Computational Intelligence Research Group (CIRG)

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Sapientia.ro Sapientia.ro
Neptun  Moodle  ISSUU  Könyvtár  Könyvtár közös keresője 
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Computational Intelligence Research Group (CIRG)

Main topics include: clustering and semi-supervised clustering algorithms, evolutionary algorithms, digital signal and image processing, speech technology: continuous speech recognition, speech synthesis and language resources development, big data applications, modeling and identification and control of physiological systems, protein interaction network analysis (PINA), virtualization techniques.  

 

Group members

  • László SZILÁGYI – group leader
  • Senior researchers: Margit ANTAL, Attila BIRÓ, Lehel DÉNES-FAZAKAS, József DOMOKOS, Ágnes GYŐRFI, David Andrei ICLĂNZAN, László LEFKOVITS, Katalin Tünde JÁNOSI-RANCZ
  • PhD students: Szabolcs CSAHOLCZI, Mirtill-Boglárka NAGHI, Katalin KOCSIS (FERENCZ), Ioan Marius Pisak-Lukáts, József Palatka, Róbert ROMÁN, Gerlinda-Boglárka KIS, Attila KOVÁCS
  • Technical staff: Konrád József KISS
  • Students since 2010: Tímea Fülöp, Zsolt-Levente Kucsván, Zoltán Kapás, Bálint Borsos, Zsófia Szabó, Adél Mészáros, Andrea-Melinda Kőble, Mózes Vidámi, Lajos Lóránd Nagy, Örs Darabont, Gellért Dénesi, Emese Fábián, Zsuzsa Réka Varga, Lehel Medvés, Lehel Crăciun, Lehel Szabó

Partner institutions

  • BME Budapest (Hungary), Dept. of Control Engineering and Information Technology, Balázs Benyó, http://iit.bme.hu 
  • Canterbury University of Christchurch (New Zealand), Geoffrey Chase
  • Queens University of Kingston (Canada), Gábor Fichtinger
  • University of Málaga, Antonio Cuesta-Vargas
  • Óbuda University, Budapest (Hungary), Levente Kovács, György Eigner
  • University of Lyon, France, Előd Egyed-Zsigmond
  • Mircea Giurgiu, Speech Processing Group, Technical University of Cluj-Napoca, http://speech.utcluj.ro  
  • UMFST Tg. Mureş, Sándor M. Szilágyi

Proiecte de cercetare finanțate

  • KPI grant: Multi-atlas based segmentation of medical images for diagnostics and therapy planning (2019/05-2021/05, 10000 EUR)
  • KPI grant: Detection and segmentation of tubular shapes and structures in low-resolution volumetric image data (2017/03–2018/08, 20000 RON)  
  • KPI grant: MRI brain tumor segmentation applying machine learning algorithms (2017/03–2018/08, 20000 RON)  
  • János Bolyai Research Fellowship (László Szilágyi), 2018/09-2021/08, 4.5m HUF
  • Collegium Talentum Fellowship (Tímea Fülöp), 2019/09-2020/07, 1.5m HUF
  • Collegium Talentum Fellowship (Mirtill-Boglárka Naghi), 2023/09-2026/07, 3x1.5m HUF
  • Collegium Talentum Fellowship (Robert Roman), 2025/09-2026/07, 1.5m HUF
  • Székely Előfutár Fellowship (Zoltán Kapás, 2016-2017, 1000 USD), (Bálint Borsos, 2018-2019, 1000 USD)
  • Accenture Student Research Fellowship (Zsófia Szabó, 2018, 4000 RON), (Bálint Borsos, 2018, 4000 RON) 

Organized conferences

  • INES 2020. 2021, 2022, 2023, 2024, 2025, INES 2026 ¬– Program Committee Co-chair: László Szilágyi
  • ICONIP 2019 – Program Committee members: László Szilágyi, David Iclănzan
  • PSIVT 2017 – Program Committee member: László Szilágyi
  • MDAI 2012, 2013, 2014, … 2023, 2024, 2025, 2026 – Program Committee member: László Szilágyi

Conference participations

  • ICONIP 2016 – International Conference on Neural Information Processing – Kyoto (Japan)
  • ICONIP 2017 – International Conference on Neural Information Processing – Guangzhou (China)
  • ICONIP 2018 – International Conference on Neural Information Processing – Siem Reap (Cambodia)
  • ICONIP 2019 – International Conference on Neural Information Processing – Sydney (Australia)
  • ICONIP 2020 – International Conference on Neural Information Processing – Bangkok (Thailand) – online
  • IEEE SMC 2019 – IEEE International Conference on Systems, Man, and Cybernetics – Bari (Italy)
  • IEEE SMC 2020 – IEEE International Conference on Systems, Man, and Cybernetics – Toronto (Canada) – online
  • IEEE SMC 2023 – IEEE International Conference on Systems, Man, and Cybernetics – Honolulu (USA)
  • IEEE SMC 2024 – IEEE International Conference on Systems, Man, and Cybernetics – Kuching (Malaysia)
  • MDAI 2016 – Modeling Decision for Artificial Intelligence – Sant Julia de Loria (Andorra)
  • MDAI 2018 – Modeling Decision for Artificial Intelligence – Palma de Mallorca (Spain)
  • MDAI 2019 – Modeling Decision for Artificial Intelligence – Milan (Italy)
  • PSIVT 2017 – Pacific-Rim Symposium on Image and Video Technology – Wuhan (China)
  • CIARP 2017 – Ibero-American Congress on Pattern Recognition – Valparaíso (Chile)
  • CIARP 2019 – Ibero-American Congress on Pattern Recognition – La Habana (Cuba)
  • CIARP 2024 – Ibero-American Congress on Pattern Recognition – Talca (Chile)
  • MICCAI 2017 – Medical Image Computation and Computer Assisted Interventions – Athens (Greece)
  • FSKD 2018 – International Conference on Fuzzy Systems and Knowledge Discovery – Huangshan (China)
  • EMBC 2019 – Annual International Conference of the IEEE EMBS – Berlin (Germany)
  • IFAC World Congress 2023 – Yokohama (Japan)
  • IFAC World Congress 2026 – Busan (S. Korea)

 

Publications

Peer-reviewed journal articles:

  1. Biró A, Kovács L, Szilágyi L: Bioinformatics-Inspired IMU Stride Sequence Modeling for Fatigue Detection Using Spectral–Entropy Features and Hybrid AI in Performance Sports. SENSORS 26(2):525, 2026
  2. Dénes-Fazakas L, Mateas IC, Berciu AG, Szilágyi L, Kovács L, Dulf EH: A Real Time Multi Modal Computer Vision Framework for Automated Autism Spectrum Disorder Screening. ELECTRONICS 15 (6), 1287, 2026
  3. Kis GB, Kovács L, Szilágyi L: Automated Retinal Vessel Segmentation Using U-Net Deep Learning Model. ACTA POLYTECHNICA HUNGARICA 22 (12):127-142, 2026
  4. Dénes -Fazakas L, Kovács L, Eigner Gy, Szilágyi L: Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach. SENSORS 25(5):1531, 2025
  5. Dénes -Fazakas L, Kovács L, Eigner Gy, Szilágyi L: Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification. BIOMEDICINES 12(10):2388, 2024
  6. Dénes-Fazakas L, Szilágyi L, Kovács L, Di Gaetano A, Eigner Gy: Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes. BIOMEDICINES 12(9):2143, 2024
  7. Dénes-Fazakas L, Simon B, Hartveg Á, Szilágyi L, Kovács L, Mosavi A, Eigner Gy: Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor. COMPUTERS IN BIOLOGY AND MEDICINE 182:109167, 2024
  8. Biró A, Cuesta-Vargas AI, Szilágyi L: AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets. SENSORS 24(1):132, 2024
  9. Dénes-Fazakas L, Simon B, Hartveg Á, Kovács L, Dulf ÉH, Szilágyi L, Eigner Gy: Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks. SENSORS 24(8):2412, 2024
  10. Simon B, Hartveg Á, Dénes-Fazakas L, Eigner Gy, Szilágyi L: Advancing Medical Assistance: Developing an Effective Hungarian-Language Medical Chatbot with Artificial Intelligence. INFORMATION 15(6):297, 2024
  11. Szilágyi L, Kovács L: Special Issue: Artificial Intelligence Technology in Medical Image Analysis. APPLIED SCIENCES 14(5):2180, 2024
  12. Biró A, Szilágyi SM, Szilágyi L: Optimal Training Dataset Preparation for AI-Supported Multilanguage Real-Time OCRs Using Visual Methods. APPLIED SCIENCES 13(24):13107, 2023.
  13. Bíró A, Szilágyi SM, Szilágyi L, Cuesta-Vargas AI, Martín-Martín J: Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer. SENSORS 23(7):3595, 2023
  14. Bíró A, Cuesta-Vargas AI, Martín-Martín J, Szilágyi L, Szilágyi SM: Synthetized Multilanguage OCR Using CRNN and SVTR Models for Realtime Collaborative Tools. APPLIED SCIENCES 13(7):4419, 2023
  15. Szepesi P, Szilágyi L: Detection of pneumonia using convolutional neural networks and deep learning. BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 42:1012-1022, 2022
  16. Dénes-Fazakas L, Siket M, Szilágyi L, Kovács L, Eigner Gy: Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals. SENSORS 22(21):8568, 2022
  17. Kopcsóné Németh IA, Nádor Cs, Szilágyi L, Lehotsky Á, Haidegger T: Establishing a Learning Model for Correct Hand Hygiene Technique in a NICU. JOURNAL OF CLINICAL MEDICINE 11:4276, 2022
  18. Bíró A, Jánosi-Rancz KT, Szilágyi L, Cuesta-Vargas AI, Martín-Martín J, Szilágyi SM: Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools. APPLIED SCIENCES 12:5977, 2022
  19. Lefkovits Sz, Lefkovits L, Szilágyi L: HGG and LGG Brain Tumor Segmentation in Multi-modal MRI Using Pretrained Convolutional Neural Networks of Amazon Sagemaker, Applied Sciences 12(7):3620, 2022
  20. 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, 2021
  21. 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.
  22. Antal M, Egyed-Zsigmond E: Intrusion detection using mouse dynamics, IET Biometrics, 8(5):285-294, 2019, IET Digital Library, IF: 1.836.
  23. 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
  24. 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: 3.354
  25. 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: 1.545
  26. 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
  27. 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
  28. 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
  29. 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
  30. Lehotsky Á, Szilágyi L, Ferenci T, Kovács L, Pethes R, Wéber Gy, Haidegger T: Quantitative impact of direct, personal feedback on hand hygiene technique. Journal of Hospital Infection 91(1):81–84, 2015, ISSN 0195-6701, IF: 2.655
  31. Gosztolya G, Szilágyi L: Application of fuzzy and possibilistic c-means clustering models in blind speaker clustering. Acta Polytechnica Hungarica 12(7):41-56 (2015), ISSN 1785-8860, IF: 0.544
  32. Szilágyi L, Szilágyi SM: Generalization rules for the suppressed fuzzy c-means algorithm. Neurocomputing 139:298–309, 2014, ISSN 0925-2312, IF: 2.083
  33. Szilágyi SM, Szilágyi L: A fast hierarchical clustering algorithm for large-scale protein sequence data sets. Computers in Biology and Medicine 48:94–101, 2014, ISSN 0010-4825, IF: 1.240
  34. Szilágyi L: Lessons to learn from a mistaken optimization. Pattern Recognition Letters 36(1):29–35, 2014, ISSN 0167-6855, IF: 1.551
  35. Magdás A, Szilágyi L, Belényi B, Incze A: Ambulatory monitoring derived blood pressure variability and cardiovascular risk factors in elderly hypertensive patients. Bio-Medical Materials and Engineering 24(6):2563–2569, 2014, ISSN 0959-2989, IF: 1.091
  36. Szilágyi L, Haidegger T, Lehotsky Á, Nagy M, Csonka EA, Sun XY, Ooi KL, Fisher D: A large-scale assessment of hand hygiene quality and the effectiveness of the “WHO 6-steps”. BMC Infectious Diseases 13(249):1-10, 2013, ISSN 1471-2334, IF: 2.561
  37. Szilágyi L: Robust spherical shell clustering using fuzzy-possibilistic product partition. International Journal of Intelligent Systems 28(6):524-539, 2013, ISSN 1098-111X, IF: 1.411
  38. Szilágyi L, Szilágyi SM, Benyó B: Efficient inhomogeneity compensation using fuzzy c-means clustering models. Computer Methods and Programs in Biomedicine 108(1):80-89, 2012, ISSN 0169-2607, IF: 1.555
  39. Szilágyi SM, Szilágyi L, Benyó Z: A patient specific electro-mechanical model of the heart. Computer Methods and Programs in Biomedicine 101(2):183-200, 2011, ISSN 0169-2607, IF: 1.516
  40. Szilágyi L, Szilágyi SM, Benyó B, Benyó Z: Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models. Biomedical Signal Processing and Control 6(1):3-12, 2011, ISSN 1746-8094, IF: 1.000
  41. Szilágyi L, Medvés L, Szilágyi SM: A modified Markov clustering approach to unsupervised classification of protein sequences. Neurocomputing 73(13-15):2332-2345, 2010, ISSN 0925-2312, IF: 1.429
  42. Szilágyi L, Szilágyi SM, Benyó Z: Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models. Soft Computing 14(5):495-505, 2010, ISSN 1432-7643, IF: 1.512
  43. Naghi MB, Kovács L, Szilágyi L: A generalized fuzzy-possibilistic c-means clustering algorithm. Acta Universitatis Sapientiae – Informatica 15(2):404–431, 2023.
  44. Biró A, Cuesta-Vargas AI, Szilágyi L: Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis. Acta Universitatis Sapientiae – Informatica 15(2):359–403, 2023.
  45. Palatka J, Kovács L, Szilágyi L: Enhanced imagistic methodologies augmenting radiological image processing in interstitial lung diseases. Acta Universitatis Sapientiae, Informatica 15(1):146–139, 2023
  46. Pisak-Lukáts IM, Kovács L, Szilágyi L: A feature selection strategy using Markov clustering, for the optimization of brain tumor segmentation from MRI data. Acta Universitatis Sapientiae, Informatica 14(2):316–337, 2022
  47. Győrfi Á, Kovács L, Szilágyi L: A two-stage U-net approach to brain tumor segmentation from multi-spectral MRI records. Acta Universitatis Sapientiae, Informatica 14(2):223–247, 2022
  48. Szilágyi L, Lefkovits L, Iclănzan D: A review on suppressed fuzzy c-means clustering models. Acta Universitatis Sapientiae, Informatica 12(2):302–324, 2020, ISSN 2066-7760
  49. 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, ISSN 2066-7760
  50. 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 10(1):110-132, 2018, ISSN 2066-7760

 

Conference papers

  1. Biró A, Kovács L, Szilágyi L: A Bioinformatics-Driven, Biostatistical Pathway Framework with Multimodal Machine Learning for Athlete Burnout Prediction on Physiological Signals. ICCC 2026 Brisbane/Uluru, pp. N/A
  2. Biró A, Kovács L, Szilágyi L: Bioinformatics-Guided NSGA-II Pathway-Sparse Optimization for Interpretable Neurodegenerative Risk Detection. ICCC 2026 Brisbane/Uluru, pp. N/A
  3. Biró A, Kovács L, Szilágyi L: Risk-Aware Genetic Algorithms for Reliable Triathlon Relays: A Cantelli-Regularized Applied Mathematics Pipeline. ICCC 2026 Brisbane/Uluru, pp. N/A
  4. Roman R, Kovács L, Szilágyi L: Automatic Detection of Bacterial and Viral Pneumonia from X-Ray Scans. ICCC 2026 Brisbane/Uluru, pp. N/A
  5. Simon B, Borkó B, Borkó B, Héger A, Hartveg A, Szász L, Dénes-Fazakas L, Eigner Gy, Kovács L, Szilágyi L: Sleep Efficiency Prediction with Machine Learning and Deep Neural Networks. SAMI 2026 Stará Lesná (SK), pp. 579-584
  6. Biró A, Kovács L, Szilágyi L: Bioinformatics-Inspired Robust Smith-Waterman-Guided Lineup Optimization in Basketball Using HMM Surrogates, PWMs, and Cantelli-Bounded Genetic Algorithms. SAMI 2026 Stará Lesná (SK), pp. 565-572.
  7. Biró A, Kovács L, Szilágyi L: Adapting Hidden Markov Models and Local Alignment Algorithms for Real-Time Lineup Change Decisioning in Ice Hockey. SAMI 2026 Stará Lesná (SK), pp. 379-384
  8. Biró A, Kovács L, Szilágyi L: Bayesian Biostatistical and Physiological Modeling for Optimal Triathlon Relay Team Assembly from Historical Results. SAMI 2026 Stará Lesná  (SK), pp. 707-714
  9. Naghi MB, Derzsi D, Kovács L, Szilágyi L: Automatic Hyperparameter Optimization of Self-Tuning Possibilistic Fuzzy C-Means Using Genetic Algorithms. CINTI 2025 Budapest, pp. 799-804.
  10. Biró A, Kovács L, Szilágyi L: Tactical Lineup Changes Simulation in Competitive High-Speed Team Sports via Hoeffding’s Inequality and Concentration Bounds. CINTI 2025 Budapest, pp. 713-720.
  11. Biró A, Kovács L, Szilágyi L: Disrupting the Critical 10 Seconds: An Optimization Framework for Lineup Changes and Strategy Shifts in Ice Hockey. CINTI 2025 Budapest, pp. 805-812.
  12. Biró A, Kovács L, Szilágyi L: Game-Based Learning and Gamified Efficiency for Team Performance Estimation in Professional Basketball. ICETA 2025 Stará Lesná (SK), pp. 95-100.
  13. Lam LD, Szilágyi L, Dung NV, Chuyen MT: Sparse Deep Neural Networks for Pubic Symphysis-Fetal Head Segmentation. ICHST 2025 DaNang (Vietnam), 1-6,
  14. Simon B, Hartveg Á, Dénes-Fazakas L, Eigner Gy, Szilágyi L: Medical Assistant Chatbot on Microcontroller. INES 2025 Palermo, pp. 125–130.
  15. Simon B, Hartveg Á, Szász L, Dénes-Fazakas L, Szilágyi L, Eigner Gy: Enhancing Diabetes Management Through LSTM Analysis of Physical Activity Effects. INES 2025 Palermo, pp. 115–120.
  16. Palatka J, Dénes-Fazakas L, Kovács L, Szilágyi L: Advancing Interstitial Lung Disease Diagnosis: a CNN Approach for High-Resolution Computed Tomography Image Classification. INES 2025 Palermo, pp. 15–20.
  17. Roman R, Kovács L, Szilágyi L: Detection of Pneumonia from X-Ray Scans Using Deep Learning Algorithms and Computer Vision. INES 2025 Palermo, pp. 137–142.
  18. Biró A, Szilágyi L: An Enhanced AI Pipeline for the Detection and Preliminary Diagnosis of Pneumonia and Pulmonary Malformations in Athletes with YOLOv11. INES 2025 Palermo, pp. 231–236.
  19. Csaholczi Sz, Kovács L, Szilágyi L: Brain Tumor Segmentation from Multi-Spectral MRI Records: Classical Machine Learning or Convolutional Neural Networks? ICCC 2025 Seychelles, pp. 97–102.
  20. Biró A, Szilágyi L: Gamified AI-Driven Video Monitoring for Enhanced Rehabilitation in Performance Sports. ICCC 2025 Seychelles, pp. 47–52.
  21. Dénes-Fazakas L, Simon B, Hartveg Á, Csaholczi Sz, Eigner Gy, Kovács L, Szilágyi L: Exploring Vision Transformer Architectures for Brain Tumor Classification: A Comprehensive Study. SACI 2025 Timisoara-Budapest, pp. 343–348.
  22. Naghi MB, Kovács L, Szilágyi L: A Parameter Selection Strategy for the Generalized Fuzzy-Possibilistic C-Means Algorithm. SAMI 2025 Stará Lesná (SK), pp. 533–538.
  23. Biró A, Cuesta-Vargas AI, Szilágyi L: Enhanced Spatial-Temporal Analysis for EEG-Based Microsleep Detection: Integrating Kalman Filtering with Voronoi Tessellation and Adaptive Coverage Control. IEEE SMC 2024 Kuching (Malaysia), pp. 4855–4860.
  24. Naghi MB, Kreinovich V, Kovács L, Szilágyi L: A Self-Tuning Version for the Fuzzy-Possibilistic Product Partition c-Means Algorithm. IEEE SMC 2024 Kuching (Malaysia), pp. 4861–4867.
  25. Csaholczi Sz, Györfi Á, Kovács L, Szilágyi L: Segmentation of Brain Tumor Parts from Multi-spectral MRI Records Using Deep Learning and U-Net Architecture. CIARP 2024 Talca (Chile), LNCS vol. 15369, pp. 191–204.
  26. Dénes-Fazakas L, Sándor-Rokaly K, Szász L, Csuzi H, Kovács L, Szilágyi L, Eigner Gy: Exploring the Integration of Differential Equations in Neural Networks: Theoretical Foundations, Applications, and Future Directions. CINTI 2024 Budapest, pp. 221–226.
  27. Dénes-Fazakas L, Szilágyi L, Eigner Gy, Kosheleva O, Kreinovich V, Phuong NH: Why Bump Reward Function Works Well in Training Insulin Delivery Systems. AICI 2024 Hanoi. Studies in Systems, Decision and Control (SSDC), 543:7–13, 2024.
  28. Biró A, Jánosi-Rancz TK, Szilágyi L: Real-time Artificial Intelligence Text Analysis for Identifying Burnout Syndromes in High-Performance Athletes. SAMI 2024 Stará Lesná (SK), pp. 253–258.
  29. Biró A, Cuesta-Vargas AI, Szilágyi L: AI-controlled training method for performance hardening or injury recovery in sports. SAMI 2024 Stará Lesná (SK), pp. 259–264.
  30. Román R, Dávid L, Szilágyi L: Development of A Novel Solar Photovoltaic Energy Converter To Increase Off-Grid Solar Powerplant Energy Efficiency, Decrease Energy Storage Costs And Increase Monetary Return On Investment. SAMI 2024 Stará Lesná (SK), pp. 303–308.
  31. Simon B, Hartveg Á, Siket M, Dénes-Fazakas L, Eigner Gy, Kovács L, Szilágyi L: Data Collection Studies for the Better Understanding of Factors in Type 1 Diabetes Management. ICCC 2024 Hanoi, pp. 375–380.
  32. Csaholczi Sz, Kovács L, Szilágyi L: Brain Tumor Classification Using Convolutional Neural Networks and Deep Learning. ICCC 2024 Hanoi, pp. 399–404.
  33. Simon B, Hartveg Á, Siket M, Dénes-Fazakas L, Eigner Gy, Kovács L, Szilágyi L: Translating Hungarian language dialects using natural language processing models. SACI 2024 Timisoara, pp. 375–380.
  34. Nagy M, Simon B, Szász L, Siket M, Dénes-Fazakas L, Eigner Gy, Süli PP, Kovács L, Szilágyi L: Web application development for diabetes patients. SACI 2024 Timisoara, pp. 573–579.
  35. Potyok C, Simon B, Hartveg Á, Siket M, Dénes-Fazakas L, Eigner Gy, Balázs M, Kovács L, Szilágyi L: Mobile application development for diabetes patient. SACI 2024 Timisoara, pp. 559–564.
  36. Dénes-Fazakas L, Dénes-Fazakas G, Eigner Gy, Kovács L, Szilágyi L: Review of Reinforcement Learning-Based Control Algorithms in Artificial Pancreas Systems for Diabetes Mellitus Management. SACI 2024 Timisoara, pp. 565–571.
  37. Dénes-Fazakas L, Csaholczi Sz, Eigner Gy, Kovács L, Szilágyi L: Using Resizing Layer in U-Net to Improve Memory Efficiency. Lecture Notes in Networks and Systems (LNNS), 1026:38–48, 2024.
  38. Biró A, Martín-Martín J, Szilágyi L: Applied AI for real-time detection of lesions and tumors following severe head injuries, SISY 2023 Pula (HR), pp. 653–658.
  39. Dénes-Fazakas L, Kovács L, Eigner Gy, Szilágyi L: A Modified VGG Architecture for Brain Tumor Classification (a.k.a. Brain Tumor Segmentation from Multi-Spectral MRI Records Using a U-net Cascade Architecture). IEEE SMC 2023 Honolulu, pp. 3003–3008.
  40. Györfi Á, Kovács L, Szilágyi L: Brain Tumor Segmentation from Multi-Spectral MRI Records Using a U-Net Cascade Architecture. IEEE SMC 2023 Honolulu, pp. 1327–1332.
  41. Dénes-Fazakas L, Siket M, Szilágyi L, Eigner Gy, Kovács L: Effect of Hyperparameters of Reinforcement Learning in Blood Glucose Control. IEEE SMC 2023 Honolulu, pp. 1333–1340.
  42. Biró A, Martín-Martín J, Szilágyi L: SRPE and ACWR to Control Fatigue Levels and Minimize Injuries in Performance Sports. IEEE SMC 2023 Honolulu, pp. 2808–2813.
  43. Simon B, Hartveg Á, Dénes-Fazakas L, Eigner Gy, Szilágyi L: Translating Hungarian language dialects using natural language processing models. CINTI 2023 Budapest, pp. 309–315.
  44. Dénes-Fazakas L, Szilágyi L, Eigner Gy, Kosheleva O, Ceberio M, Kreinovich V: Which activation function works best for training artificial pancreas: empirical fact and its theoretical explanation. SSCI 2023 Ciudad de Mexico, pp. 496–500.
  45. Szilágyi L, Györfi Á, Dénes-Fazakas L, Csaholczi Sz, Pisak-Lukáts IM, Kovács L: Challenges and Difficulties of Multi-Spectral MRI Based Brain Tumor Detection and Segmentation. ICHST 2023, Hanoi, paper no. 16, pp. 1–6.
  46. Naghi MB, Kovács L, Szilágyi L: A self-tuning version for the possibilistic fuzzy c-means clustering algorithm. FUZZIEEE 2023, Incheon (S. Korea), paper no. 123, pp. 1-6.
  47. Dénes-Fazakas L, Eigner Gy, Kovács L, Szilágyi L: Two U-net Architectures for Infant Brain Tissue Segmentation from Multi-Spectral MRI Data. 22nd IFAC World Congress (IFAC WC 2023, Yokohama JP), paper no. 1892, 6pp.
  48. Dénes-Fazakas L, Siket M, Szilágyi L, Kovács L, Eigner Gy: Investigation of reward functions for controlling blood glucose level using reinforcement learning. SACI 2023 Timișoara, pp. 387-392.
  49. Naghi MB, Kovács L, Szilágyi L: A review on advanced c-means clustering models based on fuzzy logic. SAMI 2023, Herl’any (SK), pp. 293-298.
  50. Dénes-Fazakas L, Siket M, Kertész G, Szilágyi L, Kovács L, Eigner Gy: Control of Type 1 Diabetes Mellitus using direct reinforcement learning based controller. IEEE SMC 2022 Prague, pp. 1512-1517.
  51. Győrfi Á, Csaholczi Sz, Pisak-Lukáts IM, Dénes-Fazakas L, Kőble A, Shvets O, Eigner Gy, Kovács L, Szilágyi L: Effect of spectral resolution on the segmentation quality of magnetic resonance imaging data. INES 2022, Chania (GR), pp. 53-58.
  52. Dénes-Fazakas L, Eigner Gy, Szilágyi L: Segmentation of 6-month infant brain tissues from multi-spectral MRI records using a U-Net neural network architecture. ICCC 2022, Reykjavík, pp. 77-82.
  53. Pisak-Lukáts IM, Szilágyi L: Markov clustering based feature selection for brain tumor segmentation from multi-spectral MRI records. SAMI 2022 Poprad, (SK), 6p, 2022.
  54. Kőble A, Győrfi Á, Csaholczi Sz, Surányi B, Dénes-Fazakas L, Kovács L, Szilágyi L: Identifying the most suitable histogram normalization technique for machine learning based segmentation of multispectral brain MRI data. AFRICON 2021 Online, pp. 71-76, 2021.
  55. Iclănzan D, Lung RI, Kucsván ZsL, Surányi B, Kovács L, Szilágyi L: The role of atlases and multi-atlases in brain tissue segmentation based on multispectral magnetic resonance image data. AFRICON 2021 Online, pp. 55-60, 2021.
  56. Csaholczi Sz, Kovács L, Szilágyi L: Automatic segmentation of brain tumor parts from MRI data using a random forest classifier. SAMI 2021 Herl'any, (SK), pp. 471–475.
  57. Surányi B, Kovács L, Szilágyi L: Segmentation of brain tissues from infant MRI records using machine learning techniques. SAMI 2021 Herl'any, (SK), pp. 455–460, 2021.
  58. Csaholczi Sz, Iclanzan D, Kovács L, Szilágyi L: Brain tumor segmentation from multi-spectral MR image data using random forest classifier. ICONIP 2020 Bangkok online, Lecture Notes in Computer Science vol. 12532, pp. 174-184, 2020.
  59. Vidámi M, Szilágyi L, Iclanzan D: Real valued card counting strategies for the game of Blackjack. ICONIP 2020 Bangkok online. Lecture Notes in Computer Science vol. 12533, pp. 63-73, 2020.
  60. Dénes-Fazakas L, Szilágyi L, Tasic J, Kovács L, Eigner Gy: Detection of physical activity using machine learning methods. CINTI 2020 Budapest, pp. 167–172, 2020.
  61. 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 SMC 2020 Toronto, online), pp. 1699-1704.
  62. Fülöp T, Győrfi Á, Surányi B, Kovács L, Szilágyi L: Brain tumor segmentation from MRI data using ensemble learning and multi-atlas. SAMI 2020, Herl'any (SK), pp. 111-116, 2020. The paper received the Baltazár Frankovic Award.
  63. 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. SAMI 2020, Herl'any (SK), pp. 325-328, 2020.
  64. Iclănzan D, Szilágyi L: Learning to generate ambiguous sequences. ICONIP 2019 Sydney. Lecture Notes in Computer Science vol. 11953, pp. 110-121, 2019.
  65. Győrfi Á, Kovács L, Szilágyi L: Brain tumor segmentation from multispectral MR image data using ensemble learning methods. CIARP 2019 La Habana (Cuba), Lecture Notes in Computer Science vol. 11896, pp. 326-335.
  66. Győrfi Á, Karetka-Mezei Z, Iclanzan D, Kovács L, Szilágyi L: A study on histogram normalization for brain tumor segmentation from multispectral MR image data. CIARP 2019 La Habana (Cuba). Lecture Notes in Computer Science vol. 11896, pp. 375-384.
  67. Győrfi Á, Kovács L, Szilágyi L: Brain tumor detection and segmentation from magnetic resonance image data using ensemble learning methods. IEEE SMC 2019, Bari (Italy), pp. 919-924.
  68. Győrfi Á, Kovács L, Szilágyi L: A feature ranking and selection algorithm for brain tumor segmentation in multi-spectral magnetic resonance image data. IEEE EMBC 2019 Berlin, pp. 804-807.
  69. Lefkovits Sz, Lefkovits L, Szilágyi L: Applications of different CNN architectures for palm vein identification. MDAI 2019, Milano. Lecture Notes in Computer Science vol. 11676, pp. 295-306, 2019
  70. Lefkovits Sz, Lefkovits L, Szilágyi L: CNN architectures for dorsal hand vein identification. WSCG 2019 Plzen (Czechia), 10 pp, 2019,
  71. 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.
  72. Iclănzan D, Szilágyi SM, Szilágyi L: Evolving computationally efficient hashing for similarity search. ICONIP 2018 Siem Reap (Cambodia). Lecture Notes in Computer Science vol. 11302, pp. 552-563, 2018.
  73. Szilágyi L, Lefkovits Sz, Kucsván ZsL: A self-tuning possibilistic c-means clustering algorithm. MDAI 2018, Palma de Mallorca. Lecture Notes in Computer Science vol. 11144, pp. 255-266, 2018.
  74. Umenhoffer T, Tóth M, Kacsó EÁ, Szécsi L, Szlávecz Á, Somogyi P, Szilágyi L, Kubovje A, Szerafin T, Szirmay-Kalos L, Benyó B: Modelling and simulation framework of aortic valve for hemodynamic evaluation of aortic root replacement surgery outcomes. IFAC BMS 2018 Sao Paolo (Brazil), IFAC-PapersOnLine 51(27):258-263, 2018.
  75. Szabó Zs, Kapás Z, Lefkovits L, Győrfi Á, Szilágyi SM, Szilágyi L: Automatic segmentation of low-grade brain tumor using a random forest classifier and Gabor features. FSKD 2018 Huangshan (China), pp. 1106-1113.
  76. 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. PSIVT 2017, Wuhan. Lecture Notes in Computer Science vol. 10749, pp. 137-149, 2018
  77. Lefkovits Sz, Emerich S, Szilágyi L: Biometric system based on registration of dorsal hand vein configurations. PSIVT 2017, Wuhan. Human Behavior Analysis Workshop. Lecture Notes in Computer Science vol. 10799, pp. 17-29, 2018
  78. Szilágyi L, Szilágyi SM: A possibilistic c-means clustering model with cluster size estimation. CIARP 2017 Valparaíso (Chile). Lecture Notes in Computer Science vol. 10659, pp. 661-668, 2018
  79. 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
  80. Kapás Z, Lefkovits L, Szilágyi L: Automatic detection and segmentation of brain tumor using random forest approach. MDAI 2016 Andorra, Lecture Notes in Computer Science vol. 9880, pp. 301-312, 2016
  81. Szilágyi L, Dénesi G, Enăchescu C: Fast color quantization via fuzzy clustering. ICINIP 2016 Kyoto. Lecture Notes in Computer Science vol. 9950, pp. 95-103 2016.
  82. Szilágyi L, Szilágyi SM, Enăchescu C: A study on cluster size sensitivity of fuzzy c-means algorithm variants. ICINIP 2016 Kyoto. Lecture Notes in Computer Science vol. 9948, pp. 470-478, 2016.
  83. Szilágyi L: A unified theory of fuzzy c-means clustering models with improved partition. MDAI 2015 Skövde (SWE). Lecture Notes in Computer Science vol. 9321, pp. 129-140. 2015.
  84. Szilágyi L, Nagy LL, Szilágyi SM: Recent advances in improving the memory efficiency of the TRIBE MCL algorithm. ICONIP 2015, Istanbul. Lecture Notes in Computer Science vol. 9490, pp. 28-35, 2015.
  85. Szilágyi L, Lefkovits L, Iantovics BL, Iclănzan D, Benyó B: Automatic brain tumor segmentation in multispectral MRI volumetric records. ICONIP 2015, Istanbul. Lecture Notes in Computer Science vol. 9492, pp. 174-181, 2015.
  86. Iantovics BL, Szilágyi L, Pintea CM: Societal intelligence – A new perspective for highly intelligent systems. ICONIP 2015, Istanbul. Lecture Notes in Computer Science vol. 9492, pp. 606-614, 2015.
  87. Iclănzan D, Szilágyi L: Neural population coding of stimulus features. ICONIP 2015, Istanbul. Lecture Notes in Computer Science vol. 9492, pp. 263-270, 2015.
  88. Szilágyi L, Lefkovits L, Benyó B: Automatic brain tumor segmentation in multispectral MRI volumes using a fuzzy c-means cascade algorithm. FSKD 2015 Zhangjiajie (China), pp. 310-316
  89. Szilágyi L, Szilágyi SM, Hirsbrunner B: A fast and memory-efficient hierarchical graph clustering algorithm. ICONIP 2014 Kuching (Malaysia). LNCS vol. 8834, pp. 247-254, 2014.
  90. Szilágyi L, Kovács L, Szilágyi SM: Synthetic test data generation for hierarchical graph clustering methods. ICONIP 2014 Kuching (Malaysia). LNCS vol. 8835, pp. 303-310, 2014.
  91. Szalay P, Szilágyi L, Benyó Z, Szilágyi SM: Sensor drift compensation using fuzzy inference system and sparse-grid quadrature filter in blood glucose control. ICONIP 2014 Kuching (Malaysia). LNCS vol. 8835, pp. 445-453, 2014.
  92. Szilágyi L, Varga ZsR, Szilágyi SM: Application of the fuzzy-possibilistic product partition in elliptic shell clustering. MDAI 2014 Tokyo. LNCS vol. 8825, pp. 158-169, 2014.
  93. Szilágyi L, Dénesi G, Szilágyi SM: Fast color reduction using approximative c-means clustering models. FUZZ-IEEE 2014 Beijing, pp. 194-201, 2014.
  94. Szilágyi L, Szilágyi SM: Fast implementations of Markov clustering for protein sequence grouping. MDAI 2013 Barcelona. LNCS vol. 8234, pp. 214-225, 2013.
  95. Haidegger T, Nagy M, Lehotsky Á, Szilágyi L: Digital imaging for the education of proper surgical hand disinfection. MICCAI 2011 Toronto. LNCS vol. 6893, pp. 619-626, 2011
  96. Szilágyi L: Fuzzy-Possibilistic Fuzzy Partition: a novel robust approach to c-means clustering. MDAI 2011. LNCS vol. 6820, pp. 150-161, 2011.
  97. Szilágyi L, Szilágyi SM, Benyó Z: A unified approach to c-means clustering models. FUZZ-IEEE 2009 Jeju Island (S. Korea), pp. 456-461, 2009
  98. Szilágyi L, Iclănzan D, Szilágyi SM, Dumitrescu D, Hirsbrunner B: A generalized c-means clustering model optimized via evolutionary computation. FUZZ-IEEE 2009, Jeju Island (S. Korea), pp. 451-455, 2009
  99. Szilágyi L, Iclănzan D, Szilágyi SM, Dumitrescu D: GeCiM: A novel generalized approach to c-means clustering. CIARP 2008, La Habana (Cuba). LNCS vol. 5197, pp. 235-242, 2008.
  100. Szilágyi L, Benyó Z, Szilágyi SM, Adam HS: MR brain image segmentation using an enhanced fuzzy c-means algorithm. IEEE EMBC 2003 Cancún, pp. 724–726, 2003

 

Patents

  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.
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The Sapientia Hungarian University of Transylvania is the independent university of the Hungarian community in Romania, which aims at providing education to the members of our community and performing scientific research on a high professional level. 

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