Machine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy

Dust source mapping using satellite imagery and machine learning models

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...

Malignant melanoma in Rome, Italy, 1970-9.

An epidemiological study was conducted in the city of Rome and the incidence of malignant melanoma was determined retrospectively for the years 1970-9. All patients had legal residence in Rome and a histological diagnosis of melanoma during that period; only cases of malignant melanoma of the skin (ICD 172, 8th revision) were considered. A total of 17 public and 82 private hospitals were involv...

Hysterectomy and socioeconomic position in Rome, Italy.

STUDY OBJECTIVE There exists conflicting evidence regarding the higher risk of hysterectomy among women of a lower educational and economic level. This study aims to assess whether in Italy socioeconomic level is related to hysterectomy undertaken for different medical reasons. DESIGN An area based index was used to assign socieconomic status (SES; four levels defined) to 3141 women (aged 35 ...

Machine learning for functional brain mapping

Machine Learning for Robots A Comparison of Di erent Paradigms

For robots to be truly exible they need to be able to learn to adapt to partially known or dynamic environments to teach themselves new tasks and to compensate for sensor and e ector defects The problem of robot learning has been an intensively stud ied research topic over the last decade In this paper we critically examine four major formulations of the robot learning problem inductive concept...