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- All Subjects: Computer Science
- Creators: Meuth, Ryan
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
- Status: Published
for my thesis, is an action-platformer, with the intent of metroidvania-style progression
and an interconnected world map.
The current version of Helix is a turn based role-playing game, with the intent of roguelike
gameplay and a dark fantasy theme. We will first be exploring the challenges that came
with programming my own game - not quite from scratch, but also without a prebuilt
engine - then transition into game design and how Helix has evolved from its original form
to what we see today.
The Oasis app is a self-appraisal tool for potential or current problem gamblers to take control of their habits by providing periodic check-in notifications during a gambling session and allowing users to see their progress over time. Oasis is backed by substantial background research surrounding addiction intervention methods, especially in the field of self-appraisal messaging, and applies this messaging in a familiar mobile notification form that can effectively change user’s behavior. User feedback was collected and used to improve the app, and the results show a promising tool that could help those who need it in the future.
Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays all have very strong visual components. Not only that, but resources for them are abundantly available online. Automata Theory, on the other hand, is the first Computer Science course students encounter that has a significant focus on deep theory. Many
of the concepts can be difficult to visualize, or at least take a lot of effort to do so. Furthermore, visualizers for finite state machines are hard to come by. Because I thoroughly enjoyed learning about Automata Theory and parsers, I wanted to create a program that involved the two. Additionally, I thought creating a program for visualizing automata would help students who struggle with Automata Theory develop a stronger understanding of it.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.