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Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow ProblemsTartakovsky, A. M. ; Marrero, C. Ortiz ; Perdikaris, Paris ; Tartakovsky, G. D. ; Barajas‐Solano, D.Water resources research, 2020-05, Vol.56 (5), p.n/a [Periódico revisado por pares]Washington: John Wiley & Sons, IncTexto completo disponível |
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eDoctor: machine learning and the future of medicineHandelman, G. S. ; Kok, H. K. ; Chandra, R. V. ; Razavi, A. H. ; Lee, M. J. ; Asadi, H.Journal of internal medicine, 2018-12, Vol.284 (6), p.603-619 [Periódico revisado por pares]England: Blackwell Publishing LtdTexto completo disponível |
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Correction: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among eldely patients in Hong KongGuan, Jingjing ; Leung, Eman ; Kwok, Kin-On ; Chen, Frank YouhuaBMC medical research methodology, 2023-02, Vol.23 (1), p.38-38, Article 38 [Periódico revisado por pares]England: BioMed Central LtdTexto completo disponível |
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Correction: The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practiceBarth, Michael ; Güllich, Arne ; Raschner, Christian ; Emrich, EikePloS one, 2020-12, Vol.15 (12), p.e0244509-e0244509 [Periódico revisado por pares]United States: Public Library of ScienceTexto completo disponível |
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Machine Learning in Psychometrics and Psychological ResearchOrrù, Graziella ; Monaro, Merylin ; Conversano, Ciro ; Gemignani, Angelo ; Sartori, GiuseppeFrontiers in psychology, 2020-01, Vol.10, p.2970-2970 [Periódico revisado por pares]Switzerland: Frontiers Media S.ATexto completo disponível |
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Material Type: Artigo
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Detecting Anomaliesin Simulated Nuclear Data Using AutoencodersMena, Pedro ; Borrelli, R A ; Kerby, LeslieNuclear technology, 2024-01, Vol.210 (1), p.112 [Periódico revisado por pares]La Grange Park: American Nuclear SocietyTexto completo disponível |
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Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron modelHuang, Faming ; Cao, Zhongshan ; Jiang, Shui-Hua ; Zhou, Chuangbing ; Huang, Jinsong ; Guo, ZizhengLandslides, 2020-12, Vol.17 (12), p.2919-2930 [Periódico revisado por pares]Berlin/Heidelberg: Springer Berlin HeidelbergTexto completo disponível |
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Error Prediction of Air Quality at Monitoring Stations Using Random Forest in a Total Error FrameworkLepioufle, Jean-Marie ; Marsteen, Leif ; Johnsrud, Mona2021Texto completo disponível |
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Material Type: Artigo
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Correction: Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patientsLiao, Hualong ; Yang, Yujie ; Zeng, Ying ; Qiu, Ying ; Chen, Yang ; Zhu, Linfang ; Fu, Ping ; Yan, Fei ; Chen, Yu ; Yuan, HuaihongBMC nephrology, 2023-04, Vol.24 (1), p.110-110, Article 110 [Periódico revisado por pares]England: BioMed Central LtdTexto completo disponível |
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Correction to: DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo softwareTabor, Zbisław ; Kabat, Damian ; Waligórski, Michael P RRadiation oncology (London, England), 2022-02, Vol.17 (1), p.44-44, Article 44 [Periódico revisado por pares]England: BioMed Central LtdTexto completo disponível |