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- Creators: Computer Science and Engineering Program
- Creators: Ira A. Fulton Schools of Engineering
- Resource Type: Text
Description
Suspect classification is a judicial process by which classes of people are determined as either suspect, quasi-suspect, or not suspect at all due to a combination of five factors: 1) minority status, 2) discrimination history, 3) political powerlessness, 4) an immutable trait, and 5) trait relevance as it relates to a discriminatory law in question. Laws that discriminate against a suspect class become immediately subject to strict scrutiny while most discriminatory laws only need to pass a rational basis test. Craig v. Boren (1976) established a precedent for the class of sex, which thereafter became subject to an intermediate level of scrutiny as a quasi-suspect class. With a more visible distinction between sex and gender today, this study seeks to determine whether gender rather than sex may become protected through heightened scrutiny by applying factors for suspect classification. In a call for heightened scrutiny for both gender and sex, this thesis argues that the suspect classification of both classes should include combinations of subclasses between gender, sex, and any other protected class. The central thesis employs a content analysis of case law, statutory law, and administrative law as it discriminates against classes of people with varying protection under the court system in the United States. In the question of whether courts should protect gender with suspect classification, the main argument calls for such action but if and only if an intersectional approach to protecting gender along with sex at a heightened level of judicial scrutiny is applied by individual judges on higher courts of review.
ContributorsTorres, Cristian Jesus (Author) / Hoekstra, Valerie (Thesis director) / Durfee, Alesha (Committee member) / School of Politics and Global Studies (Contributor) / Sandra Day O'Connor College of Law (Contributor) / School of Social Transformation (Contributor) / School of Public Affairs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
DescriptionFresh15 is an iOS application geared towards helping college students eat healthier. This is based on a user's preferences of price range, food restrictions, and favorite ingredients. Our application also considers the fact that students may have to order their ingredients online since they don't have access to transportation.
ContributorsBailey, Reece (Co-author) / Fallah-Adl, Sarah (Co-author) / Meuth, Ryan (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Simulation games are widely used in engineering education, especially for industrial engineering and operations management. A well-made simulation game aids in achieving learning objectives for students and minimal additional teaching by an instructor. Many simulation games exist for engineering education, but newer technologies now exist that improve the overall experience of developing and using these games. Although current solutions teach concepts adequately, poorly-maintained platforms distract from the key learning objectives, detracting from the value of the activities. A backend framework was created to facilitate an educational, competitive, participatory simulation of a manufacturing system that is intended to be easy to maintain, deploy, and expand.
ContributorsChandler, Robert Keith (Author) / Clough, Michael (Thesis director) / Pavlic, Theodore (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This ASU Science Book Discussion Poster was presented at the STS Research Forum and Poster Session in Chicago in conjunction with ALA 2013.
Programming is an essential part of library services. Having a regular program at the library and a wide distribution list raises awareness of the library to those associated with the university and beyond. Through programming, libraries demonstrate the vital role they play in the community. The ASU Science Book Discussion began meeting in the summer of 2011.
Programming is an essential part of library services. Having a regular program at the library and a wide distribution list raises awareness of the library to those associated with the university and beyond. Through programming, libraries demonstrate the vital role they play in the community. The ASU Science Book Discussion began meeting in the summer of 2011.
ContributorsTanner, Rene (Contributor)
Created2013
Description
This presentation was given at the Montana Library Association conference in Billings, MT in 2011 and the Arizona Library Association conference in Tucson, AZ in 2011.
ContributorsTanner, Rene (Author) / Flitner, Debbie (Author)
Created2011-11-22
Description
In 2022, a previous team of computer science and accounting students worked together to design and build a fully-functioning website to automate accounting transactions. They created dynamic accounting applications using software frameworks such as React and Express. They then used the services provided by Amazon Web Services to make the website available online. The stakeholders of the project wanted to expand upon the services provided by the website so they entrusted our team with implementing new features and applications to the software system. Using the same software frameworks and services of the previous team, we redesigned the website and increased its functionality to better meet the needs of accounting automation.
ContributorsJain, Sejal (Author) / Macabou, Elise (Co-author) / Lim, Jonathan (Co-author) / Villani, Jacob (Co-author) / Chen, Yinong (Thesis director) / Hunt, Neil (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Computer Science and Engineering Program (Contributor) / School of Public Affairs (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
Description
This thesis project focused on determining the primary causes of flight delays within the United States then building a machine learning model using the collected flight data to determine a more efficient flight route from Phoenix Sky Harbor International Airport in Phoenix, Arizona to Harry Reid International Airport in Las Vegas, Nevada. In collaboration with Honeywell Aerospace as part of the Ira A. Fulton Schools of Engineering Capstone Course, CSE 485 and 486, this project consisted of using open source data from FlightAware and the United States Bureau of Transportation Statistics to identify 5 primary causes of flight delays and determine if any of them could be solved using machine learning.
The machine learning model was a 3-layer Feedforward Neural Network that focused on reducing the impact of Late Arriving Aircraft for the Phoenix to Las Vegas route. Evaluation metrics used to determine the efficiency and success of the model include Mean Squared Error (MSE), Mean Average Error (MAE), and R-Squared Score.
The benefits of this project are wide-ranging, for both consumers and corporations. Consumers will be able to arrive at their destination earlier than expected, which would provide them a better experience with the airline. On the other side, the airline can take credit for the customer's satisfaction, in addition to reducing fuel usage, thus making their flights more environmentally friendly.
This project represents a significant contribution to the field of aviation as it proves that flights can be made more efficient through the usage of open source data.
ContributorsRosenbloom, Yonatan (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Govindillam, Sreenivasan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05
Description
Little is known about the state of Arctic sea ice at any given instance
in time. The harshness of the Arctic naturally limits the amount of in situ data that can be collected, resulting in gathered data being limited in both location and time. Remote sensing modalities such as satellite Synthetic Aperture Radar (SAR) imaging and laser altimetry help compensate for the lack of data, but suffer from uncertainty because of the inherent indirectness. Furthermore, precise remote sensing modalities tend to be severely limited in spatial and temporal availability, while broad methods are more accessible at the expense of precision. This thesis focuses on the intersection of these two problems and explores the possibility of corroborating remote sensing methods to create a precise, accessible source of data that can be used to
examine sea ice at local scale.
ContributorsBaker, John (Author) / Cochran, Douglas (Thesis director) / Wei, Hua (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
I study some comparative statics implications of disappointment-averse preferences
for optimal portfolios. Specifically, I find that risk-averse disappointment-averse investors
increase investment in a risky asset as a result of a monotone likelihood ratio improvement in
the asset’s distribution, a subset of First Order Stochastic improvements. This gives a testable
implication between the disappointment aversion model, and alternatives, including expected
utility. I also discuss previously noted implications for disappointment aversion in helping
explain the equity premium puzzle.
ContributorsWarrier, Raghav (Author) / Schlee, Edward (Thesis director) / Almacen, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Manually determining the health of a plant requires time and expertise from a human. Automating this process utilizing machine learning could provide significant benefits to the agricultural field. The detection and classification of health defects in crops by analyzing visual data using computer vision tools can accomplish this. In this paper, the task is completed using two different types of existing machine learning algorithms, ResNet50 and CapsNet, which take images of crops as input and return a classification that denotes the health defect the crop suffers from. Specifically, the models analyze the images to determine if a nutritional deficiency or disease is present and, if so, identify it. The purpose of this project is to apply the proven deep learning architecture, ResNet50, to the data, which serves as a baseline for comparison of performance with the less researched architecture, CapsNet. This comparison highlights differences in the performance of the two architectures when applied to a complex dataset with a multitude of classes. This report details the data pipeline process, including dataset collection and validation, as well as preprocessing and application to the model. Additionally, methods of improving the accuracy of the models are recorded and analyzed to provide further insights into the comparison of the different architectures. The ResNet-50 model achieved an accuracy of 100% after being trained on the nutritional deficiency dataset. It achieved an accuracy of 88.5% on the disease dataset. The CapsNet model achieved an accuracy of 90% on the nutritional deficiency dataset but only 70% on the disease dataset. In comparing the performance of the two models, the ResNet model outperformed the other; however, the CapsNet model shows promise for future implementations. With larger, more complete datasets as well as improvements to the design of capsule networks, they will likely provide exceptional performance for complex image classification tasks.
ContributorsChristner, Drew (Author) / Carter, Lynn (Thesis director) / Ghayekhloo, Samira (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05