Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"Extreme gradient boosting (XGBoost)"”.
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PREDICTING METRICS FOR NAVAL SUPPLY SYSTEMS COMMAND WHOLESALE INVENTORY OPTIMIZATION MODEL
… approaches: k-nearest neighbors (KNN) and Extreme Gradient Boosting (XGBoost). Both methods train very fast for more than 21,000 items and multiple CIPs by item. These observations are divided into four categories, and the results demonstrate that XGBoost consistently outperforms KNN. …
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Optimising credit card fraud detection through machine learning and deep learning with spatial-temporal imbalance handling
… Forest, CatBoost, Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), AdaBoost, Gaussian Naive Bayes, and Extra Trees Classifier—and two deep learning models, Gated Recurrent Unit (GRU) and Neural Network (NN). Performance was evaluated using Recall, Precision, F1 Score, ROC-AUC …
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A machine learning model for vehicle crash type prediction
… to the specific crash type. In this study, eXtreme Gradient Boosting (XGBoost) method is applied to predict the occurrence of different types of crashes. A two-layer model is proposed. The first layer is used to distinguish potential crashes from crash-free observations and the second layer …
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Machine learning for high performance computing applications
… first application used K-means clustering and Gradient Boosted Tree Regression (GBTR) to predict estimated queue time for jobs submitted to an HPC system. This method achieved a 96% accuracy when predicting whether or not a job would start prior to a specified deadline. The second application …
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Analysis of bankruptcy prediction of shipping industry - Machine Learning Approach
… advanced machine learning models, including Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) networks. A comprehensive literature review and interviews with industry practitioners were conducted to refine the variables used in the models. These models predict bankruptcy …
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Machine Learning Applications in Blockchain for Renewable Energy Systems
… comparative analysis of ensemble methods such as eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest is conducted for residual demand. To optimize these models, the study contrasts bio-inspired Swarm Intelligence, Honey Badger Algorithm (HBA), …
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DISCOVERY OF HIGH ENTROPY CERAMICS WITH LOW THERMAL CONDUCTIVITY THROUGH MACHINE LEARNING
… (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) are employed. As a result, the RF, KRR, and XGBoost exhibit excellent performance, achieving high R2 scores over 0.90. Additionally, the accuracy of this model was tested using new cases of four compounds, which …
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Machine Learning and Stochastic Simulation for Inventory Management
… Machine learning models such as CatBoost, Extreme Gradient Boosting (XGBoost) and Random Forest are proposed to forecast lead times and demand. The models are trained on datasets of 10,000+ materials, incorporating unique patterns based on factors like suppliers’ historical delivery …
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Travel Behavior Analysis and Mode Choice Prediction for Commuting to Campus - Performance Comparison of Discrete Choice and Machine Learning Models
… modes. The comparison results show that the Extreme Gradient Boosting (XGBoost) method performs better in travel mode choice prediction of the university commuters for higher overall accuracy and F1-score. In addition to the performance comparison, this thesis estimates the relative …
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Machine Learning and Multivariate Statistics for Optimizing Bioprocessing and Polyolefin Manufacturing
… of foaming. We apply two ensemble frameworks, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to build classification and regression models. Excessive foaming can interfere with the mixing of reactants and lead to problems, such as decreasing effective reactor volume, microbial …
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Enhancement of digital elevation models using tree-based ensemble machine learning algorithms
… the comparison, three recent implementations of gradient boosting, the extreme gradient boosting (XGBoost), light boosting machine (LightGBM) and categorical boosting (CatBoost) were selected for the development of a robust DEM enhancement framework. After training and testing, the models were …
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ESSAYS ON BANKING MERGERS AND ACQUISITIONS
… regression and machine learning method of XGBoost) on the prediction of bank failure or takeover. Chapter 1, titled FACTORS THAT INDICATE BANK TAKEOVER TARGET VS. BANK FAILURE, analyzes the mergers and acquisitions data for the US banking industry from 2001 to late 2015, using both …
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AI-based analysis of cerebral oxygenation in preterm and term infants
Preterm and term infants are vulnerable to brain injuries resulting from inadequate cerebral oxygenation, posing significant risks to their long-term neurodevelopment. Near-infrared spectroscopy (NIRS) is a non-invasive technology capable of monitoring regional cerebral oxygen saturation (rcSO2) …