Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 4 of 4
Filtering by
Description
With the advent of sophisticated computer technology, we increasingly see the use of computational techniques in the study of problems from a variety of disciplines, including the humanities. In a field such as poetry, where classic works are subject to frequent re-analysis over the course of years, decades, or even centuries, there is a certain demand for fresh approaches to familiar tasks, and such breaks from convention may even be necessary for the advancement of the field. Existing quantitative studies of poetry have employed computational techniques in their analyses, however, there remains work to be done with regards to the deployment of deep neural networks on large corpora of poetry to classify portions of the works contained therein based on certain features. While applications of neural networks to social media sites, consumer reviews, and other web-originated data are common within computational linguistics and natural language processing, comparatively little work has been done on the computational analysis of poetry using the same techniques. In this work, I begin to lay out the first steps for the study of poetry using neural networks. Using a convolutional neural network to classify author birth date, I was able to not only extract a non-trivial signal from the data, but also identify the presence of clustering within by-author model accuracy. While definitive conclusions about the cause of this clustering were not reached, investigation of this clustering reveals immense heterogeneity in the traits of accurately classified authors. Further study may unpack this clustering and reveal key insights about how temporal information is encoded in poetry. The study of poetry using neural networks remains very open but exhibits potential to be an interesting and deep area of work.
ContributorsGoodloe, Oscar Laurence (Author) / Nishimura, Joel (Thesis director) / Broatch, Jennifer (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
I have challenged myself to learn Python. I did this because I wanted to improve myself and my mindset around coding. My view on coding has changed immensely. I was intimidated by the social stigmas around coding, but I have become more comfortable with it. There were times when I thought that I would never understand something, but it became familiar. Through constant exposure, such as completing modules in DataCamp and Kaggle, I better understood the basics and uses of different models. The concepts I had learned before became clearer by completing a project I was genuinely interested in. I could search for a solution or ask my thesis director if I had an error. I enjoyed working with my thesis professor and failing many times. I have learned that I do not have to be a master within the year but must remain consistent with my practice. I will continue to practice and learn more about coding now with more confidence.
ContributorsMorales, Abril (Author) / Nishimura, Joel (Thesis director) / Broatch, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Natural Sciences (Contributor)
Created2023-05
Description
In this study, models will be introduced which are developed from historical UFC data and aim to predict the fight outcomes between mixed martial arts fighters within the UFC. The paper will explore multivariate linear probability regression analysis using variables which were provided and developed from a large dataset to effectively predict the probability of a fighter winning a given fight. It will analyze several multivariate regression models and compare, internally, the accuracy of each model and account for limitations within the models. Then, the model’s efficacy will be tested by recent UFC fights and adjusted to find a more accurate equation that maximizes profit in sports betting using implied probability from betting odds and comparing them to the model’s predicted probabilities.
ContributorsTufte, Nicholas (Author) / Hill, Alexander (Thesis director) / Broatch, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Natural Sciences (Contributor)
Created2022-12
Description
Gage reproducibility and repeatability methods do not account for a mix of random and fixed effects, nested factors, and repeated measures. Using a case study in fingerprint analysis, we propose a new method using linear mixed effects models to determine the decomposition of the variation components in a measurement system. The fingerprint analysis tests whether the measuring system for ridge widths is reproducible and repeatable. Using the new model and traditional measurement systems analysis metrics, we found that the current process to measure ridge widths is not adequate. Further, we discovered that it is possible to use a linear mixed model to decompose the variance of a measurement system.
ContributorsJohanson, Jena (Author) / Mancenido, Michelle (Thesis director) / Broatch, Jennifer (Committee member) / School of International Letters and Cultures (Contributor) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05