Intelligent data analysis research

The research team is mainly involved in applied data analysis research, prepares describing, exploring,

predictive and prescriptive statistical analyses in fundamental and applied research tasks. In addition to data mining methods, in their data analyses they use bioinspired methods (genetic algorithms, fuzzy systems, neural networks - including in-depth analyses and self-organizing maps) combined with classical mathematical statistical methods.

Some previous tasks: handwriting recognition, creation of an international handwriting database, expert analysis of ecological databases, examination of the effectiveness of context and semantic analysis in automatic text classification tasks, improvement of optical quality control with MI methods, development of cow rumen probe data analysis algorithms.

Keywords: intelligent data analysis, data mining, statistics, genetic algorithms, fuzzy systems, neural networks, SOM

Participants

  • Nagyné Dr. Éva Hajnal, habilitated associate professor
  • Piglerné Dr. Rozália Lakner, associate professor
  • Dr. Márta Seebauer, Associate Professor (Institute of Engineering)
  • Dr. Péter Udvardy, associate professor
  • Dr. Gergely Vakulya, research associate
  • Gaye Ediboglu Bartos, PhD student, lecturer

References

[1]     G. E. Bartos, Y. Hoşcan, A. Kauer, and É. Hajnal, “A Multilingual Handwritten Character Data: T-H-E Data,” ACTA POLYTECHNICA HUNGARICA, vol. 17, no. 9, p. 141–160, 2020.
[2]     É. Dawn, Marton. Daniel, and Rick. Mátyás, “Industry 4.0 Case Study Big Data Analysis on an Industrial Database,” in AIS 2019: 14th International Symposium on Applied Informatics and Related Areas organized in                the framework of the Hungarian Science Festival 2019 by Óbuda University, 2019, pp. 147–151.
[3]     C. Stenger-Kovács, K. Körmendi, E. Lengyel, A. Abonyi, É. Hajnal, B. Szabó, K. Buczkó, and J. Padisák, “Expanding the trait-based concept of benthic diatoms: Development of trait and species-based indices for                          conductivity as the master variable of ecological status in continental saline lakes,” ECOLOGICAL INDICATORS, vol. 95, no. 1, p. 63–74, 2018.
[4]     L. Gábor, L. József, H. É. Granny, and B.-B. Katalin, “Mathematical modeling of real-time control system for industrial wastewater management,” DESALINATION AND WATER TREATMENT, vol. 75, p. 268-273, 2017.

Tender activity

  • 2020-1.1.2-PIACI-KFI-2020-00109, Development of a cattle rumen probe based on LPWAN communication technology (2021-2024)
  • ARCONIC 2020 Industry 4.0 and virtual reality research development,
  • Establishment of EFOP-3.3.6-17-2017-00002 Science Experience Center in Székesfehérvár (2017-2020)
  • EFOP 3.4.3-16-2016-00023 Complex Institutional Developments of the Óbuda University for the Joint Improvement of the Quality and Accessibility of Higher Education (2017-2020)
  • GINOP-2.2.1-15-2017-00097 Development of a cordless forklift fleet using synergistically interoperable navigation technologies (2018-2021)
  • GINOP-2.2.1-18-2018-00015 Development of the material system and production technology of unique 3D printed plastic automotive parts with fire retardant additives (2018-2021)

Cooperating partners

Arconic Zrt.
AlbaComp Zrt.
Bosch Hungary Zrt.
Hidrofilt Zrt.
Hungarian University of Agricultural and Life Sciences
Department of Limnology, Pannon University