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.
Researchers know that different types of self-construal (independent and interdependent) vary across different cultures. Individuals from East Asian cultures are more interdependent while individuals from Western cultures are more independent. Researchers also know that perceptions and understandings of beauty differ across cultures; however, there has been limited research on the connections between self-construal and beauty with minimal research on direct appearance enhancement products. Recently, new ways to present a positive self-image outside of cosmetics or direct appearance enhancement tools have emerged, and the question is raised as to whether these will also be determined by self-construal. We leverage work on the fluidity of self concept to argue that individuals with a more fluid self-concept (interdependents) will express more interest in appearance enhancement products. In the context of a Facebook ad study with Indian (interdependent) and American (independent) consumers, we demonstrate that interdependent consumers have greater interest in indirect appearance enhancing products, measured by click-through rate, compared to independent consumers.
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.