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Ergebnis der Suche nach: "Machine Learning"
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Artikel 241 Interpreting the relationship between properties of wood and pulping & paper via machine learning algorithms combined with SHAP analysis
Enthalten in Nordic pulp & paper research journal Bd. 40, 2025, Nr. 1: 149-160. 12 S.
Online Ressource
Artikel 242 Use of machine learning methods for modelling mechanical parameters of PLA and PLA/native potato starch compound using aging data
Enthalten in International polymer processing Bd. 40, 2025, Nr. 1: 94-109. 16 S.
Online Ressource
Artikel 243 Unlocking potential: the role of chemometric modeling in pulp and paper manufacturing
Enthalten in Nordic pulp & paper research journal Bd. 40, 2025, Nr. 1: 71-82. 12 S.
Online Ressource
Artikel 244 Artificial Intelligence and Machine Learning in Reconstructive Microsurgery
Enthalten in Seminars in plastic surgery Bd. 39, 2025, Nr. 03: 190-198
Online Ressource
Artikel 245 Machine Learning Applied to Electrochemical Data Processing for Improved Analyte Quantification in Complex Saliva
Enthalten in Electroanalysis Bd. 37, 2025, Nr. 9. 11 S.
Online Ressource
Artikel 246 Controllability and diffeomorphism groups on manifolds with boundary
Enthalten in Forum mathematicum Bd. 37, 2025, Nr. 4: 1291-1308. 18 S.
Online Ressource
Artikel 247 Machine Learning Guided Design of Nerve‐On‐A‐Chip Platforms with Promoted Neurite Outgrowth (Adv. Funct. Mater. 38/2025)
Enthalten in Advanced functional materials Bd. 35, 2025, Nr. 38. 1 S.
Online Ressource
Artikel 248 Bridging Theory and Experiment: Machine Learning Potential‐Driven Insights into pH‐Dependent CO₂ Reduction on Sn‐Based Catalysts (Adv. Funct. Mater. 36/2025)
Enthalten in Advanced functional materials Bd. 35, 2025, Nr. 36. 1 S.
Online Ressource
Artikel 249 Continuous pH Monitoring in Organ‐on‐Chip Devices with Single‐Step Fabricated DNA Scaffold Sensors Conjugated Hydrogels and Machine Learning (Adv. Funct. Mater. 36/2025)
Enthalten in Advanced functional materials Bd. 35, 2025, Nr. 36. 1 S.
Online Ressource
Artikel 250 Construction of a model for predicting sensory attributes of cosmetic creams using instrumental parameters based on machine learning
Enthalten in Applied rheology Bd. 35, 2025, Nr. 1. 20 S.
Online Ressource


241 - 250 von 30825
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