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	33191	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
	33192	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
	33193	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
	33194	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
	33195	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
	33196	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
	33197	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
	33198	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
	33199	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
	33200	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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