@inproceedings{MTMT:34789639,
	title = {Approximation of Heat Transfer Coefficients by using AI techniques},
	url = {https://m2.mtmt.hu/api/publication/34789639},
	author = {Szabo-Gali, Akos and Biczó, Zoltán Bálint and Kohlhéb, Róbert and Kertész, Gábor and Masahiro, Okumiya and Kovács, Levente and Felde, Imre},
	booktitle = {Proceedings of 28th IFHTSE 2023 Congress},
	unique-id = {34789639},
	year = {2023},
	orcid-numbers = {Kertész, Gábor/0000-0002-8845-8301; Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:33861837,
	title = {A Novel Machine Learning Solution for the Inverse Heat Conduction Problem with Synthetic Datasets},
	url = {https://m2.mtmt.hu/api/publication/33861837},
	author = {Biczó, Zoltán Bálint and Szénási, Sándor and Felde, Imre},
	booktitle = {IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings},
	unique-id = {33861837},
	year = {2023},
	pages = {117-122},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:33552228,
	title = {Application of New Artificial Neural Network model to Predict Heat Transfer Coefficients during Quenching},
	url = {https://m2.mtmt.hu/api/publication/33552228},
	author = {Biczó, Zoltán Bálint and Martha, Guerrero and Kovács, Levente and Felde, Imre},
	booktitle = {27TH IFHTSE CONGRESS & EUROPEAN CONFERENCE ON HEAT TREATMENT 2022},
	unique-id = {33552228},
	year = {2022},
	pages = {63-68},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:32732425,
	title = {Safe Overfitting of Boosted Tree Algorithm in Heat Transfer Modeling},
	url = {https://m2.mtmt.hu/api/publication/32732425},
	author = {Biczó, Zoltán Bálint and Szénási, Sándor and Felde, Imre},
	booktitle = {IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics SAMI (2022)},
	doi = {10.1109/SAMI54271.2022.9780808},
	unique-id = {32732425},
	year = {2022},
	pages = {379-382},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:32076922,
	title = {Distorsion Prediction of Additive Manufacturing Process using Machine Learning Methods},
	url = {https://m2.mtmt.hu/api/publication/32076922},
	author = {Biczó, Zoltán Bálint and Felde, Imre and Szénási, Sándor},
	booktitle = {15th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2021},
	doi = {10.1109/SACI51354.2021.9465625},
	unique-id = {32076922},
	abstract = {Additive Manufacturing is a widely used technology; however, it also has several open questions. In the modelling phase, it is necessary to predict undesired distortions. There are several finite-element based simulation tools for this purpose, but these are costly and resource-intensive. This paper presents a novel approach based on several Machine Learning methods (decision trees, random forest, gradient boosted trees, support vector machines, deep learning) to speed-up this process. The results show that it is possible to give accurate predictions with these methods.},
	keywords = {machine learning; 3D printing; Computer Science, Information Systems; Additive manufacturing; Computer Science, Artificial Intelligence; auto-ML},
	year = {2021},
	pages = {249-252},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:30715665,
	title = {Activity Pattern Analysis of the Mobile Phone Network During a Large Social Event},
	url = {https://m2.mtmt.hu/api/publication/30715665},
	author = {Pintér, Gergő and Nadai, Laszlo and Bognar, Gabor and Biczó, Zoltán Bálint and Felde, Imre},
	booktitle = {2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF)},
	doi = {10.1109/RIVF.2019.8713741},
	unique-id = {30715665},
	year = {2019},
	pages = {1-5},
	orcid-numbers = {Pintér, Gergő/0000-0003-4731-3816}
}