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Ergebnis der Suche nach: "Machine Learning"
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Artikel 40171 Warranty-driven reliability analysis: a stochastic model for continuous solar power supply system with two photovoltaic cells and an inverter
Enthalten in International journal of information technology Bd. 17, 18.11.2024, Nr. 1, date:1.2025: 279-292
Online Ressource
Artikel 40172 Warum wir immer noch am Anfang sind
Enthalten in Digitale Welt Bd. 4, 3.6.2020, Nr. 3, date:7.2020: 18-20
Online Ressource
Artikel 40173 Was kann digitales Versichern? — Künstliche Intelligenz und Maschinenlernen auf dem Vertrauensprüfstand
Enthalten in Digitale Welt Bd. 5, 11.6.2021, Nr. 3, date:7.2021: 61-62
Online Ressource
Artikel 40174 Was wirklich bleibt: KI und deren praktische Anwendung
Enthalten in Digitale Welt Bd. 6, 15.9.2022, Nr. 4, date:10.2022: 52-55
Online Ressource
Artikel 40175 Wasserstein-based fairness interpretability framework for machine learning models
Enthalten in Machine learning Bd. 111, 21.7.2022, Nr. 9, date:9.2022: 3307-3357
Online Ressource
Artikel 40176 Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques
Enthalten in Environmental science and pollution research Bd. 32, 16.12.2024, Nr. 1, date:1.2025: 234-247
Online Ressource
Artikel 40177 Water jet angle prediction in supersonic crossflows: Euler–Lagrange and machine learning approaches
Enthalten in The European physical journal / Plus Bd. 139, 14.3.2024, Nr. 3, date:3.2024: 1-21
Online Ressource
Artikel 40178 Water Quality Evaluation and Monitoring Model (WQEM) Using Machine Learning Techniques with IoT
Enthalten in Water resources Bd. 51, 19.11.2024, Nr. 6, date:12.2024: 1094-1110
Online Ressource
Artikel 40179 Water quality index modelling and its application on artificial intelligence (AI) in conjunction with machine learning (ML) methodologies for mapping surface water potential zones for drinking activities
Enthalten in Discover civil engineering Bd. 2, 1.8.2025, Nr. 1, date:12.2025: 1-53
Online Ressource
Artikel 40180 Water quality parameters retrieval and nutrient status evaluation based on machine learning methods and Sentinel- 2 imagery: a case study of the Hongjiannao Lake
Enthalten in Environmental monitoring and assessment Bd. 197, 15.4.2025, Nr. 5, date:5.2025: 1-17
Online Ressource


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