Publications
OUR RESEARCH
Scientific Publications
Here you can find the comprehensive list of publications from the members of the Research Center on Computer Vision and eXtended Reality (xRAI).
Use the tag cloud to filter papers based on specific research topics, or use the menus to filter by year, type of publication, or authors.
For each paper, you have the option to view additional details such as the Abstract, Links, and BibTex record.
Research is formalized curiosity. It is poking and prying with a purpose
Zora Neale Hurston
2024
Agnolucci, Lorenzo; Galteri, Leonardo; Bertini, Marco; Bimbo, Alberto Del
Reference-based restoration of digitized analog videotapes Proceedings Article
In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1659–1668, 2024, (tex.copyright: All rights reserved).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Digital Archiving, Image Processing, Transformer Networks
@inproceedings{agnolucciReferencebasedRestorationDigitized2024,
title = {Reference-based restoration of digitized analog videotapes},
author = {Lorenzo Agnolucci and Leonardo Galteri and Marco Bertini and Alberto Del Bimbo},
url = {https://openaccess.thecvf.com/content/WACV2024/papers/Agnolucci_Reference-Based_Restoration_of_Digitized_Analog_Videotapes_WACV_2024_paper.pdf},
doi = {10.1109/WACV57701.2024.00168},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages = {1659–1668},
abstract = {Analog magnetic tapes have been the main video data storage device for several decades. Videos stored on analog videotapes exhibit unique degradation patterns caused by tape aging and reader device malfunctioning that are different from those observed in film and digital video restoration tasks. In this work, we present a reference-based approach for the resToration of digitized Analog videotaPEs (TAPE). We leverage CLIP for zero-shot artifact detection to identify the cleanest frames of each video through textual prompts describing different artifacts. Then, we select the clean frames most similar to the input ones and employ them as references. We design a transformer-based Swin-UNet network that exploits both neighboring and reference frames via our Multi-Reference Spatial Feature Fusion (MRSFF) blocks. MRSFF blocks rely on cross-attention and attention pooling to take advantage of the most useful parts of each reference frame. To address the absence of ground truth in real-world videos, we create a synthetic dataset of videos exhibiting artifacts that closely resemble those commonly found in analog videotapes. Both quantitative and qualitative experiments show the effectiveness of our approach compared to other state-of-the-art methods. The code, the model, and the synthetic dataset are publicly available at https://github.com/miccunifi/TAPE.},
note = {tex.copyright: All rights reserved},
keywords = {Artificial Intelligence, Compression Artifact Removal, Computer Vision and Pattern Recognition, Digital Archiving, Image Processing, Transformer Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Analog magnetic tapes have been the main video data storage device for several decades. Videos stored on analog videotapes exhibit unique degradation patterns caused by tape aging and reader device malfunctioning that are different from those observed in film and digital video restoration tasks. In this work, we present a reference-based approach for the resToration of digitized Analog videotaPEs (TAPE). We leverage CLIP for zero-shot artifact detection to identify the cleanest frames of each video through textual prompts describing different artifacts. Then, we select the clean frames most similar to the input ones and employ them as references. We design a transformer-based Swin-UNet network that exploits both neighboring and reference frames via our Multi-Reference Spatial Feature Fusion (MRSFF) blocks. MRSFF blocks rely on cross-attention and attention pooling to take advantage of the most useful parts of each reference frame. To address the absence of ground truth in real-world videos, we create a synthetic dataset of videos exhibiting artifacts that closely resemble those commonly found in analog videotapes. Both quantitative and qualitative experiments show the effectiveness of our approach compared to other state-of-the-art methods. The code, the model, and the synthetic dataset are publicly available at https://github.com/miccunifi/TAPE.
Tomazzoli, Claudio; Ponza, Andrea; Cristani, Matteo; Olivieri, Francesco; Scannapieco, Simone
A Cobot in the Vineyard: Computer Vision for Smart Chemicals Spraying Journal Article
In: Applied Sciences, vol. 14, no. 9, 2024, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Collaborative robotic, Computer vision, Cyber-Physical Systems, Deep Learning, Machine Learning, Precision agriculture
@article{tomazzoli_cobot_2024,
title = {A Cobot in the Vineyard: Computer Vision for Smart Chemicals Spraying},
author = {Claudio Tomazzoli and Andrea Ponza and Matteo Cristani and Francesco Olivieri and Simone Scannapieco},
url = {https://www.mdpi.com/2076-3417/14/9/3777},
doi = {10.3390/app14093777},
issn = {2076-3417},
year = {2024},
date = {2024-01-01},
journal = {Applied Sciences},
volume = {14},
number = {9},
abstract = {Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.},
keywords = {Artificial Intelligence, Collaborative robotic, Computer vision, Cyber-Physical Systems, Deep Learning, Machine Learning, Precision agriculture},
pubstate = {published},
tppubtype = {article}
}
Precision agriculture (PA) is a management concept that makes use of digital techniques to monitor and optimise agricultural production processes and represents a field of growing economic and social importance. Within this area of knowledge, there is a topic not yet fully explored: outlining a road map towards the definition of an affordable cobot solution (i.e., a low-cost robot able to safely coexist with humans) able to perform automatic chemical treatments. The present study narrows its scope to viticulture technologies, and targets small/medium-sized winemakers and producers, for whom innovative technological advancements in the production chain are often precluded by financial factors. The aim is to detail the realization of such an integrated solution and to discuss the promising results achieved. The results of this study are: (i) The definition of a methodology for integrating a cobot in the process of grape chemicals spraying under the constraints of a low-cost apparatus; (ii) the realization of a proof-of-concept of such a cobotic system; (iii) the experimental analysis of the visual apparatus of this system in an indoor and outdoor controlled environment as well as in the field.
Scannapieco, Simone; Tomazzoli, Claudio
Cnosso, a Novel Method for Business Document Automation Based on Open Information Extraction Journal Article
In: Expert Systems with Applications, vol. 245, pp. 123038, 2024, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Document automation, Information Extraction, Natural language analysis, Natural Language Processing, Semantic Role Labeling
@article{scannapieco_cnosso_2024,
title = {Cnosso, a Novel Method for Business Document Automation Based on Open Information Extraction},
author = {Simone Scannapieco and Claudio Tomazzoli},
url = {https://www.sciencedirect.com/science/article/pii/S0957417423035406},
doi = {10.1016/j.eswa.2023.123038},
issn = {0957-4174},
year = {2024},
date = {2024-01-01},
journal = {Expert Systems with Applications},
volume = {245},
pages = {123038},
abstract = {The state-of-the-art in automated processing of unstructured business documents has evolved from manual labor to advanced AI systems in the span of mere decades. Such systems involve learning techniques, rule or clause sets, neural models – either used alone or in combination – for the extraction to work. As an example, rule-based processes operate on a perceived layout or positioning of the information, whereas model-based frameworks adopt a semantic, and often uninspectable, approach. Verb-Based Semantic Role Labeling (VBSRL) is a novel system presented in a former paper that uses a hybrid foundation to inform the extraction phase via a set of rules modeling natural language. We propose a new VBSRL-based document processing method, aided by valuable and innovative architectural choices, which has been implemented for the Italian language and experimented upon with promising results. Even in its infancy, in fact, the first implementation of this system shows better results than comparable IE solutions, obtaining an aggregate, average F-measure of nearly 79%.},
keywords = {Artificial Intelligence, Document automation, Information Extraction, Natural language analysis, Natural Language Processing, Semantic Role Labeling},
pubstate = {published},
tppubtype = {article}
}
The state-of-the-art in automated processing of unstructured business documents has evolved from manual labor to advanced AI systems in the span of mere decades. Such systems involve learning techniques, rule or clause sets, neural models – either used alone or in combination – for the extraction to work. As an example, rule-based processes operate on a perceived layout or positioning of the information, whereas model-based frameworks adopt a semantic, and often uninspectable, approach. Verb-Based Semantic Role Labeling (VBSRL) is a novel system presented in a former paper that uses a hybrid foundation to inform the extraction phase via a set of rules modeling natural language. We propose a new VBSRL-based document processing method, aided by valuable and innovative architectural choices, which has been implemented for the Italian language and experimented upon with promising results. Even in its infancy, in fact, the first implementation of this system shows better results than comparable IE solutions, obtaining an aggregate, average F-measure of nearly 79%.
Ferraro, Antonino; Galli, Antonio; Gatta, Valerio La; Minocchi, Mario; Moscato, Vincenzo; Postiglione, Marco
Few Shot NER on Augmented Unstructured Text from Cardiology Records Book Section
In: Barolli, Leonard (Ed.): Advances in Internet, Data & Web Technologies, vol. 193, pp. 1–12, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-53554-3 978-3-031-53555-0, (Series Title: Lecture Notes on Data Engineering and Communications Technologies).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Data Augmentation, Healthcare, Named-Entity Recognition
@incollection{ferraroFewShotNER2024,
title = {Few Shot NER on Augmented Unstructured Text from Cardiology Records},
author = {Antonino Ferraro and Antonio Galli and Valerio La Gatta and Mario Minocchi and Vincenzo Moscato and Marco Postiglione},
editor = {Leonard Barolli},
url = {https://link.springer.com/10.1007/978-3-031-53555-0_1},
doi = {10.1007/978-3-031-53555-0_1},
isbn = {978-3-031-53554-3 978-3-031-53555-0},
year = {2024},
date = {2024-01-01},
urldate = {2024-07-12},
booktitle = {Advances in Internet, Data & Web Technologies},
volume = {193},
pages = {1–12},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The principal challenge encountered in the realm of Named-Entity Recognition lies in the acquisition of high-caliber annotated data. In certain languages and specialized domains, the availability of substantial datasets suitable for training models via traditional machine learning methodologies can prove to be a formidable obstacle [10]. In an effort to address this issue, we have explored a Policy-based Active Learning approach aimed at meticulously selecting the most advantageous instances generated through a Data Augmentation procedure [3, 6]. This endeavor was undertaken within the context of a few-shot scenario in the biomedical field. Our study has revealed the superiority of this strategy in comparison to active learning techniques relying on fixed metrics or random instance selection, guaranteeing the privacy of patients from whose medical records the source data were obtained and used. However, it is imperative to note that this approach entails heightened computational demands and necessitates a longer execution duration [7].},
note = {Series Title: Lecture Notes on Data Engineering and Communications Technologies},
keywords = {Artificial Intelligence, Data Augmentation, Healthcare, Named-Entity Recognition},
pubstate = {published},
tppubtype = {incollection}
}
The principal challenge encountered in the realm of Named-Entity Recognition lies in the acquisition of high-caliber annotated data. In certain languages and specialized domains, the availability of substantial datasets suitable for training models via traditional machine learning methodologies can prove to be a formidable obstacle [10]. In an effort to address this issue, we have explored a Policy-based Active Learning approach aimed at meticulously selecting the most advantageous instances generated through a Data Augmentation procedure [3, 6]. This endeavor was undertaken within the context of a few-shot scenario in the biomedical field. Our study has revealed the superiority of this strategy in comparison to active learning techniques relying on fixed metrics or random instance selection, guaranteeing the privacy of patients from whose medical records the source data were obtained and used. However, it is imperative to note that this approach entails heightened computational demands and necessitates a longer execution duration [7].
Generosi, Andrea; Villafan, Josè Yuri; Montanari, Roberto; Mengoni, Maura
A Multimodal Approach to Understand Driver’s Distraction for DMS Proceedings Article
In: Antona, Margherita; Stephanidis, Constantine (Ed.): Universal Access in Human-Computer Interaction, pp. 250–270, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-60875-9.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Human Computer Interaction
@inproceedings{generosi_multimodal_2024,
title = {A Multimodal Approach to Understand Driver’s Distraction for DMS},
author = {Andrea Generosi and Josè Yuri Villafan and Roberto Montanari and Maura Mengoni},
editor = {Margherita Antona and Constantine Stephanidis},
doi = {10.1007/978-3-031-60875-9_17},
isbn = {978-3-031-60875-9},
year = {2024},
date = {2024-01-01},
booktitle = {Universal Access in Human-Computer Interaction},
pages = {250–270},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {This study introduces a multimodal approach for enhancing the accuracy of Driver Monitoring Systems (DMS) in detecting driver distraction. By integrating data from vehicle control units with vision-based information, the research aims to address the limitations of current DMS. The experimental setup involves a driving simulator and advanced computer vision, deep learning technologies for facial expression recognition, and head rotation analysis. The findings suggest that combining various data types—behavioral, physiological, and emotional—can significantly improve DMS’s predictive capability. This research contributes to the development of more sophisticated, adaptive, and real-time systems for improving driver safety and advancing autonomous driving technologies.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Human Computer Interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
This study introduces a multimodal approach for enhancing the accuracy of Driver Monitoring Systems (DMS) in detecting driver distraction. By integrating data from vehicle control units with vision-based information, the research aims to address the limitations of current DMS. The experimental setup involves a driving simulator and advanced computer vision, deep learning technologies for facial expression recognition, and head rotation analysis. The findings suggest that combining various data types—behavioral, physiological, and emotional—can significantly improve DMS’s predictive capability. This research contributes to the development of more sophisticated, adaptive, and real-time systems for improving driver safety and advancing autonomous driving technologies.
Mengoni, Maura; Ceccacci, Silvia; Generosi, Andrea
Emotion Recognition and Affective Computing Book Section
In: Interaction Techniques and Technologies in Human-Computer Interaction, CRC Press, 2024, ISBN: 978-1-003-49067-8.
Abstract | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Emotion Recognition
@incollection{mengoni_emotion_2024,
title = {Emotion Recognition and Affective Computing},
author = {Maura Mengoni and Silvia Ceccacci and Andrea Generosi},
isbn = {978-1-003-49067-8},
year = {2024},
date = {2024-01-01},
booktitle = {Interaction Techniques and Technologies in Human-Computer Interaction},
publisher = {CRC Press},
abstract = {This chapter explores the challenging topic of emotion recognition by affective computing. The importance of considering and understanding people’s emotions in interaction design is discussed, focusing on the role of human emotions in the entire life cycle of human–system interaction as a means to innovate products and services. The measurement of emotions is also analyzed, including the classification of human emotions and recognition methods, as well as current techniques for measuring emotional responses. An emotional-based approach and related technologies are considered in managing the entire life cycle of human–system interaction as an innovation driver. This chapter also presents how to use affective computing in cross-transversal applications, concentrating on potential applications and different case studies. This chapter concludes with a look towards a world of emotional intelligence, where affective computing plays a crucial role in collecting and analyzing emotional data to support innovative product and service experiences.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Emotion Recognition},
pubstate = {published},
tppubtype = {incollection}
}
This chapter explores the challenging topic of emotion recognition by affective computing. The importance of considering and understanding people’s emotions in interaction design is discussed, focusing on the role of human emotions in the entire life cycle of human–system interaction as a means to innovate products and services. The measurement of emotions is also analyzed, including the classification of human emotions and recognition methods, as well as current techniques for measuring emotional responses. An emotional-based approach and related technologies are considered in managing the entire life cycle of human–system interaction as an innovation driver. This chapter also presents how to use affective computing in cross-transversal applications, concentrating on potential applications and different case studies. This chapter concludes with a look towards a world of emotional intelligence, where affective computing plays a crucial role in collecting and analyzing emotional data to support innovative product and service experiences.
Ferraro, Antonino; Galli, Antonio; Gatta, Valerio La; Minocchi, Mario; Moscato, Vincenzo; Postiglione, Marco
Few Shot NER on Augmented Unstructured Text from Cardiology Records Book Section
In: Barolli, Leonard (Ed.): Advances in Internet, Data & Web Technologies, vol. 193, pp. 1–12, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-53554-3 978-3-031-53555-0, (Series Title: Lecture Notes on Data Engineering and Communications Technologies).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Data Augmentation, Healthcare, Named-Entity Recognition
@incollection{barolli_few_2024,
title = {Few Shot NER on Augmented Unstructured Text from Cardiology Records},
author = {Antonino Ferraro and Antonio Galli and Valerio La Gatta and Mario Minocchi and Vincenzo Moscato and Marco Postiglione},
editor = {Leonard Barolli},
url = {https://link.springer.com/10.1007/978-3-031-53555-0_1},
doi = {10.1007/978-3-031-53555-0_1},
isbn = {978-3-031-53554-3 978-3-031-53555-0},
year = {2024},
date = {2024-01-01},
urldate = {2024-07-12},
booktitle = {Advances in Internet, Data & Web Technologies},
volume = {193},
pages = {1–12},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The principal challenge encountered in the realm of Named-Entity Recognition lies in the acquisition of high-caliber annotated data. In certain languages and specialized domains, the availability of substantial datasets suitable for training models via traditional machine learning methodologies can prove to be a formidable obstacle [10]. In an effort to address this issue, we have explored a Policy-based Active Learning approach aimed at meticulously selecting the most advantageous instances generated through a Data Augmentation procedure [3, 6]. This endeavor was undertaken within the context of a few-shot scenario in the biomedical field. Our study has revealed the superiority of this strategy in comparison to active learning techniques relying on fixed metrics or random instance selection, guaranteeing the privacy of patients from whose medical records the source data were obtained and used. However, it is imperative to note that this approach entails heightened computational demands and necessitates a longer execution duration [7].},
note = {Series Title: Lecture Notes on Data Engineering and Communications Technologies},
keywords = {Artificial Intelligence, Data Augmentation, Healthcare, Named-Entity Recognition},
pubstate = {published},
tppubtype = {incollection}
}
The principal challenge encountered in the realm of Named-Entity Recognition lies in the acquisition of high-caliber annotated data. In certain languages and specialized domains, the availability of substantial datasets suitable for training models via traditional machine learning methodologies can prove to be a formidable obstacle [10]. In an effort to address this issue, we have explored a Policy-based Active Learning approach aimed at meticulously selecting the most advantageous instances generated through a Data Augmentation procedure [3, 6]. This endeavor was undertaken within the context of a few-shot scenario in the biomedical field. Our study has revealed the superiority of this strategy in comparison to active learning techniques relying on fixed metrics or random instance selection, guaranteeing the privacy of patients from whose medical records the source data were obtained and used. However, it is imperative to note that this approach entails heightened computational demands and necessitates a longer execution duration [7].
Workneh, Tewabe Chekole; Cristani, Matteo; Tomazzoli, Claudio
Assessing the Impact of Climate Change on Mineral-Associated Organic Carbon (MAOC) Using Machine Learning Models Proceedings Article
In: pp. 35–47, 2024.
Abstract | Links | BibTeX | Tags: Machine Learning, Mineral-associated organic carbon, Predictive Models
@inproceedings{workneh_assessing_2024,
title = {Assessing the Impact of Climate Change on Mineral-Associated Organic Carbon (MAOC) Using Machine Learning Models},
author = {Tewabe Chekole Workneh and Matteo Cristani and Claudio Tomazzoli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214791497&partnerID=40&md5=5a2e249e0da0fd4f9571af2bc00696ca},
year = {2024},
date = {2024-01-01},
volume = {3883},
pages = {35–47},
series = {CEUR Workshop Proceedings},
abstract = {This study examines the impact of climate change on Soil Organic Carbon (SOC) stocks, with a particular focus on Mineral-Associated Organic Carbon (MAOC)—a stable fraction of soil organic matter critical for long-term carbon sequestration. This study aims to develop a predictive tool for estimating MAOC at a finer spatial resolution, addressing gaps in current models and enabling cost-effective climate change mitigation strategies. Using an extensive dataset from the Zenodo repository, augmented with detailed meteorological data, machine learning techniques were employed—specifically, the Random Forest (RF) Regressor and Support Vector Machine (SVM) Regressor. The RF model not only outperformed the SVM in predictive accuracy but also identified key factors influencing MAOC content under various climate change scenarios. These findings deepen our understanding of soil carbon sequestration potential in future climate conditions, offering actionable insights for sustainable soil management and cost-effective climate change mitigation strategies. textbackslashcopyright 2024 Copyright for this paper by its authors.},
keywords = {Machine Learning, Mineral-associated organic carbon, Predictive Models},
pubstate = {published},
tppubtype = {inproceedings}
}
This study examines the impact of climate change on Soil Organic Carbon (SOC) stocks, with a particular focus on Mineral-Associated Organic Carbon (MAOC)—a stable fraction of soil organic matter critical for long-term carbon sequestration. This study aims to develop a predictive tool for estimating MAOC at a finer spatial resolution, addressing gaps in current models and enabling cost-effective climate change mitigation strategies. Using an extensive dataset from the Zenodo repository, augmented with detailed meteorological data, machine learning techniques were employed—specifically, the Random Forest (RF) Regressor and Support Vector Machine (SVM) Regressor. The RF model not only outperformed the SVM in predictive accuracy but also identified key factors influencing MAOC content under various climate change scenarios. These findings deepen our understanding of soil carbon sequestration potential in future climate conditions, offering actionable insights for sustainable soil management and cost-effective climate change mitigation strategies. textbackslashcopyright 2024 Copyright for this paper by its authors.
2023
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
In: Computers in Biology and Medicine, vol. 167, pp. 107665, 2023, ISSN: 00104825.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Generative algorithms, Rehabilitation, Synthetic data
@article{mennellaGeneratingNovelSynthetic2023,
title = {Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523011307},
doi = {10.1016/j.compbiomed.2023.107665},
issn = {00104825},
year = {2023},
date = {2023-12-01},
urldate = {2024-07-21},
journal = {Computers in Biology and Medicine},
volume = {167},
pages = {107665},
abstract = {Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation.
This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises.
A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90.
This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.},
keywords = {Artificial Intelligence, Generative algorithms, Rehabilitation, Synthetic data},
pubstate = {published},
tppubtype = {article}
}
Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation.
This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises.
A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90.
This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.
This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises.
A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90.
This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.
Russo, Raffaele; Giuseppe, Giuliano Di; Vanacore, Alessandro; Gatta, Valerio La; Ferraro, Antonino; Galli, Antonio; Postiglione, Marco; Moscato, Vincenzo
Graph-Based Approach for European Law Classification Proceedings Article
In: 2023 IEEE International Conference on Big Data (BigData), pp. 1–9, IEEE, Sorrento, Italy, 2023, ISBN: 979-8-3503-2445-7.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Semantics
@inproceedings{russoGraphBasedApproachEuropean2023,
title = {Graph-Based Approach for European Law Classification},
author = {Raffaele Russo and Giuliano Di Giuseppe and Alessandro Vanacore and Valerio La Gatta and Antonino Ferraro and Antonio Galli and Marco Postiglione and Vincenzo Moscato},
url = {https://ieeexplore.ieee.org/document/10386684/},
doi = {10.1109/BigData59044.2023.10386684},
isbn = {979-8-3503-2445-7},
year = {2023},
date = {2023-12-01},
urldate = {2024-07-12},
booktitle = {2023 IEEE International Conference on Big Data (BigData)},
pages = {1–9},
publisher = {IEEE},
address = {Sorrento, Italy},
abstract = {Deep learning, owing to its transformative influence across a myriad of sectors, has recently made its foray into the legal domain, instigated by the surge in digitization. Among the multitude of applications in this space, legal document classification emerges as a pivotal yet complex undertaking. Legal texts, characterized by unique domain-centric semantics and intricate linguistic patterns, necessitate precision-driven classification systems for numerous practical implications. This paper illuminates the challenges and opportunities in automating the classification of European Union (EU) legal documents, emphasizing the interrelationships among statutes and the hierarchical nature of legal references. In this context, we introduce a novel graph data modeling technique that adeptly marries content-centric indicators with the relational dynamics inherent among diverse legal documents. Central to our approach is a framework that melds text embeddings with graph neural networks for the classification of legal documents aligned with their subject-based directories. Empirical evaluations on the EU law dataset underline the efficacy of our model across varying granularities, from general thematic categories to intricate subtopics. This endeavor not only augments the comprehensibility and accessibility of EU jurisprudence but also holds significant implications across regulatory compliance, legal research, and policy formulation, underscoring the potential of deep learning in reshaping legal paradigms.},
keywords = {Artificial Intelligence, Semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep learning, owing to its transformative influence across a myriad of sectors, has recently made its foray into the legal domain, instigated by the surge in digitization. Among the multitude of applications in this space, legal document classification emerges as a pivotal yet complex undertaking. Legal texts, characterized by unique domain-centric semantics and intricate linguistic patterns, necessitate precision-driven classification systems for numerous practical implications. This paper illuminates the challenges and opportunities in automating the classification of European Union (EU) legal documents, emphasizing the interrelationships among statutes and the hierarchical nature of legal references. In this context, we introduce a novel graph data modeling technique that adeptly marries content-centric indicators with the relational dynamics inherent among diverse legal documents. Central to our approach is a framework that melds text embeddings with graph neural networks for the classification of legal documents aligned with their subject-based directories. Empirical evaluations on the EU law dataset underline the efficacy of our model across varying granularities, from general thematic categories to intricate subtopics. This endeavor not only augments the comprehensibility and accessibility of EU jurisprudence but also holds significant implications across regulatory compliance, legal research, and policy formulation, underscoring the potential of deep learning in reshaping legal paradigms.
Russo, Raffaele; Giuseppe, Giuliano Di; Vanacore, Alessandro; Gatta, Valerio La; Ferraro, Antonino; Galli, Antonio; Postiglione, Marco; Moscato, Vincenzo
Graph-Based Approach for European Law Classification Proceedings Article
In: 2023 IEEE International Conference on Big Data (BigData), pp. 1–9, IEEE, Sorrento, Italy, 2023, ISBN: 979-8-3503-2445-7.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Semantics
@inproceedings{russo_graph-based_2023,
title = {Graph-Based Approach for European Law Classification},
author = {Raffaele Russo and Giuliano Di Giuseppe and Alessandro Vanacore and Valerio La Gatta and Antonino Ferraro and Antonio Galli and Marco Postiglione and Vincenzo Moscato},
url = {https://ieeexplore.ieee.org/document/10386684/},
doi = {10.1109/BigData59044.2023.10386684},
isbn = {979-8-3503-2445-7},
year = {2023},
date = {2023-12-01},
urldate = {2024-07-12},
booktitle = {2023 IEEE International Conference on Big Data (BigData)},
pages = {1–9},
publisher = {IEEE},
address = {Sorrento, Italy},
abstract = {Deep learning, owing to its transformative influence across a myriad of sectors, has recently made its foray into the legal domain, instigated by the surge in digitization. Among the multitude of applications in this space, legal document classification emerges as a pivotal yet complex undertaking. Legal texts, characterized by unique domain-centric semantics and intricate linguistic patterns, necessitate precision-driven classification systems for numerous practical implications. This paper illuminates the challenges and opportunities in automating the classification of European Union (EU) legal documents, emphasizing the interrelationships among statutes and the hierarchical nature of legal references. In this context, we introduce a novel graph data modeling technique that adeptly marries content-centric indicators with the relational dynamics inherent among diverse legal documents. Central to our approach is a framework that melds text embeddings with graph neural networks for the classification of legal documents aligned with their subject-based directories. Empirical evaluations on the EU law dataset underline the efficacy of our model across varying granularities, from general thematic categories to intricate subtopics. This endeavor not only augments the comprehensibility and accessibility of EU jurisprudence but also holds significant implications across regulatory compliance, legal research, and policy formulation, underscoring the potential of deep learning in reshaping legal paradigms.},
keywords = {Artificial Intelligence, Semantics},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep learning, owing to its transformative influence across a myriad of sectors, has recently made its foray into the legal domain, instigated by the surge in digitization. Among the multitude of applications in this space, legal document classification emerges as a pivotal yet complex undertaking. Legal texts, characterized by unique domain-centric semantics and intricate linguistic patterns, necessitate precision-driven classification systems for numerous practical implications. This paper illuminates the challenges and opportunities in automating the classification of European Union (EU) legal documents, emphasizing the interrelationships among statutes and the hierarchical nature of legal references. In this context, we introduce a novel graph data modeling technique that adeptly marries content-centric indicators with the relational dynamics inherent among diverse legal documents. Central to our approach is a framework that melds text embeddings with graph neural networks for the classification of legal documents aligned with their subject-based directories. Empirical evaluations on the EU law dataset underline the efficacy of our model across varying granularities, from general thematic categories to intricate subtopics. This endeavor not only augments the comprehensibility and accessibility of EU jurisprudence but also holds significant implications across regulatory compliance, legal research, and policy formulation, underscoring the potential of deep learning in reshaping legal paradigms.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
In: Computers in Biology and Medicine, vol. 167, pp. 107665, 2023, ISSN: 00104825.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Generative algorithms, Rehabilitation, Synthetic data
@article{mennella_generating_2023,
title = {Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523011307},
doi = {10.1016/j.compbiomed.2023.107665},
issn = {00104825},
year = {2023},
date = {2023-12-01},
urldate = {2024-07-21},
journal = {Computers in Biology and Medicine},
volume = {167},
pages = {107665},
abstract = {Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation.
This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises.
A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90.
This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.},
keywords = {Artificial Intelligence, Generative algorithms, Rehabilitation, Synthetic data},
pubstate = {published},
tppubtype = {article}
}
Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation.
This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises.
A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90.
This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.
This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises.
A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90.
This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.
Buonaiuto, Giuseppe; Gargiulo, Francesco; Pietro, Giuseppe De; Esposito, Massimo; Pota, Marco
Best practices for portfolio optimization by quantum computing, experimented on real quantum devices Journal Article
In: Scientific Reports, vol. 13, no. 1, pp. 19434, 2023, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computational science, Information technology, Mathematics and computing, Quantum Physics
@article{buonaiutoBestPracticesPortfolio2023,
title = {Best practices for portfolio optimization by quantum computing, experimented on real quantum devices},
author = {Giuseppe Buonaiuto and Francesco Gargiulo and Giuseppe De Pietro and Massimo Esposito and Marco Pota},
url = {https://www.nature.com/articles/s41598-023-45392-w},
doi = {10.1038/s41598-023-45392-w},
issn = {2045-2322},
year = {2023},
date = {2023-11-01},
urldate = {2024-07-21},
journal = {Scientific Reports},
volume = {13},
number = {1},
pages = {19434},
abstract = {Abstract
In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.},
keywords = {Artificial Intelligence, Computational science, Information technology, Mathematics and computing, Quantum Physics},
pubstate = {published},
tppubtype = {article}
}
Abstract
In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.
In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions Journal Article
In: Computers in Biology and Medicine, vol. 166, pp. 107485, 2023, ISSN: 00104825.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Movement classification, Pose estimation, Rehabilitation
@article{mennellaDeepLearningSystem2023,
title = {A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523009502},
doi = {10.1016/j.compbiomed.2023.107485},
issn = {00104825},
year = {2023},
date = {2023-11-01},
urldate = {2024-07-21},
journal = {Computers in Biology and Medicine},
volume = {166},
pages = {107485},
abstract = {In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality.
This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired.
The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns.
By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Movement classification, Pose estimation, Rehabilitation},
pubstate = {published},
tppubtype = {article}
}
In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality.
This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired.
The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns.
By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.
This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired.
The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns.
By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.
Gaglio, Giuseppe Fulvio; Augello, Agnese; Pipitone, Arianna; Gallo, Luigi; Sorbello, Rosario; Chella, Antonio
Moral Mediators in the Metaverse: Exploring Artificial Morality through a Talking Cricket Paradigm Proceedings Article
In: Bruno, Alessandro; Pipitone, Arianna; Manzotti, Riccardo; Augello, Agnese; Mazzeo, Pier Luigi; Vella, Filippo; Chella, Antonio (Ed.): Proceedings of the 1st Workshop on Artificial Intelligence for Perception and Artificial Consciousness (AIxPAC 2023), pp. 30–43, CEUR, Roma, Italy, 2023, ISSN: 1613-0073.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Virtual Reality
@inproceedings{gaglioMoralMediatorsMetaverse2023,
title = {Moral Mediators in the Metaverse: Exploring Artificial Morality through a Talking Cricket Paradigm},
author = {Giuseppe Fulvio Gaglio and Agnese Augello and Arianna Pipitone and Luigi Gallo and Rosario Sorbello and Antonio Chella},
editor = {Alessandro Bruno and Arianna Pipitone and Riccardo Manzotti and Agnese Augello and Pier Luigi Mazzeo and Filippo Vella and Antonio Chella},
url = {https://ceur-ws.org/Vol-3563/#paper_9},
issn = {1613-0073},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-27},
booktitle = {Proceedings of the 1st Workshop on Artificial Intelligence for Perception and Artificial Consciousness (AIxPAC 2023)},
volume = {3563},
pages = {30–43},
publisher = {CEUR},
address = {Roma, Italy},
series = {CEUR Workshop Proceedings},
abstract = {As technological innovations continue to shape our social interactions, the Metaverse introduces im mersive experiences that reflect real-life practices, accessible by users through their avatars. However, these interactions also bring forth potential negative aspects, including discrimination and cyberbullying. While current automatic detection systems exist, educating users on appropriate behaviour remains crucial. Leveraging recent advancements in Artificial Intelligence, the paper focuses on creating virtual AI-controlled moral agents within the Metaverse to guide users in dealing with moral dilemmas. The research aims to understand how such agents impact users’ perceptions and behaviours in ethically challenging virtual environments.},
keywords = {Artificial Intelligence, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
As technological innovations continue to shape our social interactions, the Metaverse introduces im mersive experiences that reflect real-life practices, accessible by users through their avatars. However, these interactions also bring forth potential negative aspects, including discrimination and cyberbullying. While current automatic detection systems exist, educating users on appropriate behaviour remains crucial. Leveraging recent advancements in Artificial Intelligence, the paper focuses on creating virtual AI-controlled moral agents within the Metaverse to guide users in dealing with moral dilemmas. The research aims to understand how such agents impact users’ perceptions and behaviours in ethically challenging virtual environments.
Luca, Marco De; Fasolino, Anna Rita; Ferraro, Antonino; Moscato, Vincenzo; Sperlí, Giancarlo; Tramontana, Porfirio
A community detection approach based on network representation learning for repository mining Journal Article
In: Expert Systems with Applications, vol. 231, pp. 120597, 2023, ISSN: 09574174.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Community detection, Developer social network, Graph-embedding, Repository mining, Social network analysis, Social Networks
@article{de_luca_community_2023,
title = {A community detection approach based on network representation learning for repository mining},
author = {Marco De Luca and Anna Rita Fasolino and Antonino Ferraro and Vincenzo Moscato and Giancarlo Sperlí and Porfirio Tramontana},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0957417423010990},
doi = {10.1016/j.eswa.2023.120597},
issn = {09574174},
year = {2023},
date = {2023-11-01},
urldate = {2024-07-12},
journal = {Expert Systems with Applications},
volume = {231},
pages = {120597},
abstract = {In this paper, we propose a novel heterogeneous graph-based model for capturing and handling all the complex and strongly-correlated information of a software Developer Social Network (DSN) to support several analytic tasks. In particular, we challenge the problem of automatically discovering communities of software developers sharing interests for similar projects by relying on Social Network Analysis (SNA) findings. To overcome the huge graph-size issue, we leverage different graph embedding techniques. Eventually, we evaluate the proposed approach with respect to state-of-the-art approaches from an efficiency and an effectiveness point of view by carrying out an experiment involving the GitHub dataset.},
keywords = {Artificial Intelligence, Community detection, Developer social network, Graph-embedding, Repository mining, Social network analysis, Social Networks},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a novel heterogeneous graph-based model for capturing and handling all the complex and strongly-correlated information of a software Developer Social Network (DSN) to support several analytic tasks. In particular, we challenge the problem of automatically discovering communities of software developers sharing interests for similar projects by relying on Social Network Analysis (SNA) findings. To overcome the huge graph-size issue, we leverage different graph embedding techniques. Eventually, we evaluate the proposed approach with respect to state-of-the-art approaches from an efficiency and an effectiveness point of view by carrying out an experiment involving the GitHub dataset.
Gaglio, Giuseppe Fulvio; Augello, Agnese; Pipitone, Arianna; Gallo, Luigi; Sorbello, Rosario; Chella, Antonio
Moral Mediators in the Metaverse: Exploring Artificial Morality through a Talking Cricket Paradigm Proceedings Article
In: Bruno, Alessandro; Pipitone, Arianna; Manzotti, Riccardo; Augello, Agnese; Mazzeo, Pier Luigi; Vella, Filippo; Chella, Antonio (Ed.): Proceedings of the 1st Workshop on Artificial Intelligence for Perception and Artificial Consciousness (AIxPAC 2023), pp. 30–43, CEUR, Roma, Italy, 2023, ISSN: 1613-0073, (ISSN: 1613-0073).
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Virtual Reality
@inproceedings{gaglio_moral_2023,
title = {Moral Mediators in the Metaverse: Exploring Artificial Morality through a Talking Cricket Paradigm},
author = {Giuseppe Fulvio Gaglio and Agnese Augello and Arianna Pipitone and Luigi Gallo and Rosario Sorbello and Antonio Chella},
editor = {Alessandro Bruno and Arianna Pipitone and Riccardo Manzotti and Agnese Augello and Pier Luigi Mazzeo and Filippo Vella and Antonio Chella},
url = {https://ceur-ws.org/Vol-3563/#paper_9},
issn = {1613-0073},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-27},
booktitle = {Proceedings of the 1st Workshop on Artificial Intelligence for Perception and Artificial Consciousness (AIxPAC 2023)},
volume = {3563},
pages = {30–43},
publisher = {CEUR},
address = {Roma, Italy},
series = {CEUR Workshop Proceedings},
abstract = {As technological innovations continue to shape our social interactions, the Metaverse introduces im mersive experiences that reflect real-life practices, accessible by users through their avatars. However, these interactions also bring forth potential negative aspects, including discrimination and cyberbullying. While current automatic detection systems exist, educating users on appropriate behaviour remains crucial. Leveraging recent advancements in Artificial Intelligence, the paper focuses on creating virtual AI-controlled moral agents within the Metaverse to guide users in dealing with moral dilemmas. The research aims to understand how such agents impact users’ perceptions and behaviours in ethically challenging virtual environments.},
note = {ISSN: 1613-0073},
keywords = {Artificial Intelligence, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
As technological innovations continue to shape our social interactions, the Metaverse introduces im mersive experiences that reflect real-life practices, accessible by users through their avatars. However, these interactions also bring forth potential negative aspects, including discrimination and cyberbullying. While current automatic detection systems exist, educating users on appropriate behaviour remains crucial. Leveraging recent advancements in Artificial Intelligence, the paper focuses on creating virtual AI-controlled moral agents within the Metaverse to guide users in dealing with moral dilemmas. The research aims to understand how such agents impact users’ perceptions and behaviours in ethically challenging virtual environments.
Buonaiuto, Giuseppe; Gargiulo, Francesco; Pietro, Giuseppe De; Esposito, Massimo; Pota, Marco
Best practices for portfolio optimization by quantum computing, experimented on real quantum devices Journal Article
In: Scientific Reports, vol. 13, no. 1, pp. 19434, 2023, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computational science, Information technology, Mathematics and computing, Quantum Physics
@article{buonaiuto_best_2023,
title = {Best practices for portfolio optimization by quantum computing, experimented on real quantum devices},
author = {Giuseppe Buonaiuto and Francesco Gargiulo and Giuseppe De Pietro and Massimo Esposito and Marco Pota},
url = {https://www.nature.com/articles/s41598-023-45392-w},
doi = {10.1038/s41598-023-45392-w},
issn = {2045-2322},
year = {2023},
date = {2023-11-01},
urldate = {2024-07-21},
journal = {Scientific Reports},
volume = {13},
number = {1},
pages = {19434},
abstract = {Abstract
In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.},
keywords = {Artificial Intelligence, Computational science, Information technology, Mathematics and computing, Quantum Physics},
pubstate = {published},
tppubtype = {article}
}
Abstract
In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.
In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.
Mennella, Ciro; Maniscalco, Umberto; Pietro, Giuseppe De; Esposito, Massimo
A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions Journal Article
In: Computers in Biology and Medicine, vol. 166, pp. 107485, 2023, ISSN: 00104825.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Movement classification, Pose estimation, Rehabilitation
@article{mennella_deep_2023,
title = {A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions},
author = {Ciro Mennella and Umberto Maniscalco and Giuseppe De Pietro and Massimo Esposito},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0010482523009502},
doi = {10.1016/j.compbiomed.2023.107485},
issn = {00104825},
year = {2023},
date = {2023-11-01},
urldate = {2024-07-21},
journal = {Computers in Biology and Medicine},
volume = {166},
pages = {107485},
abstract = {In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality.
This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired.
The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns.
By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.},
keywords = {Artificial Intelligence, Computer Vision and Pattern Recognition, Deep Learning, Movement classification, Pose estimation, Rehabilitation},
pubstate = {published},
tppubtype = {article}
}
In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality.
This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired.
The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns.
By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.
This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired.
The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns.
By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.
Esposito, Concetta; Janneh, Mohammed; Spaziani, Sara; Calcagno, Vincenzo; Bernardi, Mario Luca; Iammarino, Martina; Verdone, Chiara; Tagliamonte, Maria; Buonaguro, Luigi; Pisco, Marco; Aversano, Lerina; Cusano, Andrea
Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy Journal Article
In: Cells, vol. 12, no. 22, pp. 2645, 2023, ISSN: 2073-4409.
Abstract | Links | BibTeX | Tags: Machine Learning, Neural networks, Raman Spectroscopy
@article{espositoAssessmentPrimaryHuman2023,
title = {Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy},
author = {Concetta Esposito and Mohammed Janneh and Sara Spaziani and Vincenzo Calcagno and Mario Luca Bernardi and Martina Iammarino and Chiara Verdone and Maria Tagliamonte and Luigi Buonaguro and Marco Pisco and Lerina Aversano and Andrea Cusano},
url = {https://www.mdpi.com/2073-4409/12/22/2645},
doi = {10.3390/cells12222645},
issn = {2073-4409},
year = {2023},
date = {2023-11-01},
urldate = {2024-10-02},
journal = {Cells},
volume = {12},
number = {22},
pages = {2645},
abstract = {We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.},
keywords = {Machine Learning, Neural networks, Raman Spectroscopy},
pubstate = {published},
tppubtype = {article}
}
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.
Esposito, Concetta; Janneh, Mohammed; Spaziani, Sara; Calcagno, Vincenzo; Bernardi, Mario Luca; Iammarino, Martina; Verdone, Chiara; Tagliamonte, Maria; Buonaguro, Luigi; Pisco, Marco; Aversano, Lerina; Cusano, Andrea
Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy Journal Article
In: Cells, vol. 12, no. 22, pp. 2645, 2023, ISSN: 2073-4409.
Abstract | Links | BibTeX | Tags: Machine Learning, Neural networks, Raman Spectroscopy
@article{esposito_assessment_2023,
title = {Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy},
author = {Concetta Esposito and Mohammed Janneh and Sara Spaziani and Vincenzo Calcagno and Mario Luca Bernardi and Martina Iammarino and Chiara Verdone and Maria Tagliamonte and Luigi Buonaguro and Marco Pisco and Lerina Aversano and Andrea Cusano},
url = {https://www.mdpi.com/2073-4409/12/22/2645},
doi = {10.3390/cells12222645},
issn = {2073-4409},
year = {2023},
date = {2023-11-01},
urldate = {2024-10-02},
journal = {Cells},
volume = {12},
number = {22},
pages = {2645},
abstract = {We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.},
keywords = {Machine Learning, Neural networks, Raman Spectroscopy},
pubstate = {published},
tppubtype = {article}
}
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.
Esposito, Concetta; Janneh, Mohammed; Spaziani, Sara; Calcagno, Vincenzo; Bernardi, Mario Luca; Iammarino, Martina; Verdone, Chiara; Tagliamonte, Maria; Buonaguro, Luigi; Pisco, Marco; Aversano, Lerina; Cusano, Andrea
Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy Journal Article
In: Cells, vol. 12, no. 22, pp. 2645, 2023, ISSN: 2073-4409.
Abstract | Links | BibTeX | Tags: Machine Learning, Neural networks, Raman Spectroscopy
@article{esposito_assessment_2023-1,
title = {Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy},
author = {Concetta Esposito and Mohammed Janneh and Sara Spaziani and Vincenzo Calcagno and Mario Luca Bernardi and Martina Iammarino and Chiara Verdone and Maria Tagliamonte and Luigi Buonaguro and Marco Pisco and Lerina Aversano and Andrea Cusano},
url = {https://www.mdpi.com/2073-4409/12/22/2645},
doi = {10.3390/cells12222645},
issn = {2073-4409},
year = {2023},
date = {2023-11-01},
urldate = {2024-10-02},
journal = {Cells},
volume = {12},
number = {22},
pages = {2645},
abstract = {We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.},
keywords = {Machine Learning, Neural networks, Raman Spectroscopy},
pubstate = {published},
tppubtype = {article}
}
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells’ Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Cusano, Andrea; Iammarino, Martina; Pisco, Marco; Spaziani, Sara; Verdone, Chiara
Raman Spectroscopy of Cells for Cancer Classification Through Machine Learning Proceedings Article
In: 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 688–693, IEEE, Milano, Italy, 2023, ISBN: 979-8-3503-0080-2.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cancer, Raman Spectroscopy
@inproceedings{aversanoRamanSpectroscopyCells2023,
title = {Raman Spectroscopy of Cells for Cancer Classification Through Machine Learning},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Andrea Cusano and Martina Iammarino and Marco Pisco and Sara Spaziani and Chiara Verdone},
url = {https://ieeexplore.ieee.org/document/10405759/},
doi = {10.1109/MetroXRAINE58569.2023.10405759},
isbn = {979-8-3503-0080-2},
year = {2023},
date = {2023-10-01},
urldate = {2024-10-02},
booktitle = {2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {688–693},
publisher = {IEEE},
address = {Milano, Italy},
abstract = {The term cancer indicates a pathological condition characterized by the uncontrolled proliferation of cells that have the ability to infiltrate the normal organs and tissues of the body, altering their structure and functioning. Therefore, since cancer is caused by DNA mutations within cells, Raman spectroscopy can be a valuable tool for gathering information about their composition. With this technique, a sample is illuminated by a beam of monochromatic light and the interaction between them produces an effect that allows to obtain information on the sample examined. This study aims to combine Raman spectroscopy with artificial intelligence to develop a model capable of distinguishing cancerous cells from healthy ones. In this regard, the experiments were conducted on a data set provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient suffering from liver cancer. Specifically, the dataset was created through a lengthy data collection process, which involved first analyzing the cells with spectroscopy and then training several machine learning, tree-based, and boosting classifiers to distinguish cancer cells from healthy ones. The main contribution of the work consists in using genetic algorithms to select the most significant frequencies. The best results are obtained using Extra Tree Classifier reaching a value of F-score up to 91%.},
keywords = {Artificial Intelligence, Cancer, Raman Spectroscopy},
pubstate = {published},
tppubtype = {inproceedings}
}
The term cancer indicates a pathological condition characterized by the uncontrolled proliferation of cells that have the ability to infiltrate the normal organs and tissues of the body, altering their structure and functioning. Therefore, since cancer is caused by DNA mutations within cells, Raman spectroscopy can be a valuable tool for gathering information about their composition. With this technique, a sample is illuminated by a beam of monochromatic light and the interaction between them produces an effect that allows to obtain information on the sample examined. This study aims to combine Raman spectroscopy with artificial intelligence to develop a model capable of distinguishing cancerous cells from healthy ones. In this regard, the experiments were conducted on a data set provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient suffering from liver cancer. Specifically, the dataset was created through a lengthy data collection process, which involved first analyzing the cells with spectroscopy and then training several machine learning, tree-based, and boosting classifiers to distinguish cancer cells from healthy ones. The main contribution of the work consists in using genetic algorithms to select the most significant frequencies. The best results are obtained using Extra Tree Classifier reaching a value of F-score up to 91%.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Cusano, Andrea; Iammarino, Martina; Pisco, Marco; Spaziani, Sara; Verdone, Chiara
Raman Spectroscopy of Cells for Cancer Classification Through Machine Learning Proceedings Article
In: 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 688–693, IEEE, Milano, Italy, 2023, ISBN: 979-8-3503-0080-2.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cancer, Raman Spectroscopy
@inproceedings{aversano_raman_2023,
title = {Raman Spectroscopy of Cells for Cancer Classification Through Machine Learning},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Andrea Cusano and Martina Iammarino and Marco Pisco and Sara Spaziani and Chiara Verdone},
url = {https://ieeexplore.ieee.org/document/10405759/},
doi = {10.1109/MetroXRAINE58569.2023.10405759},
isbn = {979-8-3503-0080-2},
year = {2023},
date = {2023-10-01},
urldate = {2024-10-02},
booktitle = {2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {688–693},
publisher = {IEEE},
address = {Milano, Italy},
abstract = {The term cancer indicates a pathological condition characterized by the uncontrolled proliferation of cells that have the ability to infiltrate the normal organs and tissues of the body, altering their structure and functioning. Therefore, since cancer is caused by DNA mutations within cells, Raman spectroscopy can be a valuable tool for gathering information about their composition. With this technique, a sample is illuminated by a beam of monochromatic light and the interaction between them produces an effect that allows to obtain information on the sample examined. This study aims to combine Raman spectroscopy with artificial intelligence to develop a model capable of distinguishing cancerous cells from healthy ones. In this regard, the experiments were conducted on a data set provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient suffering from liver cancer. Specifically, the dataset was created through a lengthy data collection process, which involved first analyzing the cells with spectroscopy and then training several machine learning, tree-based, and boosting classifiers to distinguish cancer cells from healthy ones. The main contribution of the work consists in using genetic algorithms to select the most significant frequencies. The best results are obtained using Extra Tree Classifier reaching a value of F-score up to 91%.},
keywords = {Artificial Intelligence, Cancer, Raman Spectroscopy},
pubstate = {published},
tppubtype = {inproceedings}
}
The term cancer indicates a pathological condition characterized by the uncontrolled proliferation of cells that have the ability to infiltrate the normal organs and tissues of the body, altering their structure and functioning. Therefore, since cancer is caused by DNA mutations within cells, Raman spectroscopy can be a valuable tool for gathering information about their composition. With this technique, a sample is illuminated by a beam of monochromatic light and the interaction between them produces an effect that allows to obtain information on the sample examined. This study aims to combine Raman spectroscopy with artificial intelligence to develop a model capable of distinguishing cancerous cells from healthy ones. In this regard, the experiments were conducted on a data set provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient suffering from liver cancer. Specifically, the dataset was created through a lengthy data collection process, which involved first analyzing the cells with spectroscopy and then training several machine learning, tree-based, and boosting classifiers to distinguish cancer cells from healthy ones. The main contribution of the work consists in using genetic algorithms to select the most significant frequencies. The best results are obtained using Extra Tree Classifier reaching a value of F-score up to 91%.
Aversano, Lerina; Bernardi, Mario Luca; Cimitile, Marta; Cusano, Andrea; Iammarino, Martina; Pisco, Marco; Spaziani, Sara; Verdone, Chiara
Raman Spectroscopy of Cells for Cancer Classification Through Machine Learning Proceedings Article
In: 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 688–693, IEEE, Milano, Italy, 2023, ISBN: 979-8-3503-0080-2.
Abstract | Links | BibTeX | Tags: Artificial Intelligence, Cancer, Raman Spectroscopy
@inproceedings{aversano_raman_2023-1,
title = {Raman Spectroscopy of Cells for Cancer Classification Through Machine Learning},
author = {Lerina Aversano and Mario Luca Bernardi and Marta Cimitile and Andrea Cusano and Martina Iammarino and Marco Pisco and Sara Spaziani and Chiara Verdone},
url = {https://ieeexplore.ieee.org/document/10405759/},
doi = {10.1109/MetroXRAINE58569.2023.10405759},
isbn = {979-8-3503-0080-2},
year = {2023},
date = {2023-10-01},
urldate = {2024-10-02},
booktitle = {2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {688–693},
publisher = {IEEE},
address = {Milano, Italy},
abstract = {The term cancer indicates a pathological condition characterized by the uncontrolled proliferation of cells that have the ability to infiltrate the normal organs and tissues of the body, altering their structure and functioning. Therefore, since cancer is caused by DNA mutations within cells, Raman spectroscopy can be a valuable tool for gathering information about their composition. With this technique, a sample is illuminated by a beam of monochromatic light and the interaction between them produces an effect that allows to obtain information on the sample examined. This study aims to combine Raman spectroscopy with artificial intelligence to develop a model capable of distinguishing cancerous cells from healthy ones. In this regard, the experiments were conducted on a data set provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient suffering from liver cancer. Specifically, the dataset was created through a lengthy data collection process, which involved first analyzing the cells with spectroscopy and then training several machine learning, tree-based, and boosting classifiers to distinguish cancer cells from healthy ones. The main contribution of the work consists in using genetic algorithms to select the most significant frequencies. The best results are obtained using Extra Tree Classifier reaching a value of F-score up to 91%.},
keywords = {Artificial Intelligence, Cancer, Raman Spectroscopy},
pubstate = {published},
tppubtype = {inproceedings}
}
The term cancer indicates a pathological condition characterized by the uncontrolled proliferation of cells that have the ability to infiltrate the normal organs and tissues of the body, altering their structure and functioning. Therefore, since cancer is caused by DNA mutations within cells, Raman spectroscopy can be a valuable tool for gathering information about their composition. With this technique, a sample is illuminated by a beam of monochromatic light and the interaction between them produces an effect that allows to obtain information on the sample examined. This study aims to combine Raman spectroscopy with artificial intelligence to develop a model capable of distinguishing cancerous cells from healthy ones. In this regard, the experiments were conducted on a data set provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient suffering from liver cancer. Specifically, the dataset was created through a lengthy data collection process, which involved first analyzing the cells with spectroscopy and then training several machine learning, tree-based, and boosting classifiers to distinguish cancer cells from healthy ones. The main contribution of the work consists in using genetic algorithms to select the most significant frequencies. The best results are obtained using Extra Tree Classifier reaching a value of F-score up to 91%.