Soft computing refers to computational techniques, which attempt to study, model, and analyze very complex phenomena that are affected by vagueness and uncertainty: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Earlier computational approaches could model and precisely analyze only rela- tively simple systems. More complex systems arisen in biology, medicine, the humani- ties, management sciences, and similar fields often remained intractable to conventional analytical methods. Key areas of soft computing include neural networks, evolutionary computing, fuzzy systems, and swarm intelligence. There are now cross sections with the field known as computational intelligence.
This minitrack focuses on soft computing methods and on their applications. We solicit the submission of scientific paper where the applied/defined methodologies used are ei- ther analysis or systems oriented. They may have an experimental or empirical focus. Studies are favored, which combine good theoretical results with a careful empirical veri- fication, or good empirical problem solving, planning or decision making with innovative theory building. A common denominator for all studies should be the building and use of soft computing based models.
The topics of interest include but are not limited to:
Soft Computing Methods:
Fuzzy Logic, Fuzzy Sets and Fuzzy Systems
Artificial neural networks
Soft Computing Applications:
Big Data and Intelligent Data Analysis
Predictive and Prescriptive Analytics
Information Retrieval and Filtering
Rudolf Kruse (Primary Contact)
Otto von Guericke University Magdeburg, Germany