Data Science Commons^™

Analytics Insights From Text: Machine Learning, Ai, And Sentiment Analysis On Beige Books, Charlie Smith

Graduate Theses and Dissertations (2019 - present)

Business analytics is about drawing actionable insights from data. These distinct but connected essays represent a novel approach to explore how natural language processing (NLP) advances and machine learning can transform unstructured text data into actionable conclusions. Essay 1 provides a broad framework. Essay 2 strengthens the sentiment analysis with the most recent artificial intelligence methodologies for capturing nuanced sentiment in complex texts. Essay 3 applies those insights to forecast recessions using topics that can be readily interpreted and applied.

The research demonstrates how these methodologies can be applied to enhance understanding of the same dataset, Beige Books. Published by …

Comparative Analysis Of Regression And Random Forest Models For Player Performance Prediction In The Mls, Joshua Clement Madeti

Senior Honors Theses

Advanced technology and analytics have transformed the world and have benefited several industries throughout, the sport industry being one of them. Data is constantly generated during sports and requires post-game or post-season analysis which is crucial to team and player success. In this paper, the researcher will focus on the impact of analytics on soccer and soccer players. With over three billion active fans, soccer is the most famous sport in the world yet, when it comes to analytics, it is lagging. The thesis includes a comparative study of multiple linear regression and random forest regression to explore whether these …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Describing Functionality In Natural Language May Improve Decomposition Behaviors, Matthew R. Burns

All Graduate Theses and Dissertations, Fall 2023 to Present

Problem decomposition—the ability to break complex problems into simpler parts—is a critical skill for computer programming that many beginning students struggle to develop. This research examines how using natural language to describe program functionality can help students develop better problem-solving approaches.

We created a tool called ”Natural Language Functions” (NLFs) that allows students to write descriptions of what they want their code to do in plain English, which then generates working Python functions. We studied how students used this tool compared to students who solved programming problems in traditional ways.

Our findings show that students who used the NLFs tool …

The Role Of Ai In Enhancing Teamwork, Resilience And Decision-Making: Review Of Recent Developments, Satyadhar Joshi

Harrisburg University Other Works

This paper explores the transformative impact of artificial intelligence (AI) on organizational teamwork, decision-making, and resilience. This paper furthur reviews recent literature on the integration of Artificial Intelligence (AI) in various organizational functions, focusing on its impact on innovation management, leadership paradigms, and organizational resilience. We provide groundwork required to enhance frameworks that can integrate cognitive scaffolding with antifragile team dynamics, employing behavioral economics and neurocognitive principles. We introduce methodologies for enhancing team resilience through adaptive AI systems, cross-training interventions, and pre-mortem simulation techniques. The framework addresses key challenges in confirmation bias mitigation, cultural dimension alignment, and vigilance decrement prevention. …

Towards Advancing Streamflow And Peak Flow Prediction With Machine Learning: Identifying Infrastructure At Risk, Sudan Pokharel

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Due to climate change and its impact, the need for adaptive strategies for natural disaster mitigation and resource management has never been more urgent. Central to this is water resource management, which is essential for sustainable human activities, ecological balance, and the mitigation of natural hazards like floods. Streamflow is a crucial element of water resource management and plays a vital role in planning and building water infrastructure, implementing emergency response plans, supporting flood mitigation initiatives, and regulating agricultural and industrial use. However, accurate prediction of streamflow still remains a challenge due to the complex non-linear and non-stationary interaction between …

Compartmental Disaggregation: Bridging Simulation And Sampling Methods For Synthetic Population Data Generation, Dylan Mack

McKelvey School of Engineering Graduate Student Theses & Dissertations

As agent-based models (ABMs) grow increasingly widespread in public health, their associated challenges have become all the more significant. Lauded for their ability to capture population heterogeneity, nonlinear dynamics, and emergent behaviors, disease ABMs are also computationally expensive and often require detailed inputs that describe each agent at the individual-level, known as synthetic population data. Current approaches for synthetic population data generation generally fall into one of two categories: sampling or simulation. These methods are both feasible only under restricted conditions and suffer from challenges surrounding data availability and computing power. This thesis proposes compartmental disaggregation, an intermediate method for …

Exploring The Evolution Of Global Warming Discourse: A Twitter-Based Analysis Across The United States, United Kingdom, And India, Trevor Christensen

Honors Projects in Data Science

Global warming has gained increasing attention over the past decade, with public discourse intensifying on social media platforms, particularly on Twitter. This increased discussion stems from political controversies surrounding climate change and the rise in extreme weather events. This study explores the evolution of global warming discourse on Twitter, with a focus on the United States, United Kingdom, and India. This research used a dataset of historical tweets ranging from 2010 to 2023 containing the keyword "global warming." Using natural language processing (NLP) techniques such as emotion analysis, word cloud visualization, and topic modeling (LDA), approximately twenty-eight million tweets were …

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

Rewriting War: Improving The Mlb’S Go-To Advanced Metric, Kolin Atwood

Honors Projects

Traditional Wins Above Replacement (WAR) metrics have long served as a cornerstone of player evaluation in Major League Baseball, offering a context-neutral summary of offensive, defensive, and baserunning contributions. However, this neutrality often overlooks critical factors such as game situation, lineup strength, and advanced baserunning impact. This project proposes an enhanced model, WAR-PC (Wins Above Replacement – Plus Context), that integrates three key improvements: context-dependent batting value (RE24), clutch performance (Win Probability Added, WPA), and Statcast-based baserunning metrics. Using R, player logs, and modern baseball data sources, WAR-PC was calculated for eight players from the 2023 MLB season. The revised …

The Impact Of Institutional Features On Student Retention Rates Using Regression And Random Forest Modeling, Grayson Tvrdik

Honors Projects

Student retention is a focus for higher education institutions aiming to improve student outcomes and institutional success. While previous research has often relied on qualitative assessments of college related factors, this project applies quantitative techniques at a national scale. Random forest and beta regression models were used to predict retention rates for public colleges based on institutional characteristics such as financial variables, enrollment patterns, and demographic metrics. The random forest models demonstrated higher accuracy than the beta regression models, leading us to find that financial variables and student integration factors are significant predictors of retention. Beta regression models, though less …

A Comparison Of The Bacterial Communities Of The Yamuna River (India) And Mississippi River (Usa), Jacob Gareis, Osvaldo Martinez, Silas Bergen

Research & Creative Achievement Day

This study compared the bacteria in the Yamuna River in India and the Mississippi River in the USA. Water samples were taken from 11 sites (2 in the Mississippi River and 9 in the Yamuna River). The bacteria were identified using 16S rRNA gene sequencing. Some phyla such as Proteobacteria and Bacteroidetes were seen in abundance regardless of country. However, principal-component analysis showed three distinct groupings: Mississippi/Ganga/Tons and other Yamuna River locations (7 sites), Yamuna River at Delhi (2 sites), Yamuna River below Glacier (2 sites) Additionally, the Yamuna River below Glacier had the highest bacterial diversity, with a Shannon …

Evaluating The Causal Effect Of Receiving Transthoracic Echocardiography On 28-Day Mortality In Mimic-Iii Intensive Care Unit Patients With Sepsis, Monserrath Velez, Jack O'Connor, Nicholas Della Pesca, Rio Baliga

Research & Creative Achievement Day

❏ Sepsis (an infection associated with vital organ dysfunction) is an emergent

medical condition estimated to occur in about 30% of intensive care unit

(ICU) patients[1] and is responsible for 20% of all deaths worldwide[2]

❏ Transthoracic echocardiography (TTE; an imaging modality that uses

ultrasound technology to record heart structure and function) is widely used in

medical treatment [3]

❏ 28-day mortality (whether or not a patient dies within 28 days after receiving a

treatment) is a measure considered to closely approximate ICU mortality[4]

Objectives

❏ Assess and quantify the causal effect of receiving a TTE on …

Analyzing The Sentiment Of Feminist And Non-Feminist Works, Jasmine Borie, Megan G. Falschlehner

Mathematics, Computer Science & Statistics Presentations

This presentation focuses on a group of texts that advocate for a change in the current belief system. These texts are the Feminist Manifesto, Sojourner Truth: Ain’t I a Woman?, and Civilization and Its Discontents. These first two texts advocate for women’s rights, while Freud’s book is focused on civilization’s decline and how our understanding of community can affect this. Through our presentation, we want to examine the differences in sentiment and language between the feminist texts and Freud’s texts to pinpoint whether or not sentiment changes when advocating for different beliefs.

Analyzing Cie Texts Through History Using R, Rachel A. Hart, Aaron Ditto

Mathematics, Computer Science & Statistics Presentations

In this presentation, we analyzed three separate CIE texts from different time periods. First, “The Allegory of the Cave” from 380 BC, then “The Declaration of Independence” from 1776, and lastly “The Lottery” from 1948. We compared them using tidy text techniques like sentiment lexicons, creating word clouds, and bigram analysis to see if the types of words and sentiments used have changed over time in these short texts.

A Statistical Comparison Of Selected Old Testament And New Testament Books, Branden F. Stahl, Kevin Guan, Adam Denn

Mathematics, Computer Science & Statistics Presentations

The purpose of this project was to discover similarities between sentiments in Old Testament and New Testament books of the Bible, track emotional valence and find the most common words and sentiments in the books. Text analysis was performed on Genesis, Exodus, Matthew and Luke. Word clouds were also created for these texts.

Application For Prediction Of Heart Failure; The Next Step In Machine Learning For Healthcare, Amy Adyanthaya, Dawn Bowerman, Rachel Liercke, Robert Slater

SMU Data Science Review

Heart failure (HF) is a serious medical condition affecting approximately 6.7 million U.S. adults and is expected to impact 8.5 million Americans by 2030 [1]. Heart failure is a complicated clinical ailment and characterizes the final course of numerous heart diseases [2]. This paper introduces a machine-learning-based application that utilizes Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and XGBoost models, implemented through the Python Flask framework, to predict HF risk using clinical data. The results indicate high model performance, with precision and recall metrics underscoring the application’s reliability in identifying at-risk patients. By providing real-time, accessible insights, this tool aims …

Multi-Agent Translation Team (Matt): Enhancing Low-Resource Language Translation Through Multi-Agent Workflow, Anishka Peter, Mai Dang, Michael Liu, Joaquin Dominguez, Nibhrat Lohia

SMU Data Science Review

Like humans, large language models (LLMs) benefit from revision and refinement, especially for complex tasks requiring critical thinking. Inspired by human collaborative problem-solving, this study introduces a novel multi-agent workflow designed to enhance LLM translations from English to low-resource languages. Multi-Agent Translation Team (MATT) involves the collaboration of agents that are assigned specific roles, such as translator, evaluation coordinator, and various levels of editing, to refine the initial translation into the most desired version possible. The agents work collaboratively in an iterative loop until the translation loss meets a satisfactory threshold. It stands out from other multi-agent workflows by combining …

Enhancing Animal Shelter Operations With Time Series And Machine Learning, Sakava L. Kiv, Donald L. Anderson, Shivam Negi, Jacquelyn Cheun

SMU Data Science Review

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and …

Enhancing Network Security Through Dual-Layer Log Analysis: Integrating Machine Learning Classifiers With Large Language Models For Intelligent Anomaly Detection, Anthony Burton-Cordova, O'Neil Gray, Mohammad Al Rousan

SMU Data Science Review

This paper presents an innovative approach to enhancing network security by integrating machine learning algorithms with fine-tuned large language models (LLMs) to provide an expert assistant querying. The proposed method utilizes machine learning for efficient preprocessing and feature extraction from log data, followed by the application of a fine-tuned LLM to analyze and interpret anomalies with greater accuracy. This dual-layer detection system is designed to improve the identification of subtle and sophisticated security threats. The research team’s extensive evaluation using real-world log datasets indicates that the combined approach increases detection rates and communicates results in an understandable manner, demonstrating its …