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Titolo Data di pubblicazione Autore(i) File
Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model 1-gen-2023 Borré, Andressa; Seman, Laio Oriel; Camponogara, Eduardo; Stefenon, Stefano Frizzo; Mariani, Viviana Cocco; Coelho, Leandro dos Santos
Machine Learning and Data Analytics in Pervasive Health 1-gen-2018 Oliver, N; Mayora, O; Marschollek, M
Machine Learning Applications in the Study of Parkinson’s Disease: A Systematic Review 1-gen-2023 Martorell Marugán, Jordi; Chierici, Marco; Bandres-Ciga, Sara; Jurman, Giuseppe; Carmona-Sáez, Pedro
A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems 1-gen-2017 Montagna, Fabio; Buiatti, Marco; Benatti, Simone; Rossi, Davide; Farella, Elisabetta; Benini, Luca
Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming over HTTP (DASH) 1-gen-2022 Behravesh, Rasoul; Rao, Akhila; Perez-Ramirez, Daniel F.; Harutyunyan, Davit; Riggio, Roberto; Boman, Magnus
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone 1-gen-2020 Chicco, Davide; Jurman, Giuseppe
Machine Learning for Personalized Challenges in a Gamified Sustainable Mobility Scenario 1-gen-2017 Khoshkangini, Reza; Marconi, Annapaola; Valetto, Giuseppe
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion 1-gen-2022 Donadello, Ivan; Hunter, Anthony; Teso, Stefano; Dragoni, Mauro
Machine Learning Generalisation across Different 3D Architectural Heritage 1-gen-2020 Grilli, Eleonora; Remondino, Fabio
Machine learning methods for predictive proteomics 1-gen-2008 A., Barla; Jurman, Giuseppe; Riccadonna, Samantha; Chierici, Marco; Merler, Stefano; Furlanello, Cesare
Machine learning methods to understand hepatocellular carcinoma pathology 1-gen-2003 Ciocchetta, Federica; Demichelis, Francesca; Sboner, Andrea
Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma 1-gen-2021 Semmler, Georg; Wernly, Sarah; Wernly, Bernhard; Mamandipoor, Behrooz; Bachmayer, Sebastian; Semmler, Lorenz; Aigner, Elmar; Datz, Christian; Osmani, Venet
Machine learning models for predicting endocrine disruption potential of environmental chemicals 1-gen-2019 Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare
Machine learning on historic air photographs for mapping risk of unexploded bombs 1-gen-2005 Merler, Stefano; Furlanello, Cesare; Jurman, Giuseppe
Machine learning on historic air photographs for mapping risk of unexploded bombs 1-gen-2003 Merler, Stefano; Furlanello, Cesare
A machine learning pipeline for quantitative phenotype prediction from genotype data 1-gen-2010 Guzzetta, Giorgio; Jurman, Giuseppe; Furlanello, Cesare
Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation 1-gen-2021 Mamandipoor, Behrooz; Frutos-Vivar, Fernando; Peñuelas, Oscar; Rezar, Richard; Raymondos, Konstantinos; Muriel, Alfonso; Du, Bin; Thille, Arnaud W; Ríos, Fernando; González, Marco; Del-Sorbo, Lorenzo; Del Carmen Marín, Maria; Pinheiro, Bruno Valle; Soares, Marco Antonio; Nin, Nicolas; Maggiore, Salvatore M; Bersten, Andrew; Kelm, Malte; Bruno, Raphael Romano; Amin, Pravin; Cakar, Nahit; Suh, Gee Young; Abroug, Fekri; Jibaja, Manuel; Matamis, Dimitros; Zeggwagh, Amine Ali; Sutherasan, Yuda; Anzueto, Antonio; Wernly, Bernhard; Esteban, Andrés; Jung, Christian; Osmani, Venet
Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation 1-gen-2021 Wernly, Bernhard; Mamandipoor, Behrooz; Baldia, Philipp; Jung, Christian; Osmani, Venet
Machine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks 1-gen-2023 Ullah, Subhan; Mahmood, Zahid; Ali, Nabeel; Ahmad, Tahir; Buriro, Attaullah
Machine learning-driven Scaling and Placement of Virtual Network Functions at the Network Edges 1-gen-2019 Subramanya, Tejas; Riggio, Roberto
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