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 15 of 15 for “"Data-driven prediction"”.
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Data Driven Prediction Without a Model
Ensemble data assimilation techniques, including the Ensemble Transform Kalman Filter (ETKF), have been successfully used to improve prediction skill in numerical models for weather forecasting. However, less research has been conducted on data assimilation techniques for systems with no numerical …
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Data-driven prediction of saltmarsh morphodynamics
… existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can …
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Data-driven prediction of geomagnetically-induced currents in power lines
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01
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Data-driven prediction of rail neutral temperature for continuously welded rails using rail vibration resonance frequencies
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
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Just(ifying) algorithms: Data-driven automated predictions about unobservable targets and the General Data Protection Regulation
… and make decisions about groups and individuals. Data-driven machine learning techniques, in which statistical models are developed and updated without any initial prior theory about which data are related to a classification, and how, now account for many applications. Despite increasing demand …
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A Graph Convolutional Network approach for enhancing Set Covering Problem solvers
… The method integrates GCNs and Gurobi, unifying data-driven prediction and exact solver for better computation efficiency and scalability. Experimental evaluations on benchmark SCP instances show that the Hybrid Model reduces computational time and enhances Gurobi's performance, offering a robust …
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Towards better management of organizational cybersecurity
… rating is composed of botnet, spam, and phishing data from four data sources. By conducting a large-scale field experiment using the rating system, we find a causal relationship between security awareness and protection level. Second, we develop a game-theoretical model that characterizes a …
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Realising data-centric UAV autonomy through learning-based prediction and feedback integration
… logs to a deployable closed loop — that combines data-driven prediction, evolutionary optimisation, and classical feedback to deliver reliable and interpretable autonomy. A data-driven virtual UAV is learned from real flight data using a nonlinear autoregressive model with exogenous inputs (NARX). …
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Methods to Improve Fairness and Accuracy in Machine Learning, with Applications to Financial Algorithms
… interventions are increasingly determined by data-driven prediction algorithms. These algorithms have the potential to greatly aid decision-makers, but in practice, many can be redesigned to achieve outcomes that are fundamentally fairer and more accurate. This thesis consists of three …
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Machine learning-based analytics of structured and unstructured data for enhanced bridge deterioration prediction
… increasing availability of heterogeneous bridge data from multiple sources opens unprecedented opportunities for data analytics to better predict bridge deterioration for supporting enhanced bridge maintenance decision making. Such data include structured National Bridge Inventory (NBI) and …
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Multi-Layered Randomized Architected Materials (MLRAM) as damage detection indicators for tensegrity structures
… central research themes. The first is whether data-driven models can accurately and efficiently predict material properties, offering computational and time advantages over traditional numerical approaches, while the second examines whether the proposed architected material can serve as a …
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Learning based algorithms for temperature control and fouling prediction in heat-exchangers
My thesis broadly explores different data-driven algorithmic frameworks for solving two class of problems pertaining to heat-exchangers: temperature control and fouling resistance modeling and prediction. Designing robust and accurate temperature controllers for heat exchangers is very challenging …
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Neural networks for the prediction of chaos and turbulence
… not known or computationally expensive to solve, data-driven methods are an effective tool to predict the evolution in time of the system. In this thesis, we develop data-driven methods for the prediction of prototypical, extreme and spatiotemporal chaos. The methods are based on echo state …
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The development of a real-time wave energy device control algorithm based on artificial neural network
… even the users don't know anything about the prediction model. In another word, the neural network is a data-driven prediction approach. The developed neural network is trained by a set of examples using the machine learning algorithm.;With the artificial neural network, the real-time smart …
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Refining the role of the chest radiograph in paediatric tuberculosis clinical care and research
… TB from non-TB lung disease. Using the same dataset, I present evidence that CAD (Delft’s CAD4TB v7.0) performance to distinguish TB disease from non-TB disease is suboptimal in young children, but that its performance can be significantly improved after fine-tuning with well-characterized …