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1241 |
Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques
Enthalten in BMC medical informatics and decision making Bd. 25, 6.6.2025, Nr. 1, date:12.2025: 1-14
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1242 |
Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs
Enthalten in Environmental earth sciences Bd. 80, 14.5.2021, Nr. 10, date:5.2021: 1-19
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1243 |
Predicting soil cone index and assessing suitability for wind and solar farm development in using machine learning techniques
Enthalten in Scientific reports Bd. 14, 5.2.2024, Nr. 1, date:12.2024: 1-15
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1244 |
Predicting the antenna properties of helicon plasma thrusters using machine learning techniques
Enthalten in Journal of electric propulsion Bd. 3, 5.3.2024, Nr. 1, date:12.2024: 1-24
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1245 |
Predicting the compressive strength of unreinforced brick masonry using machine learning techniques validated on a case study of a museum through nondestructive testing
Enthalten in Journal of civil structural health monitoring Bd. 10, 6.3.2020, Nr. 3, date:7.2020: 389-403
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1246 |
Predicting the daily closing price of selected shares on the Dhaka Stock Exchange using machine learning techniques
Enthalten in SN business & economics Bd. 1, 24.3.2021, Nr. 4, date:4.2021: 1-16
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1247 |
Predicting the efficiency of bare silica-based nano-fluid flooding in sandstone reservoirs for enhanced oil recovery through machine learning techniques using experimental data
Enthalten in Applied nanoscience Bd. 12, 1.7.2022, Nr. 8, date:8.2022: 2367-2377
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1248 |
Predicting the impact of internet usage on students’ academic performance using machine learning techniques in Bangladesh perspective
Enthalten in Social network analysis and mining Bd. 14, 21.3.2024, Nr. 1, date:12.2024: 1-41
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1249 |
Predicting the level of autism and improvement rate from assessment dataset using machine learning techniques
Enthalten in International journal of information technology Bd. 15, 6.3.2023, Nr. 3, date:3.2023: 1647-1652
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1250 |
Predicting the magnitude of injection-induced earthquakes using machine learning techniques
Enthalten in Natural hazards Bd. 118, 23.5.2023, Nr. 1, date:8.2023: 545-570
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