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27681 |
Using machine learning and DeepLabCut in animal behavior
Enthalten in Acta ethologica Bd. 25, 16.7.2022, Nr. 3, date:10.2022: 125-133
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27682 |
Using Machine Learning and Multi-Element Analysis to Evaluate the Authenticity of Organic and Conventional Vegetables
Enthalten in Food analytical methods Bd. 12, 25.7.2019, Nr. 11, date:11.2019: 2542-2554
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27683 |
Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department
Enthalten in BMC emergency medicine Bd. 24, 18.12.2024, Nr. 1, date:12.2024: 1-18
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27684 |
Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation
Enthalten in Scientific reports Bd. 13, 8.11.2023, Nr. 1, date:12.2023: 1-17
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27685 |
Using machine learning approach to investigate the impact of missing sensors on fault classification of multistage gear box
Enthalten in Associação Brasileira de Engenharia e Ciências Mecânicas: Journal of the Brazilian Society of Mechanical Sciences and Engineering Bd. 47, 22.2.2025, Nr. 3, date:3.2025: 1-14
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27686 |
Using machine learning approaches for predicting the compressive strength of ultra-high-performance concrete with SHAP analysis
Enthalten in Asian journal of civil engineering Bd. 26, 18.11.2024, Nr. 1, date:1.2025: 373-388
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27687 |
Using machine learning approaches to develop a fast and easy-to-perform diagnostic tool for patients with light chain amyloidosis: a retrospective real-world study
Enthalten in Annals of hematology Bd. 103, 31.10.2024, Nr. 12, date:12.2024: 5781-5798
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27688 |
Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors
Enthalten in BMC medical informatics and decision making Bd. 24, 22.7.2024, Nr. 1, date:12.2024: 1-8
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27689 |
Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes
Enthalten in Discover oncology Bd. 14, 6.12.2023, Nr. 1, date:12.2023: 1-10
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27690 |
USING MACHINE LEARNING CLASSIFIERS AND REGRESSION MODELS FOR ESTIMATING THE STAND AGES OF FALCATA PLANTATIONS FROM SENTINEL DATA
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-4/W6-2022, 2023: 465-472. 8 S.
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