@article{MTMT:34778079,
	title = {Enacting algorithms: Evolution of the algorythmics storytelling},
	url = {https://m2.mtmt.hu/api/publication/34778079},
	author = {Kátai, Zoltán and Osztián, Pálma Rozália and Iclanzan, Dávid András},
	doi = {10.1007/s10639-024-12617-y},
	journal-iso = {EDUC INF TECHNOL},
	journal = {EDUCATION AND INFORMATION TECHNOLOGIES},
	volume = {29},
	unique-id = {34778079},
	issn = {1360-2357},
	abstract = {Visual storytelling, particularly through dance choreographies as showcased in previous AlgoRythmics performances, has been effective in communicating relatively straightforward algorithms in an engaging and memorable way. Nevertheless, when addressing complex algorithmic concepts, an approach with greater expressiveness and flexibility becomes necessary. Consequently, this study introduces stage performances as an innovative solution, using cinematic representation to successfully convey and communicate these intricate concepts and processes. To evaluate the effectiveness of this approach, a short film was designed, produced, and showcased to a second-semester CS2 university course audience studying programming techniques. Following an opening scene that establishes the context, the subsequent three acts vividly depict ad hoc, greedy, and dynamic programming solutions in response to the posed programming challenge. After the screening, a questionnaire was administered, built on four key constructs of the Technology Acceptance Model, as well as other potential facilitating factors. The study reveals 100% positive perceptions of educational benefits, with the vast majority of students expressing agreement regarding the utility, enjoyment, engagement, creativity, filmic quality, and cognitive benefits of short films. Additionally, a remarkable 96% reported the intent to utilize this approach. Our subsequent Structural Equation Modeling analysis discovered that students whose learning styles were in sync with this approach demonstrated a robust correlation between their perception of the method’s value, their enjoyment of the process, and their overall attitude towards this pedagogical method. This study confirms the potential of visual storytelling through short films as an effective tool for delivering programming education. The findings provide valuable insights for computer science educators seeking to engage learners and convey complex information in an attractive and effective way.},
	year = {2024},
	eissn = {1573-7608},
	pages = {1},
	orcid-numbers = {Kátai, Zoltán/0000-0003-2343-3629}
}

@article{MTMT:32100812,
	title = {Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak},
	url = {https://m2.mtmt.hu/api/publication/32100812},
	author = {Farkas, Csaba and Iclanzan, Dávid András and Vekov, Géza Károly and Olteán Péter, Boróka},
	doi = {10.7717/peerj.10790},
	journal-iso = {PEERJ},
	journal = {PEERJ},
	volume = {9},
	unique-id = {32100812},
	issn = {2167-8359},
	year = {2021},
	eissn = {2167-8359},
	pages = {1-32}
}

@article{MTMT:31936094,
	title = {A review on suppressed fuzzy c-means clustering models},
	url = {https://m2.mtmt.hu/api/publication/31936094},
	author = {Szilágyi, László and Lefkovits, László and Iclanzan, Dávid András},
	doi = {10.2478/ausi-2020-0018},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {12},
	unique-id = {31936094},
	issn = {1844-6086},
	year = {2020},
	eissn = {2066-7760},
	pages = {302-324}
}

@inproceedings{MTMT:31853526,
	title = {Real Valued Card Counting Strategies for the Game of Blackjack},
	url = {https://m2.mtmt.hu/api/publication/31853526},
	author = {Vidámi, Mózes and Szilágyi, László and Iclanzan, Dávid András},
	booktitle = {Neural Information Processing},
	doi = {10.1007/978-3-030-63833-7_6},
	unique-id = {31853526},
	year = {2020},
	pages = {63-73}
}

@article{MTMT:31827690,
	title = {Comparing epidemiological models with the help of visualization dashboards},
	url = {https://m2.mtmt.hu/api/publication/31827690},
	author = {Olteán Péter, Boróka and Farkas, Csaba and Vekov, Géza Károly and Iclanzan, Dávid András},
	doi = {10.2478/ausi-2020-0016},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {12},
	unique-id = {31827690},
	issn = {1844-6086},
	year = {2020},
	eissn = {2066-7760},
	pages = {260-282}
}

@article{MTMT:31782899,
	title = {Brain Tumor Segmentation from Multi-spectral MR Image Data Using Random Forest Classifier},
	url = {https://m2.mtmt.hu/api/publication/31782899},
	author = {Csaholczi, Szabolcs and Iclanzan, Dávid András and Kovács, Levente and Szilágyi, László},
	doi = {10.1007/978-3-030-63830-6_15},
	journal-iso = {LNCS},
	journal = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {12532},
	unique-id = {31782899},
	issn = {0302-9743},
	year = {2020},
	eissn = {1611-3349},
	pages = {174-184},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:31121296,
	title = {Learning to Generate Ambiguous Sequences},
	url = {https://m2.mtmt.hu/api/publication/31121296},
	author = {Iclanzan, Dávid András and Szilágyi, László},
	booktitle = {Neural Information Processing},
	doi = {10.1007/978-3-030-36708-4_10},
	unique-id = {31121296},
	year = {2019},
	pages = {110-121}
}

@article{MTMT:31121281,
	title = {A Study on Histogram Normalization for Brain Tumour Segmentation from Multispectral MR Image Data},
	url = {https://m2.mtmt.hu/api/publication/31121281},
	author = {Győrfi, Ágnes and Karetka-Mezei, Zoltán and Iclanzan, Dávid András and Kovács, Levente and Szilágyi, László},
	doi = {10.1007/978-3-030-33904-3_35},
	journal-iso = {LNCS},
	journal = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {11896},
	unique-id = {31121281},
	issn = {0302-9743},
	year = {2019},
	eissn = {1611-3349},
	pages = {375-384},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:30986120,
	title = {Automatic detection of hard and soft exudates from retinal fundus images},
	url = {https://m2.mtmt.hu/api/publication/30986120},
	author = {Borsos, Balint and Nagy, Laszlo and Iclanzan, Dávid András and Szilágyi, László},
	doi = {10.2478/ausi-2019-0005},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {11},
	unique-id = {30986120},
	issn = {1844-6086},
	abstract = {According to WHO estimates, 400 million people suffer from diabetes, and this number is likely to double by year 2030. Unfortunately, diabetes can have severe complications like glaucoma or retinopathy, which both can cause blindness. The main goal of our research is to provide an automated procedure that can detect retinopathy-related lesions of the retina from fundus images. This paper focuses on the segmentation of so-called white lesions of the retina that include hard and soft exudates. The established procedure consists of three main phases. The preprocessing step compensates the various luminosity patterns found in retinal images, using background and foreground pixel extraction and a data normalization operator similar to Z-transform. This is followed by a modified SLIC algorithm that provides homogeneous superpixels in the image. The final step is an ANN-based classification of pixels using fifteen features extracted from the neighborhood of the pixels taken from the equalized images and from the properties of the superpixel where the pixel belongs. The proposed methodology was tested using high-resolution fundus images originating from the IDRiD database. Pixelwise accuracy is characterized by a 54% Dice score in average, but the presence of exudates is detected with 94% precision.},
	keywords = {diabetic retinopathy; Image segmentation; exudate detection},
	year = {2019},
	eissn = {2066-7760},
	pages = {65-79}
}

@inproceedings{MTMT:31121339,
	title = {Evolving Computationally Efficient Hashing for Similarity Search},
	url = {https://m2.mtmt.hu/api/publication/31121339},
	author = {Iclanzan, Dávid András and Szilágyi, Sándor Miklós and Szilágyi, László},
	booktitle = {Neural Information Processing},
	doi = {10.1007/978-3-030-04179-3_49},
	unique-id = {31121339},
	year = {2018},
	pages = {552-563}
}