Knowledge discovery and representation

Division of Electronics

Modern scientific research and technological advancement are based on the collection, presentation and analysis of large amounts of data. In the Department, we investigate, develop and implement complex measurement systems and computational methods for modeling and discovering knowledge from data. The work is strongly interdisciplinary and the results represent advances in the technical sciences and domains of applications.

Tomislav Šmuc

Predstojnik

PhD Tomislav Šmuc

+385 1 456 1085
Fax: +38514680114

Administration

Moira Španović

+385 1 468 0114

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The mission of the Department is the development of techniques and approaches capable of tackling data and information overload, a common problem for most  scientific disciplines and contemporary technology environments. Our techniques and approaches encompass a blend of signal processing and machine learning data mining, with modern programming paradigms, knowledge acquisition and representation technologies. The main application disciplines include biomedical engineering, biology (genomics/proteomics), and intelligent instrumentation and process modeling. Participation in graduate and postgraduate education also plays an important role in our activities.

Group research revolves  around knowledge discovery within hot topics in modern biology, using novel data mining and machine learning paradigms and techniques.

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Development of machine learning algorithms and their applications in data mining and knowledge discovery tasks. 

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  • Analysis of stochastic signals, time series and data structures
  • Advanced measurement systems and signal processing techniques
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Dragan Gamberger
dr.

senior scientist
+385 1 456 1142

Rajko Horvat

+385 1 456 1159

Ivan Marić
dr. sc.

Senior scientist
+385 1 456 1191

Branka Medved-Rogina
Ph.D.

Senior Research Associate
+385 1 468 0090

Ivan Michieli
PhD.

senior scientist
+385 1 456 1023

Strahil Ristov

senior associated researcher
+385 1 456 1029

Tomislav Šmuc
PhD

Senior scientist
+385 1 456 1085
Konstrukcija GMDH modela

Computational Intelligence Methods in Measurement Systems

Principal Investigator: Ivan Marić

The goal of the project is to investigate the possibilities of development and application of the artificial intelligence methods and machine learning techniques in measurement systems ranging from the development of simple models to the synthesis of sophisticated measurement procedures and smart instruments capable not only to execute simple commands but to perform rather complex tasks. The research will be focused on algorithms and methods for reducing the processing time and the complexity of measurement procedures and for increasing the accuracy, flexibility and the reliability of both embedded and distributed measurement systems.

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Schematic depiction of the e-LICO platform

e-LICO

Principal Investigator: Tomislav Šmuc

e-LICO is an EU FP7 collaborative (STREP) project under the call Intelligent Content and Semantics. in Data Mining and Data-Intensive Science". e-LICO  goal is to build a virtual laboratory for interdisciplinary collaborative research in data mining and data-intensive sciences. The proposed e-lab  comprises three layers: the e-science and data mining layers will form a generic research environment that can be adapted to different scientific domains by customizing the application layer. 

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Machine learning of predictive models in computational biology

Principal Investigator: Tomislav Šmuc

Methods of computational knowledge discovery based on machine learning and data mining algorithms represent  mainstream analytical support for diverse problems in biology. This project represents an effort to create and apply new methodologies and optimize the use of cutting-edge knowledge discovery techniques to solve scientific problems in targeted areas of molecular biology and biochemistry. The problems we are targeting are of interest for some very specific features: the number of variables for the description of an entity is typically very large, while the number of reliably annotated examples generally small.

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