Journal papers:
1. Győrfi Á, Szilágyi L, Kovács L: A fully automatic procedure for brain tumor segmentation from multi-spectral MRI records using ensemble learning and atlas-based data enhancement. Applied Sciences 11(2):564, pp. 1–24, 2021.
2. Szilágyi L, Lefkovits Sz, Szilágyi SM: Self-tuning possibilistic c-means clustering models. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27(Suppl. 1): 143-159, 2019.
3. Antal M, Egyed-Zsigmond E: Intrusion detection using mouse dynamics, IET Biometrics, 8(5):285-294, 2019, IET Digital Library, IF: 1.836.
4. Lehotsky Á, Szilágyi L, Bánsághi Sz, Szerémy P, Wéber Gy, Haidegger T: Towards objective hand hygiene technique assessment – validation of the UV dye based hand rubbing quality assessment procedure. Journal of Hospital Infection 97(1):26-29, 2017, ISSN 0195-6701, IF: 3.126*
5. Lehotsky Á, Morvai J, Szilágyi L, Bánsághi Sz, Benkó A, Haidegger T: Hand hygiene technique assessment using electronic equipment in 26 Hungarian healthcare institutes (Hungarian). Orvosi Hetilap 158(29):1151-1156, 2017, ISSN 0030-6002, IF: 0.346*
6. Frigy A, Magdás A, Moga VD, Coteţ OG, Kozlovszky M, Szilágyi L: Increase of short-term heart rate variability induced by blood pressure measurements during ambulatory blood pressure monitoring. Computational and Mathematical Methods in Medicine, article ID 5235319, pp. 1–5, 2017, ISSN 1748-6718, IF: 0.937*
7. Antal M, Szabó LZs, Tordai T: Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus. MOBILE INFORMATION SYSTEMS, Volume 2018 (2018), Article ID 3127042, 15 pages
8. Balog A, Loxdale H, Bálint J, Benedek K, Szabó KA, Jánosi-Rancz KT, Domokos E: The arbuscular mycorrhizal fungus Rhizophagus irregularis affects arthropod colonization on sweet pepper in both the field and greenhouse, JOURNAL OF PEST SCIENCE, 2017, Volume 90, Issue 3, pp 935–946
9. Varga V, Jánosi-Rancz KT, Kálmán B: Conceptual Design of Document NoSQL Database with Formal Concept Analysis, ACTA POLYTECHNICA HUNGARICA, Journal of Applied Sciences, Volume 13, Number 2, p. 229-248, 2016
10. Szilágyi L, Szilágyi SM: A modified two-stage Markov clustering algorithm for large and sparse networks. Computer Methods and Programs in Biomedicine 135:15-26, 2016, ISSN 0169-2607, IF: 2.503
11. Lehotsky Á, Szilágyi L, Demeter-Iclănzan A, Haidegger T, Wéber Gy: Education of hand rubbing technique to prospective medical staff, employing UV-based digital imaging technology. Acta Microbiologica et Immunologica Hungarica 63(2):217-228, 2016, ISSN 1217-8950, IF: 0.921
12. Magdás A, Szilágyi L, Incze A: Can ambulatory blood pressure variability contribute to individual cardiovascular risk stratification? Computational and Mathematical Methods in Medicine, article ID 7816830, pp. 1–5, 2016, ISSN 1748-6718, IF: 0.937
13. Szilágyi L, Iclănzan D, Kapás Z, Szabó Zs, Győrfi Á, Lefkovits L: Low and high grade glioma segmentation in multispectral brain MRI data. Acta Universitatis Sapientiae, Informatica 10(1):110-132, 2018.
14. Borsos B, Nagy L, Iclănzan D, Szilágyi L: Automatic detection of hard and soft exudates from retinal fundus images. Acta Universitatis Sapientiae, Informatica 11(1):65–79, 2019.
15. Szilágyi L, Lefkovits L, Iclănzan D: A review on suppressed fuzzy c-means clustering models. Acta Universitatis Sapientiae, Informatica 12(2):302–324, 2020.
16. Szabó IÁ, Fárr A, Kocsis I, Máthé L, Szilágyi L, Frigy A: The Early Repolarization ECG Pattern – An Update. Acta Medica Marisiensis 63(4):165-169, 2017, ISSN 2068-3324
17. Szilágyi SM, Marton Popovici M, Szilágyi L: Automatic segmentation techniques of the coronary artery using CT images in acute coronary syndromes. Journal of Cardiovascular Emergencies 3(1):9-17, 2017, ISSN 2457-5518
18. Lehotsky Á, Haidegger T, Róna P, Szilágyi L, Wéber Gy: Hogyan mossunk kezet 150 évvel Semmelweis után? A Természet Világa 147(4):180-181, 2016
Conference papers:
19. Csaholczi Sz, Kovács L, Szilágyi L: Automatic segmentation of brain tumor parts from MRI data using a random forest classifier. 19th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI 2021, Herl'any, Slovakia), pp. 471–475, 2021.
20. Surányi B, Kovács L, Szilágyi L: Segmentation of brain tissues from infant MRI records using machine learning techniques. 19th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI 2021, Herl'any, Slovakia), pp. 455–460, 2021.
21. Vidámi M, Szilágyi L, Iclănzan D: Real Valued Card Counting Strategies for the Game of Blackjack, International Conference on Neural Information Processing (ICONIP 2020), LNCS vol. 12533, pp. 63-73, 2020.
22. Csaholczi Sz, Iclănzan D, Kovács L, Szilágyi L: Brain tumor segmentation from multi-spectral MR image data using random forest classifier. 27th Int'l Conference on Neural Information Processing (ICONIP 2020, Bangkok), LNCS vol. 12532, pp. 174–184, 2020.
23. Dénes-Fazakas L, Szilágyi L, Tasic J, Kovács L, Eigner Gy: Detection of physical activity using machine learning methods. 20th IEEE Int'l Symposium on Computational Intelligence and Informatics (CINTI 2020, Budapest), pp. 167–172, 2020.
24. Győrfi Á, Csaholczi Sz, Fülöp T, Kovács L, Szilágyi L: Brain tumor segmentation from multi-spectral magnetic resonance image data using an ensemble learning approach. IEEE Int'l Conference on Systems, Man and Cybernetics (SMC 2020, Toronto), pp. 1699–1704, 2020.
25. Fülöp T, Győrfi Á, Csaholczi Sz, Kovács L, Szilágyi L: Brain tumor segmentation from multi-spectral MRI data using cascaded ensemble learning. 15th IEEE Int'l Conference on System of Systems Engineering, (SoSE 2020, Budapest), pp. 531–536, 2020.
26. Győrfi Á, Fülöp T, Kovács L, Szilágyi L: The effect of spectral resolution upon the accuracy of brain tumor segmentation from multi-spectral MRI data. 18th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI 2020, Herl'any, Slovakia), pp. 325–328, 2020.
27. Fülöp T, Győrfi Á, Surányi B, Kovács L, Szilágyi L: Brain tumor segmentation from MRI data using ensemble learning and multi-atlas. 18th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI 2020, Herl'any, Slovakia), pp. 111-116, 2020. Baltazár Frankovic Prize awarded to T. Fülöp.
28. Iclănzan D, Szilágyi L: Learning to generate ambiguous sequences. International Conference on Neural Information Processing (ICONIP 2019), LNCS vol. 11953, pp. 110-121, 2019.
29. Jánosi-Rancz KT, Kátai Z, Iclănzan D: Linking Formal and Informal Structures based on Members' E-mail Communication Patterns. International Conference on Neural Information Processing (ICONIP 2019), Australian Journal of Intelligent Informtion Processing Systems 17(1):9-16, 2019.
30. Antal M, Dénes-Fazakas L: User verification based on mouse dynamics: a comparison of public data sets. 13th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI 2019), pp. 143-147, 2019
31. Győrfi Á, Karetka-Mezei Z, Iclănzan D, Kovács L, Szilágyi L: A study on histogram normalization for brain tumour segmentation from multispectral MR image data. 24th Ibero-American Congress on Pattern Recognition (CIARP 2019, Havana), LNCS, vol. 11896, pp. 375–384, 2019.
32. Győrfi Á, Kovács L, Szilágyi L: Brain tumour segmentation from multispectral MR image data using ensemble learning methods. 24th Ibero-American Congress on Pattern Recognition (CIARP 2019, Havana), LNCS, vol. 11896, pp. 326–335, 2019.
33. Győrfi Á, Kovács L, Szilágyi L: Brain tumor detection and segmentation from magnetic resonance image data using ensemble learning methods. IEEE Int'l Conference on Systems, Man and Cybernetics (SMC 2019, Bari), pp. 909–914, 2019
34. Győrfi Á, Kovács L, Szilágyi L: A feature ranking and selection algorithm for brain tumor segmentation in multi-spectral magnetic resonance image data. 41st Annual Int'l Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019, Berlin), pp. 804–807, 2019
35. Lefkovits Sz, Lefkovits L: Combining Subspace Methods and CNN Segmentation for Iris Identification. 17th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI 2019, Herl'any, Slovakia), pp. 305–310, 2019.
36. Lefkovits Sz, Lefkovits L, Szilágyi L: Applications of different CNN architectures for palm vein identification. Modeling Decisions for Artificial Intelligence (MDAI 2019, Milano, Italy), Lecture Notes in Computer Science vol. 11676, pp. 295-306, 2019, ISBN 978-3-030-26772-8
37. Lefkovits Sz, Lefkovits L, Szilágyi L: CNN architectures for dorsal hand vein identification. 27th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2019, Plzen, Czechia), 10 pp, 2019, http://wscg.zcu.cz/wscg2019/Short/C59-full.PDF
38. Lefkovits Sz, Szilágyi L, Lefkovits L: Brain tumor segmentation and survival prediction using a cascade of random forests. BrainLesMICCAI 2018, Lecture Notes in Computer Science vol. 11384, pp. 334-345 (2019), ISBN 978-3-030-11725-2
39. Szabó Zs, Kapás Z, Győrfi Á, Lefkovits L, Szilágyi SM, Szilágyi L: Automatic segmentation of low-grade brain tumor using a random forest classier and Gabor features. 14th Int'l Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2018, Huangshan, China), pp. 1106–1113, 2018.
40. Szilágyi L, Lefkovits Sz, Kucsván ZsL: A self-tuning possibilistic c-means clustering algorithm. Modeling Decisions for Artificial Intelligence (MDAI 2018, Palma de Mallorca, Spain), Lecture Notes in Computer Science vol. 11144, pp. 255-266, 2018.
41. Lefkovits Sz, Emerich S, Szilágyi L: Biometric system based on registration of dorsal hand vein configurations. 8th Pacic-Rim Symposium on Image and Video Technology (PSIVT 2017, Wuhan), LNCS, vol. 10799, pp. 17–29, 2018.
42. Kapás Z, Lefkovits L, Iclănzan D, Győrfi Á, Iantovics BL, Lefkovits Sz, Szilágyi SM, Szilágyi L: Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a Random Forest Approach. 8th Pacic-Rim Symposium on Image and Video Technology (PSIVT 2017, Wuhan), LNCS, vol. 10749, pp. 137–149 (2018)
43. Antal M, Bandi A: Finger or Stylus: Their Impact on the Performance of On-line Signature Verification Systems, 6th Int. Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, MACRo, October 27-28, Tirgu Mures, Romania, pp. 11-22, 2017
44. Lefkovits L, Lefkovits Sz, Szilágyi L: Brain tumor segmentation with optimized random forest. BrainLes MICCAI, LNCS vol. 10154, pp. 88-99 (2016), ISBN 978-3-319-55523-2
45. Kapás Z, Lefkovits L, Szilágyi L: Automatic detection and segmentation of brain tumor using random forest approach. In: Torra V, Narukawa Y, Navarro-Arribas G, Yañez C (eds.): Modeling Decisions for Artificial Intelligence, Springer International Publishing Switzerland, LNCS vol. 9880, pp. 301-312 (2016), ISBN 978-3-319-45655-3
46. Szilágyi L, Dénesi G, Enăchescu C: Fast color quantization via fuzzy clustering. In: Hirose A, Ozawa S, Doya K, Ikeda K, Lee MH, Liu DR (eds.): 23rd International Conference on Neural Information Processing, Springer International Publishing Switzerland, LNCS vol. 9950, pp. 95-103 (2016), ISBN 978-3-319-46680-4
47. Szilágyi L, Szilágyi SM, Enăchescu C: A study on cluster size sensitivity of fuzzy c-means algorithm variants. In: Hirose A, Ozawa S, Doya K, Ikeda K, Lee MH, Liu DR (eds.): 23rd International Conference on Neural Information Processing, Springer International Publishing Switzerland, LNCS vol. 9948, pp. 470-478 (2016), ISBN 978-3-319-46671-2
48. Lefkovits L, Lefkovits Sz, Szilágyi L: Brain Tumor Segmentation with Optimized Random Forest. BrainLesMICCAI 2016, Lecture Notes in Computer Science vol. 10154, pp. 88-99 (2016), ISBN 978-3-319-55523-2
49. Kiss T, Jánosi-Rancz KT: Developing railway interlocking systems with session types and Event-B, 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics, 2016, p. 93-98
50. Antal M, Nemes L: The MOBIKEY Keystroke Dynamics Password Database: Benchmark Results. Computer Science Online Conference, CSOC 2016. ISBN: 978-3-319-33620-6, In book: Software Engineering Perspectives and Application in Intelligent Systems, Springer, pp. 35-46.
51. Antal M, Nemes Gy: Gender Recognition from Mobile Biometric Data. 11th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI 2016), pp. 243-248.
Találmányok, prototípusok, termékek, demók
1. Haidegger T, Lehotsky Á, Nagy M, Szilágyi L: Method and apparatus for hand disinfection control quality. US Patent 9,424,735 B2, 23 August 2016.
