Ricerc@Sapienza

This paper follows the recent literature on real estate price prediction and proposes to take advantage of machine learning techniques to better explain which variables are more important in describing the real estate market evolution. We apply the random forest algorithm on London real
estate data and analyze the local variables that influence the interaction between housing demand, supply and price. The variables choice is based on an urban point of view, where the main force driving the market is the interaction between local factors like population growth, net migration,

Increased life expectancy in developed countries has led researchers to pay more attention to mortality projection to anticipate changes in mortality rates. Following the scheme proposed in Deprez et al. (Eur Actuar J 7(2):337–352, 2017) and extended by Levantesi and Pizzorusso (Risks 7(1):26, 2019), we propose a novel approach based on the combination of random forest and two-dimensional P-spline, allowing for accurate mortality forecasting.

Objective: The automatic detection of migraine states using electrophysiological recordings may play a key role in migraine diagnosis and early treatment. Migraineurs are characterized by a deficit of habituation in cortical information processing, causing abnormal changes of somatosensory evoked potentials. Here, we propose a machine learning approach to utilize somatosensory evoked potential-based biomarkers for migraine classification in a noninvasive setting.

Invasive pulmonary aspergillosis (IPA) is typically considered a disease of immunocompromised patients, but, recently, many cases have been reported in patients without typical risk factors. The aim of our study is to develop a risk predictive model for IPA through machine learning techniques (decision trees) in patients with influenza. We conducted a retrospective observational study analyzing data regarding patients diagnosed with influenza hospitalized at the University Hospital “Umberto I” of Rome during the 2018-2019 season. We collected five IPA cases out of 77 influenza patients.

This pioneering Special Issue aims at providing the state-of-the-art on open energy data analytics; its availability in the different contexts, i.e., country peculiarities; and at different scales, i.e., building, district, and regional for data-aware planning and policy-making. Ten high-quality papers were published after a demanding peer review process and are commented on in this Editorial.

A Model-free approach is particularly valuable for Structural Health Monitoring because real structures are often too complex to be modelled accurately, requiring anyhow a large quantity of sensor data to be processed. In this context, this paper presents a machine learning technique that analyses data acquired by swarm of a sensor. The proposed algorithm uses unsupervised learning and is based on the use principal component analysis and symbolic data analysis: PCA extracts features from the acquired data and use them as a template for clustering.

In this paper we present a novel mechanism to
get explanations that allow to better understand
network predictions when dealing with sequential
data. Specifically, we adopt memory-based net-
works — Differential Neural Computers — to ex-
ploit their capability of storing data in memory and
reusing it for inference. By tracking both the mem-
ory access at prediction time, and the information
stored by the network at each step of the input
sequence, we can retrieve the most relevant input

The use of mobile phones or smartphones has become so widespread that most people rely on them for many services and applications like sending e-mails, checking the bank account, accessing cloud platforms, health monitoring, buying on-line and many other applications where sharing sensitive data is required. As a consequence, security functions are important in the use of smartphones, especially because most of the applications require the identification and authentication of the device in mobility.

In the last few years, with the exponential diffusion of smartphones, services for turn-by-turn navigation have seen a surge in popularity. Current solutions available in the market allow the user to select via an interface the desired transportation mode, for which an optimal route is then computed. Automatically recognizing the transportation system that the user is travelling by allows to dynamically control, and consequently update, the route proposed to the user.

Uncovering hidden relations in complex data sets is a key step to making sense of the data, which is a hot topic in our era of data deluge. Graph-based representations are examples of tools able to encode relations in a mathematical structure enabling the uncovering of patterns like clusters and paths. However, graphs only capture pairwise relations encoded in the presence of edges, but there are many forms of interaction that cannot be reduced to pairwise relations. To overcome the limitations of graph-based representations, it is necessary to incorporate multiway relations.