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

38891 - 38900 von 39185
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Artikel 38891 Using published pathway figures in enrichment analysis and machine learning
Enthalten in BMC genomics Bd. 24, 25.11.2023, Nr. 1, date:12.2023: 1-13
Online Ressource
Artikel 38892 Using quantum atomics and machine learning to advance picotechnology
Enthalten in Theoretical chemistry accounts Bd. 143, 26.9.2024, Nr. 10, date:10.2024: 1-9
Online Ressource
Artikel 38893 Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach
Enthalten in Earth science informatics Bd. 15, 3.2.2022, Nr. 1, date:3.2022: 485-496
Online Ressource
Artikel 38894 Using remote sensing, process-based crop models, and machine learning to evaluate crop rotations across 20 million hectares in Western Australia
Enthalten in Agronomy for sustainable development Bd. 42, 12.12.2022, Nr. 6, date:12.2022: 1-14
Online Ressource
Artikel 38895 Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content
Enthalten in Scientific reports Bd. 14, 15.4.2024, Nr. 1, date:12.2024: 1-14
Online Ressource
Artikel 38896 Using Smartphone Survey Data and Machine Learning to Identify Situational and Contextual Risk Factors for HIV Risk Behavior Among Men Who Have Sex with Men Who Are Not on PrEP
Enthalten in Prevention science Bd. 20, 9.5.2019, Nr. 6, date:8.2019: 904-913
Online Ressource
Artikel 38897 Using Sparrow Search Algorithm for tuning hyperparameters of hybrid machine learning models
Enthalten in Signal, image and video processing Bd. 19, 4.7.2025, Nr. 10, date:10.2025: 1-20
Online Ressource
Artikel 38898 Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning
Enthalten in Scientific reports Bd. 13, 18.12.2023, Nr. 1, date:12.2023: 1-12
Online Ressource
Artikel 38899 Using symbolic machine learning to assess and model substance transport and decay in water distribution networks
Enthalten in Scientific reports Bd. 14, 8.2.2024, Nr. 1, date:12.2024: 1-14
Online Ressource
Artikel 38900 Using synthetic data to develop machine learning models to predict the performance of fiber-reinforced concrete
Enthalten in Asian journal of civil engineering Bd. 26, 11.3.2025, Nr. 5, date:5.2025: 2209-2221
Online Ressource


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