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.
This project is a critical analysis of the works of 6 American war veterans and how they demonstrate trauma in their narratives. The texts covered here are Philip Red Eagle’s Red Earth (2007), John A. Williams’ Captain Blackman (1972), Roy Scranton’s War Porn (2016), Tim O’Brien’s The Things They Carried (1990), Kurt Vonnegut’s Slaughterhouse-Five (1969), and Joseph Heller’s Catch-22 (1961).
The United States is an empire. It was founded as such and continues to be one to this day. However, during the most prominent periods of imperial expansion, anti-imperialist organizations and politicians often rise up to oppose these further imperialist actions. This thesis paper examines the rhetoric used by these organizations and politicians, particularly through their speeches and platforms. The primary focus is on the role of American exceptionalism in this rhetoric, and what American anti-imperialism not rooted in this concept looks like. This analysis will be done by looking at a few key specific texts from these organizations and politicians, including (but not limited to) the platform of the Anti-Imperialist League and the speech Representative Barbara Lee gave to explain her lone no vote on the Authorization for Use of Military Force in Afghanistan in 2001.
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.