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26141 |
Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study
Enthalten in BMC cancer Bd. 24, 5.4.2024, Nr. 1, date:12.2024: 1-14
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26142 |
Prediction of the lattice constants of pyrochlore compounds using machine learning
Enthalten in Soft computing Bd. 26, 27.6.2022, Nr. 17, date:9.2022: 8307-8315
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26143 |
Prediction of the local buckling strength and load‐displacement behaviour of SHS and RHS members using Deep Neural Networks (DNN) – Introduction to the Deep Neural Network Direct Stiffness Method (DNN‐DSM)
Enthalten in Steel construction Bd. 15, 2022, Nr. S1, Hollow Sections: 78-90. 13 S.
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26144 |
Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques
Enthalten in Scientific reports Bd. 15, 6.3.2025, Nr. 1, date:12.2025: 1-12
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26145 |
Prediction of the R 3 Test-Based Reactivity of Supplementary Cementitious Materials: A Machine Learning Approach Utilizing Physical and Chemical Properties
Enthalten in International journal of concrete structures and materials Bd. 18, 1.11.2024, Nr. 1, date:12.2024: 1-22
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26146 |
Prediction of the rate of penetration using logistic regression algorithm of machine learning model
Enthalten in Arabian journal of geosciences Bd. 14, 28.10.2021, Nr. 21, date:11.2021: 1-13
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26147 |
Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases
Enthalten in Journal of medical systems Bd. 49, 3.2.2025, Nr. 1, date:12.2025: 1-13
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26148 |
Prediction of the small molecule selectivity index against influenza virus strain A/H1N1 using machine learning methods
Enthalten in Russian chemical bulletin Bd. 74, 28.4.2025, Nr. 3, date:3.2025: 851-864
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26149 |
Prediction of the sound absorption performance for micro-perforated panel based on machine learning
Enthalten in Journal of engineering and applied science Bd. 72, 20.2.2025, Nr. 1, date:12.2025: 1-20
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26150 |
Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning
Enthalten in BMC biomedical engineering Bd. 6, 17.1.2024, Nr. 1, date:12.2024: 1-19
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