Trent University Graduate Thesis Collection

Item Description
Identifier
tula:etd
Type
Collection
Language
eng
Extent
1 item
Rights
Copyright for all items in the Trent University Graduate Thesis Collection is held by the author, with all rights reserved, unless otherwise noted.
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Displaying 1 - 3 of 3

Representation Learning with Restorative Autoencoders for Transfer Learning

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Year: 2020, 2020
Member of: Trent University Graduate Thesis Collection
Name(s): Creator (cre): Fichuk, Dexter Lamont, Thesis advisor (ths): McConnell, Sabine, Degree committee member (dgc): Hurley, Richard, Degree granting institution (dgg): Trent University
Abstract: <p>Deep Neural Networks (DNNs) have reached human-level performance in numerous tasks in the domain of computer vision. DNNs are efficient for both classification and the more complex task of image segmentation. These networks are typically trained on thousands of images, which are often hand-labelled by domain experts. This bottleneck creates a promising research area: training accurate… more

Development of a Cross-Platform Solution for Calculating Certified Emission Reduction Credits in Forestry Projects under the Kyoto Protocol of the UNFCCC

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Year: 2020, 2020
Member of: Trent University Graduate Thesis Collection
Name(s): Creator (cre): McIntyre, Gregory, Thesis advisor (ths): Ponce-Hernandez, Raul, Thesis advisor (ths): Hurley, Richard, Degree committee member (dgc): Hircock, Brian, Degree granting institution (dgg): Trent University
Abstract: <p>This thesis presents an exploration of the requirements for and development of a software tool to calculate Certified Emission Reduction (CERs) credits for afforestation and reforestation projects conducted under the Clean Development Mechanism (CDM). We examine the relevant methodologies and tools to determine what is required to create a software package that can support a wide variety… more

Fraud Detection in Financial Businesses Using Data Mining Approaches

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Year: 2020, 2020
Member of: Trent University Graduate Thesis Collection
Name(s): Creator (cre): Moudarres, Anissa Nour, Thesis advisor (ths): McConnell, Sabine, Thesis advisor (ths): Hurley, Richard, Degree granting institution (dgg): Trent University
Abstract: <p>The purpose of this research is to apply four methods on two data sets, a Synthetic</p><p>dataset and a Real-World dataset, and compare the results to each other with the</p><p>intention of arriving at methods to prevent fraud. Methods used include Logistic Regression,</p><p>Isolation Forest, Ensemble Method and Generative Adversarial Networks.</p… more