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 20 of 60 for “"random forest model"”.
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Soil rutting prediction using Random Forest model
Soil rutting in forest operations is a critical phenomenon, characterized by depressions or tracks on the forest floor, often caused by heavy machinery use such as logging equipment. These disturbances can have profound impacts on forest health and ecosystem integrity, disrupting soil structure, …
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Comparison of growth curve models for assessing height in a South African birth cohort
… predictors of that change. Various mixed effect models were fit and compared to neural networks in terms of model fit, interpretability of parameters as well as predictive power. The best fitting mixed-effect model was the Berkey-Reed 2nd order model. The neural network compared well with this …
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Pavement Resilience Assessment Using Pavement Condition Data Before and After Hurricane Harvey
… system data and statistical and machine learning models. Taking Hurricane Harvey into consideration, pre- and post- Harvey pavement conditions were compared, and statistical and machine learning models were used for assessing distress types, severity levels, and distress distribution across …
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Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
… performance of machine learning classification models against logistic regression in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (UMSC) with scores from the Tailored Adaptive Personality Assessment System (TAPAS). The …
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Forecasting demand for district heating using different forecasting methods
… system. This thesis compares five different models for such forecasts. First, the Auto-Regressive Integrated Moving Average model, or ARIMA, predicted the general average usage based on previous data and was used as a benchmark for other models. Another regression model was created, LOWESS or …
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A comparative analysis of machine learning models for forecasting JSE Stock Returns
… examines the application of machine learning models to predict the cross-section of Johannesburg Stock Exchange (JSE)- listed share returns. Four models are developed and compared using monthly data from 2005 to 2021: neural networks, random forest, long short- term memory (LSTM) networks, and …
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Analytics for hotels : demand prediction and decision optimization
… decision optimization problem, we first build a random forest model to predict demand under given prices, and then plug the predictions into a mixed integer program to optimize the prices and capacity allocation decisions. We present in the numerical results that our demand forecast model can …
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Supporting student success with machine learning and visual analytics
… data collected between 2007 and 2011 to train a random forest model that predicts whether or not a student will dropout. Finally, we used the confidence level of the model’s prediction to represent a students “likelihood of success”, which is displayed on a beeswarm plot as part of an application …
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Survival Prediction For Brain Tumor Patients Using Gene Expression Data
… the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. …
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Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation
… decisions. First, we build a price-sensitive random forest model to predict the number of daily bookings for each customer market segment. We feed these predictions into a mixed integer linear program (MILP) to optimize prices and capacity allocations at the same time. We prove that the MILP …
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A Tacticians Guide to Conflict, Vol. 1: Advancing Explanations & Predictions of Intrastate Conflict
… Tactical decision-makers are left using models that rely on highly aggregated, country level data to create proper courses of actions (COAs) to address or predict conflict. The shortcoming is that conflicts morph quite rapidly and structural variables can struggle capture such dynamic …
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Machine Learning techniques to discover and understand the population of flare stars in MeerLICHT data
… this work, we develop generic machine learning models that classify a given transient object from the observed light curve. We train random forest (sect 4.1.1) and multilayer perceptron neural network (sect 4.1.3) models on simulated LSST PLAsTiCC data and real data from the MeerLICHT survey. We …
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Association of Fall-Related Injuries and Different Diagnoses in Older Adults of Ontario: A Machine Learning Approach
… machine learning algorithms: decision tree, random forest, and extreme gradient boosting tree (XGBoost). Secondary data from two Ontario health administrative databases (NACRS, DAD) covering the period 2006-2015 were analyzed. Older adults (aged ≥ 65 years) who sought treatment for FRIs in …
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Applications of Machine Learning in Source Attribution and Gene Function Prediction
… in host prediction for S. Typhimurium using the Random Forest model, underscored by SHAP value analyses which elucidated key predictive features. Next, the focus is shifted to the prediction of Gene Ontology terms for Arabidopsis genes using single-cell RNA-seq data. This analysis offers a …
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Development of an Automated Coin Grading System: Integrating Image Preprocessing, Feature Extraction, and ML Modeling
… processed using a multi-layer perceptron (MLP) model and a random forest model. The best-performing model is then selected to grade the coins by analyzing their overall wear patterns and color characteristics. Our grading system has demonstrated an accuracy of up to 91.3% in predicting the …
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Applications of Machine Learning in Apple Crop Yield Prediction
… Machine learning methods have the ability to model complex relationships between input and output features. This study considers the following machine learning methods for apple yield prediction: multiple linear regression, artificial neural networks, random forests and gradient boosting. The …
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Internet of Things and anomaly detection for the iron ore mining industry
… of machines, a network of sensors, a database, a random forest prediction model, an algorithm for adjusting its cutoff parameter dynamically, and a predictive maintenance algorithm. It can preventively detect and maybe fix poor quality events in the iron ore concentration factory, improving the …
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Predicting mortality for patients in critical care : a univariate flagging approach
… of considerable interest. The most widely used models utilize data from early in a patient's stay to predict risk of death. While research has shown that use of daily information, including trends in key variables, can improve predictions of patient prognosis, this problem is challenging as the …
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Not All Biomass is Created Equal: An Assessment of Social and Biophysical Factors Constraining Wood Availability in Virginia
… and distribution are needed for effective forest management and planning. This study focuses on predicting the probability of harvest at forested FIA plot locations in Virginia. Classification and regression trees, conditional inferences trees, random forest, balanced random forest, …
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Machine learning methodologies for high dimensional biomedical & bioinformatics applications
… non-negative matrix factorization as a topic model to investigate the potential of using triage notes to classify patient disposition in addressing the issue of emergency department crowding. For the second project on computer vision, we propose a novel implementation of the neural style …
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