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

38651 - 38660 von 38717
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Artikel 38651 When are they coming? Understanding and forecasting the timeline of arrivals at the FC Barcelona stadium on match days
Enthalten in Machine learning Bd. 113, 26.3.2024, Nr. 5, date:5.2024: 2765-2794
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Artikel 38652 When not to use machine learning: A perspective on potential and limitations
Enthalten in Materials Research Society: MRS bulletin Bd. 47, 21.10.2022, Nr. 9, date:9.2022: 968-974
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Artikel 38653 When physics meets machine learning: a survey of physics-informed machine learning
Enthalten in Machine learning for computational science and engineering Bd. 1, 7.5.2025, Nr. 1, date:6.2025: 1-23
Online Ressource
Artikel 38654 When will China’s industrial carbon emissions peak? Evidence from machine learning
Enthalten in Environmental science and pollution research Bd. 30, 27.3.2023, Nr. 20, date:4.2023: 57960-57974
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Artikel 38655 Where Is the Geography? A Study of the Predictors of Obesity Using UK Biobank Data and Machine Learning
Enthalten in Journal of geovisualization and spatial analysis Bd. 7, 12.6.2023, Nr. 2, date:12.2023: 1-21
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Artikel 38656 Where you go is who you are: a study on machine learning based semantic privacy attacks
Enthalten in Journal of Big Data Bd. 11, 12.3.2024, Nr. 1, date:12.2024: 1-31
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Artikel 38657 Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?
Enthalten in Biomedical engineering online Bd. 22, 29.8.2023, Nr. 1, date:12.2023: 1-25
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Artikel 38658 Which model is more efficient in carbon emission prediction research? A comparative study of deep learning models, machine learning models, and econometric models
Enthalten in Environmental science and pollution research Bd. 31, 15.2.2024, Nr. 13, date:3.2024: 19500-19515
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Artikel 38659 Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach
Enthalten in Cardiovascular diabetology Bd. 23, 21.6.2024, Nr. 1, date:12.2024: 1-13
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Artikel 38660 Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model
Enthalten in Medical & biological engineering & computing Bd. 60, 11.5.2022, Nr. 7, date:7.2022: 2039-2049
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