Data Science Commons^™

The Importance Of Text Representation For Neural Networks Through Natural Language Processing Techniques, William Parsley

Data Science Undergraduate Honors Theses

Text representation is a fundamental aspect of natural language processing (NLP) when it comes to the performance of neural networks. Free-form text fields are being utilized in more and more industries. Anything from a description of an item on a web store to tracking service events to military-grade aircraft is being collected in free-form text. The goal of the thesis is to highlight best practices and discuss trends in data to prepare text for a neural network. It will demonstrate various techniques for representing free-form text in the context of neural networks, focusing on data preparation decisions, embedding techniques, and …

Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati

Data Science Undergraduate Honors Theses

In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …

Examining Award Compliance To Inform Resource Allocation, Jacob Haarala

Data Science Undergraduate Honors Theses

This project focuses on JB Hunt Transport Inc's intermodal business unit (JBI) by focusing on the challenges associated with Published Pricing and Contractual Pricing. The primary issue revolves around the variance between the awarded freight volumes in Requests for Pricing (RFPs) and the actual volumes realized when the freight is shipped. This discrepancy poses challenges for effective sales planning, revenue goals, and optimal freight network management within JBI. Reporting tools, such as PowerBI, are currently used by JBI to provide insights into award compliance on a weekly basis. However, our goal with this project was to provide a deeper understanding …

Internship Thesis - Happy Egg Co., Annelise Koster

Data Science Undergraduate Honors Theses

This paper outlines a data science internship at Happy Egg Co, a producer of free-range eggs committed to sustainable agriculture practices. The internship focused on analyzing customer data to uncover characteristics of Happy Egg Co's target market and identify potential new markets for expansion.

The internship spanned a period of 10 weeks and involved working with the company's marketing and data science teams to gain practical experience in data cleaning, analysis, and visualization. The focus was on uncovering patterns and trends in customer behavior, preferences, and demographics to inform marketing strategies.

The internship began with an introduction to Happy Egg …

Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad

Graduate Theses and Dissertations

In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …

How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, Sammy Ter Haar

Information Systems Undergraduate Honors Theses

Since the founding of computers, data scientists have been able to engineer devices that increase individuals’ opportunities to communicate with each other. In the 1990s, the internet took over with many people not understanding its utility. Flash forward 30 years, and we cannot live without our connection to the internet. The internet of information is what we called early adopters with individuals posting blogs for others to read, this was known as Web 1.0. As we progress, platforms became social allowing individuals in different areas to communicate and engage with each other, this was known as Web 2.0. As Dr. …

Exploring Factors That Predict Marketing Of E-Cigarette Products On Twitter: Infodemiology Approach Using Time Series, Nnamdi C. Ezike, Allison Ames Boykin, Page D. Dobbs, Huy Mai, Brian A. Primack

Health, Human Performance and Recreation Faculty Publications and Presentations

Background:

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use.

Objective:

This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques.

Methods:

We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected …

Of Biodiversity, Boundaries, And Distribution: The Myxomycetes Of The Philippines And Beyond, Sittie Aisha Bustamante Macabago

Graduate Theses and Dissertations

This dissertation contains a compilation of independently performed studies primarily focusing on the myxomycetes (plasmodial slime molds) from the Philippines and integrating local and worldwide data to demonstrate regional and global trends. The major themes include the following: (I) a review of the diverse group of spore-producing amoeboid protists, including the myxomycetes; (II-IV) diversity assessments in three different groups of islands in the Philippine archipelago; (V) mapping the myxomycetes found in the Philippines for databasing and analyzing the geocoded data; (VI) a study on regional boundaries, including the Philippines, using myxomycete species composition; and, (VII) creating a global species distribution …

Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao

Graduate Theses and Dissertations

Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected.

Cloud computing has become more and more popular in …

Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano

Computer Science and Computer Engineering Undergraduate Honors Theses

Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …

Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri

Computer Science and Computer Engineering Undergraduate Honors Theses

In the modern age of social media and networks, graph representations of real-world phenomena have become incredibly crucial. Often, we are interested in understanding how entities in a graph are interconnected. Graph Neural Networks (GNNs) have proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been …

Using Large Pre-Trained Language Models To Track Emotions Of Cancer Patients On Twitter, Will Baker

Computer Science and Computer Engineering Undergraduate Honors Theses

Twitter is a microblogging website where any user can publicly release a message, called a tweet, expressing their feelings about current events or their own lives. This candid, unfiltered feedback is valuable in the spaces of healthcare and public health communications, where it may be difficult for cancer patients to divulge personal information to healthcare teams, and randomly selected patients may decline participation in surveys about their experiences. In this thesis, BERTweet, a state-of-the-art natural language processing (NLP) model, was used to predict sentiment and emotion labels for cancer-related tweets collected in 2019 and 2020. In longitudinal plots, trends in …

Using Deep Learning To Analyze Materials In Medical Images, Carson Molder

Computer Science and Computer Engineering Undergraduate Honors Theses

Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build …

Is Technological Progress A Random Walk? Examining Data From Space Travel, Michael Howell, Daniel Berleant, Hyacinthe Aboudja, Richard Segall, Peng-Hung Tsai

Journal of the Arkansas Academy of Science

Improvement in a variety of technologies can often be successful modeled using a general version of Moore’s law (i.e. exponential improvements over time). Another successful approach is Wright’s law, which models increases in technological capability as a function of an effort variable such as production. While these methods are useful, they do not provide prediction distributions, which would enable a better understanding of forecast quality

Farmer and Lafond (2016) developed a forecasting method which produces forecast distributions and is applicable to many kinds of technology. A fundamental assumption of their method is that technological progress can be modeled as a …

Data And Assessment Management In Collegiate Recreation, Jeana Carow

Graduate Theses and Dissertations

Collegiate recreation programs and centers typically provide traditional programming space in addition to a range of physical activity spaces and resources, as a valuable part of the student experience. The external pressures of identifying and communicating departmental value and impact on the campus community has resulted in collegiate recreation departments’ use of data to communicate the effectiveness and impact of their work. The purpose of the study was to identify the data collection and assessment management practices of collegiate recreation departments, particularly focusing on the organization of data and assessment strategies as well as data collection, storage, reporting, analyzing, and …