@article {, title = {A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing}, journal = {IEEE Journal of Translational Engineering in Health and Medicine}, volume = {8}, year = {2020}, pages = {1-12}, doi = {10.1109/JTEHM.2020.3031107}, author = {E. Frontoni and L. Romeo and M. Bernardini and S. Moccia and L. Migliorelli and M. Paolanti and A. Ferri and P. Misericordia and A. Mancini and P. Zingaretti} } @article {, title = {A Decision Support System for Diabetes Chronic Care Models based on General Practitioner engagement and EHR data sharing}, journal = {IEEE Journal of Translational Engineering in Health and Medicine}, year = {2020}, doi = {10.1109/JTEHM.2020.3031107}, url = {https://ieeexplore.ieee.org/document/9223696}, author = {E. Frontoni and L. Romeo and M. Bernardini and S. Moccia and L. Migliorelli and M. Paolanti and A. Ferri and P. Misericordia and A. Mancini and P. Zingaretti} } @article {, title = {Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach}, journal = {Artificial Intelligence in Medicine}, year = {2020}, pages = {101847}, author = {Bernardini, Michele and Morettini, Micaela and Romeo, Luca and Frontoni, Emanuele and Burattini, Laura} } @article {, title = {Faster R-CNN approach for detection and quantification of DNA damage in comet assay images}, journal = {Computers in Biology and Medicine}, volume = {123}, year = {2020}, pages = {103912}, author = {Rosati, Riccardo and Romeo, Luca and Silvestri, Sonia and Marcheggiani, Fabio and Tiano, Luca and Frontoni, Emanuele} } @article {, title = {Machine Learning in Capital Markets: Decision Support System for Outcome Analysis}, journal = {IEEE Access}, volume = {8}, year = {2020}, pages = {109080-109091}, doi = {10.1109/ACCESS.2020.3001455}, author = {R. Rosati and L. Romeo and C. A. Goday and T. Menga and E. Frontoni} } @article {, title = {Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0}, journal = {Expert Systems with Applications}, volume = {140}, year = {2020}, pages = {112869}, author = {Romeo, Luca and Loncarski, Jelena and Paolanti, Marina and Bocchini, Gianluca and Mancini, Adriano and Frontoni, Emanuele} } @article {, title = {Measurement of Users{\textquoteright} Well-Being Through Domotic Sensors and Machine Learning Algorithms}, journal = {IEEE Sensors Journal}, volume = {20}, year = {2020}, pages = {8029-8038}, doi = {10.1109/JSEN.2020.2981209}, author = {S. Casaccia and L. Romeo and A. Calvaresi and N. Morresi and A. Monteri{\`u} and E. Frontoni and L. Scalise and G. M. Revel} } @conference {, title = {A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study}, booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}}, year = {2020}, month = {7}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, organization = {International Joint Conferences on Artificial Intelligence Organization}, url = {https://doi.org/10.24963/ijcai.2020/593}, author = {Romeo, Luca and Armentano, Giuseppe and Nicolucci, Antonio and Vespasiani, Marco and Vespasiani, Giacomo and Frontoni, Emanuele}, editor = {Christian Bessiere} } @article {, title = {A sequential deep learning application for recognising human activities in smart homes}, journal = {Neurocomputing}, volume = {396}, year = {2020}, pages = {501{\textendash}513}, author = {Liciotti, Daniele and Bernardini, Michele and Romeo, Luca and Frontoni, Emanuele} } @article {, title = {SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0}, journal = {Information}, volume = {11}, year = {2020}, pages = {202}, author = {Calabrese, Matteo and Cimmino, Martin and Fiume, Francesca and Manfrin, Martina and Romeo, Luca and Ceccacci, Silvia and Paolanti, Marina and Toscano, Giuseppe and Ciandrini, Giovanni and Carrotta, Alberto and others} } @article {, title = {Transfer learning for informative-frame selection in laryngoscopic videos through learned features}, journal = {Medical \& Biological Engineering \& Computing}, year = {2020}, author = {Ilaria Patrini and Michela Ruperti and Sara Moccia and Leonardo S. Mattos and Emanuele Frontoni and Elena De Momi} } @article {, title = {Discovering the Type 2 Diabetes in Electronic Health Records using the Sparse Balanced Support Vector Machine}, journal = {IEEE Journal of Biomedical and Health Informatics }, year = {2019}, abstract = {

The diagnosis of Type 2 Diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient{\textquoteright}s follow-up. Recent years have witnessed an increasing amount of available Electronic Health Record (EHR) data and Machine Learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges such as overfitting, model interpretability and computational cost. Starting from these motivations, we introduced a ML method called Sparse Balanced Support Vector Machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named FIMMG dataset). In particular, among all the EHR features related to exemptions, examination and drug prescriptions we have selected only those collected before T2D diagnosis from a uniform age group of subjects. We demonstrated the reliability of the introduced approach with respect to other ML and Deep Learning approaches widely employed in the state-of-the-art for solving this task. Results evidence that the SB-SVM overcomes the other state-of-the-art competitors providing the best compromise between predictive performance and computation time. Additionally, the induced sparsity allows to increase the model interpretability, while implicitly managing high dimensional data and the usual unbalanced class distribution.

}, keywords = {Decision Support System, Electronic Health Record, Machine Learning, Support Vector Machine, Type 2 Diabetes}, issn = {2168-2208}, doi = {10.1109/JBHI.2019.2899218}, url = {https://ieeexplore.ieee.org/abstract/document/8641396}, author = {Michele Bernardini and Luca Romeo and Paolo Misericordia and Emanuele Frontoni} } @article {, title = {The KIMORE dataset: KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation}, journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering}, volume = {27}, year = {2019}, pages = {1436{\textendash}1448}, author = {Capecci, Marianna and Ceravolo, Maria Gabriella and Ferracuti, Francesco and Iarlori, Sabrina and Monteri{\`u}, Andrea and Romeo, Luca and Verdini, Federica} } @article {, title = {Multiple instance learning for emotion recognition using physiological signals}, journal = {IEEE Transactions on Affective Computing}, year = {2019}, author = {Romeo, Luca and Cavallo, Andrea and Pepa, Lucia and Berthouze, Nadia and Pontil, Massimiliano} } @article {, title = {Robotic retail surveying by deep learning visual and textual data}, journal = {Robotics and Autonomous Systems}, volume = {118}, year = {2019}, pages = {179{\textendash}188}, author = {Paolanti, Marina and Romeo, Luca and Martini, Massimo and Mancini, Adriano and Frontoni, Emanuele and Zingaretti, Primo} } @article {, title = {TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records}, journal = {Computers in Biology and Medicine}, volume = {112}, year = {2019}, pages = {103358}, author = {Bernardini, Michele and Morettini, Micaela and Romeo, Luca and Frontoni, Emanuele and Burattini, Laura} } @article {Paolanti2018, title = {Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection}, journal = {Sensors}, volume = {18}, number = {10}, year = {2018}, month = {oct}, pages = {3471}, publisher = {{MDPI} {AG}}, doi = {10.3390/s18103471}, url = {https://doi.org/10.3390/s18103471}, author = {Marina Paolanti and Luca Romeo and Daniele Liciotti and Annalisa Cenci and Emanuele Frontoni and Primo Zingaretti} } @article {, title = {Prospective motor control obeys to idiosyncratic strategies in autism}, journal = {Scientific reports}, volume = {8}, year = {2018}, pages = {1{\textendash}9}, author = {Cavallo, Andrea and Romeo, Luca and Ansuini, Caterina and Podda, Jessica and Battaglia, Francesca and Veneselli, Edvige and Pontil, Massimiliano and Becchio, Cristina} } @article {frontoni2017shelf, title = {Shelf space re-allocation for out of stock reduction}, journal = {Computers \& Industrial Engineering}, year = {2017}, publisher = {Elsevier}, abstract = {

A planogram is a detailed visual map that establishes product positions over a shelf in a retail store. It is designed to support an innovative merchandising approach, to increase sales and profits, to supply the best location of a product for suppliers and to better manage the shelves. Product selection and the shelf space reserved to each product is a central activity for retailers and Shelf Out of Stock (SOOS) events are often strongly related to planogram design. In this paper we present a solution to optimally re-allocate shelf space to minimize Out of Stock (OOS) events. The approach uses SOOS data coming in real time from a sensor network technology, named Shelf Detector System, and an Integer Linear Programming model that integrates a space elastic demand function. Experimental results, based on a real scenario in the diaper category in Belgium, have proved that the system can efficiently calculate a proper solution able to re-allocate space and reduce OOS events.

}, doi = {doi:10.1016/j.cie.2017.01.021}, author = {Emanuele Frontoni and Fabrizio Marinelli and Roberto Rosetti and Primo Zingaretti} } @conference {frontoni2016optimal, title = {Optimal production planning by reusing components}, booktitle = {Control and Automation (MED), 2016 24th Mediterranean Conference on}, year = {2016}, pages = {1272{\textendash}1277}, publisher = {IEEE}, organization = {IEEE}, author = {Emanuele Frontoni and Fabrizio Marinelli and Marina Paolanti and Roberto Rosetti and Primo Zingaretti} } @conference {Frontoni2014, title = {A heuristic approach to evaluate occurrences of products for the planogram maintenance}, booktitle = {MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings}, year = {2014}, note = {cited By 0}, abstract = {Planogram is a detailed visual map of products in a retail store and establishes the position of products in order to increase sales and to supply the best location for suppliers. So, the aims of {\textquoteright}correct{\textquoteright} planogram are several and are: increasing sales, increasing profits, introducing a new item, supporting an innovative merchandising approach, and better manages the shelves. Deviating from the planogram defeats the purpose of any of those goals. A fundamental aspect in the retail operations is to maintain the integrity of the planogram. This work intends to provide a solution to this problem, proposing a system that individually identifies the presence of a specific product in the image of a shelf. This even though the product is moved, rotated, misplaced, or even in poor lighting conditions. This paper presents a method to find and count multiple instances of the same object that occurs into an image of a shelf in a store without using classifiers. The procedures here described are based on a heuristic algorithm that involves morphological operation, template matching and histogram comparison. Experimental results are presented in order to verify the effectiveness of the proposed approach. They demonstrate that the algorithm provides satisfactory results when the user manually chooses the most significant label of the product to find in the shelf image.}, doi = {10.1109/MESA.2014.6935615}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911965097\&partnerID=40\&md5=0de9992bbb2157ad432a05fee65d6dfc}, author = {Emanuele Frontoni and Marco Contigiani and Ribighini, G.} } @conference {Rossi2014, title = {Interoperability issues among smart home technological frameworks}, booktitle = {MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings}, year = {2014}, note = {cited By 0}, abstract = {Population aging may be seen both as a human success story, the triumph of public health, medical advancements and economic development over diseases and injures, and as one of the most challenging phenomena that society faces in this century. Assistive technology in all its possible implementations (from Telemedicine to Ambient Assisted Living, and Ambient Intelligence) represents an emerging answer to the needs of the new generation of older adults whose desire is to live longer with a higher quality of life. Objective of this paper is to present the results of a public financed action for the development and implementation of an {\textquoteright}integration platform{\textquoteright} for Ambient Assisted Living that includes features of home automation (energy management, safety, comfort, etc.) and introduces {\textquoteright}smart objects{\textquoteright}, to monitor activities of daily living and detect any abnormal behavior that may represent a danger, or highlight symptoms of some incipient disease.}, doi = {10.1109/MESA.2014.6935626}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911958222\&partnerID=40\&md5=969da407b2d8bad8fa3e16dbc7d254a0}, author = {Rossi, L. and Belli, A. and De Santis, A. and Diamantini, C. and Emanuele Frontoni and Gambi, E. and Palma, L. and Pernini, L. and Pierleoni, P. and Potena, D. and Raffaeli, L. and Spinsante, S. and Primo Zingaretti and Cacciagrano, D. and Corradini, F. and Culmone, R. and De Angelis, F. and Merelli, E. and Re, B.} } @article {Frontoni2013509, title = {Customers{\textquoteright} activity recognition in intelligent retail environments}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8158 LNCS}, year = {2013}, note = {cited By 6}, pages = {509-516}, abstract = {This paper aims to propose a novel idea of an embedded intelligent system where low cost embedded vision systems can analyze human behaviors to obtain interactivity and statistical data, mainly devoted to customer behavior analysis. In this project we addressed the need for new services into the shop, involving consumers more directly and instigating them to increase their satisfaction and, as a consequence, their purchases. To do this, technology is very important and allows making interactions between costumers and products and between customers and the environment of the shop a rich source of marketing analysis. We construct a novel system that uses vertical RGBD sensor for people counting and shelf interaction analysis, where the depth information is used to remove the affect of the appearance variation and to evaluate customers{\textquoteright} activities inside the store and in front of the shelf, with products. Also group interactions are monitored and analyzed with the main goal of having a better knowledge of the customers{\textquoteright} activities, using real data in real time. Even if preliminary, results are convincing and most of all the general architecture is affordable in this specific application, robust, easy to install and maintain and low cost. {\textcopyright} 2013 Springer-Verlag.}, doi = {10.1007/978-3-642-41190-8_55}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84887121570\&partnerID=40\&md5=8a6e9b3442d8d4dbd47b404bd8248a7b}, author = {Emanuele Frontoni and Raspa, P. and Adriano Mancini and Primo Zingaretti and Valerio Placidi} } @article {De Giovanni2013627, title = {A heuristic and an exact method for the gate matrix connection cost minimization problem}, journal = {International Transactions in Operational Research}, volume = {20}, number = {5}, year = {2013}, note = {cited By 1}, pages = {627-643}, abstract = {In many applications, a sequencing of patterns (electronic circuit nodes, cutting patterns, product orders, etc.) has to be found in order to optimize some given objective function, giving rise to the so-called open stack problems. We focus on a problem related to the optimization of gate matrix layouts: electronic circuits are obtained by connecting gates and one seeks a gate layout permutation that minimizes connection costs under restrictions on the circuit area. In the literature, the connection costs and circuit area are also known as time of open stacks and maximum number of open stacks, respectively. We propose a genetic algorithm providing heuristic solutions and a branch-and-cut algorithm based on a new linear integer programming formulation that represents, to the best of our knowledge, the first exact method proposed in the literature. The algorithms have been tested on real instances and on data sets from the literature. The computational results give evidence that the proposed methods provide solutions that improve the ones found by the approaches presented in the literature. {\textcopyright} 2013 International Federation of Operational Research Societies.}, doi = {10.1111/itor.12025}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84881022755\&partnerID=40\&md5=243ec854395721c11c14eec4036e7a7b}, author = {De Giovanni, L. and Gionata Massi and F. Pezzella and Pfetsch, M.E. and Rinaldi, G. and Ventura, P.d} } @conference {Raspa2010, title = {Selection of Lithium cells for EV battery pack using self organizing maps}, booktitle = {EVS 2010 - Sustainable Mobility Revolution: 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition}, year = {2010}, note = {cited By 0}, abstract = {A challenging problem in energy storage systems for electric vehicles is the efficiency use of lithium multicell batteries. Because of production tolerances, unbalanced cells can be overstressed during usage leading to the reduction of the available capacity and premature failure of the battery pack. In order to reduce this problem the Universit{\`a} Politecnica delle Marche and FAAM Group S.p.A. developed a method for the selection and classification of homogenous cells to form uniform battery pack using Self Organizing Maps neural networks. Experimental data are collected from a set of LiFePo4 cells tested in FAAM laboratories. The selection considers both experimental data and identified characteristics: Discharge Voltage, Open Circuit Voltage, Total Capacity and identified parameters from Randle{\textquoteright}s equivalent circuit modelling. The State of Charge variability within each selected group of cells has been chosen as the clustering criterion to find the method which gives the best results in terms of homogeneity of the battery. Simulation results consider an experimental EV load profile and show a great reduction of the SOC variability and, consequently, in the balance of the battery pack for all the methods presented compared to random selection. Capacity and discharge voltage based methods give the best results over all.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84907415017\&partnerID=40\&md5=42dc9ff2c6cfe464e796818859e805ce}, author = {Raspa, P. and Frinconi, L. and Adriano Mancini and Cavalletti, M. and Sauro Longhi and L. Fulimeni and P. Bellesi and Isidori, R.} } @article {Antonicelli1989155, title = {Evaluation of efficacy and tolerability of the association Captopril 50 mg + hydrochlorothiazide 15 mg in the elderly [VALUTAZIONE DELL{\textquoteright}EFFICACIA E TOLLERABILITA DELL{\textquoteright}ASSOCIAZIONE A DOSE FISSA DI CAPTOPRIL (CPT) 50 MG E IDROCLOROTIAZIDE (HCTZ) 15 MG IN SO}, journal = {European Review for Medical and Pharmacological Sciences}, volume = {11}, number = {2}, year = {1989}, note = {cited By 0}, pages = {155-161}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0024956512\&partnerID=40\&md5=83288f643078d8c4d318448d3a1b48fc}, author = {Antonicelli, R. and Raffaelli, S. and Bossini, A. and Paciaroni, E. and Primo Zingaretti and Botta, G.F.} }