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 5 of 5 for “"Gradient Boosting Model"”.
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Prediction of loss to follow-up in postpartum others living with HIV
… statistics, and several machine learning models.. An extreme gradient boosting model was developed and validated to predict the risk of loss to follow-up within the first 9 months postpartum based on routinely available patient data at the point of discharge after delivery. Model …
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Estimating End-User Throughput Using Service Provider Cell Traces Via Gradient Boosting
… traces, and consequently, we build a Regularized Gradient Boosting model to predict the user's throughput using traces that are exclusively collected from the service provider's resources. Our approach shows that using our imputing and prediction approaches, we can accurately estimate the user …
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A natural language processing approach to improve demand forecasting in long supply chains
… processing (NLP) techniques in a deep learning model, known as NEMO, to forecast the demand of a commodity -- without requiring downstream companies to share information. In addition, this thesis compares the effectiveness of such an approach with other non-deep learning approaches, specifically …
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A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay
… Box-Pierce goodness-of-fit tests for time series models which has been an important research topic over the last few decades. All previously proposed tests are focused on changes of the test statistics. Instead, I adopted a different approach that takes the best performing test and modifying the …
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Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01