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20161 |
WGO: a similarly encoded whale-goshawk optimization algorithm for uncertain cloud manufacturing service composition
Enthalten in Autonomous intelligent systems Bd. 5, 5.3.2025, Nr. 1, date:12.2025: 1-13
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20162 |
What can we learn about the psychiatric diagnostic categories by analysing patients' lived experiences with Machine-Learning?
Enthalten in BMC psychiatry Bd. 22, 24.6.2022, Nr. 1, date:12.2022: 1-17
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20163 |
What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? A case-control study in orthodontically treated patients
Enthalten in Progress in orthodontics Bd. 25, 26.8.2024, Nr. 1, date:12.2024: 1-11
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20164 |
What do users in a polycystic ovary syndrome (PCOS) forum think about the treatments they tried: Analysing treatment sentiment using machine learning
Enthalten in Physical and engineering sciences in medicine Bd. 48, 14.4.2025, Nr. 2, date:6.2025: 723-741
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20165 |
What do we want to know about MOOCs? Results from a machine learning approach to a systematic literature mapping review
Enthalten in International journal of educational technology in higher education Bd. 19, 14.10.2022, Nr. 1, date:12.2022: 1-22
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20166 |
What factors preventing the older adults in China from living longer: a machine learning study
Enthalten in BMC geriatrics Bd. 24, 22.7.2024, Nr. 1, date:12.2024: 1-11
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20167 |
WHAT IDENTIFIES A WHALE BY ITS FLUKE? ON THE BENEFIT OF INTERPRETABLE MACHINE LEARNING FOR WHALE IDENTIFICATION
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. V-2-2020, 2020: 1005-1012
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20168 |
What influences the perception of fairness in urban and rural China? An analysis using machine learning
Enthalten in Humanities and Social Sciences Communications Bd. 12, 10.10.2025, Nr. 1, date:12.2025: 1-14
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20169 |
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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20170 |
What makes on-farm experimental data suitable for data-driven decision-making? Implications of trial design and spatial distribution of field data for machine learning models
Enthalten in Precision agriculture Bd. 26, 20.9.2025, Nr. 5, date:10.2025: 1-23
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Administration
