SCAKD 2016 – The Second International Workshop on Soft Computing Applications and Knowledge Discovery. Proceedings of the Second International Workshop on Soft Computing Applications and Knowledge Discovery. July 18, 2016
This volume contains the papers presented at the Second International Workshop on Soft Computing Applications and Knowledge Discovery (SCAKD 2016) held on July 18, 2016 at the National Research University Higher School of Economics, Moscow, Russia. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost in real life tasks. The workshop proposes to present high quality scientific results and promising research in the area of soft computing and data mining, particularly by young researchers, with an objective of bringing them to the focus while promoting collaborative research activities. By holding the workshop in conjunction with CLA 2016, we hope to provide the participants exposure and interaction with eminent scientists, engineers, and researchers in the related fields. Each submission has been reviewed by at least two Program Committee members. Six regular papers have been accepted for publication as well as four research proposals. The program also includes one invited industry talk by the representatives of ExactPro company on Using intelligent systems and structural analysis to ensure orderly operations of the modern trading and exchange platforms. We would like to thank all the authors of submitted papers and the Program Committee members for their commitment. We are grateful to our invited speaker and our sponsors: National Research University Higher School of Economics (Moscow, Russia), Russian Foundation for Basic Research, and ExactPro. Finally, we would like to acknowledge the EasyChair system which helped us to manage the reviewing process.
An ionogram is a display of the data produced by an ionosonde. It is a graph of the virtual height of the ionosphere plotted against frequency. In addition to "useful signal", an ionogram almost always contains noise of different nature, a so called background noise. That is why the signal filtering task becomes so important. There are two groups of methods to this end. The first group features methods of computer vision for image processing, namely, different filters and image binarization. The second group includes adapted clustering methods. In this paper, we show how several methods work for filtering "useful signal" from noise and emissions.