A5080190018- Complex Network Analysis Techniques
- Bioinformatics and Genomic Networks
- Online Learning and Analytics
- Mental Health Research Topics
- Theoretical and Computational Physics
- Time Series Analysis and Forecasting
- Misinformation and Its Impacts
- Anomaly Detection Techniques and Applications
- Pancreatitis Pathology and Treatment
- Pancreatic and Hepatic Oncology Research
- Artificial Intelligence in Healthcare and Education
- Sentiment Analysis and Opinion Mining
- Higher Education Learning Practices
- Hungarian Social, Economic and Educational Studies
- Network Security and Intrusion Detection
- Complex Systems and Time Series Analysis
- Software System Performance and Reliability
- Gastrointestinal Bleeding Diagnosis and Treatment
- Computational Drug Discovery Methods
- Stochastic processes and statistical mechanics
- Statistics Education and Methodologies
- Opinion Dynamics and Social Influence
- Sports Analytics and Performance
- Topological and Geometric Data Analysis
- Medical Education and Admissions
Budapest University of Technology and Economics
2016-2025
Semmelweis University
2025
Montavid Thermodynamic Research Group
2018-2024
Institute for Computer Science and Control
2022
Corvinus University of Budapest
2022
Aquincum Institute of Technology
2021
University of Debrecen
2019-2020
Abstract Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for early identification students at risk dropping out has gained a lot interest recently. However, there been little discussion on dropout prediction using interpretable (IML) explainable artificial intelligence (XAI) tools.In this work, data large public Hungarian university, we demonstrate how IML XAI can support...
Abstract Functional tissue units form the basic building blocks of organs and are important for understanding modeling healthy physiological function organ changes that occur during disease states. In this comprehensive catalog 22 anatomically based, nested functional from 10 human organs, we document definition, physical dimensions, blood vasculature connections, cellular composition. All anatomy terms mapped to multi-species Uber-anatomy Ontology (Uberon) cells Cell support computational...
Predicting student performance, preventing failure and identifying the factors influencing dropout are issues that have attracted a great deal of research interest recently. In this study, we employ evaluate several machine learning algorithms to identify students at-risk predict university programs based on data available at time enrollment (secondary school personal details). We also present data-driven decision support platform for education directorate stakeholders. The models built...
The early identification of college students at risk dropout is great interest and importance all over the world, since leaving higher education associated with considerable personal social costs. In Hungary, especially in STEM undergraduate programs, rate particularly high, much than EU average. this work, using advanced machine learning models such as deep neural networks gradient boosted trees, we aim to predict final academic performance Budapest University Technology Economics....
Abstract Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk in-hospital mortality in admitted due overt GIB. analyzed prospective, multicenter Hungarian GIB Registry’s data. The predictive performance XGBoost CatBoost algorithms with Glasgow-Blatchford (GBS), pre-endoscopic Rockall ABC scores were compared. evaluated our using five-fold cross-validation, was measured...
At many universities, student evaluations of teaching (SET) are used for determining promotion, tenure, and other financial benefits professors, which gives incentive them to try increase their scores. However, previous research, mainly based on US data, indicates that SET scores not only depend effectiveness quality, but also several factors, most notably grades. In this study, we contribute stream literature by investigating the effects grade inflation using four years data from two...
Abstract Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently use are either too complicated or require data that unavailable on admission lack sufficient predictive value. We therefore aimed to develop tool aid prediction. The XGBoost machine learning algorithm processed from 2387 patients with AP. confidence of model was estimated by bootstrapping method and interpreted via 10th 90th percentiles prediction scores. Shapley...
High dropout rates and delayed completion in higher education are associated with considerable personal social costs. In Latin America, 50% of students drop out, only the remaining ones graduate on time. Therefore, there is an urgent need to identify at risk understand main factors dropping out. Together emergence efficient computational methods, rich data accumulated educational administrative systems have opened novel approaches promote student persistence. order support research related...
Due to the big data accumulated in educational administrative systems and due advance of machine learning techniques, a new scientific discipline has emerged last few years, namely science. An important research objective this field is predict dropout improve graduation rates, particular STEM higher education.The goal study identify students at risk dropping out large Hungarian technical university using predictive analytical tools. We use 10,196 who finished their undergraduate studies...
Complex networks have attracted a great deal of research interest in the last two decades since Watts & Strogatz, Barab\'asi Albert and Girvan Newman published their highly-cited seminal papers on small-world networks, scale-free community structure complex respectively. These fundamental initiated new era establishing an interdisciplinary field called network science. Due to multidisciplinary nature field, diverse but not divided science has emerged past 20 years. This paper honors...
Abstract The fractal nature of complex networks has received a great deal research interest in the last two decades. Similarly to geometric fractals, fractality can also be defined with so-called box-covering method. A network is called if minimum number boxes needed cover entire follows power-law relation size boxes. been associated various properties throughout years, for example, disassortativity, repulsion between hubs, long-range-repulsive correlation, and small edge betweenness...
Abstract This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping content popularity media. Using Latent Dirichlet Allocation BERTopic models, we define sets 120 New York Times (NYT) topics to compare with 899,766 image-with-text memes from Reddit, showing that aligns many same topical patterns observed outlets. Topicality is formalized based temporal distributions over past 5...
Curriculum prerequisite networks have a central role in shaping the course of university programs. The analysis has attracted lot research interest recently since designing an appropriate network is great importance both academically and economically. It determines learning goals program also huge impact on completion time dropping out. In this article, we introduce data-driven probabilistic student flow approach to characterize study distribution graduation based topology rate courses. We...
An important problem in higher education is to find the most suitable admission procedure that can distinguish between students with high academic potential and future dropouts. Admissions usually rely on pre-enrolment achievement measures; therefore, it crucial these selection criteria have predictive validity achievement. In this study, we use sophisticated statistical learning methods, such as receiver operating characteristic curve analysis, logistic Tobit regression analyse of Hungarian...
Abstract In a round‐robin tournament, team may lack the incentive to win if its final rank does not depend on outcome of matches still be played. This paper introduces classification scheme determine these weakly (where one is indifferent) or strongly both teams are stakeless in double contest with four teams. The probability that such arise can serve as novel fairness criterion compare and evaluate match schedules. Our approach illustrated by UEFA Champions League group stage. A simulation...
Research on fractal networks is a dynamically growing field of network science. A central issue to analyze fractality with the so-called box-covering method. As this problem known be NP-hard, plethora approximating algorithms have been proposed throughout years. This study aims establish unified framework for comparing by collecting, implementing, and evaluating these methods in various aspects including running time approximation ability. work might also serve as reference both researchers...
Understanding what (and to extent) psychological factors affect university performance has attracted a lot of research interest recently. In this paper, we use logistic regression models study the incremental predictive power positive over pre-enrollment achievement measures on academic performance. The is based data 302 business and economics undergraduate students from Budapest University Technology Economics. Coping proved be most important factor that sheds light importance stress...
An essential task in higher education is to construct a fair admission procedure. A great deal of research has been conducted on central aspect admission: predictive validity. However, the best our knowledge, this first study that investigates how validity composite score could be improved without redesigning tests and introducing new measures. In study, relying existing instruments Hungarian nationally standardized university entrance score, we an alternative not only but also lower...
The anomaly detection method presented by this article has a special feature: it not only indicates whether or an observation is anomalous but also tells what exactly makes unusual. Hence, provides support to localize the reason of anomaly. proposed approach model based; relies on multivariate probability distribution associated with observations. Since rare events are present in tails distributions, we use copula functions, which able fat-tailed distributions well. procedure scales well;...
Understanding, modeling and visualizing student performance academic progress have attracted considerable research attention recently. In particular, early leaving retention of students are central problems associated with significant personal social cost. this study, we provide an efficient visualization tool to analyze flow patterns by alluvial Sankey diagrams. We tracked the more than 30,000 undergraduate from Budapest University Technology Economics enrolled between 2010 2017. Our method...