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: Machine Learning
Description
Energy poverty is a pressing issue in agricultural areas that affects the livelihoods of millions of people worldwide. The lack of access to modern energy services in rural communities hinders the development of the agricultural sector and limits economic opportunities. To address this issue, this thesis aims to develop a predictive modeling framework using machine learning techniques to identify feasible interventions that can improve energy access in specific rural agricultural regions.
Machine learning plays a pivotal role in addressing energy poverty in rural agricultural regions. By leveraging the power of advanced data analytics and predictive modeling, machine learning algorithms can analyze vast datasets related to energy usage, agricultural practices, geographic factors, and socioeconomic conditions. These algorithms can uncover valuable insights and patterns that are not readily apparent through traditional analytical methods. Moreover, machine learning enables the development of predictive models that can forecast energy demand and identify optimal strategies for improving energy access in rural areas. These models can take into account various variables, such as crop cycles, weather conditions, and community needs, to recommend interventions that are tailored to the specific requirements of each region.
ContributorsKonatam, Saisumana (Author) / Osburn, Steven (Thesis director) / Kerner, Hanah (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
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 aims to advance healthcare and heart disease prevention by utilizing the Python programming language and various machine learning algorithms for heart disease detection. Being one of the main causes of death worldwide, cardiovascular disease is a serious global health concern. One person passes away from cardiovascular disease every 33 seconds in the United States alone. As the leading cause of death, early identification becomes critical for early intervention and prevention. The study addresses key research questions, including the role of machine learning in enhancing heart disease detection, comparative analysis of the six machine learning models, and the importance of predictive indicators. By leveraging machine learning algorithms for medical data interpretation, the thesis contributes insights into early disease detection.
ContributorsLa, Nikki (Author) / Sheehan, Connor (Thesis director) / Connor, Dylan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
ContributorsYang, Branden (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Ahn, Gail-Joon (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
The purpose of this research is to create predictive models for a leading sustainability certification - the B Corporation certification issued by the non-profit company B Lab based on the B Impact Assessment. This certification is one of many that are currently being used to assess sustainability in the corporate world, and this research seeks to understand the relationships between a corporation's characteristics (e.g. market, size, country) and the B Certification. The data used for the analysis comes from a B Lab upload to data.world, providing descriptive information on each company, current certification status, and B Impact Assessment scores. Further data engineering was used to include attributes on publicly traded status and years certified. Comparing Logistic Regression and Random Forest Classification machine learning methods, a predictive model was produced with 87.58% accuracy discerning between certified and de-certified B Corporations.
ContributorsBrandwick, Katelynn (Author) / Samara, Marko (Thesis director) / Tran, Samantha (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (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
The cerebellum is recognized for its role in motor movement, balance, and more recently, social behavior. Cerebellar injury at birth and during critical periods reduces social preference in animal models and increases the risk of autism in humans. Social behavior is commonly assessed with the three-chamber test, where a mouse travels between chambers that contain a conspecific and an object confined under a wire cup. However, this test is unable to quantify interactive behaviors between pairs of mice, which could not be tracked until the recent development of machine learning programs that track animal behavior. In this study, both the three-chamber test and a novel freely-moving social interaction test assessed social behavior in untreated male and female mice, as well as in male mice injected with hM3Dq (excitatory) DREADDs. In the three-chamber test, significant differences were found in the time spent (female: p < 0.05, male: p < 0.001) and distance traveled (female: p < 0.05, male: p < 0.001) in the chamber with the familiar conspecific, compared to the chamber with the object, for untreated male, untreated female, and mice with activated hM3Dq DREADDs. A social memory test was added, where the object was replaced with a novel mouse. Untreated male mice spent significantly more time (p < 0.05) and traveled a greater distance (p < 0.05) in the chamber with the novel mouse, while male mice with activated hM3Dq DREADDs spent more time (p<0.05) in the chamber with the familiar conspecific. Data from the freely-moving social interaction test was used to calculate freely-moving interactive behaviors between pairs of mice and interactions with an object. No sex differences were found, but mice with excited hM3Dq DREADDs engaged in significantly more anogenital sniffing (p < 0.05) and side-side contact (p < 0.05) behaviors. All these results indicate how machine learning allows for nuanced insights into how both sex and chemogenetic excitation impact social behavior in freely-moving mice.
ContributorsNelson, Megan (Author) / Verpeut, Jessica (Thesis director) / Bimonte-Nelson, Heather (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05