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Ergebnis der Suche nach: "Machine Learning"
im Bestand: Gesamter Bestand

36871 - 36880 von 37302
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Artikel 36871 Use of machine learning to predict creativity among nurses: a multidisciplinary approach
Enthalten in BMC nursing Bd. 24, 15.5.2025, Nr. 1, date:12.2025: 1-12
Online Ressource
Artikel 36872 USE OF MULTIVARIATE MACHINE LEARNING ANALYSIS TECHNIQUES FOR FLOOD RISK PREVENTION
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLII-3/W4, 2018: 549-554
Online Ressource
Artikel 36873 Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models
Enthalten in Journal of cheminformatics Bd. 3, 7.2.2011, Nr. 1, date:12.2011: 1-12
Online Ressource
Artikel 36874 Use of systems with deep learning and machine learning for the detection and classification of malignant vs. benign spinal fractures with MRI: can deep/machine learning help us further for detection and characterization?
Enthalten in European radiology Bd. 33, 26.5.2023, Nr. 7, date:7.2023: 5058-5059
Online Ressource
Artikel 36875 Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa
Enthalten in BMC infectious diseases Bd. 23, 19.7.2023, Nr. 1, date:12.2023: 1-13
Online Ressource
Artikel 36876 Usefulness of machine learning softwares to screen titles of systematic reviews: a methodological study
Enthalten in Systematic Reviews Bd. 12, 15.4.2023, Nr. 1, date:12.2023: 1-14
Online Ressource
Artikel 36877 User structural information in priority-based ranking for top-N recommendation
Enthalten in Advances in computational intelligence Bd. 3, 6.1.2023, Nr. 1, date:2.2023: 1-16
Online Ressource
Artikel 36878 User training for machine learning controlled upper limb prostheses: a serious game approach
Enthalten in Journal of neuroEngineering and rehabilitation Bd. 18, 12.2.2021, Nr. 1, date:12.2021: 1-15
Online Ressource
Artikel 36879 Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling
Enthalten in BMC medical research methodology Bd. 22, 23.11.2022, Nr. 1, date:12.2022: 1-10
Online Ressource
Artikel 36880 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
Online Ressource


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