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The Computer Science Journal ( is published by the AGH University of Science and Technology, Krakow, Poland. The Editors of the Journal are members of the Faculty of Computer Science, Electronics and Telecommunications and the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. The Editorial Board consists of many renowned researchers from all over the world.

Original papers on theoretical and applied computer science problems are published. Example areas of interest are: theoretical aspects of computer science, pattern recognition and processing, evolutionary algorithms, neural networks, multi-agent systems, computer networks management, distributed, grid and cloud computing.

Our journal is indexed by: Google Scholar, CrossRef metadata search, Directory of Open Access Journals, Open Archives Initiative, Digital Libraries Federation, BazTech, Index Copernicus, Ulrich's Periodicals Directory, EBSCOhost Applied Sciences, DBLP, ERIH PLUS and SCOPUS and Emerging Sources Citation Index - part of Clarivate Web of Science.

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Pattern Recognition and Intelligient Data Exploration

Since the works of Hollerith in 1890s, machines have been used to aid humans in processing of vast amounts of data. Nowadays, as we explore natural, technological and business processes for which analytical models are not yet uncovered, the focus is shifted towards intelligent data recognition, analysis and understanding techniques.

At the Department of Computer Science, we have gained proficiency in developing and applying pattern recognition solutions. Our expertise includes methods for automatic classification and clustering, by using supervised and unsupervised learning coupled with advanced noise removal techniques. Our tools for data visualisation and dimensionality reduction provide intuitive support for data analysts. We also excel in automatic information extraction with image processing algorithms and interaction network analysis.

Main application schemes of our methods include intelligent system control, exploratory data analysis and decision support. Our clustering techniques have been used for surveillance and safe-guarding of IBR2 pulsed nuclear reactor and in supervising high-performance computer simulations. Ensemble classification methods of ours have been employed in an industrial drug discovery support system. Our algorithms have also proven successful in handling pattern recognition problems emerging from life sciences, e.g. genomic and proteomic data exploration, as well as cancer detection.

Our activities have been funded by grants from Polish State Agencies: KBN in 1993-95, MNiI in 2004-06 and MEiN in 2005-07, as well as funds from the University of Minnesota Digital Technology Center in 2003-04.

We collaborate on extending, customising and applying our algorithms with leading foreign and national research and industry partners, including:

  • Minnesota Supercomputing Institute, – computer simulation results analysis,
  • University of Minnesota Medical School, USA – drug discovery, cancer patterns analysis,
  • University of Southern California, USA – earthquake patterns exploration,
  • Joint Institute for Nuclear Research, Russia – nuclear reactor surveillance,
  • Fujitsu Group, Poland – drug discovery support,
  • Collegium Medicum of the Jagiellonian University, Poland – cancer detection.

Research team: Witold Dzwinel, Krzysztof Boryczko, Tomasz Arodź, Marcin Kurdziel


Tags: Research