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.
Obsessive Compulsive Disorder is a psychiatric disorder that affects 2-3% of children. OCD causes anxiety, fear, upsetting thoughts, and obsessions/compulsions. These symptoms can manifest in different ways and kids can become stuck in a stressful cycle of anxiety and the need to act on compulsions. Currently on the children's book market, OCD is an underrepresented topic. I chose to design a children's book that tackles the stigma of OCD in a form that is easy for children to understand.
Obsessive Compulsive Disorder is a psychiatric disorder that affects 2-3% of children. OCD causes anxiety, fear, upsetting thoughts, and obsessions/compulsions. These symptoms can manifest in different ways and kids can become stuck in a stressful cycle of anxiety and the need to act on compulsions. Currently on the children's book market, OCD is an underrepresented topic. I chose to design a children's book that tackles the stigma of OCD in a form that is easy for children to understand.
I created an annotated bibliography on the many factors that affect eyewitnesses recollection and testimony.
With the rapid increase of technological capabilities, particularly in processing power and speed, the usage of machine learning is becoming increasingly widespread, especially in fields where real-time assessment of complex data is extremely valuable. This surge in popularity of machine learning gives rise to an abundance of potential research and projects on further broadening applications of artificial intelligence. From these opportunities comes the purpose of this thesis. Our work seeks to meaningfully increase our understanding of current capabilities of machine learning and the problems they can solve. One extremely popular application of machine learning is in data prediction, as machines are capable of finding trends that humans often miss. Our effort to this end was to examine the CVE dataset and attempt to predict future entries with Random Forests. The second area of interest lies within the great promise being demonstrated by neural networks in the field of autonomous driving. We sought to understand the research being put out by the most prominent bodies within this field and to implement a model on one of the largest standing datasets, Berkeley DeepDrive 100k. This thesis describes our efforts to build, train, and optimize a Random Forest model on the CVE dataset and a convolutional neural network on the Berkeley DeepDrive 100k dataset. We document these efforts with the goal of growing our knowledge on (and usage of) machine learning in these topics.
