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14 results on '"Csizmadia, Gábor"'

4. Musical Preferences among Students Aged 9–19: A Study on Musical Genres and Styles.

Author

Szabó, Norbert, Földi, Fanni, Oo, Tun Zaw, Csizmadia, Gábor, and Józsa, Krisztián

Subjects
POPULAR music genres, MUSICAL style, PSYCHOLOGY of students, AGE groups, MUSIC education, MUSIC videos, FOLK music
Abstract

This study aimed to explore students' musical genre preferences, exploring variations across gender and age groups in Hungary. Additionally, we investigated the relationships among the popularity of musical genres and connections between musical programs and related activities. With the help of stratified sampling, we examined the opinions of students aged 9–19 about each genre (N = 1159) using specific musical examples. In this study, musical genres were classified into four main types based on related music videos, including classical popular music (CPM), rock music (RM), classical art music (CAM), and folk music (FM). Different types of analyses, such as t-tests, one-way and two-way ANOVAs, and correlational analyses such as correlation coefficients (r), chi-square tests, and eta squares, were employed in this study. The results showed that the popularity of the four pieces of music classified as CPM, RM, CAM, and FM had a moderately strong correlation with each other. The correlations between FM, CPM, and RM did not differ from each other, but they showed significantly weaker correlation values than the correlation between FM and CAM. Moreover, significant differences were found in the students' perceptions of the genres based on their gender and age groups. Among the four performances, the girls had a greater preference compared to the boys for each one, except for the folk music piece. The youngest age group (9–12) showed the highest preference for the CPM, although the difference compared to the oldest age group (17–19) was not statistically significant. Therefore, this study is beneficial for music education, focusing on students' genre preferences, including CPM, RM, CAM, and FM. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Human Activity Recognition of Children with Wearable Devices Using LightGBM Machine Learning

Author

Csizmadia, Gábor, Liszkai-Peres, Krisztina, Ferdinandy, Bence, Miklósi, Ádám, and Konok, Veronika

Subjects
Machine Learning, Male, Wearable Electronic Devices, Multidisciplinary, Movement, Humans, Female, Human Activities, Child, Algorithms
Abstract

Human activity recognition (HAR) using machine learning (ML) methods is a relatively new method for collecting and analyzing large amounts of human behavioral data using special wearable sensors. Our main goal was to find a reliable method which could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59 – 8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM)learning algorithm, a decision based method with a 3-fold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. The overall accuracy was 0.95, which is at the top segment of the previously published similar HAR data. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.

Published
2021

14. Studies on the Variation of Agronomic Traits in Some Faba Bean (Vicia faba L.) Landraces from Romania.

Author

Vörösváry, Gábor, Holly, László, Strajeru, Silvia, Tamás, Judit, Constantinovici, Dana, Csizmadia, Gábor Málnási, and Horváth, Lajos

Subjects
FAVA bean, AGRONOMY, AGROBIODIVERSITY
Abstract

The objective of this study were to evaluate the variation of agronomic characters of some faba bean landraces collected in two different regions (Gheorgheni, Ciuc) from Romania. The seeds of these landraces were collected from local farmers. All accessions evaluated belong to the large-seeded type (V. faba var. major). The trial was conducted at the Research Centre for Agrobiodiversity, Tápiószele during 2009 cropping season. Two accessions from Gheorgheni (Lazarea, Remetea) and four accessions from Ciuc regions (Mihaileni, Ineu) were included in the trial. Two local varieties of faba bean from Hungary (Onga, Békéscsaba) were used as standards. Agronomic traits were evaluated according to the Faba bean Descriptors (IBPGR, 1985, Bioversity International, ICARDA, 2009) during the growing season. These investigations included phenophasic morphological and agronomic characters. Results showed differences among landraces for phenophasic, morphological and agronomic traits. These investigations revealed differences among the landraces for all characters studied. The variation in phenophases length was attributed to differences among agro-ecological zones. Faba bean landraces collected from two different climatic zones represent new and potentially valuable genetic resources for breeding programs. [ABSTRACT FROM AUTHOR]

Published
2011

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