AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Gao, H.; Xie, Y.; Kasneci, E.
PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education Journal Article
In: International Journal of Human-Computer Interaction, 2025, ISSN: 10447318 (ISSN).
Abstract | Links | BibTeX | Tags: Backpropagation, ChatGPT, Curricula, Educational robots, Immersive learning, Interactive learning, Language Model, Large language model, large language models, Learning mode, Machine learning education, Machine-learning, Personalized learning, Support vector machines, Teaching, Virtual Reality, Virtual-reality environment, Virtualization
@article{gao_pervrml_2025,
title = {PerVRML: ChatGPT-Driven Personalized VR Environments for Machine Learning Education},
author = {H. Gao and Y. Xie and E. Kasneci},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005776517&doi=10.1080%2f10447318.2025.2504188&partnerID=40&md5=c2c59be3d20d02c6df7750c2330c8f6d},
doi = {10.1080/10447318.2025.2504188},
issn = {10447318 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Human-Computer Interaction},
abstract = {The advent of large language models (LLMs) such as ChatGPT has demonstrated significant potential for advancing educational technologies. Recently, growing interest has emerged in integrating ChatGPT with virtual reality (VR) to provide interactive and dynamic learning environments. This study explores the effectiveness of ChatGTP-driven VR in facilitating machine learning education through PerVRML. PerVRML incorporates a ChatGPT-powered avatar that provides real-time assistance and uses LLMs to personalize learning paths based on various sensor data from VR. A between-subjects design was employed to compare two learning modes: personalized and non-personalized. Quantitative data were collected from assessments, user experience surveys, and interaction metrics. The results indicate that while both learning modes supported learning effectively, ChatGPT-powered personalization significantly improved learning outcomes and had distinct impacts on user feedback. These findings underscore the potential of ChatGPT-enhanced VR to deliver adaptive and personalized educational experiences. © 2025 Taylor & Francis Group, LLC.},
keywords = {Backpropagation, ChatGPT, Curricula, Educational robots, Immersive learning, Interactive learning, Language Model, Large language model, large language models, Learning mode, Machine learning education, Machine-learning, Personalized learning, Support vector machines, Teaching, Virtual Reality, Virtual-reality environment, Virtualization},
pubstate = {published},
tppubtype = {article}
}
The advent of large language models (LLMs) such as ChatGPT has demonstrated significant potential for advancing educational technologies. Recently, growing interest has emerged in integrating ChatGPT with virtual reality (VR) to provide interactive and dynamic learning environments. This study explores the effectiveness of ChatGTP-driven VR in facilitating machine learning education through PerVRML. PerVRML incorporates a ChatGPT-powered avatar that provides real-time assistance and uses LLMs to personalize learning paths based on various sensor data from VR. A between-subjects design was employed to compare two learning modes: personalized and non-personalized. Quantitative data were collected from assessments, user experience surveys, and interaction metrics. The results indicate that while both learning modes supported learning effectively, ChatGPT-powered personalization significantly improved learning outcomes and had distinct impacts on user feedback. These findings underscore the potential of ChatGPT-enhanced VR to deliver adaptive and personalized educational experiences. © 2025 Taylor & Francis Group, LLC.
2020
Franchini, Silvia; Terranova, Maria Chiara; Re, Giuseppe Lo; Salerno, Sergio; Midiri, Massimo; Vitabile, Salvatore
Evaluation of a Support Vector Machine Based Method for Crohn's Disease Classification Book Section
In: Esposito, Anna; Faundez-Zanuy, Marcos; Morabito, Francesco Carlo; Pasero, Eros (Ed.): Neural Approaches to Dynamics of Signal Exchanges, pp. 313–327, Springer, Singapore, 2020, ISBN: 9789811389504.
Abstract | Links | BibTeX | Tags: Crohn's disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines
@incollection{franchiniEvaluationSupportVector2020,
title = {Evaluation of a Support Vector Machine Based Method for Crohn's Disease Classification},
author = { Silvia Franchini and Maria Chiara Terranova and Giuseppe Lo Re and Sergio Salerno and Massimo Midiri and Salvatore Vitabile},
editor = { Anna Esposito and Marcos {Faundez-Zanuy} and Francesco Carlo Morabito and Eros Pasero},
doi = {10.1007/978-981-13-8950-4_29},
isbn = {9789811389504},
year = {2020},
date = {2020-01-01},
urldate = {2023-03-20},
booktitle = {Neural Approaches to Dynamics of Signal Exchanges},
pages = {313--327},
publisher = {Springer},
address = {Singapore},
series = {Smart Innovation, Systems and Technologies},
abstract = {Crohn's disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.},
keywords = {Crohn's disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines},
pubstate = {published},
tppubtype = {incollection}
}
Crohn's disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.
Franchini, Silvia; Terranova, Maria Chiara; Re, Giuseppe Lo; Salerno, Sergio; Midiri, Massimo; Vitabile, Salvatore
Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification Book Section
In: Esposito, Anna; Faundez-Zanuy, Marcos; Morabito, Francesco Carlo; Pasero, Eros (Ed.): Neural Approaches to Dynamics of Signal Exchanges, pp. 313–327, Springer, Singapore, 2020, ISBN: 9789811389504.
Abstract | Links | BibTeX | Tags: Crohn’s disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines
@incollection{franchini_evaluation_2020,
title = {Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification},
author = {Silvia Franchini and Maria Chiara Terranova and Giuseppe Lo Re and Sergio Salerno and Massimo Midiri and Salvatore Vitabile},
editor = {Anna Esposito and Marcos Faundez-Zanuy and Francesco Carlo Morabito and Eros Pasero},
url = {https://doi.org/10.1007/978-981-13-8950-4_29},
doi = {10.1007/978-981-13-8950-4_29},
isbn = {9789811389504},
year = {2020},
date = {2020-01-01},
urldate = {2023-03-20},
booktitle = {Neural Approaches to Dynamics of Signal Exchanges},
pages = {313–327},
publisher = {Springer},
address = {Singapore},
series = {Smart Innovation, Systems and Technologies},
abstract = {Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.},
keywords = {Crohn’s disease classification, Feature extraction, Feature reduction, K-fold cross-validation, machine learning, Magnetic Resonance Enterography, Medical Imaging, Supervised learning, Support vector machines},
pubstate = {published},
tppubtype = {incollection}
}
Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-MRI dataset composed of 800 patients from the University of Palermo Policlinico Hospital (400 patients with histologically proved CD and 400 healthy patients) has been used to evaluate the proposed classification technique. For each patient, a team of radiology experts has extracted a vector composed of 20 features, usually associated with CD, from the related E-MRI examination, while the histological specimen results have been used as the ground-truth for CD diagnosis. The dataset composed of 800 vectors has been used to train and validate the SVM classifier. Automatic techniques for feature space reduction have been applied and validated by the radiologists to optimize the proposed classification method, while K-fold cross-validation has been used to improve the SVM classifier reliability. The measured indexes (sensitivity: 97.07%, specificity: 96.04%, negative predictive value: 97.24%, precision: 95.80%, accuracy: 96.54%, error: 3.46%) are better than the operator-based reference values reported in the literature. Experimental results also show that the proposed method outperforms the main standard classification techniques.