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.
Displaying 11 - 20 of 20
Description
SmartAid aims to target a small, yet relevant issue in a cost effective, easily replicable, and innovative manner. This paper outlines how to replicate the design and building process to create an intelligent first aid kit. SmartAid utilizes Alexa Voice Service technologies to provide a new and improved way to teach users about the different types of first aid kit items and how to treat minor injuries, step by step. Using Alexa and RaspberryPi, SmartAid was designed as an added attachment to first aid kits. Alexa Services were installed into a RaspberryPi to create a custom Amazon device, and from there, using the Alexa Interaction Model and the Lambda function services, SmartAid was developed. After the designing and coding of the application, a user guide was created to provide users with information on what items are included in the first aid kit, what types of injuries can be treated through first aid, and how to use SmartAid. The
application was tested for its usability and practicality by a small sample of students. Users provided suggestions on how to make the application more versatile and functional, and confirmed that the application made first aid easier and was something that they could see themselves using. While this application is not aimed to replace the current physical guide solution completely, the findings of this project show that SmartAid has potential to stand in as an improved, easy to use, and convenient alternative for first aid guidance.
application was tested for its usability and practicality by a small sample of students. Users provided suggestions on how to make the application more versatile and functional, and confirmed that the application made first aid easier and was something that they could see themselves using. While this application is not aimed to replace the current physical guide solution completely, the findings of this project show that SmartAid has potential to stand in as an improved, easy to use, and convenient alternative for first aid guidance.
ContributorsHasan, Bushra Anwara (Author) / Kobayashi, Yoshihiro (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Psychology (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Fiddlevent is an event searching website written in Ruby on Rails. Fiddlevent enables any person to go online and find local events that interest him. Fiddlevent also enables merchants to post their events online. Fiddlevent explores all challenges of website development, such as project management, database design, user interface design, deployment and the software development lifecycle. Fiddlevent aims to utilize best practices for website and software development.
ContributorsThornton, Christopher Gordon (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Hurst, Charles (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
It is important for organizations and businesses to have some kind of online presence, as there are enormous benefits, including utilizing the power of web languages to provide services for people. However, creating a website is difficult, and often expensive. While successful businesses can use their profits to develop a costly website, organizations are not so lucky and can't afford to pay large amounts of money for theirs. Thus, the goal of this project was to provide a complete website to the Card Trick Quilters organization found in Show Low, Arizona. The website serves as both a learning experience, to see exactly what it takes to construct a website from the ground up, and a service project that will provide the Card Trick Quilters with a website that performs various services for its members, with functionality that is completely unique to the Arizona quilting community at large. The creation of the website required learning several different skills in regards to web design, such as databases, scripting languages, and even elements of graphic design. The uniqueness of the website comes from the creation of an online submission form for the annual quilt show hosted by the quilters, and an email reminder system where members of the community can submit their addresses and receive emails when there is an upcoming meeting. While there will no doubt be changes and improvements to the website in the future, the website is currently live and ready for the community to use.
ContributorsSchwarting, Jessa Lynn (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Computing, Informatics, and Decision Systems Engineering (Contributor)
Created2016-05
Description
My proposed project is an educational application that will seek to simplify the<br/>process of internalizing the chord symbols most commonly seen by those learning<br/>musical improvisation. The application will operate like a game, encouraging the<br/>user to identify chord tones within time limits and award points for successfully<br/>doing so.
ContributorsOwens, Kevin Bradyn (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Now that home security systems are readily available at a low cost, these systems are commonly being installed to watch over homes and loved ones. These systems are fairly easy to install and can provide 4k Ultra HD resolution. The user can configure the sensitivity and areas to monitor and receive object detection notifications. Unfortunately, once the customer starts to use the system, they often find that the notifications are overwhelming and soon turn them off. After hearing the same experience from multiple friends and family I thought it would be a good topic for my thesis. I examined a top selling security system sold at a bulk retail store and have implemented improved detection techniques that advance object detection and reduce false notifications. The additional algorithms will support the processing of both near real-time streams and saved video file processing, which existing security systems do not include.
ContributorsBustillos, Adriana (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
In this update to the ESPBot, we have introduced new libraries for a small OLED display and a beeper. This functionality can be easily expanded to multiple beepers and displays, but requires more GPIO pins, or for the user to not use some of the infrared sensors or the ultrasonic sensor. We have also relocated some of the pins. The display can be updated to display 1 of 4 predefined shapes, or to display user-defined text. New shapes can be added by defining new methods within display.ino and calling the appropriate functions while parsing the JSON data in viple.ino. The beeper can be controlled by user-defined input to play any frequency for any amount of time. There is also a function added to play the happy birthday song. More songs can be added by defining new methods within beeper.ino and calling the appropriate functions while parsing the JSON data in viple.ino. More functionality can be added to allow the user to input a list of frequencies along with a list of time so the user can define their own songs or sequences on the fly.
ContributorsWelfert, Monica Michelle (Co-author) / Nguyen, Van (Co-author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Behavioral economics suggests that emotions can affect an individual’s decision making. Recent research on this idea’s application on large societies hints that there may exist some correlation or maybe even some causation relationship between public sentiment—at least what can be pulled from Twitter—and the movement of the stock market. One major result of consistent research on whether or not public sentiment can predict the movement of the stock market is that public sentiment, as a feature, is becoming more and more valid as a variable for stock-market-based machine learning models. While raw values typically serve as invaluable points of data, when training a model, many choose to “engineer” new features for their models—deriving rates of change or range values to improve model accuracy.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.
ContributorsYu, James (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine learning applications expand to numerous fields; however, I chose to focus on machine learning with a business perspective for this thesis, specifically e-commerce.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
ContributorsMadaan, Shreya (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
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.
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.
ContributorsGarretson, Samuel Burkett (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Low-level optimization is the process of handwriting key parts of applications in assembly code that is better than what can be generated from a higher-level language. In performance-intensive applications, this is key to ensuring efficient code. This is generally something that is taught in on the job training, but knowledge of it improves college student’s skill sets and makes them more desirable employees
I have created material for a course teaching this low-level optimization with assembly code. I specifically focus on the x86 architecture, as this is one of the most prolific computer architectures. The course contains a series of lecture videos, live coding videos, and structured programming assignments to support the learning objectives. This material is presented in an entirely autonomous way, which serves as remote learning material and can be easily added as supplemental material to an existing course.
ContributorsAbraham, Jacob (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05