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Ergebnis der Suche nach: "Machine Learning"
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Artikel 15661 Revolutionizing prostate cancer screening: Machine learning-driven FTIR analysis of biofluidic EVs integrated with clinical profiles
Enthalten in Health and Technology Bd. 15, 14.4.2025, Nr. 3, date:5.2025: 563-576
Online Ressource
Artikel 15662 Rewiring care delivery through Digital Therapeutics (DTx): a machine learning-enhanced assessment and development (M-LEAD) framework
Enthalten in BMC health services research Bd. 24, 23.2.2024, Nr. 1, date:12.2024: 1-13
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Artikel 15663 RExPRT: a machine learning tool to predict pathogenicity of tandem repeat loci
Enthalten in Genome biology Bd. 25, 31.1.2024, Nr. 1, date:12.2024: 1-22
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Artikel 15664 RF sensing enabled tracking of human facial expressions using machine learning algorithms
Enthalten in Scientific reports Bd. 14, 13.11.2024, Nr. 1, date:12.2024: 1-11
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Artikel 15665 RGCVAE: relational graph conditioned variational autoencoder for molecule design
Enthalten in Machine learning Bd. 114, 27.1.2025, Nr. 2, date:2.2025: 1-26
Online Ressource
Artikel 15666 RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers
Enthalten in BMC bioinformatics Bd. 18, 30.6.2017, Nr. 1, date:12.2017: 1-22
Online Ressource
Artikel 15667 Rheological Behavior of SAE50 Oil–SnO2–CeO2 Hybrid Nanofluid: Experimental Investigation and Modeling Utilizing Response Surface Method and Machine Learning Techniques
Enthalten in Nanoscale research letters Bd. 17, 8.12.2022, Nr. 1, date:12.2022: 1-22
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Artikel 15668 Rhesus infant nervous temperament predicts peri-adolescent central amygdala metabolism & behavioral inhibition measured by a machine-learning approach
Enthalten in Translational Psychiatry Bd. 14, 15.3.2024, Nr. 1, date:12.2024: 1-11
Online Ressource
Artikel 15669 Rice plant disease diagnosing using machine learning techniques: a comprehensive review
Enthalten in SN applied sciences Bd. 4, 20.10.2022, Nr. 11, date:11.2022: 1-13
Online Ressource
Artikel 15670 Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models
Enthalten in Scientific reports Bd. 14, 17.9.2024, Nr. 1, date:12.2024: 1-17
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