C09: Computational Intelligence for Learning and Knowledge Acquisition

Paper Submission for C09

SMC2018:C09 submission site (external site)

Abstracts

Computational Intelligence technologies have made great progress in recent decades. Real world environments produce big data, which is large-scale, high-dimensional, multi-modal, sequential and ambiguous data. Since many real world problems are not considered to be well-posed mathematically, attempts of analytic approaches to find solutions met some difficulties. For dealing with such complex data, various techniques are required such as visualization by clustering of multi-modal and sequential data, automatic feature extraction by representation learning, acquisition of comprehensible knowledge from learning results and so on. Driven by such motivation, emerging computational intelligence approaches have been proposed in the soft-computing areas like artificial neural networks, evolutionary computation and fuzzy theories. As one of the successes, Deep Learning is now becoming popular in the field of computer science. According to the brisk activities, many researchers also have been able to challenge solving industrial problems. We discuss in this session the computational intelligence technologies for learning real world complex data, which will make an explicit or implicit knowledge to the real world problems that prior technologies cannot provide satisfactory solutions. The topics of this session include computational intelligence methodologies such as Deep Learning, Neural Networks, Evolutionary Computation, Fuzzy Theory, Swarm Intelligence, and other softcomputing methods, and their applications to Big Data Analysis, Image Processing, Intelligent Control System, Computer Education, Medical Informatics and so on.

Session Chairs