Katalog der Deutschen Nationalbibliothek

Ergebnis der Suche nach: "Machine Learning"
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43241 - 43250 von 43632

	43241	Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran Enthalten in Natural hazards Bd. 117, 11.6.2023, Nr. 3, date:7.2023: 3231-3255
	43242	Using Machine Learning techniques in phenomenological studies on flavour physics Enthalten in Journal of high energy physics Bd. 2022, 19.7.2022, Nr. 7, date:7.2022: 1-43
	43243	Using Machine Learning Techniques to Detect Defects in Images of Metal Structures Enthalten in Pattern recognition and image analysis Bd. 31, 21.9.2021, Nr. 3, date:7.2021: 506-512
	43244	Using Machine Learning Techniques to Detect Parallel Patterns of Multi-threaded Applications Enthalten in International journal of parallel programming Bd. 44, 4.11.2015, Nr. 4, date:8.2016: 867-900
	43245	USING MACHINE LEARNING TECHNIQUES TO FILTER VEGETATION IN COLORIZED SFM POINT CLOUDS OF SOIL SURFACES Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-1/W2-2023, 2023: 163-170. 8 S.
	43246	Using machine learning techniques to predict antimicrobial resistance in stone disease patients Enthalten in World journal of urology Bd. 40, 26.5.2022, Nr. 7, date:7.2022: 1731-1736
	43247	Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks Enthalten in Journal of Big Data Bd. 7, 24.8.2020, Nr. 1, date:12.2020: 1-16
	43248	Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data Enthalten in Scientific reports Bd. 14, 4.3.2024, Nr. 1, date:12.2024: 1-11
	43249	Using Machine Learning Techniques to Test the Load Capacity Curve Hypothesis: A Classifier-Lasso Application on Global Panel Data Enthalten in Biophysical economics and sustainability Bd. 10, 19.2.2025, Nr. 1, date:6.2025: 1-18
	43250	Using machine learning to assist with the selection of security controls during security assessment Enthalten in Empirical software engineering Bd. 25, 13.4.2020, Nr. 4, date:7.2020: 2550-2582

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