@article {,
	title = {A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	month = {04/2021},
	abstract = {<p>Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight {\textquotedblleft}suspicious{\textquotedblright} AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel{\textemdash}and the gear it adopts{\textemdash}is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.</p>
},
	keywords = {Automatic Identification System, Machine Learning, maritime surveillance, Synthetic Aperture Radar data integration},
	issn = {1424-8220},
	doi = {10.3390/s21082756},
	url = {https://www.mdpi.com/1424-8220/21/8/2756},
	author = {Alessandro Galdelli and Adriano Mancini and Carmen Ferra Vega and Anna Nora Tassetti}
}
@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 = {<p>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.</p>
},
	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}
}
