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 12 of 12 for “"Random Forest machine learning"”.
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A Machine Learning Approach to Network Intrusion Detection System Using K Nearest Neighbor and Random Forest
… of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented problems of low accuracy, but the growth in the area of machine learning over the last decade has led to vast …
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Integrating AI and GIS for Climate-Driven Malaria Monitoring and Demand-Based Resource Distribution and Supply Optimization in Low Developing Countries
… Geographically Weighted Regression (GWR), and Random Forest machine learning (R2 = .86) to analyze transmission dynamics. Key findings reveal that minimum temperature is the strongest predictor of transmission (p < .001), while rainfall acts as a reliable 30-day (Lag-1) lead indicator. Emerging …
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Detection and characterization of forest disturbances in California
Natural and anthropogenic forest disturbances are a major influence on the global carbon cycle, with the local and global impacts of a disturbance event depending in large part on the timing, intensity, and cause of the disturbance. With some disturbance agents expected to increase in frequency and …
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Computational approaches for drug design at the Protein-Protein interface
… We conclude that it is possible to carefully use random forest machine learning techniques to marginally improve these predictions. However, it is extremely diffcult to use simple physical parameters to provide added information as to the maximal affnity that a small-molecule might be able to …
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Cumulative Impacts of Stream Restoration on Watershed-Scale Flood Attenuation, Floodplain Inundation, and Nitrate Removal
… addressed the water quality component by using a random forest machine learning approach coupled with artificial neural networks to find trends and predict nitrate removal rates associated with spatial, temporal, hydrologic, and restoration features. Our results showed that hydrologic conditions …
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Management and benchmarking strategies to improve financial health status of U.S. beef operators
… 1992 – 2016. Three models (linear regression, random forest, and step-wise) were used to assess the SPA data for KPI. Upon further analyses, six variables were considered most impactful to predict Unit Cost of Production: Financial Grazing per CWT, Financial Raised/Purchased Feed per CWT, …
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A Diverse and Comprehensive Air Quality Modeling Analysis of Houston, Texas: How Will Changing Emissions, Industries, and Legislation Impact the Air and Human Health?
… Positive Matrix Factorization (PMF) with Random Forest machine learning interpreted by SHapley Additive exPlanation (SHAP). VOC and NOx data from the urban Milby Park and industrial Lynchburg Ferry sites were analyzed for the O3 seasons of 2017-2021. This revealed that NOx emissions …
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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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Unconfined compressive strength prediction using drilling parameters and analyzing feature importance through principal components analysis
… include but are not limited to basic regression, machine learning, and deep learning algorithms. Data-driven methods to identify patterns in the data to estimate geomechanical parameters are considered to be implemented for drilling operations. This study proposes methods to assist safe and …
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Development and Implementation of a Predictive Model of Documentation Status to Examine Factors Related to Health Outcomes for Undocumented Immigrants
… multiple imputation by chained equations (MICE), random forest machine learning, and support vector machine learning algorithms were employed, with each model assessed for its accuracy, precision, recall, and Matthews correlation coefficient (MCC). Aim three is investigated through the …
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Analysis of Chemical Elements in Basalts using Mislabeled Data, a Machine Learning Approach
… features is done using Logistic Regression and Random Forest to discover any new elements of interest. The models were used with other tools, such as recursive feature elimination and permutations, to increase reliability. Among the scarcely explored chemical elements are Terbium (Tb), Holmium …
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An evaluation of a data-driven approach to regional scale surface runoff modelling
… watersheds in the Chesapeake Bay area. We used a random forest algorithm to build the model, where monthly precipitation, temperature, land cover, and topographic data were used as predictors, and monthly surface runoff generated by the SWAT hydrological model was used as the response. A sub-model …