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26041 |
Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques
Enthalten in BMC bioinformatics Bd. 20, 24.12.2019, Nr. 19, date:12.2019: 1-17
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26042 |
Rapid classification of local seismic events using machine learning
Enthalten in Journal of seismology Bd. 26, 19.9.2022, Nr. 5, date:10.2022: 897-912
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26043 |
Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning
Enthalten in International journal of minerals, metallurgy and materials Bd. 29, 23.5.2022, Nr. 6, date:6.2022: 1197-1205
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26044 |
Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models
Enthalten in BMC microbiology Bd. 25, 24.1.2025, Nr. 1, date:12.2025: 1-8
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26045 |
Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning
Enthalten in Scientific reports Bd. 15, 7.1.2025, Nr. 1, date:12.2025: 1-12
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26046 |
Rapid detection of honey adulteration using machine learning on gas sensor data
Enthalten in npj science of food Bd. 9, 15.5.2025, Nr. 1, date:12.2025: 1-9
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26047 |
Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data
Enthalten in Journal of food science and technology Bd. 60, 18.2.2023, Nr. 5, date:5.2023: 1530-1540
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26048 |
Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques
Enthalten in Endoscopy International Open Bd. 10, 2022, Nr. 04: E539-E543
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26049 |
Rapid Estimation of Truck Cycle Time in Open-Pit Mine Haulage Based on Feature-Optimized Machine Learning
Enthalten in Mining, metallurgy & exploration Bd. 42, 18.3.2025, Nr. 2, date:4.2025: 665-684
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26050 |
Rapid forecasting of urban flood inundation using multiple machine learning models
Enthalten in Natural hazards Bd. 108, 18.5.2021, Nr. 2, date:9.2021: 2335-2356
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