@article{MTMT:34786234,
	title = {A FESZÜLTSÉG ÉS ÁRAM HARMONIKUS TORZÍTÁSOK, AZ IEEE 519-2022 SZABVÁNY},
	url = {https://m2.mtmt.hu/api/publication/34786234},
	author = {Kovács, Attila and Somogyiné Molnár, Judit and Jármai, Károly},
	journal-iso = {GÉP},
	journal = {GÉP},
	volume = {75},
	unique-id = {34786234},
	issn = {0016-8572},
	year = {2024},
	pages = {35-38},
	orcid-numbers = {Kovács, Attila/0009-0007-1812-7025; Jármai, Károly/0000-0001-8487-4327}
}

@inproceedings{MTMT:34770083,
	title = {Transformer Models in Natural Language Processing},
	url = {https://m2.mtmt.hu/api/publication/34770083},
	author = {Kovács, László and Csépányi-Fürjes, László and Sewunetie, Walelign Tewabe},
	booktitle = {The 17th International Conference Interdisciplinarity in Engineering},
	doi = {10.1007/978-3-031-54674-7_14},
	unique-id = {34770083},
	abstract = {The development of transformer-based language models brings a paradigm shift in the world of smart applications. The ChatGPT model opened new horizons in the field of natural language understanding and generation. This paper presents a survey on the history of transformer models, on the basic architecture and application areas. The last section is devoted to two use cases experiments on the application of ChatGPT. The first domain relates to Human-Level Programming and the second focuses on the semantic functional parsing of text sentences. The performed analysis demonstrates the big potential in the transformer language models.},
	year = {2024},
	pages = {180-193},
	orcid-numbers = {Csépányi-Fürjes, László/0000-0001-5687-1671}
}

@mastersthesis{MTMT:34747185,
	title = {Rendellenesség alapú behatolás érzékelő rendszerek gépi tanulási módszerrel történő tanítása},
	url = {https://m2.mtmt.hu/api/publication/34747185},
	author = {Göcs, László},
	unique-id = {34747185},
	year = {2024}
}

@mastersthesis{MTMT:34726333,
	title = {MapReduce Performance Analysis and Modelling of Auto-Scaling Applications Behaviours},
	url = {https://m2.mtmt.hu/api/publication/34726333},
	author = {Gavua, Ebenezer Komla},
	unique-id = {34726333},
	year = {2024}
}

@article{MTMT:34721788,
	title = {A Comparative Study of ChatGPT-Based and Hybrid Parser-based Sentence Parsing Methods for Semantic Graph-Based Induction},
	url = {https://m2.mtmt.hu/api/publication/34721788},
	author = {Sewunetie, Walelign Tewabe and Kovács, László},
	doi = {10.1109/ACCESS.2024.3360480},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	unique-id = {34721788},
	issn = {2169-3536},
	year = {2024},
	eissn = {2169-3536}
}

@article{MTMT:34695021,
	title = {Structure-Aware Scheduling Algorithm for Deadline-Constrained Scientific Workflows in the Cloud},
	url = {https://m2.mtmt.hu/api/publication/34695021},
	author = {Al-Haboobi, Ali and Kecskeméti, Gábor},
	doi = {10.14569/IJACSA.2024.0150280},
	journal-iso = {IJACSA},
	journal = {INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS},
	volume = {15},
	unique-id = {34695021},
	issn = {2158-107X},
	abstract = {Cloud computing provides pay-per-use IT services through the Internet. Although cloud computing resources can help scientific workflow applications, several algorithms face the problem of meeting the user’s deadline while minimising the cost of workflow execution. In the cloud, selecting the appropriate type and the exact number of VMs is a major challenge for scheduling algorithms, as tasks in workflow applications are distributed very differently. Depending on workflow requirements, algorithms need to decide when to provision or de-provision VMs. Therefore, this paper presents an algorithm for effectively selecting and allocating resources. Based on the workflow structure, it decides the type and number of VMs to use and when to lease and release them. For some structures, our proposed algorithm uses the initial rented VMs to schedule all tasks of the same workflow to minimise data transfer costs. We evaluate the performance of our algorithm by simulating it with synthetic workflows derived from real scientific workflows with different structures. Our algorithm is compared with Dyna and CGA approaches in terms of meeting deadlines and execution costs. The experimental results show that the proposed algorithm met all the deadline factors of each workflow, while the CGA and Dyna algorithms met 25% and 50%, respectively, of all the deadline factors of all workflows. The results also show that the proposed algorithm provides more cost-efficient schedules than CGA and Dyna.},
	year = {2024},
	eissn = {2156-5570},
	pages = {792-802},
	orcid-numbers = {Kecskeméti, Gábor/0000-0001-5716-8857}
}

@CONFERENCE{MTMT:34654257,
	title = {Analyzing and Designing an Optimization System for In-Plant Complex Production Based on Industry 4.0},
	url = {https://m2.mtmt.hu/api/publication/34654257},
	author = {Akkad, Mohammad Zaher and Bányai, Tamás},
	booktitle = {9th International Conference on Advanced Engineering and Technology (ICAET)},
	doi = {10.4028/p-Es6Xrb},
	unique-id = {34654257},
	abstract = {Industry 4.0 symbolizes various applications and technologies that have many possible positive effects within the industrial field. The complex production area contains longer product cycle times and multiple levels of subassemblies, and this reflects a higher need for raising production efficiency and optimizing the resources and time. Adopting developed Industry 4.0 technologies is considered a promising way for achieving this since they contribute directly to real-time data analysis, remote operation, and complete product life analysis next to further tools that allow more profound and inclusive analysis in the target complex production system. This article discusses the integration of Industry 4.0 technologies with in-plant complex production processes. A proposed system with an optimization purpose is designed and described that focuses on using multi-level integration processes effectively.},
	year = {2024},
	pages = {53-58},
	orcid-numbers = {Akkad, Mohammad Zaher/0000-0002-1269-6274}
}

@article{MTMT:34550360,
	title = {A Hybrid Approach for Automatic Question Generation from Program Codes},
	url = {https://m2.mtmt.hu/api/publication/34550360},
	author = {Alshboul, Jawad and Baksáné Varga, Erika},
	doi = {10.14569/IJACSA.2024.0150102},
	journal-iso = {IJACSA},
	journal = {INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS},
	volume = {15},
	unique-id = {34550360},
	issn = {2158-107X},
	year = {2024},
	eissn = {2156-5570},
	pages = {10-17}
}

@article{MTMT:34518995,
	title = {Automatic question generation using extended dependency parsing},
	url = {https://m2.mtmt.hu/api/publication/34518995},
	author = {Sewunetie, Walelign Tewabe and Kovács, László},
	doi = {10.11591/ijeecs.v33.i2.pp1108-1115},
	journal-iso = {IJEECS},
	journal = {INDONESIAN JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE},
	volume = {33},
	unique-id = {34518995},
	issn = {2502-4752},
	abstract = {The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures.},
	year = {2024},
	eissn = {2502-4760},
	pages = {1108-1115}
}

@mastersthesis{MTMT:34498054,
	title = {Utilizing Data-Balancing Techniques to Improve AI-Based Prediction of Software Bugs and Code Smells},
	url = {https://m2.mtmt.hu/api/publication/34498054},
	author = {Alnor Adam Khleel, Nasraldeen},
	doi = {10.14750/ME.2024.012},
	unique-id = {34498054},
	year = {2024}
}