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Neuigkeiten 24. Dezember 2025 bis 1. Januar 2026: Die Deutsche Nationalbibliothek bleibt an beiden Standorten geschlossen. // 24 December 2025 to 1 January 2026: The German National Library will be closed at both locations.
 
 
 


Ergebnis der Suche nach: "Machine Learning"
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Artikel 201 Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study
Enthalten in Biomedical engineering Bd. 70, 2025, Nr. 4: 385-391. 07 S.
Online Ressource
Artikel 202 Classification of anemic condition based on photoplethysmography signals and clinical dataset
Enthalten in Biomedical engineering Bd. 70, 2025, Nr. 4: 359-370. 12 S.
Online Ressource
Artikel 203 Prediction of remaining surgery duration based on machine learning methods and laparoscopic annotation data
Enthalten in Biomedical engineering Bd. 70, 2025, Nr. 3: 229-239. 11 S.
Online Ressource
Artikel 204 Gesture recognition from surface electromyography signals based on the SE-DenseNet network
Enthalten in Biomedical engineering Bd. 70, 2025, Nr. 3: 207-216. 10 S.
Online Ressource
Artikel 205 A new neural network–assisted hybrid chaotic hiking optimization algorithm for optimal design of engineering components
Enthalten in Materials testing Bd. 67, 2025, Nr. 6: 1069-1078. 10 S.
Online Ressource
Artikel 206 A self-portrayal of GI Junior Fellow Franziska Boenisch: trustworthy machine learning for individuals
Enthalten in Information technology Bd. 67, 2025, Nr. 1: 8-12. 5 S.
Online Ressource
Artikel 207 Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders
Enthalten in Reviews on advanced materials science Bd. 64, 2025, Nr. 1. 21 S.
Online Ressource
Artikel 208 Advanced hybrid machine learning models for estimating chloride penetration resistance of concrete structures for durability assessment: optimization and hyperparameter tuning
Enthalten in Reviews on advanced materials science Bd. 64, 2025, Nr. 1. 16 S.
Online Ressource
Artikel 209 Analyzing the compressive strength, eco-strength, and cost–strength ratio of agro-waste-derived concrete using advanced machine learning methods
Enthalten in Reviews on advanced materials science Bd. 64, 2025, Nr. 1. 18 S.
Online Ressource
Artikel 210 Equation-driven strength prediction of GGBS concrete: a symbolic machine learning approach for sustainable development
Enthalten in Reviews on advanced materials science Bd. 64, 2025, Nr. 1. 18 S.
Online Ressource


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