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 107 for “"Data-driven methods"”.
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Data-Driven Methods for Low-Energy Nuclear Theory
<p>The term data-driven describes computational methods for numerical problem solvingwhich have been developed by the field of data science; these are at the intersection of computer science,mathematics, and statistics. When applied to a domain science like nuclear physics, especially with the …
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Data-driven methods for personalized product recommendation systems
… studies using: (i) point-of-sale transaction data from a large U.S. e-tailer, and, (ii) ticket transaction data from a premier global airline. The results demonstrate that our approaches result in significant improvements on the order of 3-7% lifts in expected revenue over current industry …
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Data-driven methods for design of model predictive controllers
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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Characterizing and Detecting Online Deception via Data-Driven Methods
… techniques to track targets. We collect a large dataset of email messages using disposable email services and measure the landscape of email tracking. In the fourth part of this thesis (Chapter 6), we move on to phishing websites. We implement a powerful tool to detect squatting domains and train …
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DATA-DRIVEN METHODS FOR REDUCING WRONG-WAY CRASHES ON FREEWAYS
… errors and related crashes. Wrong-way crash data from Illinois Department of Transportation (IDOT) crash database were collected with 632 possible wrong-way crashes. The real wrong-way crashes were further identified by reviewing the wrong-way crash reports hardcopies and information from …
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Data-driven methods for statistical verification of uncertain nonlinear systems
… statistical verification frameworks that combine data-driven statistical learning techniques and control system verification. First, two frameworks are introduced for verification of deterministic systems with binary and non-binary evaluations of each trajectory's robustness. These frameworks …
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Application of data-driven methods in nuclear fuel performance analysis
… in this thesis therefore revolve around applying data-driven methods to address these issues. First, discrepancies always exist between code predictions and real-world responses, thus uncertainties must be quantified for the code predictions for benefit of decision making, operation safety and …
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Optimal Data-driven Methods for Subject Classification in Public Health Screening
… subject misclassification. We develop an optimal data-driven framework, which integrates optimization and data analytics methodologies, for subject classification in disease screening, with the aim of minimizing classification errors. In particular, our framework utilizes data analytics …
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Data-driven Methods for Resting-state fMRI Biomarker Discovery in Mental Illness
The objective of this dissertation is to develop data-driven methods for discovering the biological markers of mental illness from functional neuroimaging data. We focused on two broader research areas, the first of which is the characterization of brain networks in functional magnetic resonance …
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Data-driven methods for the extraction, grouping and application of terroir units
Data-driven methods for the delineation of viticultural zones have become increasingly relevant, necessitated by the transition from subjective, expert-based methods to objective and transparent methods. The use of subjective expert knowledge to inform zone boundaries can lack empirical evidence, …
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Physics-Aware Optimization and Data-Driven Methods for Low-Carbon Power Systems
… evaluate algorithms without access to real-world data? To address these questions this thesis proposes two physics-aware optimization frameworks that coordinate grid-edge resources towards meeting three goals: improving grid efficiency, ensuring grid operability, and supporting clean energy …
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Data-Driven Methods for Modeling and Predicting Multivariate Time Series using Surrogates
Modeling and predicting multivariate time series data has been of prime interest to researchers for many decades. Traditionally, time series prediction models have focused on finding attributes that have consistent correlations with target variable(s). However, diverse surrogate signals, such as …
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Data-driven Methods in Mechanical Model Calibration and Prediction for Mesostructured Materials
… based in Bayesian inference, which integrates data from simulations and physical experiments, has been applied to a study involving a mesostructured material fabricated by fused deposition modeling. Calibration results provide insights on what values these parameters converge to as well as …
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Promoting autonomy for language learning powered by data-driven methods and learner-centred design
This thesis explores innovative, data-driven methods to enhance autonomy in language learning. While it presents interdisciplinary work, the main focus is exploring the learner-centred design of three applications aiming to promote self-regulated behaviours among language learners. The following …
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Well Integrity Mapping Using Hybrid Model Based on Physics of Failure and Data-Driven Methods
… a hybrid model based on Physics of failure and data driven algorithms that can estimate remaining useful life of production casing in high pressure, high temperature, and sour well conditions. A unique degradation modeling and prognostics framework and analysis are presented in this study. A …
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Computational Approaches for Time Series Analysis and Prediction. Data-Driven Methods for Pseudo-Periodical Sequences.
Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. …
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Computational Approaches for Time Series Analysis and Prediction. Data-Driven Methods for Pseudo-Periodical Sequences.
Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. …
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Data-driven methods to improve resource utilization, fraud detection, and cyber-resilience in smart grids
… using machine learning and statistical methods, improve resource utilization, fraud detection, and cyber-resilience in smart grids. The modern power grid, known as the smart grid, uses computer communication networks to improve efficiency by transporting control and monitoring messages …
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Compound-Gaussian-regularized inverse problems: theory, algorithms, and neural networks
… and tomographic imaging. Model-based and data-driven methods are two prevalent classes of approaches used to solve linear inverse problems. Model-based methods incorporate certain assumptions, such as the image prior distribution, into an iterative estimation algorithm, often, as an …
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