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Economical Camera-based Measuring Scheme for Roundabout Traffic

Periodica Polytechnica Transportation engineering 2025年第2期53卷 235-241页

作者： Kovács, Tamás Kovács, Lóránt Department of Information Technology GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki út 10. KecskemétH-6000 Hungary

Counting the turning movements in a four-leg roundabout is a challenging task and often executed by vehicle recognition and tracking on traffic videos. In order to obtain accurately all the 12 flow values of the origin-destination (O-D) matrix we need to measure arbitrary chosen 12 linearly independent flow values. However, the larger the area to be observed the more cameras are needed because the good pixel resolution of the vehicles and the proper camera inclination angles are essential. Here we propose a novel measuring scheme, which observes only two roundabout legs instead of observing all the four, as a result obtaining all the 12 flow values requires a reduced number of cameras. The directly measured flow values and the mathematical method of determining the O-D matrix are given in details. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Object recognition

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Effects of machining parameters, coolant oil composition, and coatings on the output characteristics of turning of X5CrNi18-10 stainless steel

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Wear 2025年

作者： Kónya, Gábor Kovács, Zsolt F. Department of Innovative Vehicles and Materials GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki St. 10. KecskemétH-6000 Hungary

Austenitic stainless steels are widely used in the automotive industry, the aerospace industry, and the food industry alike. Due to their applications, most components are manufactured by machining, leading to a broad interest in the machinability of these materials, especially those containing chromium and nickel, as these alloying elements impart high strength and poor thermal conductivity to the base material. In this research, the effects of dry machining, emulsion oil concentration (3 %, 6 %, 9 %, 12 %, 15 %, and 18 %) and uncoated, (Al,Ti)N and TiN tools with identical geometry were investigated on cutting force, tool wear and Ra, Rz, Rsk, Rku, Rpk, Rvk, A2 parameters using same cutting parameters. The coatings reduced cutting forces and tool wear by reducing the coefficient of friction and the chemical reactivity between the tool and the machined workpiece material. Additionally, the use of emulsions further reduced the cutting force, flank and crater tool wear by providing extra cooling and lubrication in the cutting zone compared to dry machining. Increasing the oil concentration resulted in higher cutting forces, as the oil adhered to the cutting edge, increasing the edge radius. In terms of surface roughness (Ra, Rz), dry machining resulted in the lowest values, which is related to tool wear, as an increasing tool tip radius produces more favourable surface roughness. For the Rsk-Rku parameters, the use of emulsion generally reduced these values, thereby enhancing the wear resistance of the surface. Dry machining yielded the most favourable Rvk, Rpk, and A2 parameters, which characterize the surface's oil retention capability and wear resistance. Considering tool wear and tribological characteristics, it is not advisable to increase the oil concentration above 9 %. © 2025

关键词： Austenitic stainless steel

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AI-based Adaptive Programming Education for Socially Disadvantaged Students: Bridging the Digital Divide

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TechTrends 2025年 1-18页

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

In the context of the digital economy, programming proficiency is an essential competency that promotes upward socio-economic mobility and expands career opportunities. However, students from socially disadvantaged backgrounds often face significant barriers to acquiring these skills, such as limited access to technology and educational resources. This study explores the impact of AI-based adaptive programming education on socially disadvantaged students' learning outcomes and engagement levels. The 122 participants in the research were divided into an experimental group (EG) that received AI-driven adaptive instruction, and a control group (CG) taught through the traditional curriculum during the 13-week experimental period. In the research, the combined pre-test/post-test assessments and self-report engagement questionnaires were used to focus on behavioural, emotional, and cognitive engagement. The findings showed that the EG demonstrated higher levels in programming knowledge and full-scale engagement across behavioural, emotional, and cognitive dimensions compared to the CG. This difference was confirmed through ANOVA, while ANCOVA, which controlled for students' socio-economic factors, further confirmed that the AI system had a positive impact on students’ results regardless of their socio-economic background. Indeed, current research findings suggest that AI-based adaptive learning environments can hold a promising opportunity to narrow educational gaps, boost engagement, and contribute toward better learning outcomes for socially disadvantaged students.

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Cutting Tests and Performance Evaluation of Recycled Pet in Fused Filament Fabrication

SSRN

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SSRN 2025年

作者： Kónya, Gábor Tóth, László Gerse, Péter Palásti, Ferenc Hansághy, Pál Ronkay, Ferenc Department of Innovative Vehicles and Materials GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki str. 10. KecskemétH-6000 Hungary

Fused Deposition Modelling (FDM) technology in 3D printing offers numerous advantages;however, surface quality remains a significant challenge. This study investigates how the hardness and melt flow properties of the raw material affect surface roughness and how this can be improved by post-machining. Modified recycled poly(ethylene terephthalate) (RPET) was used as the raw material in the experiments, considering aspects of the circular economy. The material properties were adjusted by varying the ratio of ethylene-butyl-acrylate-glycidyl-methacrylate (EBA-GMA) additive. It was demonstrated that a decrease in the melt flow rate (MFR) results in more uniform deposited layers, leading to smoother surfaces. By systematically varying turning parameters and applying statistical analysis, it was shown that feed rate has the most significant impact on surface roughness. Using a feed rate of 0.1 mm/rev reduced the average surface roughness from 13–25 µm to 2–4 µm. Scanning electron microscopy (SEM) revealed that post-processing surface characteristics were minimally influenced by material properties, suggesting that the results are likely applicable to 3D printing with other materials as well. © 2025, The Authors. All rights reserved.

关键词： Fused Deposition Modeling

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade

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Social sciences and Humanities Open 2025年 11卷

作者： Kovari, Attila Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét Hungary

This review examines the current state of integration and impact of AI-enhanced collaborative learning in the higher education sector. Given the rapid advances in technology, AI has enormous potential for application in educational settings, with benefits in terms of personalizing learning, better engaging learners and improving learning outcomes. Artificial intelligence tools, in particular machine learning, natural language processing and recommender algorithms, facilitate collaborative learning by enabling personalized learning through feedback and group work. Furthermore, this review concludes that predictive analytics and multimodal approaches supported by artificial intelligence have been shown to enhance student engagement and motivation, while personalized learning systems and recommender algorithms ensure the effectiveness of collaborative learning environments. It also identifies two other critical issues: good task design and effective emotional engagement and social presence in AI-based environments. In addition, it highlights some of the problems and ethical considerations arising from the integration of AI like transparency, data protection, and a balance between full automation and human touch. This review aims to integrate the current state and future opportunities of AI-enhanced collaborative learning within a higher education context to inform educators, researchers, and policy makers in pursuit of improving teaching and learning practices. © 2025 The Author

关键词： AI-Powered collaborative learning Educational outcomes Higher education Personalized learning Social presence Student engagement

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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Explainable AI chatbots towards XAI ChatGPT: A review

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Heliyon 2025年第2期11卷 e42077页

作者： Kovari, Attila Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eszterhazy ter 1 Eger 3300 Hungary Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Tancsics M. 1/A Dunaujvaros 2400 Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10 Kecskemét 6000 Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Óbuda University Bécsi street 96/B Budapest 1034 Hungary

Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale language models like ChatGPT. This paper aims to provide a critical review of explainable AI (XAI) methodologies for AI chatbots, with a particular focus on ChatGPT. Its main objectives are to investigate the applied methods that improve the explainability of AI chatbots, identify the challenges and limitations within them, and explore future research directions. Such goals emphasize the need for transparency and interpretability of AI systems to build trust with users and allow for accountability. While integrating such interdisciplinary methods, such as hybrid methods combining knowledge graphs with ChatGPT, enhancing explainability, they also highlight industry needs for explainability and user-centred design. This will be followed by a discussion of the balance between explainability and performance, then the role of human judgement, and finally the future of verifiable AI. These are the avenues through which insights can be used to guide the development of transparent, reliable and efficient AI chatbots. © 2025 The Author

关键词： AI chatbots ChatGPT Controllable AI Explainable AI (XAI) Natural language processing (NLP) Transparency

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Supporting the Pedagogical Assessment Process in Educational Institutions with Social Background Data Analysis and WTCAi System Integration

Supporting the Pedagogical Assessment Process in Educational...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Éva Karl György Molnár József Cserkó Beáta Orosz Eniko Nagy Várkerti Primary School Várpalota Hungary Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Győr Hungary Kandó Kálmán Faculty of Electrical Engineering Budapest Óbuda University Hungary János Faculty of Humanities Education and Social Sciences Széchenyi University Apáczai Csere Győr Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Óbuda University Doctoral School of Innovation Management Budapest Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Primary educational institutions aim to prepare children for further education and successful integration into the workforce. To consistently achieve this, they must meet the demands and expectations imposed by the constantly changing economic and social environments. Research shows that applying a constructive pedagogical approach is the most successful path, best fitting the expectations above. An essential and integral part of the educational process is the pedagogical supervision and evaluation procedure, aimed to assess performance and improve and support the learning process. A sad feature of the current system is that it needs to support individual learning paths sufficiently. Additionally, communication issues arising from the generational gap between teachers and students jeopardize the validity of the assessment tools. Educational institutions' advancement and continuous development can only be realized if the pedagogical supervision and evaluation system progresses and evolves to meet new challenges. Our research aims to develop such a method. We are working on creating a complex evaluation method supported by artificial intelligence (WTCAi - When The Child Ask with AI), which suggests appropriate items for assessment and evaluation to subject teachers. To achieve our main objective, we have planned to collect additional social background data from our students. Through analyzing these data, we hope to uncover numerous correlations that significantly influence the obtained data and results, thereby steering the development of the supervision-evaluation system in the most appropriate direction.

关键词： Economics Data analysis Correlation Fitting Education System integration Artificial intelligence Informatics Machine intelligence

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Impact of montmorillonite reinforcement on the physical recyclability of biobased and petroleum-based polyesters

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Heliyon 2025年第6期11卷

作者： Taha, Zoubeida Taha Bata, Attila Molnár, Béla Ronkay, Ferenc Department of Innovative Vehicles and Materials GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét H-6000 Hungary Doctoral School of Materials Sciences and Technologies Óbuda University Bécsi str. 96/b Budapest H-1034 Hungary Imsys Ltd Material Testing Laboratory Mozaik Street 14/A. Budapest H-1033 Hungary Department of Polymer Engineering Faculty of Mechanical Engineering Budapest University of Technology and Economics Muegyetem rkp. 3 Budapest H-1111 Hungary Jászberény Campus Eszterházy Károly Catholic University Eger Hungary

Montmorillonite (MMT) is commonly utilized to enhance the gas barrier, stiffness, and flame resistance of thermoplastic polyesters. This study examines the effect of MMT on the stability of various polyester properties during physical recycling. The primary objective was to determine whether MMT-reinforced materials exhibit more pronounced property changes compared to unreinforced counterparts during the recycling process. The research focused on two petroleum-based polymers—poly(ethylene terephthalate) (PET) and poly(butylene terephthalate) (PBT)—as well as two bio-based polymers, poly(lactic acid) (PLA) and poly(butylene succinate) (PBS). Key property changes affecting the usability of injection-moulded products, such as shrinkage, flexural strength, stiffness, and notched Izod impact strength, were measured. The underlying reasons for these changes were elucidated through investigations involving solution viscosity, morphology, and electron microscopy. The findings suggest that recycling with MMT leads to property alterations similar to those seen in unreinforced materials;however, the rate of change is considerably greater in nanocomposites. In particular, linear shrinkage decreased by 0.01–0.60 % and volumetric shrinkage by 1.0–2.6 % after the second extrusion cycle. Flexural strength increased by up to 3.8 % for PBT/MMT and 4.3 % for PLA/MMT nanocomposites, while impact strength showed a decrease of up to 13 % in PET nanocomposite. Additionally, MMT increased the crystallization temperature by 11–12 °C for PET and by 1–2 °C for the other polyester, highlighting its nucleation effect. This study also demonstrated that MMT does not uniformly enhance polyester recyclability;rather, its effects vary significantly depending on the polymer matrix. Additionally, a generalized model was developed to predict the variations in crystallinity and density in relation to changes in mechanical properties across all material types. Thus, these results provide valuable insight

关键词： Biopolymer Crystallinity Nanocomposites Polyesters Recycling

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