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25991 |
Rapid Post-Wildfire Burned Vegetation Assessment with Google Earth Engine (Case Study: 2023 Canada Wildfires)
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-3/W3-2024, 2024: 45-53. 9 S.
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25992 |
Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning
Enthalten in Analytical and bioanalytical chemistry Bd. 414, 30.3.2022, Nr. 13, date:5.2022: 3959-3970
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25993 |
Rapid traversal of vast chemical space using machine learning-guided docking screens
Enthalten in Nature computational science Bd. 5, 13.3.2025, Nr. 4, date:4.2025: 301-312
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25994 |
Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine
Enthalten in European Association for Predictive, Preventive and Personalised Medicine: The EPMA journal Bd. 13, 27.5.2022, Nr. 2, date:6.2022: 285-298
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25995 |
Rates of convergence for regression with the graph poly-Laplacian
Enthalten in Sampling theory, signal processing, and data analysis Bd. 21, 27.11.2023, Nr. 2, date:12.2023: 1-40
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25996 |
Rational design and glass-forming ability prediction of bulk metallic glasses via interpretable machine learning
Enthalten in Journal of materials science Bd. 58, 19.5.2023, Nr. 21, date:6.2023: 8833-8844
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25997 |
RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning
Enthalten in BMC medical informatics and decision making Bd. 24, 16.9.2024, Nr. 2, date:4.2024: 1-15
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25998 |
Re-assessing accuracy degradation: a framework for understanding DNN behavior on similar-but-non-identical test datasets
Enthalten in Machine learning Bd. 114, 14.2.2025, Nr. 3, date:3.2025: 1-22
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25999 |
Re-evaluation of machine learning models for predicting ultimate bearing capacity of piles through SHAP and Joint Shapley methods
Enthalten in Neural computing & applications Bd. 36, 16.10.2023, Nr. 2, date:1.2024: 697-715
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26000 |
Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer
Enthalten in Scientific reports Bd. 13, 5.10.2023, Nr. 1, date:12.2023: 1-14
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