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ContributorsShah, Beejal (Author) / Nakamura, Mutsumi (Thesis director) / Balasooriya, Janaka (Committee member) / Wilkerson, Kelly (Committee member) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2012-12
Description
We created an Android application, Impromp2, which allows users to search for and save events of interest to them in the Phoenix area. The backend, built on the Parse platform, gathers events daily using Web services and stores them in a database. Impromp2 was designed to improve upon similarly-purposed apps available for Android devices in several key ways, especially in user interface design and data interaction capability. This is a full-stack software project that explores databases and their performance considerations, Web services, user interface design, and the challenges of app development for a mobile platform.
ContributorsNorth, Joseph Robert (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Faucon, Philippe (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
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
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
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
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
For my thesis project, I have developed a cash register web application for the Arizona State University Barrett Dining Hall. I previously worked at the Barrett Dining Hall, and I would occasionally step in as a cashier. This work is how I came to be familiar with the system and all its inefficiencies. The system requires multiple user inputs to implement even the most basic of tasks, is not user-friendly, and therefore very prone to error. In the event that multiple incorrect inputs are entered, the software will freeze, and the user will have to turn off the computer and turn it back on. In theory, this application is an improvement over the software system that is currently in place in that the user interface has been specifically designed to be user-friendly. This application reduces the number of required user inputs by automating certain tasks (such as pricing and determining the meal period), thereby reducing the chance of user error. It is also an improvement in that it allows students to log in to the system to view how many meals they have left, how much M&G is in their account, and how many guest passes they have left. This functionality is extremely important because this is a feature that is not currently in place, and is something that students have actively complained about. Currently, if students want to check on their meal plan, they have to either physically go to a dining hall and ask the cashier, or call a toll-free number. The two technologies used to develop this application are C# and XML. These technologies were chosen because I wanted to learn something new for this project to broaden my knowledge. I also happened to be taking a class at the start of this project that utilized C# and XML for Web Applications, and it seemed like the perfect opportunity to transfer over the skills I had been learning.
ContributorsLewis, Q. Mariha Paishance (Author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This project is a full integrated development environment implementing the LEGv8 assembly language standard, to be used in classroom settings. The LEGv8 assembly language is defined by the ARM edition of "Computer Organization and Design: The Hardware/Software Interface" by David A. Patterson and John L. Hennessy as a more approachable alternative to the full ARMv8 instruction set. The MIPS edition of that same book is used in the Computer Organization course at ASU. This class makes heavy use of the "MARS" MIPS simulator, which allows students to write and run their own MIPS assembly programs. Writing assembly language programs is a key component of the course, as assembly programs have many design difficulties as compared to a high-level language. This project is a fork of the MARS project. The interface and functionality remain largely the same aside from the change to supporting the LEGv8 syntax and instruction set. Faculty used to the MARS environment from teaching Computer Organization should only have to adjust to the new language standard, as the editor and environment will be familiar. The available instructions are basic arithmetic/logical operations, memory interaction, and flow control. Both floating-point and integer operations are supported, with limited support of conditional execution. Only branches can be conditionally executed, per LEGv8. Directives remain in the format supported by MARS, as documentation on ARM-style directives is both sparse and agreeable to this standard. The operating system functions supported by the MARS simulator also remain, as there is no generally standardized requirements for operating system interactions.
ContributorsWhite, Josiah Jeremiah (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This paper details the specification and implementation of a single-machine blockchain simulator. It also includes a brief introduction on the history & underlying concepts of blockchain, with explanations on features such as decentralization, openness, trustlessness, and consensus. The introduction features a brief overview of public interest and current implementations of blockchain before stating potential use cases for blockchain simulation software. The paper then gives a brief literature review of blockchain's role, both as a disruptive technology and a foundational technology. The literature review also addresses the potential and difficulties regarding the use of blockchain in Internet of Things (IoT) networks, and also describes the limitations of blockchain in general regarding computational intensity, storage capacity, and network architecture. Next, the paper gives the specification for a generic blockchain structure, with summaries on the behaviors and purposes of transactions, blocks, nodes, miners, public & private key cryptography, signature validation, and hashing. Finally, the author gives an overview of their specific implementation of the blockchain using C/C++ and OpenSSL. The overview includes a brief description of all the classes and data structures involved in the implementation, including their function and behavior. While the implementation meets the requirements set forward in the specification, the results are more qualitative and intuitive, as time constraints did not allow for quantitative measurements of the network simulation. The paper concludes by discussing potential applications for the simulator, and the possibility for future hardware implementations of blockchain.
ContributorsRauschenbach, Timothy Rex (Author) / Vrudhula, Sarma (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The areas of cloud computing and web services have grown rapidly in recent years, resulting in software that is more interconnected and and widely used than ever before. As a result of this proliferation, there needs to be a way to assess the quality of these web services in order to ensure their reliability and accuracy. This project explores different ways in which services can be tested and evaluated through the design of various testing techniques and their implementations in a web application, which can be used by students or developers to test their web services.
ContributorsHilliker, Mark Paul (Author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05