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Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.
ContributorsBala, Shantanu (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
With the development of technology, there has been a dramatic increase in the number of machine learning programs. These complex programs make conclusions and can predict or perform actions based off of models from previous runs or input information. However, such programs require the storing of a very large amount of data. Queries allow users to extract only the information that helps for their investigation. The purpose of this thesis was to create a system with two important components, querying and visualization. Metadata was stored in Sedna as XML and time series data was stored in OpenTSDB as JSON. In order to connect the two databases, the time series ID was stored as a metric in the XML metadata. Queries should be simple, flexible, and return all data that fits the query parameters. The query language used was an extension of XQuery FLWOR that added time series parameters. Visualization should be easily understood and be organized in a way to easily find important information and details. Because of the possibility of a large amount of data being returned from a query, a multivariate heat map was used to visualize the time series results. The two programs that the system performed queries on was Energy Plus and Epidemic Simulation Data Management System. By creating such a system, it would be easier for people of the project's fields to find the relationship between metadata that leads to the desired results over time. Over the time of the thesis project, the overall software was completed, however the software must be optimized in order to take the enormous amount of data expected from the system.
ContributorsTse, Adam Yusof (Author) / Candan, Selcuk (Thesis director) / Chen, Xilun (Committee member) / Barrett, The Honors College (Contributor) / School of Music (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Although previous research has explored the relationship between social media use and well-being, many studies are contradictory of each other and conclude varying findings relating to social media use and outspokenness. This study explores the relationship between active and passive social media use, perceived social media expertise, and outspokenness using the potentially mediating variable of perceived social acceptance. 162 participants, recruited through Amazon Mechanical Turk (MTurk) and ASU’s SONA systems, completed a survey relating to their own use of social media, perceived social acceptance, and outspokenness. Contradictory to my first hypotheses, no significant correlations were found between social media use and social media expertise. However, correlation analyses revealed that active social media use is related to an increased amount of perceived social media expertise (r = 0.23, p < .004). Perceived social media expertise was significantly positively correlated with outspokenness (r = 0.19, p < 0.015); however, it was not correlated with perceived social acceptance. When examining these relationships separately by gender, a strong association was found for males between active social media use and outspokenness, whereas passive social media use and outspokenness were negatively correlated for females. The results of this study add to previous research in the field of social media and outspokenness and lend new ideas for future research on these topics, such as exploring the gender differences that are associated with these variables. Further research in the area is needed for a more complete understanding of how one’s social media use affects his/her outspokenness and how gender modifies these effects.
ContributorsRubino, Kelli Erika (Co-author) / Rubino, Kelli (Co-author) / Mickelson, Kristin (Thesis director) / Halavais, Alexander (Committee member) / Department of Psychology (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Since the advent of social media, researchers have studied how platforms like Facebook and Instagram can influence our relationships, and more specifically, how social networking sites can impact what happens when these relationships dissolve. Less is known about the newer platform Snapchat, which provides ephemeral updates as they occur to one's friend list, as well as self-destructing direct messages between individuals. The present study utilized survey responses from 84 college-aged individuals and eight semi-structured, in-depth interviews to study the relationship between using Snapchat to engage with or monitor one's ex-partner, the level of distress that results from these behaviors, and an individual's overall breakup distress level. A significant positive correlation was found between each of these variables, indicating that remaining connected with one's ex-partner on Snapchat may contribute to one's level of distress, or alternatively, that more distressed individuals are turning to Snapchat to monitor their ex-partner. Pairing this quantitative data with in-depth interviews allowed for more robust and generalizable findings. Qualitative details supported the statistical analysis to indicate that one's overall breakup distress level may be leading individuals to use Snapchat to monitor their ex-partner or exaggerate their own speed of recovery. Future research should analyze these same variables in a larger, more representative sample by following couples as their breakups occur in real-time to capture more comprehensive participant experiences.
ContributorsAter, Brittany Alexis (Author) / Parker, John (Thesis director) / Bodford, Jessica (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Marketing (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Social media sites are platforms in which individuals discuss a wide range of topics and share a huge amount of information about themselves and their interests. So much of this information is encoded through unstructured text that users post on the these types of sites. There has been a considerable amount of work done in respect to sentiment analysis on these sites to infer users' opinions and preferences. However there is a gap where it may be difficult to infer how a user feels about particular pages or topics that they have not conveyed their sentiment for in a observable form. Collaborative filtering is a common method used to solve this problem with user data, but has only infrequently been used with sentiment information in order to make inferences about users preferences. In this paper we extend previous work on leveraging sentiment in collaborative filtering, specifically to approximate user sentiment and subsequently their vote for candidates in an online election. Sentiment is shown to be an effective tool for making these types of predictions in the absence of other more explicit user preference information. In addition to this, we present an evaluation of sentiment analysis methods and tools that are used in state of the art sentiment analysis systems in order to understand which of these methods to leverage in our experiments.
ContributorsBaird, James Daniel (Author) / Liu, Huan (Thesis director) / Wang, Suhang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In 2012, Chick-fil-A president Dan Cathy's "came out" about his anti-same sex marriage views, launching an enormous negative backlash across social media networks. To counteract this, former governor Mike Huckabee called on his Facebook fans to support the company on "Chick-fil-A Appreciation Day," both on Facebook and in person. The project examines both the backlash and Appreciation Day on social media networks. Posts on the Appreciation Day Facebook event page and similar posts on Twitter were first broken down in the framework of supportive and oppositional posts and then analyzed in further contexts. Comments on official Chick-fil-A Facebook statuses were then examined in a similar fashion. The research concludes that a strong support system both online and offline were necessary for Chick-fil-A to recover from its backlash. The controversy that ensued is ultimately a case study in the growing influence of Facebook as a tool for small-scale activism.
ContributorsKuiland, Zachary Rico (Author) / Cheong, Pauline (Thesis director) / Szeli, Eva (Committee member) / Lim, Merlyna (Committee member) / Barrett, The Honors College (Contributor) / School of Criminology and Criminal Justice (Contributor) / Department of Psychology (Contributor)
Created2013-05