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34411 |
Using machine learning to explore core risk factors associated with the risk of eating disorders among non-clinical young women in China: A decision-tree classification analysis Enthalten in Journal of eating disorders Bd. 10, 10.2.2022, Nr. 1, date:12.2022: 1-11
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34412 |
Using machine learning to explore the determinants of service satisfaction with online healthcare platforms during the COVID-19 pandemic Enthalten in Service business Bd. 17, 16.5.2023, Nr. 2, date:6.2023: 449-476
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34413 |
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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34414 |
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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34415 |
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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34416 |
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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34417 |
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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34418 |
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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34419 |
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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34420 |
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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