Dániel Csikor

57788035100

Publications - 10

The role of generative AI in improving the sustainability and efficiency of HR recruitment process

Boglárka Eisingerné Balassa Dániel Csikor Réka Koteczki

Publication Name: Discover Sustainability

Publication Date: 2025-12-01

Volume: 6

Issue: 1

Page Range: Unknown

Description:

Generative artificial intelligence (GAI) is becoming increasingly important in business processes, including human resource management. GAI can offer the potential to automate repetitive tasks in recruitment processes, optimise decision making, and reduce administrative burdens. Although AI can help increase operational efficiency, environmental pressures must also be taken into account. AI models require significant computing power, resulting in high energy consumption and increased CO2 emissions. This dichotomy may raise the question of whether the efficiency gains provided by GAI outweigh the environmental burden. This article examines the environmental impacts of GAI on HRM through a case study. The research combines qualitative and quantitative methods: expert interviews are used to explore practical applications, while calculations on energy consumption, costs, and emissions are carried out by comparing traditional and AI-based recruitment methods. The results of the case study showed that the integration of GAI led to efficiency gains. The time required for the recruitment process was reduced by 13.25 h, which could save thousands of man-hours per year. At the same time, costs and energy consumption and associated carbon emissions were reduced. The study highlights the duality of “AI for sustainability” and “sustainability of AI”, highlighting that while GAI can contribute to more sustainable corporate operations, its own environmental footprint raises questions about long-term sustainability. The results will provide HR professionals, decision makers, and organisations with practical insights into the potential for sustainable use of AI.

Open Access: Yes

DOI: 10.1007/s43621-025-01484-3

Optimizing Human Resources for Efficiency and Sustainability through Business Process Modelling with Large Language Models

Boglárka Eisingerné Balassa Dániel Csikor Réka Koteczki

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 499-504

Description:

In today's business situations, effective use of human resources is critical to organisational performance and long-term growth. Employees frequently squander important time on monotonous jobs that take up their time. This problem negatively affects not only business efficiency but also labour market satisfaction and economic growth, contrary to the goals of Sustainable Development Goals (SDGs) 8 (Decent work and economic growth) and 9 (Industry, innovation and infrastructure). The aim of the research was to see how large language models (LLM) can help to optimise human resources by automating less skill-intensive, time-consuming tasks. For the analysis, a case study was conducted using the methodology of business process modelling (BPM) to compare the efficiency of a project management task ('reporting') with and without the use of ChatGPT technology. The model was used to analyse quantitative data such as process duration, labour costs, overhead costs and overhead volume. The research shows that LLM can significantly reduce the time workers spend on routine tasks, allowing them to focus on higher-value jobs that match their skills. In the case where ChatGPT was used by the participants to prepare the report, the whole process took 455.5 h less. The time savings contributed to a reduction in wage costs and overheads, which in total represents a saving of € 8,046.30. Based on the results, it is believed that LLMs have the potential to increase efficiency and sustainability.

Open Access: Yes

DOI: 10.3303/CET24114084

Induction Motor Energy Efficiency Investigation †

I. Szenasy Dániel Csikor

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

The energy efficiency of the induction motor (IM) is extremely important in the drives of electric vehicles. The first part of the article examines the possibilities of modifying the torque and efficiency curves in order to realize high-torque work points more efficiently by modifying the motor’s impedances. Later, it analyzes the flux-dependent changes in the highly load-dependent efficiency based on the literature. The FEM-type investigations of the experimental IM development carried out at the Vehicle Industry Research Center Institute of the Széchenyi István University offer new control options for increasing the efficiency of work points with lower torque and speed as well as for modifying the examined torque curve sections.

Open Access: Yes

DOI: 10.3390/engproc2024079075

How Perceptual Variables Influence the Behavioral Intention to Use Autonomous Vehicles †

Boglárka Eisingerné Balassa Dániel Csikor Réka Koteczki

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

The rapid development of technology makes the acceptance of autonomous vehicles (AVs) in modern transport a key issue. The aim of the present research was to explore the impact of AVs on behavioral intention to use. An online survey was conducted, in which factors such as perceived usefulness (PU), perceived ease of use (PEU), perceived trust (PT), social influence (SI), and behavioral intention to use (BIU) were investigated. As a result of the investigation, the correlation analysis revealed that there was a significant positive relationship between the intention and all the factors examined. The practical utility of this research is that the results will support developers and vehicle manufacturers in understanding how different social factors influence the adoption of AVs.

Open Access: Yes

DOI: 10.3390/engproc2024079023

Examining the Expected Impacts of Autonomous Vehicles on Transport, Their Cross-Impacts and the Relationships Between Impacts †

Dániel Csikor Réka Koteczki Ferenc Szauter

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

We have a promising future in improving road safety, which requires a comprehensive assessment of the impact of systems on road transport. Research is timely as we are in a transitional period until the advent of autonomous vehicles. This research has identified the factors that influence the impact of vehicle control algorithms on road transport, depending on technological feasibility, as well as the factors that influence development directions, and then used a questionnaire survey to assess the factors that are most important to society. The correlations between the impacts were analysed and, after identifying the different clusters, this research assessed the opinions of different age groups on the expected impact of self-driving vehicles and the factors influencing their introduction.

Open Access: Yes

DOI: 10.3390/engproc2024079062

Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles

Peter Foldesi Szabolcs Kocsis Szurke Dániel Csikor Ferenc Szauter László T. Kóczy

Publication Name: Energies

Publication Date: 2022-07-01

Volume: 15

Issue: 13

Page Range: Unknown

Description:

Hybrid vehicles are gaining increasing global prominence, especially in the military, where unexpected breakdowns or even power deficits are not only associated with greater expense but can also cost the lives of military personnel. In some cases, it is extremely important that all battery cells and modules deliver the specified amount of capacity. Therefore, it is recommended to introduce a new measurement line of rapid diagnostics before deployment, in addition to the usual procedures. Using the results of rapid testing, we recommend the introduction of a hierarchical three-step diagnostics and assessment procedure. In this procedure, the key factor is the building up of a hierarchical tree-structured fuzzy signature that expresses the partial interdependence or redundancy of the uncertain descriptors obtained from the rapid tests. The fuzzy signature structure has two main important components: the tree structure itself, and the aggregations assigned to the internal nodes. The fuzzy signatures that are thus determined synthesize the results from the regular maintenance data, as well as the effects of the previous operating conditions and the actual state of the battery under examination; a signature that is established this way can be evaluated by “executing the instructions” coded into the aggregations. Based on the single fuzzy membership degree calculated for the root of the signature, an overall decision can be made concerning the general condition of the batteries.

Open Access: Yes

DOI: 10.3390/en15134791

Investigating the Impact of Environmental Factors on Autonomous Vehicle Sensors †

János Hollósi Dániel Csikor Ferenc Szauter

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

The operation of autonomous vehicles requires the coordinated operation of a number of sensors to improve road safety. Data from the sensors are processed by vehicle control algorithms, which then make decisions. If there is a degradation in the efficiency of these sensors, the reliability of the whole system is affected. Different weather conditions affect the efficiency of the system. The research has identified the weather factors that affect the performance of the sensors based on a literature search. Following the literature analysis, a simulation test was carried out to investigate the extent to which the detection performance of a stereo video camera installed in an experimental autonomous vehicle is affected when part of the sensor is covered by a contaminant. The measurement was followed by a comparison of the vehicle and obstacle detection efficiency when the camera is completely clean and when part of the camera is covered.

Open Access: Yes

DOI: 10.3390/engproc2025113015

Simulation of Environment Recognition Systems for Autonomous Vehicles in CARLA Simulator †

Dániel Csikor Gábor Saly Dávid Gorza

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

Towards the introduction of autonomous vehicles, studying their functionality is becoming increasingly important. Detecting the environment in a self-driving vehicle is a very complex issue. The combination of different sensors is essential for safe and reliable operation. Detection enables the vehicle to accurately recognize and track surrounding objects, understand changes in the dynamic environment, and adapt to different situations. Improving environmental sensing and object recognition is essential for the widespread deployment of self-driving vehicles. In addition to real-world tests, simulation environments provide an opportunity to investigate the operation of autonomous vehicles. Simulations are cost-effective methods for examining the processing of information from the vehicle environment and identifying the current limitations and problems of these technologies. In the CARLA simulator environment, object detection is reproduced in realistic traffic situations. Based on the results, the detection performance was analyzed using interference matrices, F1 scores, accuracy, and coverage metrics.

Open Access: Yes

DOI: 10.3390/engproc2025113030

Ride-Sharing Services in Regional Context: Consumer Attitudes and Reuse Intentions in Western Hungary

Boglárka Eisingerné Balassa Dániel Csikor Réka Koteczki Boglárka Eisinger Balassa Ferenc Szauter

Publication Name: Applied Sciences Switzerland

Publication Date: 2026-01-01

Volume: 16

Issue: 2

Page Range: Unknown

Description:

This study examines consumer attitudes and experiences related to ride-sharing services in the Western Transdanubia region of Hungary. Despite the growing global popularity of shared mobility solutions, there is little empirical evidence on regional consumer acceptance patterns in the Hungarian context. Based on a structured questionnaire survey involving 500 respondents, this research explores the relationship between satisfaction with past ride-sharing experiences and the intention to reuse such services in the future. The results reveal a high willingness to reuse among those who have already tried ridesharing, yet the correlation analysis shows that satisfaction dimensions alone are not significant predictors of reuse intention. In contrast, attitudinal factors, such as preference over other transport modes, willingness to make recommendations, and perceived accessibility, exhibit strong correlations with acceptance of ride-sharing. The findings emphasise the key role of attitudes and trust in shaping consumer decisions. The paper contributes to the literature by providing regionally grounded empirical insights and offers practical and policy-level recommendations to support the diffusion of sustainable shared mobility services.

Open Access: Yes

DOI: 10.3390/app16021055

Performance Degradation of Object Detection Neural Networks Under Natural Visual Contamination in Autonomous Driving

János Hollósi Dániel Csikor

Publication Name: Computers

Publication Date: 2026-04-01

Volume: 15

Issue: 4

Page Range: Unknown

Description:

The operation of driver assistance systems and autonomous vehicles requires a sensor system and a control algorithm. Sensors provide information to detect people, vehicles and objects in the vehicle’s environment; however, their performance can be degraded by adverse environmental conditions and contamination. This literature review identified factors that reduce sensor visibility, such as weather conditions and external contamination. In this study, the detection efficiency of state-of-the-art neural network-based object detectors was examined in a simulation environment using a synthetic dataset. A custom dataset comprising six urban and suburban traffic scenarios was created, including clean images and ten contaminated variants per scene with increasing mud coverage. The results show that contamination leads to a measurable reduction in detection performance across all models. Smaller variants are more sensitive to degradation, while medium-complexity models provide a favorable balance between robustness and computational cost. Increasing model size yields limited additional robustness, and performance differences between architectures highlight the importance of model design. Furthermore, the spatial distribution of contamination, particularly near the image center, has a significant impact on performance in addition to its overall extent.

Open Access: Yes

DOI: 10.3390/computers15040254