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25631 |
Predicting the Addition of Information Regarding Clinically Significant Adverse Drug Reactions to Japanese Drug Package Inserts Using a Machine-Learning Model
Enthalten in Therapeutic innovation & regulatory science Bd. 58, 22.12.2023, Nr. 2, date:3.2024: 357-367
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25632 |
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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25633 |
Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
Enthalten in Building simulation Bd. 17, 16.3.2024, Nr. 5, date:5.2024: 839-855
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25634 |
Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data
Enthalten in Friction Bd. 12, 2.2.2024, Nr. 6, date:6.2024: 1299-1321
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25635 |
Predicting the complexity and mortality of polytrauma patients with machine learning models
Enthalten in Scientific reports Bd. 14, 9.4.2024, Nr. 1, date:12.2024: 1-10
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25636 |
Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods
Enthalten in Taehan-t'omok-hakhoe: KSCE journal of civil engineering Bd. 26, 6.9.2022, Nr. 11, date:11.2022: 4664-4679
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25637 |
Predicting the compressive strength of fiber-reinforced self-consolidating concrete using a hybrid machine learning approach
Enthalten in Innovative infrastructure solutions Bd. 9, 1.11.2024, Nr. 11, date:11.2024: 1-20
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25638 |
Predicting the compressive strength of high-performance concrete using an interpretable machine learning model
Enthalten in Scientific reports Bd. 14, 16.11.2024, Nr. 1, date:12.2024: 1-18
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25639 |
Predicting the compressive strength of ultra-high-performance concrete using a decision tree machine learning model enhanced by the integration of two optimization meta-heuristic algorithms
Enthalten in Journal of engineering and applied science Bd. 71, 14.2.2024, Nr. 1, date:12.2024: 1-17
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25640 |
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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