Data Science Commons^™

Mathworks Fitness Tracker, Tuan Quoc Le

2023 MathWorks Fitness Tracker Challenge-Archive

Mobile fitness app that utilizes the sensors in mobile phone in order to determine the position, velocity, number of calories burned, and other potentially useful fitness information.

Comparing Igneous Geochemical Data From Hawaii And Southern California Via Machine Learning, Miro Manestar

MS in Computer Science Project Reports

Bi-plots are commonly used in geochemical analyses. However, their use can become cumbersome in the case of multi-variate analyses. Therefore, this thesis explores the application of unsupervised machine learning techniques, specifically PCA and K-Means, to analyze large geochemical data sets from two distinct regions, Hawaii and the \acrfull{prb} in Southern California. The IBM Foundational Methodology for Data Science was utilized to ensure proper data preparation and analysis. PCA provided dimensionality reduction, revealing which features correlated most strongly with variances within the data. K-Means clustering allowed for deeper interpretation of the data. The analysis yielded valuable insights into the composition and …

Time Series Analysis Of Longitudinally Collected Standard Autoperimetry Data In Glaucoma Patients, Carlyn Childress

Honors College Theses

Glaucoma is a group of eye diseases in which damage gradually occurs to the optic nerve, which often leads to partial or complete loss of vision. As the second leading cause of blindness, there is no cure for glaucoma. Early detection and the tracking of its progression is key to managing the effects of glaucoma. Ordinary Least Squares Regression (OLSR), the most commonly used methodology for tracking glaucoma progression, is inappropriate as the longitudinally collected perimetry data from the glaucoma patients appears to be temporally correlated. Time series models, that account for temporal correlation, are better methods to analyze Mean …

Crime Prediction Using Machine Learning: The Case Of The City Of Little Rock, Zurab Sabakhtarishvili, Sijan Panday, Clayton Jensen

ATU Scholars Symposium

Crime is a severe problem in the city of Little Rock, Arkansas. In this study, we aim to develop a machine-learning model to predict criminal activities in the city and provide insights into crime patterns. We will analyze publicly available crime datasets from Little Rock Police Department from January 2017 to March 2023 to identify trends and patterns in crime occurrence. We used data cleaning and exploratory data analysis techniques, such as figured-based visualizations, to prepare the data for machine learning. We will employ the Neural Prophet, a time-series machine learning model, to predict daily crime counts. The model will …

Operation Enduring Freedom: Improving Mission Effectiveness By Identifying Trends In Successful Terrorism, Dalton Shaver

Symposium of Student Scholars

This research examines how the characteristics of terrorist attacks predict the chance of an attack succeeding, where an attack is defined as successful if the intended attack type is carried out. Data from The Global Terrorism Database (https://www.start.umd.edu/gtd) was analyzed across three geographical missions within Operation Enduring Freedom: Trans-Sahara, Horn of Africa, and the Philippines. The three models were able to distinguish between successful and unsuccessful attacks at 78.74%, 82.11%, 74.25%, respectively. Using predicted probabilities of success obtained from each logistic regression models, the medians were plotted to compare the characteristics of terrorist attacks across missions. The coefficients for each …

Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists, Graham Nash

Symposium of Student Scholars

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a …

Detecting Covid-19 Misinformation On Social Media, Jason Michaud

Honors Projects in Data Science

There have been many studies conducted over the last few years that have attempted to uncover the impacts of the COVID-19 pandemic. One of the largest areas of concern with COVID-19 is misinformation, as it is a novel virus that many report on, even if unqualified to do so. This study aims to predict whether a Tweet can be classified as misinformation, and then analyze the differences between Tweets that are labeled as either misinformation or not by this model. Machine learning models are created and validated using the CovidMis20 dataset as a training set. The dataset to be labeled …

Understanding Non-Fungible Tokens Through Social Media Discussion, David Poretsky

Honors Projects in Data Science

Non-fungible tokens (NFTs) are a peer-to-peer type of blockchain technology which is a unique digital asset transferred completely over the internet (Sarmah, 2018). NFT's have become an integral component of many financial and art communities in recent years, boasting millions of consumers (Regner, 2019). Along with the non-fungible token technology, social media has become a crucial aspect of communication in society, with many consumers of the NFT industry communicating and expressing opinions of the industry on different social media platforms. Considering the rise in popularity of NFTs in recent years, this study aims to gain a deeper understanding of the …

Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez

Senior Design Project For Engineers

Family Restaurant is a local restaurant in the greater Atlanta area that serves a variety of dishes that include an assortment of 19 different proteins. Currently, Family Restaurant places protein orders based on business intuition, and tends to over-stock and sometimes under-stock. To minimize inventory costs by reducing over-stocking and preventing under-stocking of proteins, we applied Facebook Prophet (FB Prophet), ARIMA, and XG Boost machine learning models to predict protein demand and then fed these results into a Fixed Time Period inventory model to make an overall order suggestion based on the specified time period. We trained our models on …

Hipaa Vs. Medical Research: Improving Patient Care Through Integration Of Data Privacy And Data Access, Katherine D'Ordine

Honors Projects in Data Science

The purpose of this research is to understand the current relationship between data access and data privacy in the health care industry and attempt to find a way that important health care research can still be conducted amidst HIPAA regulations. There is a lack of extensive research on the impacts of data privacy on health care research due to access regulations, so a survey was created regarding current data processes and recommendations for creating a healthier relationship between privacy and access for research. It was distributed to anyone in health care, analytics, or research to get a variety of perspectives. …

The Bellarmine Bee Bed: Organizing A Native Plant Garden Using Feedback From The Local Community, Kate Moran

Undergraduate Theses

Animal pollinators are the cornerstone of healthy ecosystems. Their survival is essential for the persistence of entire food chains: from the flowers they cross-pollinate directly, to the animals who depend on those plants for nutrition. The establishment of pollinator gardens—particularly ones that consist of native plants—is an effective way to enhance their biodiversity, abundance, and well-being.

The main goal of this thesis is to construct a pollinator garden that maximizes the benefits for animal pollinators using feedback from local gardeners. A survey was used to gather information about the popularity and preferences of 40 flowering plants, and after analyzing the …

Visualizing The Spread Of Western Music Throughout The World Using Big Data, Dakota C. Cookenmaster

Campus Research Month

Music, perhaps the most prevailing form of art throughout the ages, has impacted the world in countless ways. Due to the vast magnitude of published musical compositions, it is difficult to comprehend the full extent of how Western music has spread from Europe to the rest of the world. Our contribution is a presentation of the history of music throughout the ages, highlighting the countries of publication by year since the 15th century. Our visualization also exhibits the top 10 most prevalent composers within the British Library, with additional information such as the composers’ number of works and lifespan.

Open Data Indicates That Collegedale Could Be A Bluezone, Tristan Deschamps, Alva Johnson

Campus Research Month

A blue zone is an indicator of exceptional health in a community. Adventists have a blue zone community in Loma Linda, but there has been little research into other Adventist populated areas that could be blue zones. Therefore, our goal is to show that open data suggests that a blue zone may exist near Southern Adventist University, specifically in Collegedale. This data has been gathered from different federal sources, including, the CDC, the US Census Bureau, the Tennessee Department of Health, official state records, and federal documents that are available to the public.

Interactive Dashboard Of Diabetes In The Us, Marc Butler

Campus Research Month

The contribution of this research project is the construction and interactive dashboard in order to facilitate the visualization of diabetes-related data to the public

An Interactive Dashboard For Southern Adventist University Weather Data, Emily Hamstra, Miguel Rivas, Ac Hybl, Harvey Alferez

Campus Research Month

A Davis Wireless Vantage Pro 2 weather station has been collecting data on Southern Adventist University’s campus since December 2019. Some of the data are uploaded and reported by Weather Underground. The remaining data are not easily accessible. This project aims to make that data available to the general public through an interactive dashboard. Weather data specific to campus may help further scientific research that requires accurate weather records.

Visualizing Literary Narratives With A Graph-Centered Approach., Meg Ermer

Campus Research Month

The art of storytelling is multifaceted and nonlinear, involving multiple characters, themes, and symbols while often jumping between the present and past. While media forms such as novels can encapsulate these complexities, it is often difficult to visualize a narrative in an easy-to-understand format. Our contribution is a graph-based system to let users organize and visualize those narratives. Events and characters are represented as nodes and their relationships are represented as edges. Neo4J is used as a database management system to store the graph and to run queries on it, and Streamlit and Pyvis are used to represent the database …

Extracting Information From Twitter Screenshots, Tarannum Zaki, Michael L. Nelson, Michele C. Weigle

Modeling, Simulation and Visualization Student Capstone Conference

Screenshots are prevalent on social media as a common approach for information sharing. Users rarely verify before sharing screenshots whether they are fake or real. Information sharing through fake screenshots can be highly responsible for misinformation and disinformation spread on social media. There are services of the live web and web archives that could be used to validate the content of a screenshot. We are going to develop a tool that would automatically provide a probability whether a screenshot is fake by using the services of the live web and web archives.

The Legacy Of Colonization And Civil Societies In South Africa, Erika Frydenlund, Melissa Miller-Felton, Bolu Ayankojo

Modeling, Simulation and Visualization Student Capstone Conference

This research analyzes the unique ways that civil societies operate in Sub-Saharan Africa in the context of post-apartheid Cape Town, South Africa. Decades after the demise of apartheid, remnants of inequality remain without the promise of actionable change. We used a computational modeling approach to understand the dynamics of migrants in the receiving community as derived from qualitative interviews conducted with 24 stakeholders in Cape Town, South Africa between 2020 and 2021. Our findings show that the presence of NGOs can promote access to resources and reduce xenophobia if they can have the right influence on government policies.

Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry

Modeling, Simulation and Visualization Student Capstone Conference

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).

The Effectiveness Of Visualization Techniques For Supporting Decision-Making, Cansu Yalim, Holly A. H. Handley

Modeling, Simulation and Visualization Student Capstone Conference

Although visualization is beneficial for evaluating and communicating data, the efficiency of various visualization approaches for different data types is not always evident. This research aims to address this issue by investigating the usefulness of several visualization techniques for various data kinds, including continuous, categorical, and time-series data. The qualitative appraisal of each technique's strengths, weaknesses, and interpretation of the dataset is investigated. The research questions include: which visualization approaches perform best for different data types, and what factors impact their usefulness? The absence of clear directions for both researchers and practitioners on how to identify the most effective visualization …