@article{MTMT:34857410,
	title = {Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization},
	url = {https://m2.mtmt.hu/api/publication/34857410},
	author = {Mehrabi Hashjin, Nastaran and Amiri, Mohammad Hussein and Mohammadzadeh, Ardashir and Mirjalili, Seyedali and Khodadadi, Nima},
	doi = {10.1007/s10586-024-04475-7},
	journal-iso = {CLUSTER COMPUT},
	journal = {CLUSTER COMPUTING},
	volume = {&},
	unique-id = {34857410},
	issn = {1386-7857},
	abstract = {This paper presents a unique hybrid classifier that combines deep neural networks with a type-III fuzzy system for decision-making. The ensemble incorporates ResNet-18, Efficient Capsule neural network, ResNet-50, the Histogram of Oriented Gradients (HOG) for feature extraction, neighborhood component analysis (NCA) for feature selection, and Support Vector Machine (SVM) for classification. The innovative inputs fed into the type-III fuzzy system come from the outputs of the mentioned neural networks. The system’s rule parameters are fine-tuned using the Improved Chaos Game Optimization algorithm (ICGO). The conventional CGO’s simple random mutation is substituted with wavelet mutation to enhance the CGO algorithm while preserving non-parametricity and computational complexity. The ICGO was evaluated using 126 benchmark functions and 5 engineering problems, comparing its performance with well-known algorithms. It achieved the best results across all functions except for 2 benchmark functions. The introduced classifier is applied to seven malware datasets and consistently outperforms notable networks like AlexNet, ResNet-18, GoogleNet, and Efficient Capsule neural network in 35 separate runs, achieving over 96% accuracy. Additionally, the classifier’s performance is tested on the MNIST and Fashion-MNIST in 10 separate runs. The results show that the new classifier excels in accuracy, precision, sensitivity, specificity, and F1-score compared to other recent classifiers. Based on the statistical analysis, it has been concluded that the ICGO and propose method exhibit significant superiority compared to the examined algorithms and methods. The source code for ICGO is available publicly at  https://nimakhodadadi.com/algorithms-%2B-codes .},
	year = {2024},
	eissn = {1573-7543},
	pages = {&}
}

@article{MTMT:34849856,
	title = {Strategies and Outcomes of Building a Successful University Research and Innovation Ecosystem},
	url = {https://m2.mtmt.hu/api/publication/34849856},
	author = {Haidegger, Tamás and Galambos, Péter and Tar, József and Kovács, Levente and Kozlovszky, Miklós and Zrubka, Zsombor and Eigner, György and Drexler, Dániel András and Szakál, Anikó and Reicher, Viktória and Árendás, Csaba and Tarsoly, Sándor and Garamvölgyi, Tivadar and Rudas, Imre},
	doi = {10.12700/APH.21.10.2024.10.2},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {34849856},
	issn = {1785-8860},
	year = {2024},
	eissn = {1785-8860},
	pages = {13-35},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139; Galambos, Péter/0000-0002-2319-0551; Tar, József/0000-0002-5476-401X; Kovács, Levente/0000-0002-3188-0800; Zrubka, Zsombor/0000-0002-1992-6087; Rudas, Imre/0000-0002-2067-8578}
}

@article{MTMT:34842684,
	title = {A meta-heuristic approach for reliability-based design optimization of shell-and-tube heat exchangers},
	url = {https://m2.mtmt.hu/api/publication/34842684},
	author = {Jafari-Asl, Jafar and Lara Montaño, Oscar D. and Mirjalili, Seyedali and Faes, Matthias G.R.},
	doi = {10.1016/j.applthermaleng.2024.123161},
	journal-iso = {APPL THERM ENG},
	journal = {APPLIED THERMAL ENGINEERING},
	volume = {248},
	unique-id = {34842684},
	issn = {1359-4311},
	year = {2024},
	eissn = {1873-5606},
	pages = {123161-123176},
	orcid-numbers = {Jafari-Asl, Jafar/0000-0001-8622-1952; Faes, Matthias G.R./0000-0003-3341-3410}
}

@article{MTMT:34836711,
	title = {Resilient Synchronization for Insecure Markovian Jump Neural Networks to Mitigate Dual Cyber Attacks},
	url = {https://m2.mtmt.hu/api/publication/34836711},
	author = {Li, Xiaohang and Shi, Peng and Zhang, Weidong and Saif, Mehrdad},
	doi = {10.1109/TCSI.2024.3383839},
	journal-iso = {IEEE T CIRCUITS-I},
	journal = {IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS},
	unique-id = {34836711},
	issn = {1549-8328},
	keywords = {NEURONS; Artificial neural networks; SYNCHRONIZATION; SWITCHES; Actuators; Cyberattack; Sensor phenomena and characterization; sensor attack; Actuator attack; Asynchronous adaptive controller; Markovian jump neural networks},
	year = {2024},
	eissn = {1558-0806},
	orcid-numbers = {Saif, Mehrdad/0000-0002-7587-4189}
}

@article{MTMT:34836311,
	title = {Comparative analysis of late gadolinium enhancement assessment techniques for monitoring fibrotic changes in myocarditis follow-up},
	url = {https://m2.mtmt.hu/api/publication/34836311},
	author = {Károlyi, Mihály and Polacin, Malgorzata and Kolossváry, Márton József and Sokolska, Justyna M. and Matziris, Ioannis and Weber, Lucas and Alkadhi, Hatem and Manka, Robert},
	doi = {10.1007/s00330-024-10756-x},
	journal-iso = {EUR RADIOL},
	journal = {EUROPEAN RADIOLOGY},
	unique-id = {34836311},
	issn = {0938-7994},
	year = {2024},
	eissn = {1432-1084},
	pages = {1-11},
	orcid-numbers = {Kolossváry, Márton József/0000-0002-5570-991X; Manka, Robert/0000-0002-3383-4998}
}

@article{MTMT:34833556,
	title = {Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification},
	url = {https://m2.mtmt.hu/api/publication/34833556},
	author = {Singh, Alka and Kumar, Rakesh and Gandomi , Amirhossein},
	doi = {10.1007/s11042-024-19216-6},
	journal-iso = {MULTIMED TOOLS APPL},
	journal = {MULTIMEDIA TOOLS AND APPLICATIONS: AN INTERNATIONAL JOURNAL},
	unique-id = {34833556},
	issn = {1380-7501},
	year = {2024},
	eissn = {1573-7721},
	orcid-numbers = {Singh, Alka/0009-0004-4891-436X}
}

@article{MTMT:34833548,
	title = {Reference Tracking MPC for Cyber-Physical Systems Under Denial-of-Service Attacks: An Omnidirectional Robot Application},
	url = {https://m2.mtmt.hu/api/publication/34833548},
	author = {Zhang, Daotong and Shi, Peng and Agarwal, Ramesh K. and Kovács, Levente},
	doi = {10.1109/JSYST.2024.3384372},
	journal-iso = {IEEE SYST J},
	journal = {IEEE SYSTEMS JOURNAL},
	unique-id = {34833548},
	issn = {1932-8184},
	keywords = {cyber-physical systems (CPSs); model predictive control (MPC); Disturbance observer; denial-of-service (DoS) attacks},
	year = {2024},
	eissn = {1937-9234},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:34833013,
	title = {Development of novel low-cost readout electronics for large field-of-view gamma camera detectors},
	url = {https://m2.mtmt.hu/api/publication/34833013},
	author = {Radnia, Aram and Alikhani, Amirhossein and Teimourian, Behnoosh and Nejad, Mahyar Yousef and Farahani, Mohammad Hossein and Pashaei, Fakhereh and Rahmim, Arman and Zaidi, Habib and Ay, Mohammad Reza},
	doi = {10.1016/j.ejmp.2024.103357},
	journal-iso = {PHYS MEDICA},
	journal = {PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS},
	volume = {121},
	unique-id = {34833013},
	issn = {1120-1797},
	year = {2024},
	eissn = {1724-191X},
	pages = {103357},
	orcid-numbers = {Teimourian, Behnoosh/0000-0001-8234-7435; Rahmim, Arman/0000-0002-9980-2403}
}

@article{MTMT:34833010,
	title = {Assessment of Surgeons’ Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study},
	url = {https://m2.mtmt.hu/api/publication/34833010},
	author = {Takács, Kristóf and Lukács, Eszter and Levendovics, Renáta and Pekli, Damján and Szijártó, Attila and Haidegger, Tamás},
	doi = {10.3390/s24092915},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {24},
	unique-id = {34833010},
	abstract = {Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human–robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.},
	year = {2024},
	eissn = {1424-8220},
	orcid-numbers = {Takács, Kristóf/0000-0001-5417-6026; Lukács, Eszter/0009-0001-7585-281X; Levendovics, Renáta/0000-0002-3030-254X; Haidegger, Tamás/0000-0003-1402-1139}
}

@article{MTMT:34831712,
	title = {Deep learning for enhanced brain Tumor Detection and classification},
	url = {https://m2.mtmt.hu/api/publication/34831712},
	author = {Agarwal, Monika and Rani, Geeta and Kumar, Ambeshwar and K, Pradeep Kumar and Manikandan, R. and Gandomi , Amirhossein},
	doi = {10.1016/j.rineng.2024.102117},
	journal-iso = {RESULT ENGIN},
	journal = {RESULTS IN ENGINEERING},
	volume = {22},
	unique-id = {34831712},
	year = {2024},
	eissn = {2590-1230}
}