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 178 for “"neural network model"”.
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A neural network model for resource leveling
A new neural network model aimed at solving the resource leveling (RL) problem in construction is developed. The model is derived by mapping a formulation of the RL problem as a quadratic augmented Lagrangian multiplier (QALM) optimization, onto an artificial neural network (ANN) architecture …
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Evidential Deep Learning for uncertainty quantification in jet tagging deep neural network model
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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An artificial neural network model for predicting freeway work zone delays with big data
… developed. Therefore, the development of a sound model for predicting delays or road users is desirable. A comprehensive literature review on existing work zone delay prediction models (i.e., deterministic queuing model and shock wave model) is conducted in this study, which explores the …
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Policy Subsystem Portfolio Management: A Neural Network Model of the Gulf of Mexico Program
… environmental policy subsystem. The study uses neural network theory to model the Gulf of Mexico Program's allocation of implementation funds. The Gulf of Mexico Program is a prototype effort to institutionalize a policy subsystem. A project implementation fund is at the core of the Gulf of …
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Exchange rate forecasting with an artificial neural network model : can we beat a random walk model?
… the foreign exchange market and for theoretical modelling in international finance. Owing to the importance of the movements of exchange rates in our real life, such as financial hedges and investment abroad, this research investigates the possibility of an accurate pattern of the exchange rate …
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Development of an artificial neural network model to predict expert judgement of leather handle from instrumentally measured parameters
… such characterisation by developing Artificial Neural Network models to investigate the relationship between the subjective assessment of leather handle and its measureable physical characteristics. Two collections of commercial leather samples provided by TFL and PITTARDS were studied in this …
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A comparison of neural network and regression models for Navy retention modeling
… thesis evaluates a possible use of artificial neural networks for military manpower and personnel analysis. Two neural network models were constructed to predict the reenlistment behavior of a select group of individuals in the Navy, from a sample of 680 individuals. The data were extracted …
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Neural Network Prediction of Math and Reading Proficiency as Reported in the Educational Longitudinal Study 2002 Based on Non-Curricular Variables
… employed tool for constructing predictive models is multiple linear regression. This research sought to compare the performance of a three-layer back propagation neural network to that of traditional multiple linear regression in predicting math and reading proficiency from 103 …
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Advanced methods for prediction of animal-related outages in overhead distribution systems
… along with historical outage data for prediction models. Poisson regression model, neural network model, wavelet based neural network model and Bayesian model combined with Monte Carlo simulations are applied to the weekly data of different cites. Even though Poisson regression models, Bayesian …
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Utilizing Oil-Soluble Tracers to Evaluate the Production Profile in Multistage Fractured Horizontal Wells
… was simulated using CMG-STARS. The simulated model was first history matched to validate the reservoir parameters. A sensitivity analysis was then performed to determine the dominating factors that influenced the accuracy of using oil-soluble tracers to estimate the production contribution …
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Evolution as a design tool to inform biomolecular engineering
… disulfide engineering were developed- 1) A neural network model to predict disulfide bonds within existing structures from mutual information and a continuous distributed representation of protein sequence. 2) A methodology incorporating statistics on the structural information of disulfide …
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Machine Learning techniques to discover and understand the population of flare stars in MeerLICHT data
… this work, we develop generic machine learning models that classify a given transient object from the observed light curve. We train random forest (sect 4.1.1) and multilayer perceptron neural network (sect 4.1.3) models on simulated LSST PLAsTiCC data and real data from the MeerLICHT survey. We …
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On the effect of dynamic adjustment of recurrent network parameters on learning
The thesis examines sequential learning in a neural network model derived by M. I. Jordan and J. L. Elman. In each of three experiments, different network parameters are systematically altered in a series of simulations. Each simulation measures learning ability for a specific network …
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Investigation of a multi-layer perceptron network to model and control a non-linear system.
… optimal predictive controller incorporating a neural network model of a non-linear process. The scheme is based on a Multi-Layer Perceptron neural net-work as a modelling tool for a real non-linear, dual tank, liquid level process. A neural network process model is developed and evaluated …
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Estimating Impervious Surface Cover in Flathead County, Montana
… landscapes. In this study, an Artificial Neural Network model was developed to update NLCD impervious surface product (2011) in Flathead County, Montana. Four Landsat 8 images from 2015 and 2016 were used to characterize imperviousness. This multi-temporal analytical method was designed to …
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Investigation and modeling of unsaturated flow through swelling soils
… the results has been limited, and the developed models have suffered from a lack of a mechanism to reliably define water flow and experimental techniques for measuring the unsaturated soil hydraulic properties. The intent of the present research is to elucidate and to investigate the theory of …
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Representation learning of recipes
… embedded in a joint vector space using a novel neural network model. Experiments on cross-modal retrieval and vector space arithmetic demonstrate the utility and generalizability of both the per-component and joint embeddings.
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Autonomous Vehicle Perception Quality Assessment
… complexity scores. In this study, we propose a neural network to classify complexity. Subsequently, we study image-based perception quality assessment by using image saliency and 2D object detection algorithms to create an image-based quality index. We then develop a neural network model to …
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