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26011 |
Prediction of compressive strength of granite: use of machine learning techniques and intelligent system
Enthalten in Earth science informatics Bd. 16, 15.11.2023, Nr. 4, date:12.2023: 4113-4129
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26012 |
Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis
Enthalten in Journal of building pathology and rehabilitation Bd. 9, 24.5.2024, Nr. 2, date:12.2024: 1-20
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26013 |
Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach
Enthalten in BMC medical informatics and decision making Bd. 23, 17.1.2023, Nr. 1, date:12.2023: 1-17
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26014 |
Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department
Enthalten in Scientific reports Bd. 15, 27.2.2025, Nr. 1, date:12.2025: 1-12
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26015 |
Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning
Enthalten in BMC pediatrics Bd. 24, 5.3.2024, Nr. 1, date:12.2024: 1-9
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26016 |
Prediction of coronary heart disease based on klotho levels using machine learning
Enthalten in Scientific reports Bd. 15, 27.5.2025, Nr. 1, date:12.2025: 1-8
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26017 |
Prediction of COVID-19 patients’ participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches
Enthalten in BMC health services research Bd. 24, 10.7.2024, Nr. 1, date:12.2024: 1-13
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26018 |
Prediction of creep degradation in Fe-Cr-Ni single-crystal alloys for high-temperature applications: a molecular-dynamics and machine-learning approach
Enthalten in Mechanics of time-dependent materials Bd. 29, 11.12.2024, Nr. 1, date:3.2025: 1-20
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26019 |
Prediction of creep properties of Co–10Al–9W superalloys with machine learning
Enthalten in Journal of materials science Bd. 59, 13.3.2024, Nr. 11, date:3.2024: 4571-4585
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26020 |
Prediction of cross-sectional features of SPR joints based on the punch force-displacement curve using machine learning
Enthalten in The international journal of advanced manufacturing technology Bd. 128, 25.8.2023, Nr. 9-10, date:10.2023: 4023-4034
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