Deep Learning @ INAF

Session

Session 2

18 Sept 2019, 16:20

Conveners

Session 2: Data Science and Astrophysics

• Chair: Vito Conforti (Istituto Nazionale di Astrofisica (INAF))

13. Improving accuracy of Machine Learning algorithms with Feature Selection in the context of particle background discrimination for the AGILE telescope

Leonardo Baroncelli (Istituto Nazionale di Astrofisica (INAF))

18/09/2019, 16:20

The aim of this work is to optimise the training process of machine learning algorithms in order to improve their performance on the background particles discrimination task, for the AGILE gamma ray telescope. It has been used a supervised dataset that describes the interactions between the high energy particles with the AGILE detectors. The dataset is composed of thousands of particles...

14. Discovery of new QSOs by means of Canonical Correlation Analysis and photometric catalogs.

Giorgio Calderone (Istituto Nazionale di Astrofisica (INAF))

18/09/2019, 16:45

The several astronomical surveys carried on in the past, and those currently ongoing, produced a huge amount of data stored in their legacy catalogs. Besides the primary scientific goals pursued by the individual experiments, there probably is a lot of information hidden in those databases, still waiting to be extracted. This task is of crucial importance both because it provides further...

10. A real time approach targeted at buyer prediction, using behaviour classification in Tourism Web Analytics

Massimo Deriu (CRS4)

18/09/2019, 17:10

Given the continuously growing importance of digital marketing in the tourism sector, understanding customer’s purchase behaviour is a critical issue for the online competition. Competitors who are able to identify prospective patterns can create a value to lift business.

This work presents a supervised machine-learning (ML) approach, focused on the buyer prediction, in the context of online...

15. Discussion

18/09/2019, 17:35