@article{MTMT:34789663,
	title = {Optimizing Speech Emotion Recognition with Deep Learning and Grey Wolf Optimization: A Multi-Dataset Approach},
	url = {https://m2.mtmt.hu/api/publication/34789663},
	author = {Suryakant, Tyagi and Szénási, Sándor},
	doi = {10.3390/a17030090},
	journal-iso = {ALGORITHMS},
	journal = {ALGORITHMS},
	volume = {17},
	unique-id = {34789663},
	keywords = {Neural network; LSTM; Deep learning; speech emotion recognition},
	year = {2024},
	eissn = {1999-4893},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:34543148,
	title = {Resource estimation for executing program codes using machine learning},
	url = {https://m2.mtmt.hu/api/publication/34543148},
	author = {Kovács, András and Szénási, Sándor and Lovas, Róbert},
	booktitle = {IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics : SAMI 2024 : Proceedings},
	doi = {10.1109/SAMI60510.2024.10432832},
	unique-id = {34543148},
	year = {2024},
	pages = {249-252},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717; Lovas, Róbert/0000-0001-9409-2855}
}

@inproceedings{MTMT:34543059,
	title = {Exploring the Potential of Convolutional Neural Networks in Sequential Data Analysis: A Comparative Study with LSTMs and BiLSTMs},
	url = {https://m2.mtmt.hu/api/publication/34543059},
	author = {Suryakant, Tyagi and Szénási, Sándor},
	booktitle = {IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics : SAMI 2024 : Proceedings},
	doi = {10.1109/SAMI60510.2024.10432861},
	unique-id = {34543059},
	year = {2024},
	pages = {243-248},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@article{MTMT:34444922,
	title = {Semantic speech analysis using machine learning and deep learning techniques: a comprehensive review},
	url = {https://m2.mtmt.hu/api/publication/34444922},
	author = {Suryakant, Tyagi and Szénási, Sándor},
	doi = {10.1007/s11042-023-17769-6},
	journal-iso = {MULTIMED TOOLS APPL},
	journal = {MULTIMEDIA TOOLS AND APPLICATIONS: AN INTERNATIONAL JOURNAL},
	unique-id = {34444922},
	issn = {1380-7501},
	abstract = {Human cognitive functions such as perception, attention, learning, memory, reasoning, and problem-solving are all significantly influenced by emotion. Emotion has a particularly potent impact on attention, modifying its selectivity in particular and influencing behavior and action motivation. Artificial Emotional Intelligence (AEI) technologies enable computers to understand a user's emotional state and respond appropriately. These systems enable a realistic dialogue between people and machines. The current generation of adaptive user interference technologies is built on techniques from data analytics and machine learning (ML), namely deep learning (DL) artificial neural networks (ANN) from multimodal data, such as videos of facial expressions, stance, and gesture, voice, and bio-physiological data (such as eye movement, ECG, respiration, EEG, FMRT, EMG, eye tracking). In this study, we reviewed existing literature based on ML and data analytics techniques being used to detect emotions in speech. The efficacy of data analytics and ML techniques in this unique area of multimodal data processing and extracting emotions from speech. This study analyzes how emotional chatbots, facial expressions, images, and social media texts can be effective in detecting emotions. PRISMA methodology is used to review the existing survey. Support Vector Machines (SVM), Naïve Bayes (NB), Random Forests (RF), Recurrent Neural Networks (RNN), Logistic Regression (LR), etc., are commonly used ML techniques for emotion extraction purposes. This study provides a new taxonomy about the application of ML in SER. The result shows that Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) are found to be the most useful methodology for this purpose.},
	year = {2023},
	eissn = {1573-7721},
	pages = {1-30},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:34395056,
	title = {Fuzzy-based Approach for Road Accident Risk Estimation on GPU},
	url = {https://m2.mtmt.hu/api/publication/34395056},
	author = {Mogyorósi, Péter and Szénási, Sándor},
	booktitle = {IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI 2023) : Proceedings},
	unique-id = {34395056},
	year = {2023},
	pages = {415-419},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:34391764,
	title = {Simulation of Fluvial Erosion},
	url = {https://m2.mtmt.hu/api/publication/34391764},
	author = {Klopp, Bálint and Szénási, Sándor},
	booktitle = {IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI 2023) : Proceedings},
	doi = {10.1109/CINTI59972.2023.10381916},
	unique-id = {34391764},
	year = {2023},
	pages = {281-284},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:34377320,
	title = {Bacteria Colony Simulation},
	url = {https://m2.mtmt.hu/api/publication/34377320},
	author = {Polónyi, Richard William and Szénási, Sándor},
	booktitle = {IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI 2023) : Proceedings},
	doi = {10.1109/CINTI59972.2023.10381973},
	unique-id = {34377320},
	year = {2023},
	pages = {249-253},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:34354098,
	title = {Speech Emotion Recognition using AdaMax and Weighted Adam Optimizers},
	url = {https://m2.mtmt.hu/api/publication/34354098},
	author = {Suryakant, Tyagi and Szénási, Sándor},
	booktitle = {IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI 2023) : Proceedings},
	doi = {10.1109/CINTI59972.2023.10382013},
	unique-id = {34354098},
	year = {2023},
	pages = {177-183},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:34343000,
	title = {Lightweight Blockchain Simulation with Transaction Graph Visualizer},
	url = {https://m2.mtmt.hu/api/publication/34343000},
	author = {Sipos, Miklós and Szénási, Sándor},
	booktitle = {IEEE 23rd International Symposium on Computational Intelligence and Informatics (CINTI 2023) : Proceedings},
	doi = {10.1109/CINTI59972.2023.10382093},
	unique-id = {34343000},
	year = {2023},
	pages = {31-36},
	orcid-numbers = {Sipos, Miklós/0009-0005-9783-6051; Szénási, Sándor/0000-0002-7292-0717}
}

@inproceedings{MTMT:34164420,
	title = {Speech Emotion Recognition using Long Short-Term Memory Models and Grey Wolf Optimization},
	url = {https://m2.mtmt.hu/api/publication/34164420},
	author = {Suryakant, Tyagi and Szénási, Sándor},
	booktitle = {SISY 2023 IEEE 21st International Symposium on Intelligent Systems and Informatics},
	doi = {10.1109/SISY60376.2023.10417879},
	unique-id = {34164420},
	year = {2023},
	pages = {643-648},
	orcid-numbers = {Szénási, Sándor/0000-0002-7292-0717}
}