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Oblasti výskumu

Nature provides a very valuable source of inspiration for computer science. We are adopting algorithms (e.g. evolutionary) and principles from biology and nature (e.g. bee hive) to achieve better results in various computational problems. Artificial neural networks model brain structures and neural networks of living beings, providing excellent results in classification, prediction and regression tasks. As a part of data mining, we also focus on text mining and knowledge discovery from text-based resources, including topics such as opinion mining.

Data Mining, Machine Learning, Neural Networks, Nature and Biology Inspired Computing

Pavol Návrat
Mária Bieliková
His research interests range from information interactions of people on the Web as manifested by information recommendation or spreading within social networks, to social insect inspired computing and to modelling of software artifacts.
Aims her research to human interactions on the Web with special emphasis on user modelling and personalization, context awareness, collaborations and usability. This includes research of methods for automated analysis and modelling user feedback, and its evaluation by (multi/group) user studies employing eye trackers.

Mária Lucká
Associate Professor
Viera Rozinajová
Associate Professor
Focuses in her research on efficient algorithms and processing of big data sets, with applications in bioinformatics and energy data. Intelligent data analysis methods include clustering of big data, parallel methods and high performance computing.
Covers by her research interests data science in broader sense, in particular she concentrates on advanced methods of predictive modeling, cluster analysis, anomaly detection and optimization.

Michal Barla
Anna Bou Ezzeddine
Covers by his research interests an area of clickstream data analysis for user modeling with a special focus on unsupervised methods, including neural networks.
Explores bio-inspired optimization methods, intelligent data processing techniques with a focus on stream data, self-adapting methods.

Gabriela Grmanová
Michal Kompan
Her research is oriented on the field of Data mining. She explores mainly advanced methods of clustering and predictive modeling.
Aims his research at problems in the recommender systems and users’ behavior prediction.

Peter Lacko
Marián Šimko
His research interests include artificial intelligence, neural networks and parallel and distributed computing.
Focuses on information extraction and knowledge discovery from text-based content, by employing ontology engineering and natural language processing. His interests include processing of resources in Slovak language.

Selected recent publications

  1. KAŠŠÁK, Ondrej - KOMPAN, Michal - BIELIKOVÁ, Mária
    Student Behavior in a Web-based Educational System: Exit Intent Prediction.
    The Engineering Applications of Artificial Intelligence Journal, Elsevier, Vol.51, Issue Mining the Humanities: Technologies and Applications, pp. 136-149, 2016

  2. BIELIKOVÁ, Mária - KOMPAN, Michal - ZELENÍK, Dušan
    Effective Hierarchical Vector-based News Representation for Personalized Recommendation.
    Computer Science and Information Systems, Vol. 9, No. 1. pp. 303-322, 2012. ISSN 1820-0214.

  3. BARLA, Michal - ŠIMEK, Miroslav - BIELIKOVÁ, Mária
    Comparing Eye-tracking Data using Machine Learning.
    Journal of Eye Movement Research. Vol. 8, No. 4, p. 252. 2015.

  4. GRMANOVÁ, Gabriela - LAURINEC, Peter - ROZINAJOVÁ, Viera - BOU EZZEDDINE, Anna - LUCKÁ, Mária - LACKO, Peter - VRABLECOVÁ, Petra - NÁVRAT, Pavol
    Incremental Ensemble Learning for Electricity Load Forecasting.
    Acta Polytechnica Hungarica. Vol. 13, No. 2 (2016), pp. 97-117.

  5. BOU EZZEDDINE, Anna - KASALA, Štefan - NÁVRAT, Pavol
    Applying the Firefly Approach to the DNA Fragments Assembly Problem.
    Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae Sectio Computatorica. Vol. 42 (2014), pp. 69-81. ISSN 0138-9491.

  6. KORENEK, Peter - ŠIMKO, Marián
    Sentiment Analysis on Microblog Utilizing Appraisal Theory.
    World Wide Web. Vol. 17, No. 4, 2014, pp. 847-867.

Industry collaboration

  • Data analysis Orange SK


  • data centre – 736 cores, 10TB ram, 100TB disk
  • smart – 16 nodes, 128 cores, 0.6TB ram, 48TB disk
  • graphic card computations:
    • GTX 980 Ti – 2816 cores, 4GB DDR5
    • 2x GTX 960 – 1024 cores, 4GB DDR5