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Showing 1 to 11 of 11 for “"Random Forest. XGBoost"”.
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Εφαρμογή αλγορίθμων μηχανικής μάθησης για την υποστήριξη θεραπευτικής απόφασης και την πρόβλεψη χρήσης ενδοστοματικού νάρθηκα σε ασθενείς με υπνική άπνοια
… Εφαρμόστηκαν οι αλγόριθμοι μηχανικής μάθησης Random Forest, XGBoost και Support Vector Machines τόσο για την πολυκατηγορική πρόβλεψη (τύπος νάρθηκα) όσο και για τη δυαδική πρόβλεψη (επιλογή ή μη θεραπείας με νάρθηκα). Εφαρμόστηκαν, επίσης, τεχνικές επιλογής χαρακτηριστικών, διασταυρούμενης …
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Ανάπτυξη Γεωχωρικού Μοντέλου Αξιολόγησης Πλημμυρικής Επιδεκτικότητας με Χρήση Τεχνολογιών Τηλεπισκόπησης και Μεθόδων Μηχανικής Μάθησης. Εφαρμογή στην Νήσο της Ρόδου
… και εφαρμόζονται δενδρικά μοντέλα ταξινόμησης (Random Forest, XGBoost και CART) για την παραγωγή πιθανοτικών χαρτών επιδεκτικότητας, με προγνωστικές μεταβλητές που περιλαμβάνουν μορφομετρικά/τοπογραφικά χαρακτηριστικά (π.χ. υψόμετρο, κλίση, προσανατολισμό, καμπυλότητες), δείκτες υδρολογικής …
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An AI-driven loan brokerage platform: integrating socio-economic factors, consumer financial behaviour, and multi-criteria decision analysis for responsible lending.
… advanced machine learning algorithms including random forest, XGBoost, and AdaBoost. A knowledge graph is constructed to map causal dependencies among features, enhancing transparency and providing interpretable insights into how socio-economic conditions influence eligibility outcomes. …
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InSAR time series analysis and machine learning for ground subsidence monitoring and susceptibility mapping in Midvaal, South Africa
… machine learning algorithms including Random Forest, XGBoost, LightGBM, and CNN were employed to create ground subsidence susceptibility maps and classify the region into five risk zones: Very Low, Low, Moderate, High, and Very High. Random Forest (RF) achieved the highest predictive …
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Intelligent low-complexity widely deployable diagnostic tools for wireless edge device security using machine learning
… algorithms, including support vector machines, Random Forest, XGBoost, K Nearest Neighbors and DNNs, evaluate the developed feature set in each application. The designed data analytics and features enable more fundamental approaches to achieve similar accuracy and generalization results, on …
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Detekce stresu s využitím biologických a enviromentálních dat
… V rámci klasifikace byly porovnány modely Random Forest, SVM a XGBoost, přičemž všechny dosáhly srovnatelné úspěšnosti (průměrnou přesnost přesahující 90 %). Pro další analýzu byl zvolen algoritmus Random Forest, a to zejména pro jeho vysokou odolnost vůči šumu a transparentní …
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Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies
Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The …
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Advancing Fisheries and Aquaculture Management with Machine Learning: Bycatch Risk Prediction and Autonomous Mortality Modeling
Machine learning is proving to play an increasingly important role in many fields, including ecology and fisheries sciences. Machine learning models offer many advantages over traditional statistical analysis methods, such as being well suited for analyzing large data sets, capable of incorporating …
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Geochemical investigation of the co-evolution of life and environment in the Neoproterozoic Era
The co-evolution of life and the environment stands as a cornerstone in Earth's 4.5-billion-year history. Environmental fluctuations have wielded substantial influence over biological evolution, while life forms have, in turn, reshaped Earth's surface and climate. This dissertation centers on a …