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27761 |
Using machine learning methods to predict electric vehicles penetration in the automotive market
Enthalten in Scientific reports Bd. 13, 23.5.2023, Nr. 1, date:12.2023: 1-16
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27762 |
Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
Enthalten in BMC medical informatics and decision making Bd. 20, 2.10.2020, Nr. 1, date:12.2020: 1-10
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27763 |
Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)
Enthalten in Optical memory and neural networks Bd. 30, 29.12.2021, Nr. 4, date:10.2021: 291-297
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27764 |
Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction
Enthalten in Scientific reports Bd. 13, 22.12.2023, Nr. 1, date:12.2023: 1-13
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27765 |
Using machine learning models to improve stroke risk level classification methods of China national stroke screening
Enthalten in BMC medical informatics and decision making Bd. 19, 10.12.2019, Nr. 1, date:12.2019: 1-7
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27766 |
Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia
Enthalten in BMC pulmonary medicine Bd. 24, 1.7.2024, Nr. 1, date:12.2024: 1-9
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27767 |
Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance
Enthalten in Scientific reports Bd. 15, 9.5.2025, Nr. 1, date:12.2025: 1-11
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27768 |
Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis
Enthalten in BMC medical informatics and decision making Bd. 20, 29.9.2020, Nr. 1, date:12.2020: 1-13
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27769 |
Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients
Enthalten in Scientific reports Bd. 13, 8.2.2023, Nr. 1, date:12.2023: 1-11
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27770 |
Using machine learning prediction models for quality control: a case study from the automotive industry
Enthalten in Computational management science Bd. 20, 16.3.2023, Nr. 1, date:12.2023: 1-28
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Administration
