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26271 |
Proposal and evaluation of tsunami disaster drill support system using tablet computer
Enthalten in International journal of information technology Bd. 15, 13.9.2023, Nr. 8, date:12.2023: 4029-4039
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26272 |
Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach
Enthalten in Nutrition & metabolism Bd. 17, 17.11.2020, Nr. 1, date:12.2020: 1-8
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26273 |
Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns
Enthalten in Frontiers of structural and civil engineering Bd. 18, 26.7.2024, Nr. 8, date:8.2024: 1169-1194
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26274 |
Proposing a digital twin-based sustainable water governance system for rural Indian villages
Enthalten in International journal of information technology Bd. 17, 18.1.2025, Nr. 3, date:4.2025: 1777-1783
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26275 |
Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study
Enthalten in BMC pregnancy and childbirth Bd. 21, 12.3.2021, Nr. 1, date:12.2021: 1-17
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26276 |
Proposing a machine learning-based model for predicting nonreassuring fetal heart
Enthalten in Scientific reports Bd. 15, 6.3.2025, Nr. 1, date:12.2025: 1-8
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26277 |
Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique
Enthalten in Scientific reports Bd. 15, 28.2.2025, Nr. 1, date:12.2025: 1-11
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26278 |
Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models
Enthalten in Stochastic environmental research and risk assessment Bd. 37, 6.3.2023, Nr. 7, date:7.2023: 2513-2540
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26279 |
Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning
Enthalten in BMC psychiatry Bd. 20, 10.11.2020, Nr. 1, date:12.2020: 1-10
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26280 |
Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
Enthalten in Nature medicine Bd. 28, 21.7.2022, Nr. 7, date:7.2022: 1455-1460
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Administration
