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27711 |
Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand
Enthalten in Scientific reports Bd. 15, 25.2.2025, Nr. 1, date:12.2025: 1-10
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27712 |
Using machine learning to find the hidden relationship between RTT and TCP throughput in WiFi
Enthalten in European Association for Speech, Signal and Image Processing: EURASIP journal on wireless communications and networking Bd. 2021, 20.12.2021, Nr. 1, date:12.2021: 1-18
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27713 |
Using machine learning to forecast domestic homicide via police data and super learning
Enthalten in Scientific reports Bd. 13, 21.12.2023, Nr. 1, date:12.2023: 1-8
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27714 |
Using Machine Learning to Forecast Future Earnings
Enthalten in Atlantic economic journal Bd. 48, 21.1.2021, Nr. 4, date:12.2020: 543-545
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27715 |
Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics
Enthalten in International journal of disaster risk science Bd. 15, 21.2.2024, Nr. 1, date:2.2024: 116-133
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27716 |
Using machine learning to identify gene interaction networks associated with breast cancer
Enthalten in BMC cancer Bd. 22, 17.10.2022, Nr. 1, date:12.2022: 1-9
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27717 |
Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis
Enthalten in Journal of cancer research and clinical oncology Bd. 149, 28.10.2023, Nr. 19, date:12.2023: 17479-17493
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27718 |
Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs
Enthalten in Journal of animal science and biotechnology Bd. 13, 17.5.2022, Nr. 1, date:12.2022: 1-12
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27719 |
Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model
Enthalten in Scientific reports Bd. 9, 12.3.2019, Nr. 1, date:12.2019: 1-15
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27720 |
Using machine learning to mine mental health diagnostic groups from emergency department presentations before and during the COVID-19 pandemic
Enthalten in Discover mental health Bd. 3, 6.11.2023, Nr. 1, date:12.2023: 1-22
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Administration
