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17951 |
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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17952 |
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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17953 |
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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17954 |
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
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17955 |
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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17956 |
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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17957 |
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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17958 |
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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17959 |
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
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17960 |
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
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