Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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- All Subjects: artificial intelligence
Description
This Honors thesis was written in partial fulfillment of the requirements for a Bachelor of Science in Human Systems Engineering with Honors. The project consists of a literature review that explores the uses and applications of Machine Learning and Artificial Intelligence techniques in the field of commercial aviation. After a brief introduction and explanation of the most commonly used algorithms in the field of aviation, it explores the applications of Machine Learning techniques for risk reduction, and for the betterment of in-flight operations, and pilot selection, training, and assessment.
ContributorsInderberg, Laura (Author) / Gray, Robert (Thesis director) / Demir, Mustafa (Committee member) / Barrett, The Honors College (Contributor) / Human Systems Engineering (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2023-12
Description
This thesis provides an analysis of the potential issues of using ChatGPT, as despite its benefits it does have its concerns that may deter societal progress. The thesis first provides insight into how ChatGPT generates text and provides insight into how the process of generating its outputs can lead to a variety of issues in the output such as hallucinated and biased output. After explaining how these issues occur, the thesis focuses on the impact of these issues in important industries such as medicine, education, and security, comparing them to popular open-source models such as Llama and Falcon.
ContributorsTsai, Brandon (Author) / Martin, Thomas (Thesis director) / Shakarian, Paulo (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
In today's dynamic societal landscape, the critical evaluation of public policies holds paramount importance. This thesis project, undertaken by a Barrett Honors student of Public Policy, endeavors to contribute to this essential discourse by creating a policy critique podcast.
The primary objective of this project is to produce two podcast episodes that delve into the intricacies of housing policy. Through a meticulous examination, these episodes will dissect the implications of the selected housing policy. By offering a nuanced perspective, the podcast aims to illuminate the complexities inherent in housing policy issues, enriching the understanding of listeners.
This thesis project represents a scholarly endeavor to engage in informed discussions about the efficacy and implications of policies, specifically housing policies. Through the medium of podcasting, the project seeks to bridge the gap between academic research and public discourse to foster a deeper understanding of housing policy among a diverse audience.
Ultimately, this thesis project aims to contribute to the ongoing dialogue surrounding public policies, particularly in the realm of housing policy, by providing listeners with valuable insights and fostering critical thinking about contemporary policy challenges.
ContributorsCruz, Corinn (Author) / Uebelherr, Joshua (Thesis director) / Gaughan, Monica (Committee member) / Barrett, The Honors College (Contributor) / School of Public Affairs (Contributor)
Created2024-05
ContributorsCruz, Corinn (Author) / Uebelherr, Joshua (Thesis director) / Gaughan, Monica (Committee member) / Barrett, The Honors College (Contributor) / School of Public Affairs (Contributor)
Created2024-05
ContributorsCruz, Corinn (Author) / Uebelherr, Joshua (Thesis director) / Gaughan, Monica (Committee member) / Barrett, The Honors College (Contributor) / School of Public Affairs (Contributor)
Created2024-05
Description
In the field of botany, it is often necessary for plants to be identified based on their phenotypical characteristics, whether in person or using previously collected image samples. This work can be tedious and challenging for a human botanist to complete, as datasets can be large and several species of plants strongly resemble each other. Various machine learning techniques, both supervised and unsupervised, can address this task with varying degrees of accuracy and efficiency thanks to their ability to identify subtle patterns in data. The objective of this research is to both conduct a review of previous studies that measure the effectiveness of various machine learning methods for plant identification and to build and test various models to draw up a comparison of the accuracies and efficiencies of the set of techniques.
A review of the existing literature found that any of the studied machine learning techniques can yield a high level of accuracy when used in the correct situations and on a suitable dataset. The results gathered from the models built from this research show that all else being equal, complex convolutional neural networks perform the best on this task, yielding an accuracy of 85.4% on the larger dataset. The other models tested in descending order of accuracy on the same dataset are k-nearest neighbors, random forest, k-means clustering, and a decision tree classifier.
ContributorsOlsen, Laela (Author) / Carter, Lynn Robert (Thesis director) / Bhargav, Vishnu (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Supply chain sustainability has become an increasingly important topic for corporations
due to consumer demands, regulatory requirements, and employee retention and productivity.
Since more and more stakeholders are beginning to care about sustainability, companies are
looking at how they can reduce their carbon footprint without it leading to higher costs. Although
sustainable supply chain operations are often associated with higher costs, new technology has
surfaced within the last decade that makes this association come into question. This paper serves as an investigation on whether or not implementation of recent technology will not only make for more sustainable supply chains, but also bring cost savings to a company. For the sake of simplicity, this paper analyzes the topic within the context of the consumer packaged goods (CPG) industry. The three categories of technology that were evaluated are artificial intelligence, Internet of Things, and data integration systems. Internship projects and/or published case studies and articles were examined to explore the relationship between the technology, supply chain sustainability, and costs. The findings of this paper indicate that recent technology offers companies innovative sustainability solutions to supply chains without sacrificing cost. This calls for CPG companies to invest in and implement technology that allows for more sustainable supply chains. Shying away from this because of cost concerns is no longer necessary.
ContributorsDixon, Logan (Author) / Printezis, Antonios (Thesis director) / Macias, Jeff (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
Description
This work focuses on combining multiple different technologies to produce a scalable, full-stack music generation and sharing application meant to be deployed to a cloud environment while keeping operating costs as low as possible. The key feature of this app is that it allows users to generate tracks from scratch by providing a text description, or customize existing tracks by supplying both an audio file and a track description. Users will be able to share these tracks with other users, via this app, so that they can collaborate with others and jumpstart their creative process, allowing creators to produce more content for their fans. A web app was developed; Contak. This application requires a database, REST API, object storage, music generation artificial intelligence models, and a web application (GUI) to interact with the user. In order to define the best music generation model, a small exploratory study was conducted to compare the quality of different music generation models, including MusicGen, MusicLM, and Riffusion. Results found that the MusicGen model, selected for this work, outperformed the competing models: MusicLM and Riffusion. This exploratory study includes rankings of the three models based on how well each one adhered to a text description of a track. The purpose was to test the hypothesis that MusicGen produces higher quality music that adheres to text descriptions better than other models because it encodes audio at a higher bit rate (32 kHz). While the web app generates high quality tracks with above average text adherence, the main limitation of this work is the response time needed to generate tracks from existing audio using the currently available backend infrastructure, as this can take up to 7 minutes to complete. In the future, this app can be deployed to a cloud environment with GPU acceleration to improve response times and throughput. Additionally, new methods of input besides text and audio input can be implemented using MIDI instructions and the Magenta music model, providing increased track generation precision for advanced music creators with MIDI experience.
ContributorsZamora, Michael (Author) / Chavez Echeagaray, Maria (Thesis director) / Prim, Tadi (Committee member) / Day, Kimberly (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
The 2023 SAG-AFTRA strike signifies a pivotal moment in the entertainment industry's evolution. This paper explores the historical context of labor disputes, the transition from network television to streaming, and the key issues of the strike, including residual payments and AI-generated scripts. The strike's economic implications, production delays, shareholder perspectives, and public sentiment are analyzed, revealing potential industry-transforming consequences. The stark differences between SAG-AFTRA's demands and the Alliance of Motion Picture and Television Producers (AMPTP) offers are examined. The paper concludes with recommendations for fair compensation and creative control, considering the 10-year impact of AI and alternative approaches, emphasizing the need for industry recognition of the contributions of writers and actors in the ever-changing entertainment landscape.
ContributorsLewis, Madison (Author) / Koretz, Lora (Thesis director) / Moore, James (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2023-12
Description
The Crystals in All of Us is a children's book creative project that is meant to reflect the impact a community has on a child, and vice versa. Made specifically for the special needs students I work with, it is a generalizable lesson. It incorporates various art principles, as well as child and human development theories, such as Vygotsky's Zone of Proximal Development.
ContributorsRoodettes, Nicolas (Author) / Fonseca-Chavez, Vanessa (Thesis director) / Williams, Wendy (Committee member) / Barrett, The Honors College (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Division of Teacher Preparation (Contributor)
Created2023-12