TY - JOUR AU - Kátai, Zoltán AU - Osztián, Pálma Rozália AU - Iclanzan, Dávid András TI - Enacting algorithms: Evolution of the algorythmics storytelling JF - EDUCATION AND INFORMATION TECHNOLOGIES J2 - EDUC INF TECHNOL VL - 29 PY - 2024 SP - 1 SN - 1360-2357 DO - 10.1007/s10639-024-12617-y UR - https://m2.mtmt.hu/api/publication/34778079 ID - 34778079 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Farkas, Csaba AU - Iclanzan, Dávid András AU - Vekov, Géza Károly AU - Olteán Péter, Boróka TI - Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak JF - PEERJ J2 - PEERJ VL - 9 PY - 2021 SP - 1 EP - 32 PG - 32 SN - 2167-8359 DO - 10.7717/peerj.10790 UR - https://m2.mtmt.hu/api/publication/32100812 ID - 32100812 LA - English DB - MTMT ER - TY - JOUR AU - Szilágyi, László AU - Lefkovits, László AU - Iclanzan, Dávid András TI - A review on suppressed fuzzy c-means clustering models JF - ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA J2 - ACTA UNIV SAP INFORM VL - 12 PY - 2020 IS - 2 SP - 302 EP - 324 PG - 23 SN - 1844-6086 DO - 10.2478/ausi-2020-0018 UR - https://m2.mtmt.hu/api/publication/31936094 ID - 31936094 LA - English DB - MTMT ER - TY - CHAP AU - Vidámi, Mózes AU - Szilágyi, László AU - Iclanzan, Dávid András ED - King, Irwin ED - Chan, Jonathan H. ED - Kwok, James T. ED - Leung, Andrew Chi-Sing ED - Pasupa, Kitsuchart ED - Yang, Haiqin TI - Real Valued Card Counting Strategies for the Game of Blackjack T2 - Neural Information Processing PB - Springer Netherlands SN - 9783030638221 T3 - Communications in Computer and Information Science, ISSN 1865-0929 ; 1333. PY - 2020 SP - 63 EP - 73 PG - 11 DO - 10.1007/978-3-030-63833-7_6 UR - https://m2.mtmt.hu/api/publication/31853526 ID - 31853526 N1 - ICONIP 2020 Bangkok LA - English DB - MTMT ER - TY - JOUR AU - Olteán Péter, Boróka AU - Farkas, Csaba AU - Vekov, Géza Károly AU - Iclanzan, Dávid András TI - Comparing epidemiological models with the help of visualization dashboards JF - ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA J2 - ACTA UNIV SAP INFORM VL - 12 PY - 2020 IS - 2 SP - 260 EP - 282 PG - 23 SN - 1844-6086 DO - 10.2478/ausi-2020-0016 UR - https://m2.mtmt.hu/api/publication/31827690 ID - 31827690 LA - English DB - MTMT ER - TY - JOUR AU - Csaholczi, Szabolcs AU - Iclanzan, Dávid András AU - Kovács, Levente AU - Szilágyi, László TI - Brain Tumor Segmentation from Multi-spectral MR Image Data Using Random Forest Classifier JF - LECTURE NOTES IN COMPUTER SCIENCE J2 - LNCS VL - 12532 PY - 2020 SP - 174 EP - 184 PG - 11 SN - 0302-9743 DO - 10.1007/978-3-030-63830-6_15 UR - https://m2.mtmt.hu/api/publication/31782899 ID - 31782899 N1 - Chapter 15 Yang, Haiqin (szerk.) Cham : Springer International Publishing AG, 2020 ISBN: 978-3-030-63830-6, 978-3-030-63829-0 LNCS vol. 12532 Computational Intelligence Research Group, Sapientia - Hungarian University of Transylvania, Tîrgu Mureş, Romania University Research, Innovation and Service Center (EKIK), Óbuda University, Budapest, Hungary Conference code: 251889 Cited By :1 Export Date: 10 June 2021 Correspondence Address: Szilágyi, L.; Computational Intelligence Research Group, Romania; email: szilagyi.laszlo@nik.uni-obuda.hu Funding details: Horizon 2020 Framework Programme, H2020, 679681 Funding details: European Research Council, ERC Funding details: Magyar Tudományos Akadémia, MTA Funding details: Ministry for Innovation and Technology Funding text 1: This project was supported by the Sapientia Foundation – Institute for Scientific Research. The work of L. Kovács was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 679681). The work of L. Szilágyi was supported by the Hungarian Academy of Sciences through the János Bolyai Fellowship program, and by the ÚNKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology. Computational Intelligence Research Group, Sapientia - Hungarian University of Transylvania, Tîrgu Mureş, Romania University Research, Innovation and Service Center (EKIK), Óbuda University, Budapest, Hungary Conference code: 251889 Cited By :1 Export Date: 4 August 2021 Correspondence Address: Szilágyi, L.; Computational Intelligence Research Group, Romania; email: szilagyi.laszlo@nik.uni-obuda.hu Funding details: Horizon 2020 Framework Programme, H2020, 679681 Funding details: European Research Council, ERC Funding details: Magyar Tudományos Akadémia, MTA Funding details: Ministry for Innovation and Technology Funding text 1: This project was supported by the Sapientia Foundation – Institute for Scientific Research. The work of L. Kovács was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 679681). The work of L. Szilágyi was supported by the Hungarian Academy of Sciences through the János Bolyai Fellowship program, and by the ÚNKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology. LA - English DB - MTMT ER - TY - CHAP AU - Iclanzan, Dávid András AU - Szilágyi, László ED - Gedeon, Tom ED - Wong, Kok Wai ED - Lee, Minho TI - Learning to Generate Ambiguous Sequences T2 - Neural Information Processing PB - Springer Netherlands CY - Cham SN - 9783030367077 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 11953. PY - 2019 SP - 110 EP - 121 PG - 12 DO - 10.1007/978-3-030-36708-4_10 UR - https://m2.mtmt.hu/api/publication/31121296 ID - 31121296 N1 - ICONIP 2019, Sydney LA - English DB - MTMT ER - TY - JOUR AU - Győrfi, Ágnes AU - Karetka-Mezei, Zoltán AU - Iclanzan, Dávid András AU - Kovács, Levente AU - Szilágyi, László TI - A Study on Histogram Normalization for Brain Tumour Segmentation from Multispectral MR Image Data JF - LECTURE NOTES IN COMPUTER SCIENCE J2 - LNCS VL - 11896 PY - 2019 SP - 375 EP - 384 PG - 10 SN - 0302-9743 DO - 10.1007/978-3-030-33904-3_35 UR - https://m2.mtmt.hu/api/publication/31121281 ID - 31121281 N1 - Edited by:Nystrom I (Nystrom, I) ; Heredia, YH (Heredia, YH) ; Nunez, VM (Nunez, VM) Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications (COIARP 2019) ISBN978-3-030-33904-3978-3-030-33903-6 Chapter 35 CIARP 2019, La Habana LA - English DB - MTMT ER - TY - JOUR AU - Borsos, Balint AU - Nagy, Laszlo AU - Iclanzan, Dávid András AU - Szilágyi, László TI - Automatic detection of hard and soft exudates from retinal fundus images JF - ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA J2 - ACTA UNIV SAP INFORM VL - 11 PY - 2019 IS - 1 SP - 65 EP - 79 PG - 15 SN - 1844-6086 DO - 10.2478/ausi-2019-0005 UR - https://m2.mtmt.hu/api/publication/30986120 ID - 30986120 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Iclanzan, Dávid András AU - Szilágyi, Sándor Miklós AU - Szilágyi, László ED - Cheng, Long ED - Leung, Andrew Chi Sing ED - Ozawa, Seiichi TI - Evolving Computationally Efficient Hashing for Similarity Search T2 - Neural Information Processing PB - Springer Netherlands CY - Cham SN - 9783030041786 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 11302. PY - 2018 SP - 552 EP - 563 PG - 12 DO - 10.1007/978-3-030-04179-3_49 UR - https://m2.mtmt.hu/api/publication/31121339 ID - 31121339 N1 - ICONIP 2018, Siem Reap LA - English DB - MTMT ER -