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30991 - 31000 von 31067

	30991	What is the impact of national public expenditure and its allocation on neonatal and child mortality? A machine learning analysis Enthalten in BMC public health Bd. 23, 28.4.2023, Nr. 1, date:12.2023: 1-11
	30992	What Machine Learning Can Do for Focusing Aerogel Detectors Enthalten in Physics of atomic nuclei Bd. 86, 9.11.2023, Nr. 5, date:10.2023: 864-868
	30993	What We Are Missing: Using Machine Learning Models to Predict Vitamin C Deficiency in Patients with Metabolic and Bariatric Surgery Enthalten in Obesity surgery Bd. 33, 15.4.2023, Nr. 6, date:6.2023: 1710-1719
	30994	What we can do with one qubit in quantum machine learning: ten classical machine learning problems that can be solved with a single qubit Enthalten in Quantum machine intelligence Bd. 6, 12.11.2024, Nr. 2, date:12.2024: 1-24
	30995	What’s in a name? – gender classification of names with character based machine learning models Enthalten in Data mining and knowledge discovery Bd. 35, 12.5.2021, Nr. 4, date:7.2021: 1537-1563
	30996	When are they coming? Understanding and forecasting the timeline of arrivals at the FC Barcelona stadium on match days Enthalten in Machine learning Bd. 113, 26.3.2024, Nr. 5, date:5.2024: 2765-2794
	30997	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
	30998	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
	30999	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
	31000	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

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