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Artikel 40091 Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran
Enthalten in Natural hazards Bd. 117, 11.6.2023, Nr. 3, date:7.2023: 3231-3255
Online Ressource
Artikel 40092 Using Machine Learning techniques in phenomenological studies on flavour physics
Enthalten in Journal of high energy physics Bd. 2022, 19.7.2022, Nr. 7, date:7.2022: 1-43
Online Ressource
Artikel 40093 Using Machine Learning Techniques to Detect Defects in Images of Metal Structures
Enthalten in Pattern recognition and image analysis Bd. 31, 21.9.2021, Nr. 3, date:7.2021: 506-512
Online Ressource
Artikel 40094 Using Machine Learning Techniques to Detect Parallel Patterns of Multi-threaded Applications
Enthalten in International journal of parallel programming Bd. 44, 4.11.2015, Nr. 4, date:8.2016: 867-900
Online Ressource
Artikel 40095 USING MACHINE LEARNING TECHNIQUES TO FILTER VEGETATION IN COLORIZED SFM POINT CLOUDS OF SOIL SURFACES
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-1/W2-2023, 2023: 163-170. 8 S.
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Artikel 40096 Using machine learning techniques to predict antimicrobial resistance in stone disease patients
Enthalten in World journal of urology Bd. 40, 26.5.2022, Nr. 7, date:7.2022: 1731-1736
Online Ressource
Artikel 40097 Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks
Enthalten in Journal of Big Data Bd. 7, 24.8.2020, Nr. 1, date:12.2020: 1-16
Online Ressource
Artikel 40098 Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data
Enthalten in Scientific reports Bd. 14, 4.3.2024, Nr. 1, date:12.2024: 1-11
Online Ressource
Artikel 40099 Using Machine Learning Techniques to Test the Load Capacity Curve Hypothesis: A Classifier-Lasso Application on Global Panel Data
Enthalten in Biophysical economics and sustainability Bd. 10, 19.2.2025, Nr. 1, date:6.2025: 1-18
Online Ressource
Artikel 40100 Using machine learning to assist with the selection of security controls during security assessment
Enthalten in Empirical software engineering Bd. 25, 13.4.2020, Nr. 4, date:7.2020: 2550-2582
Online Ressource


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