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Ergebnis der Suche nach: "Machine Learning"
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Artikel 881 Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images
Enthalten in International journal of information technology 14.2.2025: 1-14
Online Ressource
Artikel 882 CoCoSo method based on generalized distance measure with trapezoidal fuzzy multi-numbers for solving multi-criteria decision-making method problems
Enthalten in International journal of information technology 14.2.2025: 1-13
Online Ressource
Artikel 883 Identification of biomarkers associated with phagocytosis regulatory factors in coronary artery disease using machine learning and network analysis
Enthalten in Mammalian genome 14.2.2025: 1-13
Online Ressource
Artikel 884 Leveraging machine learning and optimization models for enhanced seaport efficiency
Enthalten in Maritime economics & logistics 14.2.2025: 1-42
Online Ressource
Artikel 885 Machine Learning Prediction and Optimization of Cold Metal Transfer Welding Parameters for Enhancing the Mechanical and Microstructural Properties of Austenitic-Ferritic Stainless-Steel Joints
Enthalten in Journal of materials engineering and performance 14.2.2025: 1-22
Online Ressource
Artikel 886 Novel R-C-C fusion machine learning method for automatic damage detection of heritage buildings using 3D point cloud data
Enthalten in Journal of civil structural health monitoring 14.2.2025: 1-18
Online Ressource
Artikel 887 Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach
Enthalten in European radiology 14.2.2025: 1-11
Online Ressource
Artikel 888 Electrospinning Technology, Machine Learning, and Control Approaches: A Review
Enthalten in Advanced engineering materials 13.02.2025. 29 S.
Online Ressource
Artikel 889 An effective of bio-inspired multiverse optimization based controller placement in software defined networks environment
Enthalten in International journal of information technology 13.2.2025: 1-14
Online Ressource
Artikel 890 Application of Non-destructive Methods for Predicting the Concrete’s Compressive Strength, Using Hyper-Tuned Machine Learning Techniques
Enthalten in Iranian journal of science and technology / Transactions of civil engineering 13.2.2025: 1-17
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