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 222 for “"Predictive Modeling"”.
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Predictive modeling of combustion processes
… reaction mechanisms generated by RMG are purely predictive and elementary rate coefficient from any reliable source can be added to RMG database to improve the quality of its predictions. The goal of my thesis was two fold, first to extend the capabilities and database of RMG and to release it as …
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Scalable predictive modeling for spatiotemporally evolving phenomena
… and calibration, which can be difficult for modeling phenomena at the continental scale. This has occurred alongside the availability of diverse data that can be leveraged by model-fitting algorithms. This dissertation focuses on leveraging deep learning methods to model spatiotemporally …
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Predictive Modeling of Early Stage Parkinsons Disease
Background: Early stage (preclinical) detection of Parkinsons disease (PD) remains challenged yet is crucial to both differentiate it from other disorders and facilitate timely administration of neuroprotective treatment as it becomes available. Objective: In a cross-validation paradigm, dual …
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Predictive modeling of fuel efficiency of trucks
… trips to adapt to the immediate necessities. A predictive model was developed to calculate the change in Miles per Gallon (MPG) whenever a re-route is performed on a region of a particular distribution area. The data that was used, was from the Dallas center which is one of the distribution …
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Predictive Modeling of Volatile Organic Compound Measurements
… that the project seek to address is: Can simple predictive models be applied to VOC data? This thesis focuses on building simple predictive models for forecasting VOC concentration, with the ultimate goal of predicting the flow of human traffic in a given space during different times of the day. …
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Predictive Modeling of Chemical Reactivity for Sustainability
… material and process design. However, modeling reactivity at scale remains challenging due to the computational demands of quantum chemical methods and the complexity of reaction mechanisms. This thesis explores how high-throughput computational approaches, rooted in quantum chemistry …
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Predictive Modeling and Optimization of Autoinjector Manufacturing
… of assembled devices. An interpretable predictive modeling framework is developed to identify sources of subcomponent and process variability that are predictive of final lot performance. Differences in predictive accuracy across products, user demographics, geography, and with different …
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Predictive Modeling of Metal-Catalyzed Polyolefin Processes
This dissertation describes the essential modeling components and techniques for building comprehensive polymer process models for metal-catalyzed polyolefin processes. The significance of this work is that it presents a comprehensive approach to polymer process modeling applied to large-scale …
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Machine Learning based Predictive Modeling of Stochastic Systems
… in our daily lives, and interpreting and modeling them is vital for scientific advancement. Traditional methods for predictive modeling of complex signals include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to …
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Predictive Modeling of Nuclear Waste from Leaching Behavior
Made available in DSpace on 2017-07-06T18:56:23Z (GMT). No. of bitstreams: 3 Simonson_Scott_A_MS.pdf: 18773490 bytes, checksum: 4ed0b8b37642d609a0f50a1749cec89e (MD5) Simonson_Scott_A_MS_ABS.pdf: 472178 bytes, checksum: aa3ad13ccb9a02b02c0ae1ef9cd01db7 (MD5) license.txt: 4813 bytes, checksum: …
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FairML : ToolBox for diagnosing bias in predictive modeling
Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite societal gains in efficiency and productivity through deployment of these models, potential systemic flaws have not been fully addressed, particularly the …
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Wires-down predictive modeling and preventative measures optimization
In 2012, the Pacific Gas and Electric Company (PG&E) identified overhead wires-down failure events as an important metric for safety and reliability. These events occur when power lines contact the ground and are caused by a host of reasons including trees falling on wires, animals creating …
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Integrated Predictive Modeling and Analytics for Crisis Management
The surge in the application of big data and predictive analytics in fields of crisis management, such as pandemics and epidemics, highlights the vital need for advanced research in these areas, particularly in the wake of the COVID-19 pandemic. Traditional methods, which typically rely on …
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RECENT TIMBERING ACTIVITY AS A VARIABLE IN PREDICTIVE MODELING
… of recent timbering activities affects the predictive power of predictive models in regard to precontact archaeological sites. Predictive models have been used to assess the likelihood of identifying cultural resources in a given area for decades. A county-wide predictive model has not been …
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Predictive modeling of polycyclic aromatic hydrocarbon formation during pyrolysis
Polycyclic aromatic hydrocarbons (PAHs), large molecules comprised of multiple aromatic rings like anthracene or pyrene, are a notable intermediate and byproduct in combustion or pyrolysis of hydrocarbon fuels. On their own, they have been shown to pose a significant health risk, with certain PAHs …
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Predictive modeling of spatial-temporal data: A graph-centric approach
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Predictive modeling of fulfillment supply chain for delivery performance improvement
… produces an analytical framework using predictive modeling methodology to investigate further future long-term strategy around delivery.
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