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Marco Boresta
Marco Boresta
CNR - IASI
Verified email at iasi.cnr.it
Title
Cited by
Cited by
Year
Exercise levels and preferences in cancer patients: a cross-sectional study
A Avancini, V Pala, I Trestini, D Tregnago, L Mariani, S Sieri, V Krogh, ...
International journal of environmental research and public health 17 (15), 5351, 2020
912020
Data of patients undergoing rehabilitation programs
R Seccia, M Boresta, F Fusco, E Tronci, E Di Gemma, L Palagi, ...
Data in brief 30, 105419, 2020
382020
What drives a donor? A machine learning‐based approach for predicting responses of nonprofit direct marketing campaigns
D Cacciarelli, M Boresta
Journal of Philanthropy and Marketing 27 (2), e1724, 2022
162022
A mixed finite differences scheme for gradient approximation
M Boresta, T Colombo, A De Santis, S Lucidi
Journal of Optimization Theory and Applications 194 (1), 1-24, 2022
42022
Tuning parameters of deep neural network training algorithms pays off: a computational study
C Coppola, L Papa, M Boresta, I Amerini, L Palagi
Top 32 (3), 579-620, 2024
22024
Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel
M Boresta, T Giovannelli, M Roma
Health Care Management Science 27 (3), 415-435, 2024
22024
Bridging operations research and machine learning for service cost prediction in logistics and service industries
M Boresta, DM Pinto, G Stecca
Annals of Operations Research, 1-27, 2024
22024
Convolutional neural networks and multi-threshold analysis for contamination detection in the apparel industry
M Boresta, T Colombo, A De Santis
arXiv preprint arXiv:2207.12720, 2022
12022
Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome
M Boresta, AL Croella, C Gentile, L Palagi, DM Pinto, G Stecca, P Ventura
Logistics 8 (3), 79, 2024
2024
Computational issues in Optimization for Deep networks
C Coppola, L Papa, M Boresta, I Amerini, L Palagi
arXiv preprint arXiv:2405.02089, 2024
2024
Enabling gradient-based optimization methods in problems with unreliable or absent derivatives
M Boresta
Università degli Studi di Roma" La Sapienza", 2022
2022
Customer Cost Forecasting Through Patterns Learning of Optimal Capacity Allocation
DM Pinto, M Boresta, G Stecca
Available at SSRN 4259923, 2022
2022
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