Adatintenzív Mesterséges Intelligencia Módszerek és Rendszerek Kutatólaboratórium

A kutatólaboratórium tagjai:
  • Dr. Fogarassyné dr. Vathy Ágnes, egyetemi docens, laborvezető
  • Starkné dr. Werner Ágnes, egyetemi docens
  • Dr. Leitold Dániel, adjunktus
  • Dulai Tibor, mesteroktató, doktorjelölt
  • Ábrahám Gyula, PhD hallgató
  • Nagy Zsuzsanna, PhD hallgató
  • Szekér Szabolcs, PhD hallgató
Kapcsolódó kiemelt publikációk:
  • Leitold Dániel, Vathy-Fogarassy Ágnes, Abonyi János. Network-Based Analysis of Dynamical Systems. Springer Briefs in Computer Science, p.110. Springer International Publishing (2020)
  • Nagy Zsuzsanna, Werner-Stark Agnes. A Multi-perspective Online Conformance Checking Technique. In: 2020 6th International Conference on Information Management (ICIM), IEEE, 172-176, (2020)
  • Szekér Szabolcs, Vathy-Fogarassy Ágnes. Weighted nearest neighbours-based control group selection method for observational studies. PLOS ONE 15: 7 Paper: e0236531 (2020)
  • Vathy-Fogarassy Ágnes, Szekér Szabolcs, Szolár Balázs, Fogarassy György. The Efficiency of Different Distance Metrics for Keyword-Based Search in Medical Documents: A Short Case Study. Studies in Health Technology and Informatics 271, pp. 232-239, (2020)
  • Abraham Gyula, Auer Peter, Dosa Gyorgy, Dulai Tibor, Werner-Stark Agnes. A Reinforcement Learning Motivated Algorithm for Process Optimization. Periodica Polytechnica-Civil Engineering, 63:4, 961-970, (2019)
  • Fogarassy György, Vathy-Fogarassy Ágnes, Kenessey István, Kásler Miklós, Forster Tamás. Risk prediction model for long-term heart failure incidence after epirubicin chemotherapy for breast cancer – A real-world data-based, nationwide classification analysis. International Journal of Cardiology, vol. 285, 47-52, 6 p. (2019)
  • Daniel Leitold, Agnes Vathy-Fogarassy, Janos Abonyi. Empirical working time distribution-based line balancing with integrated simulated annealing and dynamic programming. Central European Journal of Operations Research 27:2, 455-473, (2019)
  • Miseta Tamas, Vathy-Fogarassy Agnes. The Effect of the Different Data Aggregation Methods and their Detail Levels to the Prediction of Bitcoin's Exchange Rate. In: Levente, Kovács; Carlos, M. Travieso-González (szerk.) Proceedings of IEEE International Work Conference on Bioinspired Intelligence IWOBI 2019, IEEE, pp. 145-152 (2019)
  • Nagy Zsuzsanna, Werner-Stark Agnes, Dulai Tibor. Using Process Mining in Real-Time to Reduce the Number of Faulty Products. In: Kamišalić Latifić, Aida; Podgorelec, Vili; Eder, Johann; Welzer, Tatjana (eds.) Advances in Databases and Information Systems: 23rd European Conference, ADBIS 2019, Springer, 89-104., (2019)
  • Szekér S, Fogarassy G, Machalik K, Vathy-Fogarassy Á. Application of Named Entity Recognition Methods to Extract Information from Echocardiography Reports. Studies in Health Technology and Informatics, vol. 260, 41-48, (2019)
  • Leitold D, Vathy-Fogarassy A, Abonyi J. Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree. Sensors 18:9, Paper: 3096, (2018)
  • Szeker S, Vathy-Fogarassy A. The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching. Studies in Health Technology and Informatics, vol. 248, 1-8, (2018)
  • Krisztina Tóth, Károly Machalik, György Fogarassy, Ágnes Vathy-Fogarassy. Applicability of Process Mining in the Exploration of Healthcare Sequences. In: Szakál, Anikó (szerk.) IEEE 30th Jubilee Neumann Colloquium, 151-155, (2017)
 
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