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42951 |
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
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42952 |
Using the artificial bee colony technique to optimize machine learning algorithms in estimating the mature weight of camels
Enthalten in Tropical animal health and production Bd. 55, 17.2.2023, Nr. 2, date:4.2023: 1-10
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42953 |
Using the grouping function of machine learning algorithm to reduce the influence of information avoidance tendency during reading behavior
Enthalten in Smart Learning Environments Bd. 10, 27.11.2023, Nr. 1, date:12.2023: 1-16
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42954 |
Using the risk of spatial extrapolation by machine-learning models to assess the reliability of model predictions for conservation
Enthalten in Landscape ecology Bd. 38, 10.4.2023, Nr. 6, date:6.2023: 1363-1372
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42955 |
Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii
Enthalten in Respiratory research Bd. 25, 3.1.2024, Nr. 1, date:12.2024: 1-11
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42956 |
Using topological data analysis and machine learning to predict customer churn
Enthalten in Journal of Big Data Bd. 11, 7.11.2024, Nr. 1, date:12.2024: 1-31
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42957 |
Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra
Enthalten in BMC bioinformatics Bd. 24, 9.1.2023, Nr. 1, date:12.2023: 1-15
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42958 |
Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity
Enthalten in BMC cardiovascular disorders Bd. 25, 27.10.2025, Nr. 1, date:12.2025: 1-13
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42959 |
Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis
Enthalten in Archives of gynecology and obstetrics Bd. 309, 3.2.2024, Nr. 3, date:3.2024: 1053-1063
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42960 |
Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum
Enthalten in Scientific reports Bd. 15, 19.2.2025, Nr. 1, date:12.2025: 1-14
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