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26131 |
Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques
Enthalten in Neural computing & applications Bd. 33, 20.7.2021, Nr. 24, date:12.2021: 17131-17145
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26132 |
Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning
Enthalten in npj computational materials Bd. 10, 17.9.2024, Nr. 1, date:12.2024: 1-10
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26133 |
Prediction of the development of acute kidney injury following cardiac surgery by machine learning
Enthalten in Critical care Bd. 24, 31.7.2020, Nr. 1, date:12.2020: 1-13
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26134 |
Prediction of the Diagrams of Fatigue Fracture of D16T Aluminum Alloy by the Methods of Machine Learning
Enthalten in Materials science Bd. 54, 4.12.2018, Nr. 3, date:11.2018: 333-338
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26135 |
Prediction of the Diameter of Biodegradable Electrospun Nanofiber Membranes: An Integrated Framework of Taguchi Design and Machine Learning
Enthalten in Journal of polymers and the environment Bd. 31, 28.4.2023, Nr. 9, date:9.2023: 4080-4096
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26136 |
Prediction of the Dynamic Behavior of Beams in Similitude Using Machine Learning Methods
Enthalten in Aerotecnica missili & spazio Bd. 98, 14.11.2019, Nr. 4, date:12.2019: 283-291
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26137 |
Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features
Enthalten in BMC bioinformatics Bd. 24, 12.9.2023, Nr. 1, date:12.2023: 1-16
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26138 |
Prediction of the Extent of Blood–Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model
Enthalten in Pharmaceutical research Bd. 42, 10.2.2025, Nr. 2, date:2.2025: 281-289
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26139 |
Prediction of the fatigue curve of high-strength steel resistance spot welding joints by finite element analysis and machine learning
Enthalten in The international journal of advanced manufacturing technology Bd. 128, 11.8.2023, Nr. 5-6, date:9.2023: 2763-2779
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26140 |
Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods
Enthalten in Environmental monitoring and assessment Bd. 191, 19.5.2019, Nr. 6, date:6.2019: 1-21
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