited in the following relevant areas.
Topics of interest include, but are not limited to:
 Data management and knowledge representation for multimedia data
 Algorithms, models, and designs for social multimedia analytics
 Machine learning for high-performance computing with multimedia big data
 User activities recognition for multimedia systems
 Web applications for multimedia big data
 Soft computing model for multimedia assisted prediction
 Pattern and feature learning in multimedia big data
 Nature-Inspired algorithms for multimedia systems
 Efficient inference methods for mobile multimedia deep networks
 Learning for ranking and recommendation on multimedia big data
 Semi-supervised learning problems in multimedia big data applications
 Deep neural networks for multimedia big data analysis and understanding
 Robotics, intelligent system and advanced manufacturing with multimedia big data
 Novel and incentive applications of multimedia big data in various fields
 Other soft computing topics for emerging multimedia applications