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32661 - 32670 von 32734

	32661	When machine learning and neural networks marry real-time scheduling Enthalten in Real-time systems Bd. 61, 7.7.2025, Nr. 2, date:6.2025: 320-325
	32662	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
	32663	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
	32664	When the crowd gets it wrong – the limits of collective wisdom in machine learning Enthalten in Scientific reports Bd. 15, 1.7.2025, Nr. 1, date:12.2025: 1-11
	32665	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
	32666	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
	32667	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
	32668	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
	32669	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
	32670	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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