TY  - JOUR
AU  - Erdős, Szilvia
AU  - Kővári, Bence András
TI  - Exploring fair scheduling aspects-Through final exam scheduling
JF  - POLLACK PERIODICA: AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES
J2  - POLLACK PERIODICA
VL  - 19
PY  - 2024
IS  - 1
SP  - 151
EP  - 156
PG  - 6
SN  - 1788-1994
DO  - 10.1556/606.2023.00780
UR  - https://m2.mtmt.hu/api/publication/34342470
ID  - 34342470
N1  - Export Date: 16 November 2023            
            Correspondence Address: Jáhn-Erdos, S.; Department of Automation and Applied Informatics, Hungary; email: Erdos.Szilvia@aut.bme.hu
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szinyéri, Bence
AU  - Kővári, Bence András
TI  - Comparison of a Deep Learning-based Axle Load Estimator and the Matrix Method in Strain Gauge-based Bridge Weigh-In-Motion Systems
T2  - 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI)
PB  - IEEE
CY  - Piscataway (NJ)
SN  - 9798350359374
T3  - International Conference on Soft Computing & Machine Intelligence ISCMI, ISSN 2640-0154
PY  - 2023
SP  - 12
EP  - 16
PG  - 5
DO  - 10.1109/ISCMI59957.2023.10458514
UR  - https://m2.mtmt.hu/api/publication/34763429
ID  - 34763429
N1  - Conference code: 198042            
            Export Date: 2 April 2024
AB  - One aspect of structural health monitoring of bridges is to monitor vehicular traffic. The demand for moni-toring bridge performance and life cycle has led to measuring traffic flow. We have previously developed a deep learning-based axle load estimator showing promising results considering COST 323 benchmark. This paper compares the deep learning-based solution to the established matrix method under different circumstances. Results show that the deep learning-based solution achieves better accuracy on several datasets of the BME-Simulated I corpus and has a better ability to handle noise than the matrix method. © 2023 IEEE.
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Trautsch, László Kálmán
AU  - Kővári, Bence András
ED  - Iványi, Péter
TI  - Solving a Final Exam Scheduling Problem with Constraint Programming
T2  - Abstract book for the19th MIKLÓS IVÁNYI INTERNATIONAL PHD & DLA SYMPOSIUM
PB  - Pollack Press
C1  - Pécs
SN  - 9789636261825
PY  - 2023
PG  - 1
UR  - https://m2.mtmt.hu/api/publication/34457300
ID  - 34457300
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Saleem, Mohammad
AU  - Szücs, Cintia Lia
AU  - Kővári, Bence András
TI  - Systematic Evaluation of Pre-Processing Approaches in Online Signature Verification
JF  - INTELLIGENT DECISION TECHNOLOGIES
J2  - INTELL DECIS TECHNOL
VL  - 17
PY  - 2023
IS  - 3
SP  - 655
EP  - 672
PG  - 18
SN  - 1872-4981
DO  - 10.3233/IDT-220247
UR  - https://m2.mtmt.hu/api/publication/34034421
ID  - 34034421
N1  - Export Date: 29 August 2023            
            Correspondence Address: Saleem, M.; Budapest University of Technology and EconomicsHungary; email: msaleem@aut.bme.hu
LA  - English
DB  - MTMT
ER  - 

TY  - CONF
AU  - Szücs, Cintia Lia
AU  - Kővári, Bence András
AU  - Charaf, Hassan
TI  - Származtatott függvénytulajdonságok alkalmazásának szerepe az online aláírás-hitelesítésben
T2  - Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája
PY  - 2023
SP  - Paper ID 45
UR  - https://m2.mtmt.hu/api/publication/34025204
ID  - 34025204
LA  - Hungarian
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Szinyéri, Bence
AU  - Kővári, Bence András
AU  - Völgyi, István Krisztián
AU  - Kollár, Dénes
AU  - Joó, Attila László
TI  - A strain gauge-based Bridge Weigh-In-Motion system using deep learning
JF  - ENGINEERING STRUCTURES
J2  - ENG STRUCT
VL  - 277
PY  - 2023
SN  - 0141-0296
DO  - 10.1016/j.engstruct.2022.115472
UR  - https://m2.mtmt.hu/api/publication/33372846
ID  - 33372846
N1  - Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics, Department of Automation and Applied Informatics, Műegyetem rkp. 3., Budapest, 1111, Hungary            
            Budapest University of Technology and Economics, Faculty of Civil Engineering, Department of Structural Engineering, Műegyetem rkp. 3., Budapest, 1111, Hungary                  
            CODEN: ENSTD            
            Correspondence Address: Szinyéri, B.; Budapest University of Technology and Economics, Műegyetem rkp. 3., Hungary; email: szinyeribence@edu.bme.hu
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szücs, Cintia Lia
AU  - Kővári, Bence András
TI  - The Usability of Derived Function Features in Online Signature Verification
T2  - 2022 9TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE, ISCMI
PB  - IEEE
CY  - New York, New York
SN  - 9798350320886
T3  - International Conference on Soft Computing & Machine Intelligence ISCMI, ISSN 2640-0154
PY  - 2022
SP  - 192
EP  - 197
PG  - 6
DO  - 10.1109/ISCMI56532.2022.10068475
UR  - https://m2.mtmt.hu/api/publication/34021323
ID  - 34021323
N1  - WoS:hiba:000985064000036 2023-12-31 19:44 befoglaló egyiknél nincsenek szerzők
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Kurai, András
AU  - Bacskay, Viktor
AU  - Bakó, Benjamin
AU  - Molnár, Zsolt
AU  - Kővári, Bence András
AU  - Kollár, Dénes
AU  - Joó, Attila László
TI  - Tekla Structures kiterjesztése valós idejű szenzoradat vizualizálással
JF  - MAGÉSZ ACÉLSZERKEZETEK
J2  - MAGÉSZ ACÉLSZERKEZETEK
VL  - 19
PY  - 2022
IS  - különszám
SP  - 66
EP  - 72
PG  - 7
SN  - 1785-4822
UR  - https://m2.mtmt.hu/api/publication/32863099
ID  - 32863099
LA  - Hungarian
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Szinyéri, Bence
AU  - Kővári, Bence András
AU  - Völgyi, István Krisztián
AU  - Kollár, Dénes
AU  - Joó, Attila László
TI  - Mélytanulás alapú tengelysúlybecslés nyúlásmérő bélyegek adatai alapján
JF  - MAGÉSZ ACÉLSZERKEZETEK
J2  - MAGÉSZ ACÉLSZERKEZETEK
VL  - 19
PY  - 2022
IS  - különszám
SP  - 58
EP  - 65
PG  - 8
SN  - 1785-4822
UR  - https://m2.mtmt.hu/api/publication/32863049
ID  - 32863049
LA  - Hungarian
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Heszler, András
AU  - Szücs, Cintia Lia
AU  - Kővári, Bence András
TI  - The Use of Confidence Indicating Prediction Score in Online Signature Verification
JF  - JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY
J2  - J ADV INFORM TECH
VL  - 13
PY  - 2022
IS  - 3
SP  - 290
EP  - 294
PG  - 5
SN  - 1798-2340
DO  - 10.12720/jait.13.3.290-294
UR  - https://m2.mtmt.hu/api/publication/32842136
ID  - 32842136
N1  - Export Date: 22 June 2022
AB  - Signature verification is an actively researched area whose goal is to decide whether unknown signatures are genuine or forged. Online signature verification applies signatures captured with an electronic device (digital tablet or pen). Online signatures contain not only spatial information but dynamics as well. There are two types of possible errors, the false prediction as genuine and the false prediction as a forgery. This paper proposes a prediction score as the classification output, which indicates the confidence of the system decision. This approach allows a trade-off between the different error types to create specialized verifiers and construct combined classifiers. This paper presents two types of combined classifiers, pre-filtering classifiers and majority voting classifiers. The proposed approaches are evaluated using the MCYT-100 dataset.
LA  - English
DB  - MTMT
ER  -