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.
Results
Showing 1 to 18 of 18 for “"Gradient Boosting Machine"”.
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Modelação e visualização da biodiversidade de briófitos em sistemas altitudinais na ilha do Pico - Açores
… incluíram, entre outros, Random Forest, Gradient Boosting Machine, Modelos Lineares Generalizados e Range Bagging, tendo por base dados de ocorrência das espécies e 19 variáveis bioclimáticas derivadas de dados de temperatura e precipitação. Considerando a complexidade das relações …
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Predicting household poverty with machine learning methods: the case of Malawi
… Therefore, this study looked at whether machinelearning models can be used on existing survey data to predict poor and non-poor households, and whether these models can predict poverty using a smaller number of features than those collected in integrated household surveys. This was …
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Predicting Gentrification Patterns in London: A Machine Learning Approach to Analysing Deprivation and Urban Change Across Neighbourhoods
… deprivation and development. The employment of machine learning models provides an opportunity to conduct gentrification forecasting beyond traditionally applied regression models. This study considers three types of ML models (random forest, gradient boosting machine, and extreme gradient …
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Credit scorecards in retail banking: enhancing interpretability through shapley values and evaluating the effectiveness of alternative data for improved accuracy
… accuracy and maintaining interpretability. While machine learning algorithms like random forest and eXtreme gradient boosting outperform traditional logistic regression in accuracy, their complex predictor variable representation hinders interpretability. To reconcile this, the study discretizes …
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An Interactive Learning Framework for Understanding Infrastructure Health Monitoring and Leveraging Machine Learning for Safety Improvement
… to accidents. The first manuscript presents a machine learning framework to predict crash injury severity using real-world crash data and roadway characteristics. By applying models such as Artificial Neural Networks (ANN), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), …
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Evaluating Predictive Models for Predicting Total Score of Beef Carcasses
… Multiple Linear Regression (MLR) with three machine learning techniques: K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting Machine (GBM).</p> <p>The analysis focuses on six key predictors: Hot Carcass Weight, Back Fat Thickness, Ribeye Area, Final Yield Grade, Final Carcass …
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Forecasting Markers of Habitual Driving Behaviors Associated with Crash Risk
… although remarkable, are reactionary and machine centered. Here we propose a method that is mixed in its approach, preventive in its aim, and predictive in its function. The method uses multimodal measurements of the driver’s physiological variables and readings of the vehicle’s driving …
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Machine Learning Applications in Blockchain for Renewable Energy Systems
… a synergistic framework that integrates advanced Machine Learning (ML) forecasting with Distributed Ledger Technology (DLT). The research first investigates the limits of predictive accuracy for community microgrids. A novel hybrid deep learning model, Bidirectional Long-Short-Term-Memory with …
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Artificial Intelligence Driven Infrastructure Management and Maintenance Plan
… pounds ≤ GVW < 150,000 pounds) vehicles using gradient boosting machine (GBM) learning algorithms. The characterization of permit vehicles was performed for Florida Weigh-in-Motion (WIM) sites and the prediction of GVW, maximum axle weight, and individual axle weights were accurately predicted …
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Measuring Political Corruption from Population Outcomes: An alternative to perception measures
… indicator of its presence.This work applies machine learning to objectively estimate corruption from this shadow, proposing both a novel theoretical approach to assessing national corruption, as well as a new quantitative corruption index. The theoretical approach outlined here begins by …
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Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. [Thesis]
… robust and effective detection of attacks. Machine learning (ML) and its subdivision Deep Learning (DL) methods offer a promise, but they can be computationally expensive in providing better detection for resource-constrained IoT devices. Therefore, this research proposes an optimization …
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Sensitivity of MEPDG Using Advanced Statistical Analyses
… such as multivariate adaptive regression spline, gradient boosting machine are employed to identify and rank the significant input variables. Results show that predicted pavement performances are sensitive to traffic input variables such as Annual Average Daily Truck Traffic (AADTT) and percent of …
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Algorithm development for characterizing coastal and inland aquatic environments using satellite remote sensing
… sensing algorithms, integrating empirical, machine-learning/deep learning approaches, and implementing automated and scalable processing workflows. Conducted under the SIMBAD (Sentinel Imagery Multiband Analysis and Dissemination) R&D initiative at Quasar Science Resources, the research …
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Predictive Modeling and Durability Analysis of Low Carbon Concrete Incorporating Recycled Materials
… composites using a hybrid approach of machine learning meta-analyses combined with experimental verification. In the first half of the dissertation, a meta-analysis of more than 750 experimental data available in the literature was performed to predict the compressive strength of …
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Towards Supporting Developers for Securing Containerized Software
… Open Container Initiatives (OCI) properties with Machine Learning (ML) models. Our results showed that Light Gradient Boosting Machine (LGBM) achieves a Mathews Correlation Coefficient (MCC) score of 0.856, whereas Logistic Regression (LR), Naive Bayesian (NB), Support Vector Machines (SVM), …
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Machine Learning Approaches for Improving Construction Materials and Pavement Systems
… Therefore, this dissertation research applies machine learning (ML) techniques to predictive modeling and optimization and forms a data-driven strategy for material selection and performance prediction. This dissertation is focused on four primary studies, each showing the application of ML in …
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USE OF ON-FARM DATA AND MILK LEUCOCYTE COMPONENTS FOR MONITORING THE HEALTH OF DAIRY COWS
… and milk production. For this reason, an eXtreme Gradient Boosting machine learning algorithm was trained and tested to classify cows in the three LS classes, using as predictors only BCS and milking on-farm data. Balance accuracy, sensitivity and specificity higher than 0.9 were found, suggesting …
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Prediction of moisture and protein in corn kernels from multiple origins based on NIR-PLSR with gradient boosting machines for feature selection
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01