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 85 for “"Machine Learning Framework"”.
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A Machine Learning Framework for Securing Patient Records
… may go undetected. This thesis proposes a novel machine learning framework using a density-based local outlier detection model, in addition to employing a Human-in-the-Loop Machine Learning (HILML) approach. The density-based outlier detection model enables patterns in EPR data to be extracted to …
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An Online Adaptive Machine Learning Framework for Autonomous Fault Detection
… system (AIS) paradigm and Online Support Vector Machines (OSVM). Together, these algorithms create the Artificial Immune System augemented Online Support Vector Machine (AISOSVM).</p> <p>The AISOSVM framework combines the strengths of the AIS and OSVM to create a fault detection system that can …
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A machine learning framework for predictive maintenance of wind turbines
… energy companies have increasingly turned to machine learning to improve wind turbine reliability. Thus, the goal of this thesis is to create a flexible and extensible machine learning framework that enables wind energy experts to define and build models for the predictive maintenance of wind …
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Tracking engagement : a machine learning framework for estimating affective engagement
… engagement during training courses by applying machine learning techniques to video images. This thesis proposes a framework to measure construction workers' engagement during training courses by unobtrusively analyzing engagement through body and pose estimation, codifying who is speaking and …
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Orion – A Machine Learning Framework for Unsupervised Time Series Anomaly Detection
… detection. New methods to detect anomalies using machine learning are continuously emerging. However, algorithms alone only solve one aspect of the problem – finding anomalies. Existing systems often fail to encompass an end-to-end detection process, to facilitate comparative analysis of various …
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A Geospatial and Machine Learning Framework for Forecasting Ground Level Ozone Pollution
<p>The major detrimental health effects of ground-level ozone (GLO) pollution make it imperative that both policy makers and ordinary citizens have access to high accuracy, high-resolution forecasts of their local area. Recently, advancements in computing power have made it possible to apply …
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An integrated cyberGIS and machine learning framework for data-intensive urban analytics
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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A GAN-Augmented Machine Learning Framework for Predicting Raman Characteristics in Carbon Nanofiber Synthesis
This thesis explores a hybrid data-driven framework for predicting the structural quality of Carbon Nanofibers (CNFs) synthesized via Chemical Vapor Deposition (CVD). Building upon prior work employing Conditional Tabular GAN (CTGAN) for data augmentation and XGBoost for quality prediction, this …
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HARP: A MACHINE LEARNING FRAMEWORK ON TOP OF THE COLLECTIVE COMMUNICATION LAYER FOR THE BIG DATA SOFTWARE STACK
… revolution requiring analyzing massive datasets. Machine learning algorithms are widely used to find meaning in a given dataset and discover properties of complex systems. At the same time, the landscape of computing has evolved towards computers exhibiting many-core architectures of increasing …
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Learning hierarchical motif embeddings for protein engineering
This thesis lays the foundation for an integrated machine learning framework for the evolutionary analysis, search and design of proteins, based on a hierarchical decomposition of proteins into a set of functional motif embeddings. We introduce, CoMET - Convolutional Motif Embeddings Tool, a …
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Counterfactual Prescriptions Via Hierarchical ML For Missed Chemotherapy Appointment Prevention
… Institute, this study develops a hierarchical machine learning framework that first predicts cancellations and then no-shows among remaining cases. Combining operational and temporal features, the model achieves F1-scores of 0.76 and 0.82, outperforming a multinomial baseline by 7–10 points on …
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Closure Modeling for Accelerated Multiscale Evolution of a 1-Dimensional Turbulence Model
… multiscale approach combined with a machine learning technique to address this challenge in the context of the one-dimensional stochastic Burgers' equation, a widely used toy model for turbulence. We employ an encoder-decoder recurrent neural network to perform super-resolution …
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Machine Learning for Bias Correction in Climate Models, with Application to Forecasting Heatwaves
… their accuracy. This thesis presents a novel machine-learning framework and model to correct these biases and provide more accurate climate statistics, focusing on heatwaves. Current correction models find it challenging to generate accurate climate statistics on heatwaves due to their …
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Insurance recommendation engine using a combined collaborative filtering and neural network approach
… Both methods were deployed using the Tensorflow machine learning framework. The hybrid approach helps solve for cold start problems where users have no interaction history. The accuracy on the collaborative filtering produced 0.13 root mean square error based on implicit feedback rating of 0-1, …
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A statistical learning framework for data mining of large-scale systems : algorithms, implementation, and applications
A machine learning framework is presented that supports data mining and statistical modeling of systems that are monitored by large-scale sensor networks. The proposed algorithm is novel in that it takes both observations and domain knowledge into consideration and provides a mechanism that …
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Machine-Learned Representations of Basis Sets and Their Application in Quantum Computational Chemistry
… In this work, we introduce a general machine learning framework for fast basis set prediction in quantum computational chemistry. Our method employs an equivariant graph neural network that outputs a Hermitian matrix encoding optimized molecular orbitals. The eigenvectors of this …
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Automatic analysis of medical dialogue in the home hemodialysis domain : structure induction and summarization
… features that are integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). We demonstrate the utility of this structural abstraction by incorporating it into an automatic dialogue …
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