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27651 |
Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches
Enthalten in BMC medical informatics and decision making Bd. 25, 31.1.2025, Nr. 1, date:12.2025: 1-10
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27652 |
Using a Sociotechnical Model to Understand Challenges with Sepsis Recognition among Critically Ill Infants
Enthalten in ACI Open Bd. 06, 2022, Nr. 02: e57-e65
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27653 |
Using alcohol consumption diary data from an internet intervention for outcome and predictive modeling: a validation and machine learning study
Enthalten in BMC medical research methodology Bd. 20, 11.5.2020, Nr. 1, date:12.2020: 1-9
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27654 |
Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping
Enthalten in Environmental science and pollution research Bd. 31, 19.8.2024, Nr. 40, date:8.2024: 52740-52757
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27655 |
Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms
Enthalten in BMC medical informatics and decision making Bd. 23, 19.10.2023, Nr. 1, date:12.2023: 1-14
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27656 |
Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt
Enthalten in Geoscience Letters Bd. 10, 9.2.2023, Nr. 1, date:12.2023: 1-19
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27657 |
Using Artificial Intelligence and Machine Learning for Rapidly Identifying Manufacturing Failures
Enthalten in Journal of failure analysis and prevention Bd. 22, 14.10.2022, Nr. 5, date:10.2022: 1813-1815
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27658 |
Using Big Data-machine learning models for diabetes prediction and flight delays analytics
Enthalten in Journal of Big Data Bd. 7, 17.9.2020, Nr. 1, date:12.2020: 1-18
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27659 |
USING BISPECTRAL FULL-WAVEFORM LIDAR TO MAP SEAMLESS COASTAL HABITATS IN 3D
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLIII-B3-2022, 2022: 463-470. 8 S.
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27660 |
Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum
Enthalten in Scientific reports Bd. 14, 20.5.2024, Nr. 1, date:12.2024: 1-9
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