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László Szilágyi
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2020 – today
- 2024
- [j22]Lehel Dénes-Fazakas, Barbara Simon, Ádám Hartvég, László Szilágyi, Levente Kovács, Amir Mosavi, György Eigner:
Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor. Comput. Biol. Medicine 182: 109167 (2024) - [j21]Barbara Simon, Ádám Hartvég, Lehel Dénes-Fazakas, György Eigner, László Szilágyi:
Advancing Medical Assistance: Developing an Effective Hungarian-Language Medical Chatbot with Artificial Intelligence. Inf. 15(6): 297 (2024) - [j20]Attila Biró, Antonio Ignacio Cuesta-Vargas, László Szilágyi:
AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets. Sensors 24(1): 132 (2024) - [j19]Lehel Dénes-Fazakas, Barbara Simon, Ádám Hartvég, Levente Kovács, Eva-Henrietta Dulf, László Szilágyi, György Eigner:
Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks. Sensors 24(8): 2412 (2024) - [c91]Szilveszter Nádas, Lars Ernström, László Szilágyi, Gyanesh Patra, Dmitri Krylov, Jonathan Lynam:
To QoE or not to QoE. ANRW 2024 - [c90]Szabolcs Csaholczi, Ágnes Györfi, Levente Kovács, László Szilágyi:
Segmentation of Brain Tumor Parts from Multi-spectral MRI Records Using Deep Learning and U-Net Architecture. CIARP (2) 2024: 191-204 - [c89]Csaba Potyok, Barbara Simon, Ádám Hartvég, Máté Siket, Lehel Dénes-Fazakas, György Eigner, Milán Balázs, Levente Kovács, László Szilágyi:
Mobile Application Development for Diabetes Patient. SACI 2024: 559-564 - [c88]Lehel Dénes-Fazakas, Gyozo Dénes Fazakas, György Eigner, Levente Kovács, László Szilágyi:
Review of Reinforcement Learning-Based Control Algorithms in Artificial Pancreas Systems for Diabetes Mellitus Management. SACI 2024: 565-572 - [c87]Miklós Nagy, Barbara Simon, László Szász, Máté Siket, Lehel Dénes-Fazakas, György Eigner, Patrik Péter Süli, Levente Kovács, László Szilágyi:
Web Application Development for Diabetes Patients. SACI 2024: 573-580 - [c86]Barbara Simon, Ádám Hartvég, Máté Siket, Lehel Dénes-Fazakas, Levente Kovács, László Szilágyi, Gábor Kertész, György Eigner:
Enhancing Diabetes Management Through GRU Analysis of Physical Activity Effects. SISY 2024: 27-32 - 2023
- [j18]Attila Biró, Sándor Miklós Szilágyi, László Szilágyi, Jaime Martín-Martín, Antonio I. Cuesta-Vargas:
Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer. Sensors 23(7): 3595 (2023) - [c85]Mirtill-Boglárka Naghi, Levente Kovács, László Szilágyi:
A Self-Tuning Version for the Possibilistic Fuzzy $c-\text{means}$ Clustering Algorithm. FUZZ 2023: 1-6 - [c84]Lehel Dénes-Fazakas, Máté Siket, László Szilágyi, György Eigner, Levente Kovács:
Investigation of reward functions for controlling blood glucose level using reinforcement learning. SACI 2023: 387-392 - [c83]László Szilágyi:
Fuzzy-Possibilistic Product Partition: the First Twelve Years. SISY 2023: 15-16 - [c82]Attila Biró, Antonio Ignacio Cuesta-Vargas, László Szilágyi:
Applied AI for Real-Time Detection of Lesions and Tumors Following Severe Head Injuries. SISY 2023: 653-658 - [c81]Ágnes Gyorfi, Levente Kovács, László Szilágyi:
Brain Tumor Segmentation from Multi-Spectral MRI Records Using a U-net Cascade Architecture. SMC 2023: 1327-1332 - [c80]Lehel Dénes-Fazakas, Máté Siket, László Szilágyi, György Eigner, Levente Kovács:
Effect of Hyperparameters of Reinforcement Learning in Blood Glucose Control. SMC 2023: 1333-1340 - [c79]Attila Biró, Antonio Ignacio Cuesta-Vargas, László Szilágyi:
sRPE and ACWR to Control Fatigue Levels and Minimize Injuries in Performance Sports. SMC 2023: 2808-2813 - [c78]Lehel Dénes-Fazakas, Levente Kovács, György Eigner, László Szilágyi:
Brain Tumor Segmentation from Multi-Spectral MRI Records Using a U-Net Cascade Architecture. SMC 2023: 3003-3008 - [c77]Lehel Dénes-Fazakas, László Szilágyi, György Eigner, Olga Kosheleva, Martine Ceberio, Vladik Kreinovich:
Which Activation Function Works Best for Training Artificial Pancreas: Empirical Fact and Its Theoretical Explanation. SSCI 2023: 496-500 - [i1]Irén A. Kopcsóné Németh, Csaba Nádor, László Szilágyi, Ákos Lehotsky, Tamás Haidegger:
Establishing a Learning Model for Correct Hand Hygiene Technique in a NICU. CoRR abs/2305.01366 (2023) - 2022
- [j17]Lehel Dénes-Fazakas, Máté Siket, László Szilágyi, Levente Kovács, György Eigner:
Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals. Sensors 22(21): 8568 (2022) - [c76]Lehel Dénes-Fazakas, Máté Siket, Gábor Kertész, László Szilágyi, Levente Kovács, György Eigner:
Control of Type 1 Diabetes Mellitus using direct reinforcement learning based controller. SMC 2022: 1512-1517 - 2021
- [j16]Paolo Valerio Testa, László Szilágyi, Corrado Carta, Frank Ellinger:
A Complementary Ring Mixer Driven by a Single-Ended LO in 22-nm FD-SOI CMOS for K and Ka-Bands. IEEE Open J. Circuits Syst. 2: 293-303 (2021) - [c75]David Iclanzan, Rodica Ioana Lung, Zsolt Levente Kucsván, Béla Surányi, Levente Kovács, László Szilágyi:
The role of atlases and multi-atlases in brain tissue segmentation based on multispectral magnetic resonance image data. AFRICON 2021: 1-6 - [c74]Andrea Koble, Ágnes Gyorfi, Szabolcs Csaholczi, Béla Surányi, Lehel Dénes-Fazakas, Levente Kovács, László Szilágyi:
Identifying the most suitable histogram normalization technique for machine learning based segmentation of multispectral brain MRI data. AFRICON 2021: 1-6 - 2020
- [c73]Mózes Vidámi, László Szilágyi, David Iclanzan:
Real Valued Card Counting Strategies for the Game of Blackjack. ICONIP (2) 2020: 63-73 - [c72]Szabolcs Csaholczi, David Iclanzan, Levente Kovács, László Szilágyi:
Brain Tumor Segmentation from Multi-spectral MR Image Data Using Random Forest Classifier. ICONIP (1) 2020: 174-184 - [c71]Ágnes Gyorfi, Szabolcs Csaholczi, Tímea Fülöp, Levente Kovács, László Szilágyi:
Brain Tumor Segmentation from Multi-Spectral Magnetic Resonance Image Data Using an Ensemble Learning Approach. SMC 2020: 1699-1704 - [c70]Tímea Fülöp, Ágnes Gyorfi, Szabolcs Csaholczi, Levente Kovács, László Szilágyi:
Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning*. SoSE 2020: 531-536
2010 – 2019
- 2019
- [j15]László Szilágyi, Szidónia Lefkovits, Sándor M. Szilágyi:
Self-Tuning Possibilistic c-Means Clustering Models. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 27(Supplement-1): 143-159 (2019) - [j14]László Szilágyi, Jan Plíva, Ronny Henker, David Schoeniger, Jaroslaw P. Turkiewicz, Frank Ellinger:
A 53-Gbit/s Optical Receiver Frontend With 0.65 pJ/bit in 28-nm Bulk-CMOS. IEEE J. Solid State Circuits 54(3): 845-855 (2019) - [c69]Ágnes Gyorfi, Levente Kovács, László Szilágyi:
Brain Tumour Segmentation from Multispectral MR Image Data Using Ensemble Learning Methods. CIARP 2019: 326-335 - [c68]Ágnes Gyorfi, Zoltán Karetka-Mezei, David Iclanzan, Levente Kovács, László Szilágyi:
A Study on Histogram Normalization for Brain Tumour Segmentation from Multispectral MR Image Data. CIARP 2019: 375-384 - [c67]Ágnes Gyorfi, Levente Kovács, László Szilágyi:
A Feature Ranking and Selection Algorithm for Brain Tumor Segmentation in Multi-Spectral Magnetic Resonance Image Data. EMBC 2019: 804-807 - [c66]David Iclanzan, László Szilágyi:
Learning to Generate Ambiguous Sequences. ICONIP (1) 2019: 110-121 - [c65]Szidónia Lefkovits, László Lefkovits, László Szilágyi:
Applications of Different CNN Architectures for Palm Vein Identification. MDAI 2019: 295-306 - [c64]Ágnes Gyorfi, Levente Kovács, László Szilágyi:
Brain Tumor Detection and Segmentation from Magnetic Resonance Image Data Using Ensemble Learning Methods. SMC 2019: 909-914 - [c63]László Szilágyi, Jan Plíva, Ronny Henker, Frank Ellinger:
A Mixed-Signal Offset-Compensation System for Multi-Gbit/s Optical Receiver Frontends. VLSI-SoC 2019: 46-51 - [c62]László Szilágyi, Jan Plíva, Ronny Henker:
Offset-Compensation Systems for Multi-Gbit/s Optical Receivers. VLSI-SoC (Selected Papers) 2019: 235-255 - 2018
- [c61]David Iclanzan, Sándor Miklós Szilágyi, László Szilágyi:
Evolving Computationally Efficient Hashing for Similarity Search. ICONIP (2) 2018: 552-563 - [c60]László Szilágyi, Szidónia Lefkovits, Zsolt Levente Kucsván:
A Self-tuning Possibilistic c-Means Clustering Algorithm. MDAI 2018: 255-266 - [c59]Szidónia Lefkovits, László Szilágyi, László Lefkovits:
Brain Tumor Segmentation and Survival Prediction Using a Cascade of Random Forests. BrainLes@MICCAI (2) 2018: 334-345 - 2017
- [j13]Attila Frigy, Annamária Magdás, Victor-Dan Moga, Ioana Georgiana Cotet, Miklós Kozlovszky, László Szilágyi:
Increase of Short-Term Heart Rate Variability Induced by Blood Pressure Measurements during Ambulatory Blood Pressure Monitoring. Comput. Math. Methods Medicine 2017: 5235319:1-5235319:5 (2017) - [c58]László Szilágyi:
Robust Clustering Algorithms Employing Fuzzy-Possibilistic Product Partition. Fuzzy Sets, Rough Sets, Multisets and Clustering 2017: 101-121 - [c57]László Szilágyi, Sándor M. Szilágyi:
A Possibilistic c-means Clustering Model with Cluster Size Estimation. CIARP 2017: 661-668 - [c56]Waltenegus Dargie, David Schoeniger, László Szilágyi, Xin An, Ronny Henker, Frank Ellinger:
A Highly Adaptive and Energy-Efficient Optical Interconnect for On-Board Server Communications. ICCCN 2017: 1-8 - [c55]Guido Belfiore, László Szilágyi, Ronny Henker, Frank Ellinger:
25 Gbit/s adaptive 3-tap FFE VCSEL driver in 28-nm CMOS for data center communications. ICTON 2017: 1-4 - [c54]Szidónia Lefkovits, Simina Emerich, László Szilágyi:
Biometric System Based on Registration of Dorsal Hand Vein Configurations. PSIVT Workshops 2017: 17-29 - [c53]Zoltán Kapás, László Lefkovits, David Iclanzan, Ágnes Gyorfi, Barna László Iantovics, Szidónia Lefkovits, Sándor Miklós Szilágyi, László Szilágyi:
Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a Random Forest Approach. PSIVT 2017: 137-149 - [c52]Jan Plíva, Mahdi M. Khafaji, László Szilágyi, Ronny Henker, Frank Ellinger:
Opto-electrical analog front-end with rapid power-on and 0.82 pJ/bit for 28 Gb/s in 14 nm FinFET CMOS. SoCC 2017: 253-257 - [e2]József Domokos, Lorinc Márton, László Szilágyi, Zoltán Forgó:
International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics, Târgu Mureș, Romania, October 27-28, 2017. Scientia Publishing House 2017, ISBN 978-973-1970-39-4 [contents] - 2016
- [j12]Annamária Magdás, László Szilágyi, Alexander Incze:
Can Ambulatory Blood Pressure Variability Contribute to Individual Cardiovascular Risk Stratification? Comput. Math. Methods Medicine 2016: 7816830:1-7816830:5 (2016) - [j11]László Szilágyi, Sándor M. Szilágyi:
A modified two-stage Markov clustering algorithm for large and sparse networks. Comput. Methods Programs Biomed. 135: 15-26 (2016) - [c51]László Szilágyi, Gellért Dénesi, Calin Enachescu:
Fast Color Quantization via Fuzzy Clustering. ICONIP (4) 2016: 95-103 - [c50]László Szilágyi, Sándor M. Szilágyi, Calin Enachescu:
A Study on Cluster Size Sensitivity of Fuzzy c-Means Algorithm Variants. ICONIP (2) 2016: 470-478 - [c49]Zoltán Kapás, László Lefkovits, László Szilágyi:
Automatic Detection and Segmentation of Brain Tumor Using Random Forest Approach. MDAI 2016: 301-312 - [c48]László Lefkovits, Szidónia Lefkovits, László Szilágyi:
Brain Tumor Segmentation with Optimized Random Forest. BrainLes@MICCAI 2016: 88-99 - 2015
- [j10]Guido Belfiore, László Szilágyi, Ronny Henker, Udo Jörges, Frank Ellinger:
Design of a 56 Gbit/s 4-level pulse-amplitude-modulation inductor-less vertical-cavity surface-emitting laser driver integrated circuit in 130 nm BiCMOS technology. IET Circuits Devices Syst. 9(3): 213-220 (2015) - [c47]László Szilágyi, László Lefkovits, Balázs Benyó:
Automatic Brain Tumor Segmentation in multispectral MRI volumes using a fuzzy c-means cascade algorithm. FSKD 2015: 285-291 - [c46]László Szilágyi, Lajos Loránd Nagy, Sándor Miklós Szilágyi:
Recent Advances in Improving the Memory Efficiency of the TRIBE MCL Algorithm. ICONIP (2) 2015: 28-35 - [c45]László Szilágyi, László Lefkovits, Barna Iantovics, David Iclanzan, Balázs Benyó:
Automatic Brain Tumor Segmentation in Multispectral MRI Volumetric Records. ICONIP (4) 2015: 174-181 - [c44]David Iclanzan, László Szilágyi:
Neural Population Coding of Stimulus Features. ICONIP (4) 2015: 263-270 - [c43]László Barna Iantovics, László Szilágyi, Camelia-Mihaela Pintea:
Societal Intelligence - A New Perspective for Highly Intelligent Systems. ICONIP (4) 2015: 606-614 - [c42]László Szilágyi, Guido Belfiore, Ronny Henker, Frank Ellinger:
A high-voltage DC bias architecture implementation in a 17 Gbps low-power common-cathode VCSEL driver in 80 nm CMOS. ISCAS 2015: 2385-2388 - [c41]László Szilágyi:
Random Process Simulation Using Petri Nets. MACRo 2015: 177-182 - [c40]László Szilágyi:
A Unified Theory of Fuzzy c-Means Clustering Models with Improved Partition. MDAI 2015: 129-140 - [c39]László Szilágyi, Ronny Henker, Frank Ellinger:
A 0.68 pJ/bit inductor-less optical receiver for 20 Gbps with 0.0025 mm2 area in 28 nm CMOS. SoCC 2015: 35-39 - [e1]József Domokos, Lorinc Márton, László Szilágyi, Zoltán Forgó:
International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics, Târgu Mureș, Romania, March 6-7, 2015. Scientia Publishing House 2015, ISBN 978-973-1970-39-4 [contents] - 2014
- [j9]Sándor M. Szilágyi, László Szilágyi:
A fast hierarchical clustering algorithm for large-scale protein sequence data sets. Comput. Biol. Medicine 48: 94-101 (2014) - [j8]László Szilágyi, Sándor M. Szilágyi:
Generalization rules for the suppressed fuzzy c-means clustering algorithm. Neurocomputing 139: 298-309 (2014) - [j7]László Szilágyi:
Lessons to learn from a mistaken optimization. Pattern Recognit. Lett. 36: 29-35 (2014) - [c38]László Szilágyi, Gellert Denesi, Sándor Miklós Szilágyi:
Fast color reduction using approximative c-means clustering models. FUZZ-IEEE 2014: 194-201 - [c37]László Szilágyi, Sándor Miklós Szilágyi, Béat Hirsbrunner:
A Fast and Memory-Efficient Hierarchical Graph Clustering Algorithm. ICONIP (1) 2014: 247-254 - [c36]László Szilágyi, Levente Kovács, Sándor Miklós Szilágyi:
Synthetic Test Data Generation for Hierarchical Graph Clustering Methods. ICONIP (2) 2014: 303-310 - [c35]Péter Szalay, László Szilágyi, Zoltán Benyó, Levente Kovács:
Sensor Drift Compensation Using Fuzzy Interference System and Sparse-Grid Quadrature Filter in Blood Glucose Control. ICONIP (2) 2014: 445-453 - [c34]László Szilágyi, Zsuzsa Réka Varga, Sándor Miklós Szilágyi:
Application of the Fuzzy-Possibilistic Product Partition in Elliptic Shell Clustering. MDAI 2014: 158-169 - [c33]László Szilágyi, Ronny Henker, Frank Ellinger:
An inductor-less ultra-compact transimpedance amplifier for 30 Gbps in 28 nm CMOS with high energy-efficiency. MWSCAS 2014: 957-960 - [c32]László Szilágyi, Gellert Denesi, Levente Kovács, Sándor M. Szilágyi:
Comparison of various improved-partition fuzzy c-means clustering algorithms in fast color reduction. SISY 2014: 197-202 - 2013
- [j6]László Szilágyi:
Robust Spherical Shell Clustering Using Fuzzy-Possibilistic Product Partition. Int. J. Intell. Syst. 28(6): 524-539 (2013) - [c31]Sándor Miklós Szilágyi, László Szilágyi, Béat Hirsbrunner:
Study of Electric and Mechanic Properties of the Implanted Artificial Cardiac Tissue Using a Whole Heart Model. CIARP (2) 2013: 230-237 - [c30]Sándor M. Szilágyi, László Szilágyi, Béat Hirsbrunner:
Modeling the Influence of High Fibroblast Level on Arrhythmia Development and Obstructed Depolarization Spread. CinC 2013: 45-48 - [c29]Sándor M. Szilágyi, László Szilágyi, Béat Hirsbrunner:
Simulation of Arrhythmia using Adaptive Spatio-temporal Resolution. CinC 2013: 365-368 - [c28]László Szilágyi, Sándor M. Szilágyi:
Efficient Markov clustering algorithm for protein sequence grouping. EMBC 2013: 639-642 - [c27]László Szilágyi, Sándor Miklós Szilágyi:
Fast Implementations of Markov Clustering for Protein Sequence Grouping. MDAI 2013: 214-225 - 2012
- [j5]László Szilágyi, Sándor M. Szilágyi, Balázs Benyó:
Efficient inhomogeneity compensation using fuzzy c-means clustering models. Comput. Methods Programs Biomed. 108(1): 80-89 (2012) - 2011
- [j4]László Szilágyi, Sándor M. Szilágyi, Balázs Benyó, Zoltán Benyó:
Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models. Biomed. Signal Process. Control. 6(1): 3-12 (2011) - [j3]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
A patient specific electro-mechanical model of the heart. Comput. Methods Programs Biomed. 101(2): 183-200 (2011) - [c26]László Szilágyi, Sándor M. Szilágyi, David Iclanzan, Lehel Szabó:
Efficient 3D Curve Skeleton Extraction from Large Objects. CIARP 2011: 133-140 - [c25]László Szilágyi, David Iclanzan, Lehel Craciun, Sándor M. Szilágyi:
An Efficient Approach to Intensity Inhomogeneity Compensation Using c-Means Clustering Models. CIARP 2011: 312-319 - [c24]László Szilágyi, Csaba Dobó-Nagy, Balázs Benyó:
Identification of the Root Canal from Dental Micro-CT Records. CIARP 2011: 339-346 - [c23]László Szilágyi:
Fuzzy-Possibilistic Product Partition: A Novel Robust Approach to c-Means Clustering. MDAI 2011: 150-161 - [c22]Tamás Haidegger, Melinda Nagy, Ákos Lehotsky, László Szilágyi:
Digital Imaging for the Education of Proper Surgical Hand Disinfection. MICCAI (3) 2011: 619-626 - [c21]Melinda Nagy, László Szilágyi, Ákos Lehotsky, Tamás Haidegger, Balázs Benyó:
An image-guided tool to prevent hospital acquired infections. Image Processing 2011: 79623Z - 2010
- [j2]László Szilágyi, Lehel Medvés, Sándor M. Szilágyi:
A modified Markov clustering approach to unsupervised classification of protein sequences. Neurocomputing 73(13-15): 2332-2345 (2010) - [j1]László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó:
Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models. Soft Comput. 14(5): 495-505 (2010) - [c20]László Szilágyi, Sándor M. Szilágyi, Csilla Kiss:
A Generalized Approach to the Suppressed Fuzzy c-Means Algorithm. MDAI 2010: 140-151
2000 – 2009
- 2009
- [c19]László Szilágyi, David Iclanzan, Sándor M. Szilágyi, Dan Dumitrescu, Béat Hirsbrunner:
A generalized c-means clustering model using optimized via evolutionary computation. FUZZ-IEEE 2009: 451-455 - [c18]László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó:
A unified approach to c-means clustering models. FUZZ-IEEE 2009: 456-461 - [c17]László Szilágyi, Sándor M. Szilágyi, Balázs Benyó, Zoltán Benyó:
Application of hybrid c-means clustering models in inhomogeneity compensation and MR brain image segmentation. SACI 2009: 105-110 - [c16]Sándor M. Szilágyi, László Szilágyi, David Iclanzan, Zoltán Benyó:
A weighted patient specific electromechanical model of the heart. SACI 2009: 111-116 - 2008
- [c15]László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó:
A Thorough Analysis of the Suppressed Fuzzy C-Means Algorithm. CIARP 2008: 203-210 - [c14]László Szilágyi, David Iclanzan, Sándor M. Szilágyi, Dan Dumitrescu:
GeCiM: A Novel Generalized Approach to C-Means Clustering. CIARP 2008: 235-242 - [c13]Sándor M. Szilágyi, László Szilágyi, Levente K. Görög, Constantin T. Luca, Dragos Cozma, Gabriel Ivanica, Zoltán Benyó:
An Enhanced Accessory Pathway Localization Method for Efficient Treatment of Wolff-Parkinson-White Syndrome. CIARP 2008: 269-276 - [c12]László Szilágyi, Sándor M. Szilágyi, László Dávid, Zoltán Benyó:
Multi-stage FCM-Based Intensity Inhomogeneity Correction for MR Brain Image Segmentation. ICANN (2) 2008: 527-536 - [c11]László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó:
Analytical and Numerical Evaluation of the Suppressed Fuzzy C-Means Algorithm. MDAI 2008: 146-157 - [c10]Lehel Medvés, László Szilágyi, Sándor M. Szilágyi:
A Modified Markov Clustering Approach for Protein Sequence Clustering. PRIB 2008: 110-120 - 2007
- [c9]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
Adaptive ECG Compression Using Support Vector Machine. CIARP 2007: 594-603 - [c8]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
Echocardiographic Image Sequence Compression Based on Spatial Active Appearance Model. CIARP 2007: 841-850 - [c7]Sándor M. Szilágyi, László Szilágyi, Attila Frigy, Levente K. Görög, Zoltán Benyó:
Unified Neural Network Based Pathologic Event Reconstruction Using Spatial Heart Model. CIARP 2007: 851-860 - [c6]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
Volumetric Analysis of the Heart Using Echocardiography. FIMH 2007: 81-90 - [c5]László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó:
A Modified Fuzzy C-Means Algorithm for MR Brain Image Segmentation. ICIAR 2007: 866-877 - [c4]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
Spatial Visualization of the Heart in Case of Ectopic Beats and Fibrillation. PSIVT 2007: 548-561 - [c3]László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó:
Efficient Feature Extraction for Fast Segmentation of MR Brain Images. SCIA 2007: 611-620 - [p3]László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó:
A Modified FCM Algorithm for Fast Segmentation of Brain MR Images. Analysis and Design of Intelligent Systems using Soft Computing Techniques 2007: 119-127 - [p2]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
Spatial Heart Simulation and Analysis Using Unified Neural Network. Analysis and Design of Intelligent Systems using Soft Computing Techniques 2007: 346-354 - [p1]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
Support Vector Machine-Based ECG Compression. Analysis and Design of Intelligent Systems using Soft Computing Techniques 2007: 737-745 - 2006
- [c2]László Szilágyi, Sándor M. Szilágyi, Gergely Fördös, Zoltán Benyó:
Quick ECG Analysis for On-Line Holter Monitoring Systems. EMBC 2006: 1678-1681 - [c1]Sándor M. Szilágyi, László Szilágyi, Zoltán Benyó:
Sensibility analysis of the Arruda localization method. EMBC 2006: 3998-4001
Coauthor Index
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