Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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16561 - 16570 von 16806

	16561	Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma Enthalten in BMC cancer Bd. 21, 9.8.2021, Nr. 1, date:12.2021: 1-11
	16562	Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case–control cohort analysis Enthalten in BMC nephrology Bd. 14, 23.7.2013, Nr. 1, date:12.2013: 1-9
	16563	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
	16564	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
	16565	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
	16566	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
	16567	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
	16568	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
	16569	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.
	16570	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