The continuing rapid growth of the volume of data generated by various sources implies the need for efficient methods of processing and acquiring knowledge. Data must be structured and stored in the right way; it must be possible to efficiently search through the data and analyze it to distil knowledge that could not be distilled without sophisticated methods and algorithms. In our research we use methods inspired by the living nature to process large collections of weakly structured data, extract knowledge from the data or, for example, reduce the dimension of high-dimensional data.
By studying natural phenomena, these methods (bio-inspired methods, soft-computing methods, etc.) allow processes and theoretical models to develop computational systems and algorithms capable of solving complex problems that cannot be solved (or with difficulties) by traditional techniques.
Our research also covers the area of formal methods with focus on knowledge-based approach to the creation of software systems. The key topics in this area include the development of methods for effective creation and verification of software systems, utilization of formal methods for the specification of software process and effective management of such process.Biomedical applications when measuring and processing large-volume data, e.g.:
- signal processing with primary emphasis on signals of biological origins
- modelling and verifying complex processes when planning radio-therapy
- sustainable development – resource optimization and active management of energy flows in smart grids with emphasis on increasing the positive impact on the environment and social level of the population
- optimization of sophisticated control methods for technological systems based on the analysis of biological processes
- Massively parallel computing on various technological platforms.
Certified methodology “Specific system of passenger´s check-in and number of transported passengers”:
Zjavka, L.; Snášel, V. Constructing Ordinary Sum Differential Equations using Polynomial Networks. Information Sciences Volume 281, Multimedia modeling, 10 October 2014, Pages 462–477, Elsevier.
Krömer, P.; Zelinka, I.; Snášel, V. Behaviour of pseudo-random and chaotic sources of stochasticity in nature-inspired optimization methods. Soft Comput. 18(4): 619-629 (2014). http://dx.doi.org/10.1007/s00500-014-1223-y.
Frolov, A. A.; Husek, D.; Polyakov, P.Y.; Snasel, V. New BFA method based on attractor neural network and likelihood maximization. NEUROCOMPUTING Volume: 132 Special Issue: SI Pages: 14-29. DOI: 10.1016/j.neucom.2013.07.047.
Rajasekhar, A.; Kumar Jatoth, R.; Abraham, A. Design of intelligent PID/PIλDμ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm (2014) Engineering Applications of Artificial Intelligence, 29, pp. 13-32. DOI: 10.1016/j.engappai.2013.12.009.
FROLOV, A.; HÚSEK, D.; BOBROV, P.; MOKIENKO, O.; TINTĚRA J. Sources of Electrical Brain Activity Most Relevant to Performance of Brain-computer Interface Based on Motor Imagery. In Brain-Computer Interface InTech. ISBN 980-953-307-960-3. Doi: http://dx.doi.org/10.5772/55166.
KRÖMER, P.; OWAIS, S.; PLATOŠ, J.; SNÁŠEL, V. Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression. In Computers & Mathematics with Applications. Volume 66, Issue 2. ISSN 0898-1221. Doi 10.1016/j.camwa.2013.02.017. (In Press, Corrected Proof available online) http://www.sciencedirect.com/science/article/pii/S0898122113001284.
FROLOV, A.; HÚSEK, D.; POLYAKOV, P.Y. Two Expectation Maximization Algorithms for Boolean Factor Analysis. In Neurocomputing. Doi: http://dx.doi.org/10.1016/j.neucom.2012.02.055.
BRANDSTETTER P.; KRECEK T. Speed and Current Control of Permanent Magnet Synchronous Motor Drive Using IMC Controllers. In Advances in Electrical and Computer Engineering. Volume 12, Issue 4, p. 3-10, 2012. ISSN 1582-7445. Doi: http://dx.doi.org/10.4316/AECE.2012.04001.
SEDANO, J.; GONZÁLEZ, S.; HERRERO, A.; BARUQUE, B.; CORCHADO, E. Mutating network scans for classifier ensemble assessment. In Logic Journal of the IGPL. Oxford University Press.
BESHAH, T., EJIGU, D., ABRAHAM, A., SNÁŠEL, V., KRÖMER, P.: Knowledge discovery from road traffic accident data in ethiopia: Data quality, ensembling and trend analysis for improving road safety. In Neural Network World. Volume 22, Issue 3, p. 215 – 244. ISSN 1210-0552. http://isda2001.softcomputing.net/nnw2012_tibebe.pdf.
KOLOSENI, D.; LAMPINEN, J.; LUUKKA P. Optimized Distance Metrics fo Differential Evolution based Nearest Prototype Classifier. In Expert Systems with Applications. Volume 39, Issue 12, p. 10564-10570. Elsevier. ISSN 0957-4174. Doi: http://dx.doi.org/10.1016/j.eswa.2012.02.144.
PENHAKER, M.; KREJCAR, O.; KASIK, V.; SNÁŠEL, V. Cloud computing environments for biomedical data services. In Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning, IDEAL’12, p. 336–343. Berlin, Heidelberg, 2012. Springer-Verlag.
BARUQUE, B., Corchado, E., Yin, H. THE S2-ENSEMBLE FUSION ALGORITHM. In International Journal of Neural Systems. Volume 21, Issue 06, p. 505–525. ISSN 0129-0657. Doi: http://dx.doi.org/10.1142/S0129065711003012.
FROLOV, A., HÚSEK, D., POLYAKOV, P.Y.; SNÁŠEL, V. New BFA Method Based on Attractor Neural Network and Likelihood Maximization. Neurocomputing, Elsevier (S)