@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-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}
}
@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}
}
@conference {,
	title = {Empowered Optical Inspection by Using Robotic Manipulator in Industrial Applications.},
	booktitle = {IROS},
	year = {2019},
	month = {11/2019},
	abstract = {<p>Nowadays the inspection of products at the end of line represents a critical phase. At this stage, it is necessary to look for defects in order to prevent the quality check to fail, and to provide information for improving the production as well. This task can be performed by using several sensing technologies, and the contactless optical inspection plays a key role. In this regard, the use of advanced robotic manipulators offers the capability to change the viewpoint of a given object and to inspect its multiple faces. We propose an approach that combines the use of photometric stereo to derive a 3D model of objects, empowered by the super-resolution that is applied on the original dataset (upstream) or on the normal images (downstream) in order to increase the quality of the final 3D model. The vision system is mounted on a robotic manipulator, able to grasp and change the viewpoint, thus offering a more completeviewoftheobjecttobeinspected.Theobtainedresults show that the developed solution increases the quality of the derived 3D models used for inspection tasks on different faces of the objects; this is achieved by using the manipulation ability offered by the adopted robotic platform.</p>
},
	doi = {10.1109/IROS40897.2019.8968473},
	url = {https://doi.org/10.1109/IROS40897.2019.8968473},
	author = {Galdelli, Alessandro and Pagnotta, Daniele Proietti and Mancini, Adriano and Freddi, Alessandro and Monteri{\`u}, Andrea and Frontoni, Emanuele}
}
@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}
}
