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28181 |
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
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28182 |
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
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28183 |
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
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28184 |
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
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28185 |
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
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28186 |
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
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28187 |
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
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28188 |
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
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28189 |
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
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28190 |
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
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