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 “"Machine learning regression"”.
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Prediction of arrival times of freight traffic on us railroads using support vector regression
… on the network. The ETA problem is posed as a machine learning regression problem and solved using a support vector regression machine trained and cross validated on over two years of historical data for a 140 mile stretch of track located primarily in Tennessee, USA. The article presents the …
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Semiparametric Methods for Two Problems in Causal Inference using Machine Learning
… interactions are available. Recent advances in machine learning have made it possible to model such systems, but their inherent biases and black-box nature pose an inferential challenge. Semiparametric methods are able to nonetheless leverage these powerful nonparametric regression procedures to …
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Forecasting linehaul transit times & on time delivery probability using quantile regression forests
… variability and incorporates them into quantile regression forest, a black box forecasting model, that will provide estimated scheduled transit times for a given probability of on-time arrival at the destination. With the use of Amazon's Q1 & Q2 2013 linehaul data, an analysis on performance …
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A Machine Learning and Computer Vision Framework for Damage Characterization and Structural Behavior Prediction
… components. In particular, image processing and machine learning regression techniques have been used to build predictive models capable of estimating internal loads (e.g., shear and moment) and damage states in RC beams, slabs, and panels based on surface crack pattern images. The predictive …
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Physics-Based Artificial Intelligence Models for Vehicle Emissions Prediction
… vehicles. This framework differs from black box machine learning models presented in previous literature because it incorporates engine combustion parameters that allow physical interpretation of the results. Based on chemical kinetics and the characteristics of diffusive combustion, NOx …
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Decision-support for decommissioning offshore platforms.
… This costing model was developed by applying machine learning regression to historical decommissioning cost data. The model predicts decommissioning options costs for five different scenarios with reasonable accuracy as indicated by an r-squared value of 0.935, implying that it is reliable for …
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Towards Energy-Efficient Edge Computing for tiny AI Applications
… and types of algorithms and datasets. Using regression-based methods, we model how these factors affect energy use. By converting categorical factors into numerical ones, we develop models that describe the relationship between factors and energy use on Raspberry Pi. This research contributes …
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A data-driven energy analysis framework to support energy efficiency of the Telecommunication sector
L'abstract è presente nell'allegato / the abstract is in the attachment
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Transcranial direct current stimulation in Alzheimer's disease
… treatment of Alzheimer’s disease (AD) into a machine learning model of the disease to understand the long term impact of the treatment on disease progression over a decade. The three main contributions of this work are: Firstly, this work proposes the extension of otherwise sparse clinical …
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Towards the Development of Cost-Effective Decentralized Applications: An Investigation of Transaction Processing Times on the Ethereum Blockchain Platform
… estimation services. Next, we construct machine learning models using comprehensive sets of Ethereum-based features in order to determine the most important features by characterizing transaction processing times. From these results we can derive numerous implications in order to help …
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Alternaria Leaf Blight and Head Rot of Broccoli: UAV-Based Disease Detection and Fungicide Resistance Management
… levels with a Silhouette Score of 0.34. Among regression models, Random Forest achieved the highest performance (MSE = 44.45, R² = 0.79), while classification models such as Random Forest, Artificial Neural Networks, and Gradient Boosting attained 88, 86, and 85% accuracy respectively. Feature …
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Essays on Tree-based Methods for Prediction and Causal Inference
… explores new variations of Bayesian tree-based machine learning algorithms. Bayesian Additive Regression Trees (BART) (Chipman et al. 2010) and Bayesian Causal Forests (BCF) (Hahn et al. 2020) are state-of-the-art machine learning methods for prediction and causal inference. A number of existing …