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Ergebnis der Suche nach: tit all "Machine Learning Techniques"
im Bestand: Gesamter Bestand

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Online Ressourcen 1 Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques
Adedara, Muyiwa Lawrence. - Kiel : Universitätsbibliothek Kiel, 2025
Online Ressource
Online Ressourcen 2 Forecasting of residential unit's heat demands: a comparison of machine learning techniques in a real-world case study
Kemper, Neele. - Augsburg : Universität Augsburg, 2025
Online Ressource
Artikel 3 A comprehensive review of deep learning and machine learning techniques for early-stage skin cancer detection: Challenges and research gaps
Enthalten in Journal of intelligent systems Bd. 34, 2025, Nr. 1. 35 S.
Online Ressource
Artikel 4 Enhancing Long-Term Reservoir Inflow Forecasting: An Integrated Approach Combining Switch Prediction Method, Ensemble Rainfall Forecasts, and Machine Learning Techniques
Enthalten in EGUsphere 04.07.2025: 1-33. 33 S.
Online Ressource
Artikel 5 Unstained Blood Smear Analysis: A Review of Rule‐Based, Machine Learning, and Deep Learning Techniques
Enthalten in Journal of biophotonics 30.06.2025. 22 S.
Online Ressource
Artikel 6 Utilizing Machine Learning Algorithms and Regression Techniques to Develop Pavement Performance Indices for Dry Freeze Climate Regions
Enthalten in Iranian journal of science and technology / Transactions of civil engineering 24.6.2025: 1-21
Online Ressource
Artikel 7 PARP1 inhibitors discovery: innovative screening strategies incorporating machine learning and fragment replacement techniques
Enthalten in Molecular diversity 11.6.2025: 1-18
Online Ressource
Artikel 8 Material Characterization of Aluminum Castings Using Machine Learning Techniques
Enthalten in International journal of metalcasting 10.6.2025: 1-6
Online Ressource
Artikel 9 Satellite imagery for land cover classification using machine learning techniques
Enthalten in Multimedia tools and applications 9.6.2025: 1-26
Online Ressource
Artikel 10 Prediction of Mechanical Properties of Additively Manufactured Parts Using Machine Learning Techniques
Enthalten in Institution of Engineers, India. Karnataka State Centre: Journal of the Institution of Engineers (India) / Series D 2.6.2025: 1-18
Online Ressource


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