Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation

Autor: Matteo Borrotti

Publikováno v: Energies, Vol 17, Iss 17, p 4348 (2024)

Building Performance Simulation extensively uses statistical learning techniques for quicker insights and improved accessibility. These techniques help understand the relationship between input variables and the desired outputs, and they can predict

Externí odkaz: https://doaj.org/article/6d33662337e24b41be5239021b645e5b

A Land-Use Perspective for Birdstrike Risk Assessment: The Attraction Risk Index.

Autor: Francesca Coccon, Matteo Zucchetta, Giulia Bossi, Matteo Borrotti, Patrizia Torricelli, Piero Franzoi

Publikováno v: PLoS ONE, Vol 10, Iss 6, p e0128363 (2015)

Collisions between aircraft and birds, birdstrikes, pose a serious threat to aviation safety. The occurrence of these events is influenced by land-uses in the surroundings of airports. Airports located in the same region might have different trends f

Externí odkaz: https://doaj.org/article/7e44ea42101f44e1aeca6c41cc9e8fac

Bayesian optimization and deep learning for steering wheel angle prediction

Autor: Alessandro, Riboni, Nicolò, Ghioldi, Antonio, Candelieri, Matteo, Borrotti

Automated driving systems (ADS) have undergone a significant improvement in the last years. ADS and more precisely self-driving cars technologies will change the way we perceive and know the world of transportation systems in terms of user experience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59a393ef2d6f8c22b7aab7c60577cafd
https://hdl.handle.net/10281/399793

Multi-objective optimisation of split-plot designs

Autor: Matteo Borrotti, Francesco Sambo, Kalliopi Mylona

Modern experiments allow scientists to tackle scientific problems of increasing complexity. Often experiments are characterised by factors that have levels which are harder to set than others. A possible solution is the use of a split-plot design. Ma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d29589d0f98aca5a39a5d011ec5d37c0
https://hdl.handle.net/10281/399794

A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading

Autor: Giorgio Lucarelli, Matteo Borrotti

Publikováno v: IFIP Advances in Information and Communication Technology
15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.247-258, ⟨10.1007/978-3-030-19823-7_20⟩
IFIP Advances in Information and Communication Technology ISBN: 9783030198220
AIAI

Part 7: Deep Learning - Convolutional ANN; International audience; Nowadays, Artificial Intelligence (AI) is changing our daily life in many application fields. Automatic trading has inspired a large number of field experts and scientists in developi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35edc67f6891533ae946da4d1b6d61b2
https://hal.inria.fr/hal-02331326/file/483292_1_En_20_Chapter.pdf