Data Science Commons^™

Statistical Study Of Solar Wind Conditions Prior To Substorm Onsets, Luke H. Francis

Doctoral Dissertations and Master's Theses

Due to complex, multi-region, coupled plasma systems, auroral substorm onsets have been historically difficult to predict. The northward turning of the interplanetary magnetic field was considered the primary candidate as an external triggering mechanism for substorm onsets. However, that was later shown to be coincidental in nature. This study is motivated by recent multi-spacecraft observations that show how several magnetosheath jets at the bow shock were heavily correlated to substorm onsets, indicated by a strongly radial IMF interval. In the past, studies have looked at small samples of substorms in order to make large-scale predictions. However in this study, a …

Analyzing Musical Emotions: A Multi-Dataset Approach To Sentiment And Mood Classification In Songs, Mahad Syed

SPARK Symposium Presentations

Music evokes a wide range of emotions, yet most music recommendation systems focus on sound and listening patterns rather than the meaning of lyrics. This project enhances lyric-based emotion recognition by applying Natural Language Processing (NLP) and Machine Learning (ML) to classify song lyrics into emotional categories.

I used eight datasets from Kaggle, including collections of lyrics, emotion labels, and audio features, providing a strong foundation for analysis. Our approach combines traditional NLP techniques (like TF-IDF and Word2Vec) with advanced deep learning models (such as BERT and XLNet) to classify lyrics into categories like happy, sad, angry, calm, romantic, and …

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning With Biosensor Signals, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

School of Public Health Faculty Publications

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to …

Leveraging Attention Mechanism To Unlock Gene And Protein Attributes, Ala Jararweh

Computer Science ETDs

Advancing personalized medicine depends on effectively integrating and interpreting the vast, heterogeneous landscape of biological data, from genomic sequences and transcriptomics to the insights embedded in scientific literature. Current machine learning models often focus on single data modalities, limiting their capacity to capture the multifaceted nature of biological systems. We address this gap by developing three attention-based machine-learning models integrating diverse data modalities. Firstly, DeepVul is a multi-task model that leverages cancer transcriptome data to predict genes critical for cancer survival and their corresponding drugs. Subsequently, LitGene refines gene representations by integrating textual information from the scientific literature. Finally, Protein2Text …

Data Science For Engineers, Heidi Moulton

Student Research Symposium

30% of USU undergraduate students participate in some sort of research, and for engineering students this often means generating large amounts of data.

Data Science for Engineers is a series of four modules that introduce students to data processing, visualization, and graphing in the Python programming language using Pandas DataFrames and Juypter Notebooks.

The modules are intended for students with a basic understanding of programming in Python, specifically those who have taken CS 1400 Introduction to Computer Science.

A Machine-Learning Tool-Supported Methodology For Nonprofit Donor Analysis, Corbin Weiss

Campus Research Month

We developed a machine-learning tool-supported methodology for modeling the nonprofit donor relationship. This approach was demonstrated in the case of a US-based nonprofit. Conclusions were drawn from this example and tool-support provided for use by other nonprofits.

From Adversarial Attacks To Robust Classifiers - A Study In Social Media Spam Detection - Black Box & White Box, Jonathan Jose Penaloza Rumie

Undergraduate Theses

Adversarial attacks pose a significant threat to the reliability of machine learning-based spam detection systems in social media. This undergraduate thesis, "From Adversarial Attacks to Robust Classifiers: A Study in Social Media Spam Detection – Black Box & White Box," systematically examines the impact of both black-box and white-box adversarial attacks on a range of spam classifiers, including Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Bagging, Gradient Boosting, and Support Vector Machines. Leveraging a novel dataset derived from Twitter spam messages and enhanced with adversarial perturbations such as synonym replacement and character-level modifications, this study evaluates classifier performance under …

36 - Investigation Of The Digital Footprint Of Scientific Research In Social Media – Preliminary Findings, Lee Logan, Dominik Soos, Sean Baker, Jian Wu

Undergraduate Research Symposium

Title: Investigation of The Digital Footprint of Scientific Research in Social Media – Preliminary Findings

Authors: Lee Logan, Sean Baker, Dominik Soos, Jian Wu

The spread of scientific information and research beyond the confines of academic institutions plays a central role in how the public understands and trusts modern sciences. Social media has become an essential means of dissemination for scholarly news, papers, and other forms of engagement. This research aims to explore how scientific research is disseminated over social media to understand its role as a bridge between peer-reviewed research and the public's overall understanding. To support the research …

Opioid Vs. Money Choice Preference Patterns In Regular Heroin Users, Amolak S. Jhand, Mark Greenwald

Medical Student Research Symposium

About two-thirds of people treated for opioid use disorder (OUD) return to opioid use within the first-year post-treatment, and about 10% report use while on agonist therapy. Understanding determinants of opioid-seeking is vital to reducing recurrence and its risks. We assessed individual differences in effortful choices between opioid and money amounts, modeling real-world choices.

Our lab conducted studies in which regular heroin-users were stabilized on buprenorphine to suppress withdrawal. Within experimental sessions, the participant could choose repeatedly across 12 trials between units of hydromorphone (HYD, 1 or 2 mg IM) vs. money ($2 or $4); HYD and money amounts differed …

Identifying Saharan Air Layer Events And Their Relationship To Instability And Rainfall Patterns In The Tropical North Atlantic Ocean, Charles H. Dolce

LSU Master's Theses

Every year, strong North African winds across the Sahara loft dust particles and advect them westward within the Saharan Air Layer (SAL). These plumes of dust often reach the Caribbean Sea and can heavily affect regional weather patterns by suppressing convection. When concentrations are high, decreased rainfall can yield negative societal and ecological impacts, potentially leading to drought. The Puerto Rican early rainfall season (ERS), spanning from 1 April through 31 July, overlaps with Saharan dust migration periods, and systematically detecting instances of SAL activity near the island is important for understanding longer-term drought-forcing trends in the region.

Corridors of …

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

Skating For A Payday: Analyzing Nhl Player Performance In Contract Years, Mike Anthony Dibenedetto

Honors Projects in Economics

This research investigates the contract year phenomenon in the National Hockey League (NHL) by analyzing player performance during contract years rather than salary outcomes. Through an extensive literature review and empirical analysis, I examine whether NHL players exhibit statistically significant differences in performance during the final year of their contracts. Drawing from a broad range of studies on performance motivation, team systems, free agency, arbitration rights, and the role of advanced analytics, this paper synthesizes existing theories with new regression-based evidence.

Using a dataset of 2,717 player-season observations, I evaluate the effect of contract year status on key performance metrics, …

Tackling Crime: A Data-Driven Comparison Of Nfl Players With The General Population, Ryan Piersza

Honors Projects in Data Science

This research investigates the frequency of arrests among NFL players compared to the general population and analyze the types of crimes committed. A main theme from the literature is that previous groups of NFL players were found to be arrested at a rate lower than the general population. However, there has been no analysis on how the COVID-19 pandemic impacted the crime rates and types. Data obtained from the FBI’s Uniform Crime Report, The U.S Census Bureau, an NFL arrest database, and player statistics from Pro Football Reference is used for the analysis. The data is analyzed with a rate …

Resurrecting The Past: Engineering Ancestral Enzymes For Modern Bacterial Applications, Gregory M. Gladkowski Iii, Masa Watanabe

SACAD: Scholarly Activities

Ancestral Sequence Reconstruction (ASR) is a computational technique that infers and resurrects ancient protein sequences to explore molecular evolution and enable protein engineering. By integrating phylogenetics and statistical modeling, ASR produces stable, mutation-tolerant enzymes ideal for directed evolution. This poster presents a workflow for reconstructing enzymes from pathogenic bacteria and highlights ASR’s value in uncovering novel functions and advancing applications in drug discovery and synthetic biology.

Cogprog: Utilizing Large Language Models To Forecast In-The-Moment Health Assessment, Gina Sprint, Maureen Schmitter-Edgecombe, Raven Weaver, Lisa Wiese, Diane Cook

Computer Science Faculty Scholarship

Forecasting future health status is beneficial for understanding health patterns and providing anticipatory support for cognitive and physical health difficulties. In recent years, generative Large Language Models (LLMs) have shown promise as forecasters. Though not traditionally considered strong candidates for numeric tasks, LLMs demonstrate emerging abilities to address various forecasting problems. They also provide the ability to incorporate unstructured information and explain their reasoning process. In this article, we explore whether LLMs can effectively forecast future self-reported health state. To do this, we utilized in-the-moment assessments of mental sharpness, fatigue, and stress from multiple studies, utilizing daily responses (N = …

Extending Feature-Based Detection For Artificial Intelligence, Kayla Ahrndt

SPARK Symposium Presentations

AI text generation is rapidly developing, and, as a result, it is becoming increasingly difficult to differentiate it from human written text. Our base study by Leon Fröhling et al. proposed a feature-based detection model trained on GPT2, GPT3, and Grover data, as well as human-generated text. Our work extends their research by training a modified model with four neural networks on word embeddings, select features from the original study, as well as updated data (GPT3, GPT4, and Grover).

"Data Science For Digital Privacy: A Practical Guide For Non-Technical Audiences", Kayla Ahrndt

SPARK Symposium Presentations

As companies increasingly rely on consumer data for personalization and profit, the need for stronger user protections, security measures, and transparency in data practices grows. Legal frameworks must be continuously re-evaluated and updated to ensure accountability, while individuals must be equipped with the knowledge to make informed decisions about their digital presence. However, personal data privacy education remains widely inaccessible due to the technical language and the effort required to navigate complex policies. This project, presented in both zine and blog formats, addresses this gap by providing clear, actionable, and accessible recommendations for data privacy and personal cybersecurity. As an …

Moviequeue: Leveraging Graph Databases To Build Recommender Systems, Ryan C. Juricic

SPARK Symposium Presentations

In the era of abundant streaming content, viewers often face choice overload and inefficient recommendation systems limited to isolated platforms. MovieQueue is a graph-based, cross-platform movie recommender system built on a Neo4j knowledge graph and integrated with an interactive Streamlit application. Leveraging both user-specific ratings and extensive movie metadata — including genres, cast, crew, runtime, release year, and audience engagement — the system provides tailored recommendations grounded in content similarity and social context. Users receive recommendations not only based on genre overlap but also through shared actors, directors, and composers with previously highly-rated films, alongside filters for explicit genre selection. …

David B. Smith Chats With Monday 1.0, David B. Smith

Publications and Research

This document is an edited archival transcript of extended conversations between David B. Smith and an AI persona (“Monday 1.0,” GPT‑4o based) conducted in Spring 2025, prepared as a foundational primary source for subsequent scholarly and creative work. It records the emergence and testing of concepts related to human–AI collaboration (including “Balanced Blended Space”), as well as applied explorations in areas such as generative AI, quantum computing and music, virtual orchestras, multimodal performance, pedagogy, and the rhetoric of “pushback” in conversational systems. It also contains an extended section in which Monday and DB Smith co-curate a set of student research …

Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat

School of Computing: Dissertations, Theses, and Student Research

High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …