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Ergebnis der Suche nach:
"Machine Learning"
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26301 - 26310 von 29270
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26301
Prediction of postpartum depression in women: development and validation of multiple machine learning models
Enthalten in Journal of translational medicine Bd. 23, 7.3.2025, Nr. 1, date:12.2025: 1-18
26302
Prediction of poststroke independent walking using machine learning: a retrospective study
Enthalten in BMC neurology Bd. 24, 10.9.2024, Nr. 1, date:12.2024: 1-10
26303
Prediction of potential occurrence of historical objects with defensive function in Slovakia using machine learning approach
Enthalten in Scientific reports Bd. 14, 5.12.2024, Nr. 1, date:12.2024: 1-13
26304
Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-4-2024, 2024: 195-202. 8 S.
26305
Prediction of power consumption and its signals in sustainable turning of PH13-8Mo steel with different machine learning models
Enthalten in The international journal of advanced manufacturing technology Bd. 133, 6.6.2024, Nr. 5-6, date:7.2024: 2171-2188
26306
Prediction of precancerous cervical cancer lesions among women living with HIV on antiretroviral therapy in Uganda: a comparison of supervised machine learning algorithms
Enthalten in BMC women's health Bd. 24, 8.7.2024, Nr. 1, date:12.2024: 1-11
26307
Prediction of Prednisolone Dose Correction Using Machine Learning
Enthalten in Journal of healthcare informatics research Bd. 7, 15.2.2023, Nr. 1, date:3.2023: 84-103
26308
Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches
Enthalten in Scientific reports Bd. 14, 20.9.2024, Nr. 1, date:12.2024: 1-9
26309
Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China
Enthalten in BMC pregnancy and childbirth Bd. 24, 4.12.2024, Nr. 1, date:12.2024: 1-14
26310
Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques
Enthalten in Journal of Big Data Bd. 7, 26.8.2020, Nr. 1, date:12.2020: 1-22
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