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25521 |
Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach
Enthalten in BMC medical imaging Bd. 24, 13.9.2024, Nr. 1, date:12.2024: 1-10
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25522 |
Predicting irrigation water quality indices in a typical mining dominated area in the Upper West region of Ghana using multiple machine learning techniques
Enthalten in Discover water Bd. 4, 15.7.2024, Nr. 1, date:12.2024: 1-23
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25523 |
Predicting land suitability for wheat and barley crops using machine learning techniques
Enthalten in Scientific reports Bd. 15, 7.5.2025, Nr. 1, date:12.2025: 1-19
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25524 |
Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme
Enthalten in Scientific reports Bd. 15, 6.1.2025, Nr. 1, date:12.2025: 1-14
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25525 |
Predicting life engagement and happiness from gaming motives and primary emotional traits before and during the COVID pandemic: a machine learning approach
Enthalten in Discover psychology Bd. 4, 24.6.2024, Nr. 1, date:12.2024: 1-16
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25526 |
Predicting load distribution in tie beam-foundation systems using machine learning and nature-inspired optimization algorithms
Enthalten in Asian journal of civil engineering Bd. 26, 5.3.2025, Nr. 4, date:4.2025: 1787-1800
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25527 |
Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app
Enthalten in Scientific reports Bd. 15, 1.6.2025, Nr. 1, date:12.2025: 1-7
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25528 |
Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study
Enthalten in BMC pregnancy and childbirth Bd. 25, 19.3.2025, Nr. 1, date:12.2025: 1-9
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25529 |
Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy
Enthalten in Scientific reports Bd. 15, 13.2.2025, Nr. 1, date:12.2025: 1-13
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25530 |
Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning
Enthalten in Scientific reports Bd. 15, 3.2.2025, Nr. 1, date:12.2025: 1-16
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Administration
