Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Marco, Podda; Davide, Bacciu; Alessio, Micheli; Roberto, Bellu; Giulia, Placidi; Luigi, Gagliardi A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor Journal Article In: Nature Scientific Reports, vol. 8, 2018. Davide, Bacciu; Antonio, Carta; Stefania, Gnesi; Laura, Semini An Experience in using Machine Learning for Short-term Predictions in Smart Transportation Systems Journal Article In: Journal of Logical and Algebraic Methods in Programming , vol. 87, pp. 52-66, 2017, ISSN: 2352-2208. Davide, Bacciu; Antonio, Carta; Stefania, Gnesi; Laura, Semini Adopting a Machine Learning Approach in the Design of Smart Transportation Systems Online van der Me, Rob; Shashaj, Ariona (Ed.): ERCIM News Magazine 2016, visited: 01.04.2016. Davide, Bacciu; Stefania, Gnesi; Laura, Semini Using a Machine Learning Approach to Implement and Evaluate Product Line Features Conference Proceedings 11th International Workshop on Automated Specification and Verification of Web Systems, WWV 2015, vol. 188, Electronic Proceedings in Theoretical Computer Science (EPTCS) 2015. Davide, Bacciu; Alessio, Micheli; Alessandro, Sperduti A Generative Multiset Kernel for Structured Data Conference Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series, vol. 7552, Springer-Verlag, BERLIN HEIDELBERG, 2012.@article{naturescirep2018,
title = {A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor},
author = {Podda Marco and Bacciu Davide and Micheli Alessio and Bellu Roberto and Placidi Giulia and Gagliardi Luigi },
url = {https://doi.org/10.1038/s41598-018-31920-6},
doi = {10.1038/s41598-018-31920-6},
year = {2018},
date = {2018-09-13},
urldate = {2018-09-13},
journal = {Nature Scientific Reports},
volume = {8},
abstract = {Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network in 2008–2014 (development set), using 12 easily collected perinatal variables. We used a cohort from 2015–2016 (N = 5810) as a test set. Among several machine learning methods we chose artificial Neural Networks (NN). The resulting predictor was compared with logistic regression models. In the test cohort, NN had a slightly better discrimination than logistic regression (P < 0.002). The differences were greater in subgroups of neonates (at various gestational age or birth weight intervals, singletons). Using a cutoff of death probability of 0.5, logistic regression misclassified 67/5810 neonates (1.2 percent) more than NN. In conclusion our study – the largest published so far – shows that even in this very simplified scenario, using only limited information available up to 5 minutes after birth, a NN approach had a small but significant advantage over current approaches. The software implementing the predictor is made freely available to the community.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{jlamp2016,
title = {An Experience in using Machine Learning for Short-term Predictions in Smart Transportation Systems},
author = {Bacciu Davide and Carta Antonio and Gnesi Stefania and Semini Laura},
editor = {Alberto Lluch Lafuente and Maurice ter Beek},
doi = {10.1016/j.jlamp.2016.11.002},
issn = {2352-2208},
year = {2017},
date = {2017-01-01},
journal = { Journal of Logical and Algebraic Methods in Programming },
volume = {87},
pages = {52-66},
publisher = {Elsevier},
abstract = {Bike-sharing systems (BSS) are a means of smart transportation with the benefit of a positive impact on urban mobility. To improve the satisfaction of a user of a BSS, it is useful to inform her/him on the status of the stations at run time, and indeed most of the current systems provide the information in terms of number of bicycles parked in each docking stations by means of services available via web. However, when the departure station is empty, the user could also be happy to know how the situation will evolve and, in particular, if a bike is going to arrive (and vice versa when the arrival station is full).
To fulfill this expectation, we envisage services able to make a prediction and infer if there is in use a bike that could be, with high probability, returned at the station where she/he is waiting. The goal of this paper is hence to analyze the feasibility of these services. To this end, we put forward the idea of using Machine Learning methodologies, proposing and comparing different solutions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To fulfill this expectation, we envisage services able to make a prediction and infer if there is in use a bike that could be, with high probability, returned at the station where she/he is waiting. The goal of this paper is hence to analyze the feasibility of these services. To this end, we put forward the idea of using Machine Learning methodologies, proposing and comparing different solutions.@online{ercim2016,
title = {Adopting a Machine Learning Approach in the Design of Smart Transportation Systems},
author = {Bacciu Davide and Carta Antonio and Gnesi Stefania and Semini Laura },
editor = {Rob van der Me and Ariona Shashaj},
url = {http://ercim-news.ercim.eu/en105/special/adopting-a-machine-learning-approach-in-the-design-of-smart-transportation-systems},
issn = {0926-4981 },
year = {2016},
date = {2016-04-01},
urldate = {2016-04-01},
organization = {ERCIM News Magazine},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
@conference{11568_766969,
title = {Using a Machine Learning Approach to Implement and Evaluate Product Line Features},
author = { Bacciu Davide and Gnesi Stefania and Semini Laura},
url = {http://dx.doi.org/10.4204/EPTCS.188.8},
doi = {10.4204/EPTCS.188.8},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings 11th International Workshop on Automated Specification and Verification of Web Systems, WWV 2015},
journal = {ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE},
volume = {188},
pages = {75--83},
series = {Electronic Proceedings in Theoretical Computer Science (EPTCS)},
abstract = {Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_156516,
title = {A Generative Multiset Kernel for Structured Data},
author = {Bacciu Davide and Micheli Alessio and Sperduti Alessandro},
doi = {10.1007/978-3-642-33269-2_8},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2012 proceedings, Springer LNCS series},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
volume = {7552},
pages = {57--64},
publisher = {Springer-Verlag},
address = {BERLIN HEIDELBERG},
abstract = {The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}