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10571 |
Modeling retroreflectivity degradation of pavement markings across the US with advanced machine learning algorithms
Enthalten in Journal of infrastructure preservation and resilience Bd. 5, 21.2.2024, Nr. 1, date:12.2024: 1-19
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10572 |
Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods
Enthalten in Scientific reports Bd. 15, 2.1.2025, Nr. 1, date:12.2025: 1-15
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10573 |
Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface
Enthalten in Scientific reports Bd. 13, 12.8.2023, Nr. 1, date:12.2023: 1-15
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10574 |
Modeling the compressive strength of concrete at different curing regimes using machine learning
Enthalten in Discover sustainability Bd. 6, 1.5.2025, Nr. 1, date:12.2025: 1-26
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10575 |
Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state
Enthalten in Scientific reports Bd. 12, 2.9.2022, Nr. 1, date:12.2022: 1-25
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10576 |
Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis
Enthalten in Global health research and policy Bd. 5, 6.5.2020, Nr. 1, date:12.2020: 1-11
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10577 |
Modeling the viscoelastic behavior of a FG nonlocal beam with deformable boundaries based on hybrid machine learning and semi-analytical approaches
Enthalten in Archive of applied mechanics Bd. 95, 20.3.2025, Nr. 4, date:4.2025: 1-32
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10578 |
Modellentwicklung und maschinelles Lernen erhöhen die Proteinausbeute
Enthalten in Biospektrum Bd. 26, 14.5.2020, Nr. 3, date:5.2020: 262-264
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10579 |
Modelling and parameter identification of coefficient of friction for deep-drawing quality steel sheets using the CatBoost machine learning algorithm and neural networks
Enthalten in The international journal of advanced manufacturing technology Bd. 124, 8.12.2022, Nr. 7-8, date:2.2023: 2229-2259
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10580 |
Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches
Enthalten in Journal of geodesy Bd. 96, 27.9.2022, Nr. 10, date:10.2022: 1-17
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