Luca Romeo

Scopus Publications

A novel multi-task multi-view approach with custom multi-label loss for fault detection in complex industrial apparatus
Riccardo Rosati, Lucia Pepa, Luca Romeo
Advanced Engineering Informatics, 2026
Machine Learning (ML) plays a crucial role in Industry 4.0, enabling predictive fault detection (FD) by analyzing vast amounts of log data. However, current ML approaches often rely on single-task learning, neglecting the diverse nature of log data and the prediction of interrelated faults. Moreover, multi-view learning (MVL) and multi-task learning (MTL) are usually applied disjointly without applying joint learning across tasks and views. To address these gaps, we a novel approach which leverages multi-task and multi-view learning frameworks, augmented by a multi-label Cross Entropy loss (MTMVL-CE). MTMVL-CE improves generalization performance between and within different fault types, enabling the classification of multiple faults in complex industrial machines. Indeed, MTMVL-CE optimizes classification performance by learning across numerous faults simultaneously, achieving an accurate representation of heterogeneous log data, robust fault classification, and feasible generalization over time. We tested our approach through extensive experiments on an real use case involving FD in a complex banknote recirculator device inside Automated Teller Machines. Our results demonstrate MTMVL-CE’s superior performance compared to MTL and MVL competitors in capturing fault interdependencies and providing accurate, reliable predictions. • MTMVL-CE: unified framework for multi-source industrial fault detection. • Jointly optimizes within-task and between-task fault dependencies. • Correlation-based regularizer models task relationships. • Validated on 7400 heterogeneous real-world ATM log records. • Superior temporal generalization compared to state-of-the-art approaches.
Ordinal evolutionary artificial neural networks for predicting diabetic nephropathy progression
Antonio Manuel Gómez-Orellana, Michele Bernardini, Rafael Ayllón-Gavilán, Víctor Manuel Vargas, Pedro Antonio Gutiérrez, César Hervás-Martínez, Luca Romeo
Applied Soft Computing, 2026
Diabetic Nephropathy (DN) is a complex, multi-factorial condition that often coexists with other diabetes-related comorbidities. Although DN progresses through a series of ordered stages, i.e. from mild to advanced, studies have primarily focused on classifying its presence or absence, i.e. a static risk prediction, rather than its progression. This gap underscores the need for advanced methodologies to predict DN progression, which could improve patient outcomes and optimise healthcare interventions. This study proposes a novel ordinal perspective to predicting DN progression using clinical Electronic Health Records (EHR) data. This approach is based on Ordinal Evolutionary Artificial Neural Networks (OEANNs), which integrate a Cumulative Link Model to perform ordinal predictions, and leverages Evolutionary Algorithms to optimise OEANNs architecture and weights by dynamically adapting to the sparsity of EHR data. The proposed ordinal perspective involves discretising the disease risk into four ordinal severity classes, each representing a different stage of disease progression. In addition, the temporal variability of DN progression is modelled by considering a feature engineering stage that constructs variables capturing early indicators of DN. Experimental results on a clinical EHR dataset demonstrate that OEANNs outperform state-of-the-art nominal and ordinal models, achieving significant improvements in ordinal metrics such as Mean Absolute Error and Quadratic Weighted Kappa. Furthermore, unlike traditional static risk prediction, OEANNs minimise misclassification errors between distant severity stages, enabling more accurate predictions of disease severity. Therefore, the proposed innovative ordinal approach bridges a critical gap in DN management, offering robust, clinically relevant predictions to inform personalised treatment planning. • Predicting Diabetic Nephropathy progression from an ordinal perspective. • The use of ordinal evolutionary ANNs outperforms other competitive approaches. • The proposed OEANN achieves accurate, robust, and clinically relevant predictions. • Provide an in-depth comparison of the proposed OEANN model against other competitors.
Machine Learning-Based Clinical Decision Support System for Hepatic Fibrosis Risk Prediction in General Practice
Michele Bernardini, Mariachiara di Cosmo, Gaia Barone, Luca Romeo, Emanuele Frontoni
ACM Transactions on Computing for Healthcare, 2026
Hepatic steatosis, or non-alcoholic fatty liver disease (NAFLD), affects a significant portion of the global population and can lead to more severe liver conditions, including hepatic fibrosis. Early and accurate risk prediction of fibrosis is crucial for timely intervention. Traditional diagnostic methods are invasive and carry risks, while imaging techniques and blood-based biomarkers have limitations in routine general practice. This study presents a machine learning-based clinical decision support system designed to assess the risk of hepatic fibrosis in patients with NAFLD using routine laboratory tests. The framework is developed using electronic health record data collected over 15 years, initially encompassing 1,272,572 patients from general practice. After applying clinical selection criteria, two cohorts of 12,960 and 25,478 patients were used for model development and evaluation. The proposed approach provides a robust foundation for monitoring fibrosis risk by implementing a novel screening method, which preprocesses predictors by leveraging well-established clinical indicators (e.g., hepatic steatosis index, fibrosis-4 index), alongside a selected minimal number of predictors, making it practical and cost-effective for widespread clinical use. The study’s findings indicate promising results for screening and monitoring fibrosis risk in NAFLD patients, achieving the best AUC of 92.97%, PRAUC of 75.44%, and Sensitivity of 79.63%.
Deep learning models for robust facial liveness detection
Oleksandr Kuznetsov, Emanuele Frontoni, Luca Romeo, Riccardo Rosati, Andrea Maranesi, Alessandro Muscatello
Multimedia Tools and Applications, 2026
Type 2 Diabetes Prediction from Multi-center Electronic Health Records in General Practice Using Machine Learning
Max Rerisi, Mariachiara Di Cosmo, Michele Bernardini, Luca Romeo
Communications in Computer and Information Science, 2026
Knee Osteoarthritis Severity Grading Using Soft Labelling and Ordinal Classification
Francisco Bérchez-Moreno, Víctor M. Vargas, Antonio M. Gómez-Orellana, David Guijo-Rubio, Luca Romeo, Edoardo Conti, Pedro A. Gutiérrez, César Hervás-Martínez
Lecture Notes in Computer Science, 2026
A machine learning algorithm for the prediction of complications incorporated in electronic medical records improves type 2 diabetes care
Antonio Nicolucci, Giacomo Vespasiani, Domenico Mannino, Giuseppina T. Russo, Giuseppe Lucisano, Maria Chiara Rossi, Paola Ponzani, Salvatore De Cosmo, Graziano Di Cianni, Cristina Lencioni, Luca Romeo, Michele Bernardini, Emanuele Frontoni, Riccardo Candido
Diabetes Research and Clinical Practice, 2025
ML-predicted surgical site infections: An epidemiological study utilizing machine learning on routinely collected healthcare data to predict infection risk
Davide Golinelli, Simona Rosa, Paola Rucci, Francesco Sanmarchi, Dario Tedesco, Carlo Biagetti, Alessio Gili, Andrea Bucci, Luca Romeo, Roberto Grilli
Smart Health, 2025
Background Surgical site infections (SSIs) are a major public health issue, causing increased morbidity, longer hospital stays, and higher healthcare costs. Despite progress in infection control, predicting and preventing SSIs is crucial for improving patient outcomes. This study examines the use of machine learning (ML) on routinely collected healthcare data (RCD) to predict SSIs in orthopaedic surgery, aiming to improve risk stratification and guide interventions. Objectives To develop and validate an ML predictive model using RCD to assess SSI risk in orthopaedic surgery patients. Methods A retrospective study was carried out with RCD from a 1.2 million population Italian Health Authority, covering surgeries from 2017 to 2021. The population included patients undergoing hip or knee arthroplasty and open reduction of fractures. Several ML algorithms, including eXtreme Gradient Boosting (XGBoost), were used for model development. The models’ performance was assessed by recall, accuracy, and area under the receiver operating characteristic curve (AUC). A feature importance analysis identified key SSI risk predictors. Results The XGBoost model demonstrated superior performance, with a recall exceeding 70% and an AUC>0.70, overcoming other methods. Significant predictors included the ASA classification, opioid use, priority class of the surgery operation, and length of hospital stay. Conclusions ML models, particularly XGBoost, effectively predict SSI risk in orthopaedic patients, offering a new approach to infection control and prevention. Incorporating ML and RCD highlights the potential for scalable, data-driven personalized medicine interventions. Future research will focus on model validation and integration into healthcare systems for enhanced patient management.
Deep learning model for detecting copy-move attack in images: Testing and verification
Aleksandra Pauls, Luca Romeo, Riccardo Rosati, Primo Zingaretti, Emanuele Frontoni, Oleksandr Kuznetsov
Advancements in Cybersecurity Next Generation Systems and Applications, 2025
The modern level of digital technology leads to the widespread use of multimedia content: photo and video images, audio, texts, presentations, etc. This trend is significantly enhanced with the development of Internet technologies. Today, every gadget connected to the Internet continuously creates, processes, and sends huge amounts of multimedia information in social networks, news channels, advertising mailings, messengers, and much more. Obviously, this greatly simplifies communication between people and provides convenient, fast, and reliable information services. However, every new technology can be used for damaging purposes. For example, the Internet is currently overflowing with fake photo and video content. Fake multimedia has an extremely negative aspect: it devalues intellectual property and copyright, compromises objective journalism, causes reputational and material damage, and much more. All this makes us develop and constantly improve new technologies for detecting fakes, verifying them repeatedly, and testing them in various Internet applications. This article explores a well-known type of image spoofing based on copy-move attacks. This simple attack can be implemented quickly, even in automatic mode, but it is extremely difficult to detect fake images in a huge stream of multimedia data. We consider a deep learning model using convolutional neural networks and perform numerous tests on different datasets. We show that this approach can indeed be used to detect some fake images. However, to build a universal protection mechanism, it is necessary to significantly extend the datasets and take into account the peculiarities of copy-move attacks.
Single- and multi-task linear models for ATMs fault classification in human-centered predictive maintenance
Riccardo Rosati, Luca Romeo, Adriano Mancini
Computers and Industrial Engineering, 2025
The recirculator, a complex component within Automated Teller Machines (ATMs) responsible for handling banknotes, poses a challenging task for fault diagnosis due to its intricate nature, which renders it impractical to integrate dedicated sensors and potential multiple faults. This paper presents advanced single-task (STL-LR) and multi-task (MTL-LR) logistic regression models explicitly designed for capturing specific and similar discriminative patterns of multiple faults. Our approach focuses on maintaining the expert human operator at the center of the model checking and development process (human-in-the-loop approach). This objective has been achieved by including training data extracted from the intervention management platform, which collects the annotations of human operators. By leveraging this data, our STL and MTL models enhance generalization performance, especially in cases where discrepancies exist between machine-reported errors and technician-observed anomalies. The results illustrate the potential of the STL-LR and MTL-LR models as the main core of PdM DSS to aid technicians in accurately pinpointing fault-prone areas. This research contributes to Industry 5.0 by presenting a novel predictive maintenance approach that evolves task-specific learning to the generalization advantages of MTL. This evolution holds promise for fostering more efficient and effective maintenance strategies in complex equipment environments. • Introduced a human-centered DSS for predictive maintenance in ATMs. • Integrated human expertise with automated diagnostics for PdM. • Collected and analyzed real-world operational data from ATMs. • Developed STL and MTL models enhancing fault detection accuracy. • Proposed edge computing for real-time fault diagnosis and response.
Preface
Communications in Computer and Information Science, 2025
Learning Ordinal-Hierarchical Constraints for Deep Learning Classifiers
Riccardo Rosati, Luca Romeo, Víctor Manuel Vargas, Pedro Antonio Gutiérrez, Emanuele Frontoni, César Hervás-Martínez
IEEE Transactions on Neural Networks and Learning Systems, 2025
Algor-ethics: charting the ethical path for AI in critical care
Jonathan Montomoli, Maria Maddalena Bitondo, Marco Cascella, Emanuele Rezoagli, Luca Romeo, Valentina Bellini, Federico Semeraro, Emiliano Gamberini, Emanuele Frontoni, Vanni Agnoletti, Mattia Altini, Paolo Benanti, Elena Giovanna Bignami
Journal of Clinical Monitoring and Computing, 2024
Enhancing copy-move forgery detection through a novel CNN architecture and comprehensive dataset analysis
Oleksandr Kuznetsov, Emanuele Frontoni, Luca Romeo, Riccardo Rosati
Multimedia Tools and Applications, 2024
Image steganalysis using deep learning models
Alexandr Kuznetsov, Nicolas Luhanko, Emanuele Frontoni, Luca Romeo, Riccardo Rosati
Multimedia Tools and Applications, 2024
Neighborhood Component Feature Selection for Multiple Instance Learning Paradigm
Giacomo Turri, Luca Romeo
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
Data augmentation strategy for generating realistic samples on defect segmentation task
Massimo Martini, Riccardo Rosati, Luca Romeo, Adriano Mancini
Procedia Computer Science, 2024
Multi-Rater Consensus Learning for Modeling Multiple Sparse Ratings of Affective Behaviour
Luca Romeo, Temitayo Olugbade, Massimiliano Pontil, Nadia Bianchi-Berthouze
IEEE Transactions on Affective Computing, 2024
Mitigating Bias in Aesthetic Quality Control Tasks: An Adversarial Learning Approach
Denis Bernovschi, Alex Giacomini, Riccardo Rosati, Luca Romeo
Procedia Computer Science, 2024
On the Integration of Artificial Intelligence and Blockchain Technology: A Perspective About Security
Oleksandr Kuznetsov, Paolo Sernani, Luca Romeo, Emanuele Frontoni, Adriano Mancini
IEEE Access, 2024
Visuo-motor interference is modulated by task interactivity: A kinematic study
Matilde Rocca, Lucia Maria Sacheli, Luca Romeo, Andrea Cavallo
Psychonomic Bulletin and Review, 2023
A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets
Michele Bernardini, Anastasiia Doinychko, Luca Romeo, Emanuele Frontoni, Massih-Reza Amini
Computers in Biology and Medicine, 2023
A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data
Víctor Manuel Vargas, Riccardo Rosati, César Hervás-Martínez, Adriano Mancini, Luca Romeo, Pedro Antonio Gutiérrez
Engineering Applications of Artificial Intelligence, 2023
Exponential loss regularisation for encouraging ordinal constraint to shotgun stocks quality assessment
Víctor Manuel Vargas, Pedro Antonio Gutiérrez, Riccardo Rosati, Luca Romeo, Emanuele Frontoni, César Hervás-Martínez
Applied Soft Computing, 2023
Optimizing Hill Climbing Algorithm for S-Boxes Generation
Alexandr Kuznetsov, Emanuele Frontoni, Luca Romeo, Nikolay Poluyanenko, Sergey Kandiy, Kateryna Kuznetsova, Eleonóra Beňová
Electronics Switzerland, 2023
Uncovering the potential of innovation ecosystems in the healthcare sector after the COVID-19 crisis
Dominique Lepore, Emanuele Frontoni, Alessandra Micozzi, Sara Moccia, Luca Romeo, Francesca Spigarelli
Health Policy, 2023
Deep learning based hierarchical classifier for weapon stock aesthetic quality control assessment
Víctor Manuel Vargas, Pedro Antonio Gutiérrez, Riccardo Rosati, Luca Romeo, Emanuele Frontoni, César Hervás-Martínez
Computers in Industry, 2023
From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0
Riccardo Rosati, Luca Romeo, Gianalberto Cecchini, Flavio Tonetto, Paolo Viti, Adriano Mancini, Emanuele Frontoni
Journal of Intelligent Manufacturing, 2023
Predictive Maintenance of ATM Machines by Modelling Remaining Useful Life with Machine Learning Techniques
Riccardo Rosati, Luca Romeo, Víctor Manuel Vargas, Pedro Antonio Gutiérrez, César Hervás-Martínez, Lorenzo Bianchini, Alessandra Capriotti, Rosario Capparuccia, Emanuele Frontoni
Lecture Notes in Networks and Systems, 2023
Decoding social decisions from movement kinematics
Giacomo Turri, Andrea Cavallo, Luca Romeo, Massimiliano Pontil, Alan Sanfey, Stefano Panzeri, Cristina Becchio
Iscience, 2022
Prediction of complications of type 2 Diabetes: A Machine learning approach
Antonio Nicolucci, Luca Romeo, Michele Bernardini, Marco Vespasiani, Maria Chiara Rossi, Massimiliano Petrelli, Antonio Ceriello, Paolo Di Bartolo, Emanuele Frontoni, Giacomo Vespasiani
Diabetes Research and Clinical Practice, 2022
Fault Diagnosis of Rotating Machinery Based on Wasserstein Distance and Feature Selection
Francesco Ferracuti, Alessandro Freddi, Andrea Monteriu, Luca Romeo
IEEE Transactions on Automation Science and Engineering, 2022
A novel deep ordinal classification approach for aesthetic quality control classification
Riccardo Rosati, Luca Romeo, Víctor Manuel Vargas, Pedro Antonio Gutiérrez, César Hervás-Martínez, Emanuele Frontoni
Neural Computing and Applications, 2022
A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign
Luca Romeo, Emanuele Frontoni
Pattern Recognition, 2022
Decision Support System Based on Deep Learning for Improving the Quality Control Task of Rifles: A Case Study in Industry 4.0
Luca Romeo, Riccardo Rosati, Emanuele Frontoni
Management and Industrial Engineering, 2022
Deep Learning Based Image Steganalysis
Alexandr Kuznetsov, Nicolas Luhanko, Emanuele Frontoni, Luca Romeo, Riccardo Rosati
2022 IEEE 9th International Conference on Problems of Infocommunications Science and Technology Pic S and T 2022 Proceedings, 2022
Multiple Instance Learning for Emotion Recognition Using Physiological Signals
Luca Romeo, Andrea Cavallo, Lucia Pepa, Nadia Bianchi-Berthouze, Massimiliano Pontil
IEEE Transactions on Affective Computing, 2022
Deep Learning Based Fuzzy Extractor for Generating Strong Keys from Biometric Face Images
Alexandr Kuznetsov, Dmytro Zakharov, Emanuele Frontoni, Luca Romeo, Riccardo Rosati
2022 IEEE 9th International Conference on Problems of Infocommunications Science and Technology Pic S and T 2022 Proceedings, 2022
Preface of the 2nd Italian Workshop on Artificial Intelligence and Applications for Business and Industries
Ceur Workshop Proceedings, 2022
Deep Learning Based Face Liveliness Detection
Aleksandr Kuznetsov, Davyd Kvaratskheliia, Andrea Maranesi, Luca Romeo, Alessandro Muscatello, Riccardo Rosati
2022 IEEE 9th International Conference on Problems of Infocommunications Science and Technology Pic S and T 2022 Proceedings, 2022
Identifying the signature of prospective motor control in children with autism
Andrea Cavallo, Luca Romeo, Caterina Ansuini, Francesca Battaglia, Lino Nobili, Massimiliano Pontil, Stefano Panzeri, Cristina Becchio
Scientific Reports, 2021
A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease Evolution
Michele Bernardini, Luca Romeo, Emanuele Frontoni, Massih-Reza Amini
IEEE Journal of Biomedical and Health Informatics, 2021
Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients
Jonathan Montomoli, Luca Romeo, Sara Moccia, Michele Bernardini, Lucia Migliorelli, Daniele Berardini, Abele Donati, Andrea Carsetti, Maria Grazia Bocci, Pedro David Wendel Garcia, Thierry Fumeaux, Philippe Guerci, Reto Andreas Schüpbach, Can Ince, Emanuele Frontoni, Matthias Peter Hilty, Mario Alfaro-Farias, Gerardo Vizmanos-Lamotte, Thomas Tschoellitsch, Jens Meier, Hernán Aguirre-Bermeo, Janina Apolo, Alberto Martínez, Geoffrey Jurkolow, Gauthier Delahaye, Emmanuel Novy, Marie-Reine Losser, Tobias Wengenmayer, Jonathan Rilinger, Dawid L. Staudacher, Sascha David, Tobias Welte, Klaus Stahl, “Agios Pavlos”, Theodoros Aslanidis, Anita Korsos, Barna Babik, Reza Nikandish, Emanuele Rezoagli, Matteo Giacomini, Alice Nova, Alberto Fogagnolo, Savino Spadaro, Roberto Ceriani, Martina Murrone, Maddalena A. Wu, Chiara Cogliati, Riccardo Colombo, Emanuele Catena, Fabrizio Turrini, Maria Sole Simonini, Silvia Fabbri, Antonella Potalivo, Francesca Facondini, Gianfilippo Gangitano, Tiziana Perin, Maria Grazia Bocci, Massimo Antonelli, Diederik Gommers, Raquel Rodríguez-García, Jorge Gámez-Zapata, Xiana Taboada-Fraga, Pedro Castro, Adrian Tellez, Arantxa Lander-Azcona, Jesús Escós-Orta, Maria C. Martín-Delgado, Angela Algaba-Calderon, Diego Franch-Llasat, Ferran Roche-Campo, Herminia Lozano-Gómez, Begoña Zalba-Etayo, Marc P. Michot, Alexander Klarer, Rolf Ensner, Peter Schott, Severin Urech, Nuria Zellweger, Lukas Merki, Adriana Lambert, Marcus Laube, Marie M. Jeitziner, Beatrice Jenni-Moser, Jan Wiegand, Bernd Yuen, Barbara Lienhardt-Nobbe, Andrea Westphalen, Petra Salomon, Iris Drvaric, Frank Hillgaertner, Marianne Sieber, Alexander Dullenkopf, Lina Petersen, Ivan Chau, Hatem Ksouri, Govind Oliver Sridharan, Sara Cereghetti, Filippo Boroli, Jerome Pugin, Serge Grazioli, Peter C. Rimensberger, Christian Bürkle, Julien Marrel, Mirko Brenni, Isabelle Fleisch, Jerome Lavanchy, Marie-Helene Perez, Anne-Sylvie Ramelet, Anja Baltussen Weber, Peter Gerecke, Andreas Christ, Samuele Ceruti, Andrea Glotta, Katharina Marquardt, Karim Shaikh, Tobias Hübner, Thomas Neff, Hermann Redecker, Mallory Moret-Bochatay, FriederikeMeyer zu Bentrup, Michael Studhalter, Michael Stephan, Jan Brem, Nadine Gehring, Daniela Selz, Didier Naon, Gian-Reto Kleger, Urs Pietsch, Miodrag Filipovic, Anette Ristic, Michael Sepulcri, Antje Heise, Marilene Franchitti Laurent, Jean-Christophe Laurent, Pedro D. Wendel Garcia, Reto Schuepbach, Dorothea Heuberger, Philipp Bühler, Silvio Brugger, Patricia Fodor, Pascal Locher, Giovanni Camen, Tomislav Gaspert, Marija Jovic, Christoph Haberthuer, Roger F. Lussman, Elif Colak
Journal of Intensive Medicine, 2021
Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World
Farshad Firouzi, Bahar Farahani, Mahmoud Daneshmand, Kathy Grise, Jaeseung Song, Roberto Saracco, Lucy Lu Wang, Kyle Lo, Plamen Angelov, Eduardo Soares, Po-Shen Loh, Zeynab Talebpour, Reza Moradi, Mohsen Goodarzi, Haleh Ashraf, Mohammad Talebpour, Alireza Talebpour, Luca Romeo, Rupam Das, Hadi Heidari, Dana Pasquale, James Moody, Chris Woods, Erich S. Huang, Payam Barnaghi, Majid Sarrafzadeh, Ron Li, Kristen L. Beck, Olexandr Isayev, Nakmyoung Sung, Alan Luo
IEEE Internet of Things Journal, 2021
Analysis, Design and Implementation of a Forecasting System for Parking Lots Occupation
Gabriele Guerrini, Luca Romeo, Davide Alessandrini, Emanuele Frontoni
Dicps 2021 Proceedings of the ACM 1st Workshop on Data Driven and Intelligent Cyber Physical Systems Part of Cps Iot Week 2021, 2021
A Clinical Decision Support System to Stratify the Temporal Risk of Diabetic Retinopathy
Michele Bernardini, Luca Romeo, Adriano Mancini, Emanuele Frontoni
IEEE Access, 2021
A Deep Learning-Based Approach for Automatic Leather Classification in Industry 4.0
Giulia Pazzaglia, Massimo Martini, Riccardo Rosati, Luca Romeo, Emanuele Frontoni
Lecture Notes in Computer Science, 2021
3D Human Pose Estimation Based on Multi-Input Multi-Output Convolutional Neural Network and Event Cameras: A Proof of Concept on the DHP19 Dataset
Alessandro Manilii, Leonardo Lucarelli, Riccardo Rosati, Luca Romeo, Adriano Mancini, Emanuele Frontoni
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
Functional evaluation of triceps surae during heel rise test: from EMG frequency analysis to machine learning approach
Francesco Ferracuti, Sandro Fioretti, Emanuele Frontoni, Sabrina Iarlori, Alessandro Mengarelli, Michele Riccio, Luca Romeo, Federica Verdini
Medical and Biological Engineering and Computing, 2021
Measuring Environmental Data and Physiological Parameters at Home to Assess the Caregiver Burden in Assistants of People with Dementia
Sara Casaccia, Andrea Calvaresi, Nicole Morresi, Lorenzo Scalise, Andrea Monteriù, Luca Romeo, Emanuele Frontoni, Gian Marco Revel
Lecture Notes in Electrical Engineering, 2021
Bias from the Wild Industry 4.0: Are We Really Classifying the Quality or Shotgun Series?
Riccardo Rosati, Luca Romeo, Gianalberto Cecchini, Flavio Tonetto, Luca Perugini, Luca Ruggeri, Paolo Viti, Emanuele Frontoni
Lecture Notes in Computer Science, 2021
Faster R-CNN approach for detection and quantification of DNA damage in comet assay images
Riccardo Rosati, Luca Romeo, Sonia Silvestri, Fabio Marcheggiani, Luca Tiano, Emanuele Frontoni
Computers in Biology and Medicine, 2020
Measurement of Users' Well-Being through Domotic Sensors and Machine Learning Algorithms
Sara Casaccia, Luca Romeo, Andrea Calvaresi, Nicole Morresi, Andrea Monteriu, Emanuele Frontoni, Lorenzo Scalise, Gian Marco Revel
IEEE Sensors Journal, 2020
A sequential deep learning application for recognising human activities in smart homes
Daniele Liciotti, Michele Bernardini, Luca Romeo, Emanuele Frontoni
Neurocomputing, 2020
Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach
Michele Bernardini, Micaela Morettini, Luca Romeo, Emanuele Frontoni, Laura Burattini
Artificial Intelligence in Medicine, 2020
SOPHIA: An event-based IoT and machine learning architecture for predictive maintenance in industry 4.0
Matteo Calabrese, Martin Cimmino, Francesca Fiume, Martina Manfrin, Luca Romeo, Silvia Ceccacci, Marina Paolanti, Giuseppe Toscano, Giovanni Ciandrini, Alberto Carrotta, Maura Mengoni, Emanuele Frontoni, Dimos Kapetis
Information Switzerland, 2020
Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0
Luca Romeo, Jelena Loncarski, Marina Paolanti, Gianluca Bocchini, Adriano Mancini, Emanuele Frontoni
Expert Systems with Applications, 2020
Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector Machine
Michele Bernardini, Luca Romeo, Paolo Misericordia, Emanuele Frontoni
IEEE Journal of Biomedical and Health Informatics, 2020
Convolutional Recurrent Neural Networks and Acoustic Data Augmentation for Snore Detection
Fabio Vesperini, Luca Romeo, Emanuele Principi, Andrea Monteriù, Stefano Squartini
Smart Innovation Systems and Technologies, 2020
Machine Learning in Capital Markets: Decision Support System for Outcome Analysis
Riccardo Rosati, Luca Romeo, Carlos Alfaro Goday, Tullio Menga, Emanuele Frontoni
IEEE Access, 2020
Supervised cnn strategies for optical image segmentation and classification in interventional medicine
Sara Moccia, Luca Romeo, Lucia Migliorelli, Emanuele Frontoni, Primo Zingaretti
Intelligent Systems Reference Library, 2020
A novel spatio-temporal multi-task approach for the prediction of diabetes-related complication: A cardiopathy case of study
Ijcai International Joint Conference on Artificial Intelligence, 2020
A decision support system for diabetes chronic care models based on general practitioner engagement and ehr data sharing
Emanuele Frontoni, Luca Romeo, Michele Bernardini, Sara Moccia, Lucia Migliorelli, Marina Paolanti, Alessandro Ferri, Paolo Misericordia, Adriano Mancini, Primo Zingaretti
IEEE Journal of Translational Engineering in Health and Medicine, 2020
TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records
Michele Bernardini, Micaela Morettini, Luca Romeo, Emanuele Frontoni, Laura Burattini
Computers in Biology and Medicine, 2019
Robotic retail surveying by deep learning visual and textual data
Marina Paolanti, Luca Romeo, Massimo Martini, Adriano Mancini, Emanuele Frontoni, Primo Zingaretti
Robotics and Autonomous Systems, 2019
The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation
Marianna Capecci, Maria Gabriella Ceravolo, Francesco Ferracuti, Sabrina Iarlori, Andrea Monteriù, Luca Romeo, Federica Verdini
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019
Towards the Design of a Machine Learning-based Consumer Healthcare Platform powered by Electronic Health Records and measurement of Lifestyle through Smartphone Data
Alessandro Ferri, Riccardo Rosati, Michele Bernardini, Leonardo Gabrielli, Sara Casaccia, Luca Romeo, Andrea Monteriù, Emanuele Frontoni
2019 IEEE 23rd International Symposium on Consumer Technologies Isct 2019, 2019
A cloud-based healthcare infrastructure for neonatal intensive-care units
Lucia Migliorelli, Annalisa Cenci, Michele Bernardini, Luca Romeo, Sara Moccia, Primo Zingaretti
Proceedings of the ASME Design Engineering Technical Conference, 2019
Augmented microscopy for DNA damage quantification: A machine learning tool for environmental, medical and health sciences
Michele Bernardini, Alessandro Ferri, Lucia Migliorelli, Sara Moccia, Luca Romeo, Sonia Silvestri, Luca Tiano, Adriano Mancini
Proceedings of the ASME Design Engineering Technical Conference, 2019
Prospective motor control obeys to idiosyncratic strategies in autism
Andrea Cavallo, Luca Romeo, Caterina Ansuini, Jessica Podda, Francesca Battaglia, Edvige Veneselli, Massimiliano Pontil, Cristina Becchio
Scientific Reports, 2018
Corrigendum to “Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject That Underwent a Rehabilitation Treatment in the Early Stage of Stroke” [J Stroke Cerebrovasc Dis 27/11 (2018) 2962-2972](S1052305718303446)(10.1016/j.jstrokecerebrovasdis.2018.06.021)
Patrizio Sale, Giorgio Ferriero, Lucio Ciabattoni, Anna Maria Cortese, Giovanni Gentile, Francesco Ferracuti, Luca Romeo, Francesco Piccione, Stefano Masiero
Journal of Stroke and Cerebrovascular Diseases, 2018
Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject that Underwent a Rehabilitation Treatment in the Early Stage of Stroke
Patrizio Sale, Giorgio Ferriero, Lucio Ciabattoni, Anna Maria Cortese, Francesco Ferracuti, Luca Romeo, Francesco Piccione, Stefano Masiero
Journal of Stroke and Cerebrovascular Diseases, 2018
Person re-identification with RGB-D camera in top-view configuration through multiple nearest neighbor classifiers and neighborhood component features selection
Marina Paolanti, Luca Romeo, Daniele Liciotti, Rocco Pietrini, Annalisa Cenci, Emanuele Frontoni, Primo Zingaretti
Sensors Switzerland, 2018
An innovative design support system for industry 4.0 based on machine learning approaches
Luca Romeo, Marina Paolanti, Gianluca Bocchini, Jelena Loncarski, Emanuele Frontoni
Proceedings of the 2018 5th International Symposium on Environment Friendly Energies and Applications Efea 2018, 2018
Machine learning-based approaches to analyse and improve the diagnosis of endothelial dysfunction
Chiara Calamanti, Marina Paolanti, Luca Romeo, Michele Bernardini, Emanuele Frontoni
2018 14th IEEE ASME International Conference on Mechatronic and Embedded Systems and Applications Mesa 2018, 2018
Machine Learning approach for Predictive Maintenance in Industry 4.0
Marina Paolanti, Luca Romeo, Andrea Felicetti, Adriano Mancini, Emanuele Frontoni, Jelena Loncarski
2018 14th IEEE ASME International Conference on Mechatronic and Embedded Systems and Applications Mesa 2018, 2018
Upper Limbs Dyskinesia Detection and Classification for Patients with Parkinson's Disease based on Consumer Electronics Devices
G. Belgiovine, M. Capecci, L. Ciabattoni, M. C. Fiorentino, A. Montcrie, L. Pepa, L. Romeo
2018 Zooming Innovation in Consumer Technologies Conference Zinc 2018, 2018
A smart sensing architecture for domestic monitoring: Methodological approach and experimental validation
Andrea Monteriù, Mario Rosario Prist, Emanuele Frontoni, Sauro Longhi, Filippo Pietroni, Sara Casaccia, Lorenzo Scalise, Annalisa Cenci, Luca Romeo, Riccardo Berta, Loreto Pescosolido, Gianni Orlandi, Gian Marco Revel
Sensors Switzerland, 2018
Collaborative design of a telerehabilitation system enabling virtual second opinion based on fuzzy logic
Marianna Capecci, Lucio Ciabattoni, Francesco Ferracuti, Andrea Monteriù, Luca Romeo, Federica Verdini
Iet Computer Vision, 2018
Complex activity recognition system based on cascade classifiers and wearable device data
L. Ciabattoni, G. Foresi, A. Monteriu, D. Proietti Pagnotta, L. Romeo, L. Spalazzi, A. De Cesare
2018 IEEE International Conference on Consumer Electronics Icce 2018, 2018
An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept
Marianna Capecci, Maria Gabriella Ceravolo, Francesco Ferracuti, Martina Grugnetti, Sabrina Iarlori, Sauro Longhi, Luca Romeo, Federica Verdini
Journal of Biomechanics, 2018
A Hidden Semi-Markov Model based approach for rehabilitation exercise assessment
Marianna Capecci, Maria Gabriella Ceravolo, Francesco Ferracuti, Sabrina Iarlori, Ville Kyrki, Andrea Monteriù, Luca Romeo, Federica Verdini
Journal of Biomedical Informatics, 2018
A sensor fusion approach for measuring emotional customer experience in an intelligent retail environment
Lucio Ciabattoni, Emanuele Frontoni, Daniele Liciotti, Marina Paolanti, Luca Romeo
IEEE International Conference on Consumer Electronics Berlin Icce Berlin, 2017
Multimedia experience enhancement through affective computing
Lucio Ciabattoni, Francesco Ferracuti, Sauro Longhi, Lucia Pepa, Luca Romeo, Federica Verdini
2017 IEEE International Conference on Consumer Electronics Icce 2017, 2017
Real-time mental stress detection based on smartwatch
Lucio Ciabattoni, Francesco Ferracuti, Sauro Longhi, Lucia Pepa, Luca Romeo, Federica Verdini
2017 IEEE International Conference on Consumer Electronics Icce 2017, 2017
Serious gaming approach for physical activity monitoring: A visual feedback based on quantitative evaluation
Lucio Ciabattoni, Francesco Ferracuti, Giuseppe Lazzaro, Luca Romeo, Federica Verdini
IEEE International Conference on Consumer Electronics Berlin Icce Berlin, 2016
Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario
M. Capecci, M. G. Ceravolo, F. Ferracuti, S. Iarlori, S. Longhi, L. Romeo, S. N. Russi, F. Verdini
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, 2016
Physical rehabilitation exercises assessment based on Hidden Semi-Markov Model by Kinect v2
M. Capecci, M. G. Ceravolo, F. Ferracuti, S. Iarlori, V. Kyrki, S. Longhi, L. Romeo, F. Verdini
3rd IEEE EMBS International Conference on Biomedical and Health Informatics Bhi 2016, 2016
A novel computer vision based e-rehabilitation system: From gaming to therapy support
Lucio Ciabattoni, Francesco Ferracuti, Sabrina Iarlori, Sauro Longhi, Luca Romeo
2016 IEEE International Conference on Consumer Electronics Icce 2016, 2016
A tool for home-based rehabilitation allowing for clinical evaluation in a visual markerless scenario
M. Capecci, M. G. Ceravolo, F. D'Orazio, F. Ferracuti, S. Iarlori, G. Lazzaro, S. Longhi, L. Romeo, F. Verdini
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, 2015
Evaluation of Unimodal and Multimodal Communication Cues for Attracting Attention in Human–Robot Interaction
Elena Torta, Jim van Heumen, Francesco Piunti, Luca Romeo, Raymond Cuijpers
International Journal of Social Robotics, 2015
Modular design of a novel wireless sensor node for smart environments
Massimo Grisostomi, Lucio Ciabattoni, Mariorosario Prist, Luca Romeo, Gianluca Ippoliti, Sauro Longhi
Mesa 2014 10th IEEE ASME International Conference on Mechatronic and Embedded Systems and Applications Conference Proceedings, 2014

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