@article{MTMT:34342470,
	title = {Exploring fair scheduling aspects-Through final exam scheduling},
	url = {https://m2.mtmt.hu/api/publication/34342470},
	author = {Erdős, Szilvia and Kővári, Bence András},
	doi = {10.1556/606.2023.00780},
	journal-iso = {POLLACK PERIODICA},
	journal = {POLLACK PERIODICA: AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES},
	volume = {19},
	unique-id = {34342470},
	issn = {1788-1994},
	year = {2024},
	eissn = {1788-3911},
	pages = {151-156},
	orcid-numbers = {Kővári, Bence András/0000-0003-1555-640X}
}

@inproceedings{MTMT:34763429,
	title = {Comparison of a Deep Learning-based Axle Load Estimator and the Matrix Method in Strain Gauge-based Bridge Weigh-In-Motion Systems},
	url = {https://m2.mtmt.hu/api/publication/34763429},
	author = {Szinyéri, Bence and Kővári, Bence András},
	booktitle = {2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI)},
	doi = {10.1109/ISCMI59957.2023.10458514},
	unique-id = {34763429},
	abstract = {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.},
	keywords = {life cycle; BRIDGES; Bridge; Loads (forces); Axles; Deep learning; Deep learning; Deep learning; structural health monitoring; Comparison of methods; Comparison of methods; LOAD ESTIMATION; Strain gages; matrix methods; Axle load estimation; Weigh-In-Motion; Weigh-in-motion (WIM); Axle load estimation; Axle loads; Bridge performance; Strain-gages; Weigh-in-motion; Weigh-in-motion systems},
	year = {2023},
	pages = {12-16},
	orcid-numbers = {Kővári, Bence András/0000-0003-1555-640X}
}

@{MTMT:34457300,
	title = {Solving a Final Exam Scheduling Problem with Constraint Programming},
	url = {https://m2.mtmt.hu/api/publication/34457300},
	author = {Trautsch, László Kálmán and Kővári, Bence András},
	booktitle = {Abstract book for the19th MIKLÓS IVÁNYI INTERNATIONAL PHD & DLA SYMPOSIUM},
	unique-id = {34457300},
	year = {2023},
	orcid-numbers = {Trautsch, László Kálmán/0000-0002-1589-3265; Kővári, Bence András/0000-0003-1555-640X}
}

@article{MTMT:34034421,
	title = {Systematic Evaluation of Pre-Processing Approaches in Online Signature Verification},
	url = {https://m2.mtmt.hu/api/publication/34034421},
	author = {Saleem, Mohammad and Szücs, Cintia Lia and Kővári, Bence András},
	doi = {10.3233/IDT-220247},
	journal-iso = {INTELL DECIS TECHNOL},
	journal = {INTELLIGENT DECISION TECHNOLOGIES},
	volume = {17},
	unique-id = {34034421},
	issn = {1872-4981},
	year = {2023},
	pages = {655-672},
	orcid-numbers = {Saleem, Mohammad/0000-0002-7274-2711; Kővári, Bence András/0000-0003-1555-640X}
}

@CONFERENCE{MTMT:34025204,
	title = {Származtatott függvénytulajdonságok alkalmazásának szerepe az online aláírás-hitelesítésben},
	url = {https://m2.mtmt.hu/api/publication/34025204},
	author = {Szücs, Cintia Lia and Kővári, Bence András and Charaf, Hassan},
	booktitle = {Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája},
	unique-id = {34025204},
	year = {2023},
	pages = {Paper ID 45},
	orcid-numbers = {Kővári, Bence András/0000-0003-1555-640X}
}

@article{MTMT:33372846,
	title = {A strain gauge-based Bridge Weigh-In-Motion system using deep learning},
	url = {https://m2.mtmt.hu/api/publication/33372846},
	author = {Szinyéri, Bence and Kővári, Bence András and Völgyi, István Krisztián and Kollár, Dénes and Joó, Attila László},
	doi = {10.1016/j.engstruct.2022.115472},
	journal-iso = {ENG STRUCT},
	journal = {ENGINEERING STRUCTURES},
	volume = {277},
	unique-id = {33372846},
	issn = {0141-0296},
	year = {2023},
	eissn = {1873-7323},
	orcid-numbers = {Kővári, Bence András/0000-0003-1555-640X; Völgyi, István Krisztián/0000-0001-7561-0522; Kollár, Dénes/0000-0002-0048-3327; Joó, Attila László/0000-0002-8010-7021}
}

@inproceedings{MTMT:34021323,
	title = {The Usability of Derived Function Features in Online Signature Verification},
	url = {https://m2.mtmt.hu/api/publication/34021323},
	author = {Szücs, Cintia Lia and Kővári, Bence András},
	booktitle = {2022 9TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE, ISCMI},
	doi = {10.1109/ISCMI56532.2022.10068475},
	unique-id = {34021323},
	year = {2022},
	pages = {192-197},
	orcid-numbers = {Kővári, Bence András/0000-0003-1555-640X}
}

@article{MTMT:32863099,
	title = {Tekla Structures kiterjesztése valós idejű szenzoradat vizualizálással},
	url = {https://m2.mtmt.hu/api/publication/32863099},
	author = {Kurai, András and Bacskay, Viktor and Bakó, Benjamin and Molnár, Zsolt and Kővári, Bence András and Kollár, Dénes and Joó, Attila László},
	journal-iso = {MAGÉSZ ACÉLSZERKEZETEK},
	journal = {MAGÉSZ ACÉLSZERKEZETEK},
	volume = {19},
	unique-id = {32863099},
	issn = {1785-4822},
	year = {2022},
	pages = {66-72},
	orcid-numbers = {Kollár, Dénes/0000-0002-0048-3327; Joó, Attila László/0000-0002-8010-7021}
}

@article{MTMT:32863049,
	title = {Mélytanulás alapú tengelysúlybecslés nyúlásmérő bélyegek adatai alapján},
	url = {https://m2.mtmt.hu/api/publication/32863049},
	author = {Szinyéri, Bence and Kővári, Bence András and Völgyi, István Krisztián and Kollár, Dénes and Joó, Attila László},
	journal-iso = {MAGÉSZ ACÉLSZERKEZETEK},
	journal = {MAGÉSZ ACÉLSZERKEZETEK},
	volume = {19},
	unique-id = {32863049},
	issn = {1785-4822},
	year = {2022},
	pages = {58-65},
	orcid-numbers = {Völgyi, István Krisztián/0000-0001-7561-0522; Kollár, Dénes/0000-0002-0048-3327; Joó, Attila László/0000-0002-8010-7021}
}

@article{MTMT:32842136,
	title = {The Use of Confidence Indicating Prediction Score in Online Signature Verification},
	url = {https://m2.mtmt.hu/api/publication/32842136},
	author = {Heszler, András and Szücs, Cintia Lia and Kővári, Bence András},
	doi = {10.12720/jait.13.3.290-294},
	journal-iso = {J ADV INFORM TECH},
	journal = {JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY},
	volume = {13},
	unique-id = {32842136},
	issn = {1798-2340},
	abstract = {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.},
	year = {2022},
	pages = {290-294}
}