COMPUTATIONAL KNOWLEDGE DISCOVERY IN SCIENTIFIC APPLICATIONS
Enormous quantities of information are available today. Effective and intelligent handling of information and knowledge is needed from manufacturing to banking, from health care to transportation. Knowledge based society of the future is impossible without computers able to practically realize complex tasks like knowledge representation, knowledge discovery and decision support. Although today practical implementation of these tasks may seem as a science fiction, from our previous experience in data analyses, data modeling, and artificial intelligence applications we have learnt that they, regardless how intellectually complex they may seem, consist of some computational parts that may be automated and actually better executed by machines than by humans.
The aim of the program is development, evaluation, implementation, and integration of complex computational methods representing fundamentals of the knowledge technologies. Having in mind that general goal, we will mainly concentrate on practical scientific applications. We suppose that in interdisciplinary collaboration of computer scientists and scientists working on very specific scientific problems, we have the best chance to recognize the problems, to learn how to solve them, and finally, if possible, to generalize the solutions. The work will be strongly multidisciplinary on very concrete scientific applications. The results should be relevant for application domains like medicine, bioinformatics, chemistry, and physics, as new and significant scientific results in the form of relations, models, or decision systems. At the same time, the results should be relevant for computer sciences, as novel theoretical and practical methodological achievements in the field of artificial intelligence.
The work consists of developing the most appropriate methodologies for the target problems. The quality of the resulting methodology is evaluated by the success in achieving the requested, domain specific results. The importance of the methodological results depends on the relevance of practical problems that have to be solved, and the ability to generalize the methodology to other similar tasks. Ultimate goal is detection of universal principles representing novel methodological breakthroughs. The significance of the research is in the fact that knowledge discovery technologies present technical fundamentals for supporting creativity of the information society and knowledge-based economy.
Intelligent image features extraction in knowledge discovery systems - University of Zagreb, Faculty of Electrical Engineering and Computing
Real life data measurement and characterization - Rudjer Boskovic Institute
Machine learning algorithms and their applications - Rudjer Boskovic Institute
Computational Intelligence Methods in Measurement Systems - Rudjer Boskovic Institute
Predictive models in health care - University of Zagreb, Medical School, Andrija Stampar School of Public Health
Generation of Potential Drugs In-silico - Faculty of Food Technology and Biotechnology, University of Zagrebu
Machine learning of predictive models in computational biology - Rudjer Boskovic Institute