Data Science Commons^™

Adapting And Evaluating A Theory-Driven, Non-Pharmacological Intervention To Self-Manage Pain, Jennifer Kawi, Chao Hsing Yeh, Lauren Grant, Johannes Thrul, Hulin Wu, Paul J Christo, Lorraine S Evangelista

Faculty, Staff and Student Publications

BACKGROUND: The existing literature has limited detail on theory-driven interventions, particularly in pain studies. We adapted Bandura's self-efficacy framework toward a theory-driven, non-pharmacological intervention using auricular point acupressure (APA) and evaluated participants' perceptions of this intervention on their pain self-management. APA is a non-invasive modality based on auricular acupuncture principles.

METHODS: We mapped our study intervention components according to Bandura's key sources of self-efficacy (performance accomplishments, vicarious experience, verbal persuasion, and emotional arousal) to facilitate the self-management of pain. Through a qualitative study design, we conducted virtual interviews at one and three months after a 4-week APA intervention among 23 …

Toward The Integration Of Behavioral Sensing And Artificial Intelligence, Subigya K. Nepal

Dartmouth College Ph.D Dissertations

The integration of behavioral sensing and Artificial Intelligence (AI) has increasingly proven invaluable across various domains, offering profound insights into human behavior, enhancing mental health monitoring, and optimizing workplace productivity. This thesis presents five pivotal studies that employ smartphone, wearable, and laptop-based sensing to explore and push the boundaries of what these technologies can achieve in real-world settings. This body of work explores the innovative and practical applications of AI and behavioral sensing to capture and analyze data for diverse purposes. The first part of the thesis comprises longitudinal studies on behavioral sensing, providing a detailed, long-term view of how …

Developing And Validating A Nomogram For Early Predicting The Need For Intestinal Resection In Pediatric Intussusception, Yuan-Yang Yu, Jia-Jie Zhang, Ya-Ting Xu, Zheng-Xiu Lin, Shi-Kun Guo, Zhong-Rong Li, Hui-Ya Huang, Xiao-Zhong Huang

Faculty, Staff and Student Publications

PURPOSE: Develop and validate a nomogram for predicting intestinal resection in pediatric intussusception suspecting intestinal necrosis.

PATIENTS & METHODS: Children with intussusception were retrospectively enrolled after a failed air-enema reduction in the outpatient setting and divided into two groups: the intestinal resection group and the non-intestinal resection group. The enrolled cases were randomly selected for training and validation sets with a split ratio of 3:1. A nomogram for predicting the risk of intestinal resection was visualized using logistic regression analysis with calibration curve, C-index, and decision curve analysis to evaluate the model.

RESULTS: A total of 547 cases were included …

Surmounting Challenges In Aggregating Results From Static Analysis Tools, Dr. Ann Marie Reinhold, Brittany Boles, A. Redempta Manzi Muneza, Thomas Mcelroy, Dr. Clemente Izurieta

Military Cyber Affairs

Aggregation poses a significant challenge for software practitioners because it requires a comprehensive and nuanced understanding of raw data from diverse sources. Suites of static-analysis tools (SATs) are commonly used to assess organizational security but simultaneously introduce significant challenges. Challenges include unique results, scales, configuration environments for each SAT execution, and incompatible formats between SAT outputs. Here, we document our experiences addressing these issues. We highlight the problem of relying on a single vendor's SAT version and offer a solution for aggregating findings across multiple SATs, aiming to enhance software security practices and deter threats early with robust defensive operations.

Artificial Intelligence And Music: Analysis Of Music Generation Techniques Via Deep Learning And The Implications Of Ai In The Music Industry, David Bryce

Honors Projects in Data Science

The use of artificial intelligence (AI) is quickly gaining relevancy in creative fields, and its emergence into the music industry comes with many unique implications. This paper examines the technical processes of creating music with AI and machine learning, the relationship between music and emotion, and finally the implications and ethical considerations for AI generated music in creative industries. As part of this project, a generative deep learning model (Music Variational Autoencoder) is explored and applied to generate music using a pre-trained training set of piano rolls. The AI reconstructions are based on self-made 4 measure electronic instrumental tracks. 46 …

Data Analysis Project For Preferred Credit Inc., Emily Smith, Greta Nesbit, Jack Simonet, Ignacio Sanchez-Romero

Celebrating Scholarship and Creativity Day (2018-)

This project focuses on transforming real data within PCI's operations into valuable insights through an approach of coding, data cleaning, and visualization. By leveraging advanced techniques, the project aims to uncover key trends and create visually compelling representations to aid decision-making within the company. The outcome will allow PCI stakeholders the ability to extract valuable insights, optimize processes, and drive initiatives for growth and competitive advantage in the finance industry.

Identifying High-Value Tactical Livestock Decisions On A Mixed Enterprise Farm In A Variable Environment, Michael Young, John Young, Ross S. Kingwell, Philip E. Vercoe

Animal production and livestock research articles

Context

Australia is renowned for its climate variation, featuring years with drought and years with floods, which result in significant production and profit variability. Accordingly, to maximise profitability, dryland farming systems need to be dynamically managed in response to unfolding weather conditions.

Aims

The aim of this study is to identify and quantify optimal tactical livestock management for different weather-years.

Methods

This study employed a whole-farm optimisation model to analyse a representative mixed enterprise farm located in the Great Southern region of Western Australia. Using this model, we investigated the economic significance of five key livestock management tactics. These included …

A Spatial Decision Support System For Rent Estimation Of Retail Spaces In Manhattan Using Geographically Weighted Regression And Spatial Regression, Andie M. Migden Miller

Theses and Dissertations

This report outlines an automated, three-phase Spatial Decision Support System that creates models to estimate rent of retail spaces across Manhattan. First, enrich data with predictors. Second, optimize spatially aware neighborhood-level models by combining GWR, spatial regression, and non-spatial regression. Finally, visualize results in an Esri-based WebApp.

Rosarugosides A And D From Osa Rugosa Flower Buds: Their Potential Anti-Skin-Aging Effects Intnf-Α-Induced Human Dermal Fibroblasts, Kang Sub Kim, So-Ri Son, Yea Jung Choi, Yejin Kim, Si-Young Ahn, Dae Sik Jang, Sullim Lee

Faculty, Staff and Student Publications

This present study investigated the anti-skin-aging properties of Rosa rugosa. Initially, phenolic compounds were isolated from a hot water extract of Rosa rugosa's flower buds. Through repeated chromatography (column chromatography, MPLC, and prep HPLC), we identified nine phenolic compounds (1-9), including a previously undescribed depside, rosarugoside D (1). The chemical structure of 1 was elucidated via NMR, HR-MS, UV, and hydrolysis. Next, in order to identify bioactive compounds that are effective against TNF-α-induced NHDF cells, we measured intracellular ROS production in samples treated with each of the isolated compounds (1- …

Understanding The Public Reaction To Major United States Environmental Policies Through Twitter, Ryan Giammarco

Honors Projects in Data Science

An increased focus on access to general data as well as a continued lack of usable environmental data have resulted in an odd phenomenon where the public does not have the opportunity to understand their environment on a deep level. The goal of this research is to understand, as a result, how people both talk and feel about certain environmental changes, particularly those in the realm of politics. Through word clouds and sentiment analysis performed with historical Twitter data collected between 2010 and 2022, we can identify the general trends in both conversation and feeling as they relate to a …

A Novel Correction For The Multivariate Ljung-Box Test, Minhao Huang

Computational and Data Sciences (PhD) Dissertations

This research introduces an analytical improvement to the Multivariate Ljung-Box test that addresses significant deviations of the original test from the nominal Type I error rates under almost all scenarios. Prior attempts to mitigate this issue have been directed at modification of the test statistics or correction of the test distribution to achieve precise results in finite samples. In previous studies, focused on designing corrections to the univariate Ljung-Box, a method that specifically adjusts the test rejection region has been the most successful of attaining the best Type I error rates. We adopt the same approach for the more complex, …

Machine Learning And Geostatistical Approaches For Discovery Of Weather And Climate Events Related To El Niño Phenomena, Sachi Perera

Computational and Data Sciences (PhD) Dissertations

El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA …

The Quantitative Analysis And Visualization Of Nfl Passing Routes, Sandeep Chitturi

Computer Science and Computer Engineering Undergraduate Honors Theses

The strategic planning of offensive passing plays in the NFL incorporates numerous variables, including defensive coverages, player positioning, historical data, etc. This project develops an application using an analytical framework and an interactive model to simulate and visualize an NFL offense's passing strategy under varying conditions. Using R-programming and data management, the model dynamically represents potential passing routes in response to different defensive schemes. The system architecture integrates data from historical NFL league years to generate quantified route scores through designed mathematical equations. This allows for the prediction of potential passing routes for offensive skill players in response to the …

Sequential Optimization For Stressor-Informed Test Planning Through Integration Of Experimental And Simulated Data, Jacob Brecheisen

Data Science Undergraduate Honors Theses

This technical report details an innovative approach in reliability engineering aimed at maximizing system durability through a synergistic use of physical experimentation and computer-based modeling. Our methodology explores the efficient design and analysis of computer experiments and physical tests to facilitate accelerated reliability growth, while leveraging a sequential integration of data from these two distinct sources: costly physical experiments, characterized by random errors, and inexpensive computer simulations, marked by inherent systematic errors. The key innovation lies in the adoption of a closed-loop design and analysis method. This method begins by identifying a viable subset of important environmental stressors—such as temperature, …

Murmurations And Root Numbers, Alexey Pozdnyakov

University Scholar Projects

We report on a machine learning investigation of large datasets of elliptic curves and L-functions. This leads to the discovery of murmurations, an unexpected correlation between the root numbers and Dirichlet coefficients of L-functions. We provide a formal definition of murmurations, describe the connection with 1-level density, and provide three examples for which the murmuration phenomenon has been rigorously proven. Using our understanding of murmurations, we then build new machine learning models in search of a polynomial time algorithm for predicting root numbers. Based on our models and several heuristic arguments, we conclude that it is unlikely for …

Global To Glocal: A Confluence Of Data Science And Earth Observations In The Advancement Of The Sdgs, Rejoice Thomas

Computational and Data Sciences (PhD) Dissertations

The United Nations' (UN) Sustainable Development Goals (SDGs), part of Agenda 2030, comprise 17 interconnected goals and 169 actionable targets, providing an effective framework for addressing diverse issues ranging from individual challenges such as poverty, hunger, and health to broader corporate and global challenges like climate change and equality. Among these interconnected SDGs, this dissertation focuses on the role of climate and infrastructure in global and local sustainability. To this end, earth observations have been conducted utilizing data science techniques to advance these SDGs. For this dissertation, the author has conducted earth studies serving the following SDGs:

1. SDG 3 (Good …

A Comprehensive Analysis Of Training Induced Heat-Related Injuries At Fort Moore, Anthony Beger

Data Science Undergraduate Honors Theses

Heat related injuries are a significant problem for the United States Armed Forces. There were over 11,000 confirmed cases of heat-related illnesses that were diagnosed at more than 230 military installations from 2018-2022. These injuries are primarily due to hyperthermia (i.e., abnormally high body temperature) resulting from extreme environmental temperatures, high humidity, medications, or excessive physical work or exercise. Fort Moore has the most heat related injuries of any installation in the U.S. Department of Defense since it is home to one of the largest U. S. Army training posts with most training involving intensive outdoor activity in high heat …

Implementation Of Explainable Ai For Bearing Fault Classification, Mohammad Mundiwala

Honors Scholar Theses

It is difficult to overstate the impact of artificial intelligence (AI) over the past decade. The rapid expansion of machine learning has stimulated a race to deploy AI in all facets of life, one such domain being machine health monitoring. There is no doubt that machine learning excels in prediction accuracy, but oftentimes, these models are cryptic and fail to provide valuable insight into their decisions. This paper presents an overview of a neural network and what it means to learn. Next, two distinct Explainable AI (XAI) techniques will be presented: Gradient Class Activation Mapping and SimplEx . Finally, these …

Advancement Of Iterative Optimization Technology Algorithms Toward Calibration-Free Process Analytical Technology Applications, Adam Rish

Electronic Theses and Dissertations

The expansion of spectroscopic process analytical technology (PAT) tools within the pharmaceutical industry has the potential to elevate the current state-of-the-art of pharmaceutical manufacturing by offering opportunities for reduced quality testing times, enhanced process control, and greater production flexibility. Spectroscopic PAT tools are dependent on multivariate models to extract the relevant information from the spectral outputs. However, there is a substantial calibration burden for developing and maintaining these multivariate models that discourages the application of PAT, despite the encouragement from regulators. This has led to an interest in calibration-free methods such as iterative optimization technology (IOT) for spectroscopic PAT that …

Spatiotemporal Negative Inventory Outlier Decomposition For Supply Chain Applications In Consumer-Packaged Goods (Cpg), Hayden Mcdonald

Data Science Undergraduate Honors Theses

Coca-Cola is a popular soft drink brand with sales occurring in every Walmart store across the world, which generates large quantities of data and requires a robust supply chain system. However, the company does not currently have a sophisticated, automated, and/or prescriptive system for detecting where, when, and why inventory outages occur and applying preventative measures to avoid loss of revenue from the absence of inventory on store shelves. This thesis proposes and applies a novel, prescriptive system for this purpose. An inventory outage can be seen as a ‘negative’ statistical outlier in a time series of inventory for an …

The Importance Of Data Preparation In A Data Science Problem, Sophia Beard

Data Science Undergraduate Honors Theses

This study is going to be based on an inventory outlier automation data science problem that is being solved to identify and prescribe inventory level outliers to help keep shelves stocked in terms of beverages. The objective of this paper will address why it is so important to understand the data that is involved in a particular data science problem and how planning ahead ensures a successful outcome in the data science world. In this data science project, Spatiotemporal Outlier Analysis for Inventory Intervention Automation, it was crucial for the team to understand, research, and visualize the data we were …

Employing Natural Language Processing To Link Customer Survey Feedback With Net Promoter Scores, Gerardo Moreno

Data Science Undergraduate Honors Theses

This project leverages Natural Language Processing (NLP) to analyze customer feedback from Sam’s Club, aiming to pinpoint key factors influencing Net Promoter Score (NPS). Using sentiment analysis, bigram, and trigram techniques, the project analyses textual data to identify underlying themes and patterns that affect customer satisfaction. These analyses reveal actionable insights into customer preferences and pain points, facilitating a deeper understanding of what drives customer satisfaction in retail environments. By correlating these findings with NPS, this paper details strategies to enhance customer experiences at Sam’s Club, ultimately aiming to improve both satisfaction levels and NPS.

A Spatiotemporal Analysis Of Violent Crime In Little Rock, Arkansas From 1999-2022, Nicole Rogers

Data Science Undergraduate Honors Theses

Little Rock, Arkansas is not only the capital and largest city in Arkansas, but it has one of the highest crime rates amongst cities with over 100,000 people in the country. According to the US Census in 2020, Little Rock had a population of 202,591. In the same year, Little Rock Police Department recorded 3,567 cases of violent crime, leading to a violent crime rate of 1,805 violent crime occurrences per 100,000 people. For perspective, Chicago’s violent crime rate was approximately half of that of Little Rock during the same time period. Crime, like other social phenomena is unevenly distributed …

Low-Resource Icd Coding Of Discharge Summaries, Ashton Williamson

All Theses

Medical coding is the process by which standardized medical codes are assigned to patient health records. This is a complex and challenging task that typically requires an expert human coder to review health records and assign codes from a classification system based on a standard set of rules. Considering the downstream use of these codes in statistical analysis, billing, and patient care, improving the accuracy and efficiency of the medical coding process through automation could have a far-reaching impact on the healthcare domain. Since health records typically consist of a large proportion of free-text documents, this problem has traditionally been …

Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi

LSU New Orleans Theses and Dissertations

This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …

Igwas: Image-Based Genome-Wide Association Of Self-Supervised Deep Phenotyping Of Retina Fundus Images, Ziqian Xie, Tao Zhang, Sangbae Kim, Jiaxiong Lu, Wanheng Zhang, Cheng-Hui Lin, Man-Ru Wu, Alexander Davis, Roomasa Channa, Luca Giancardo, Han Chen, Sui Wang, Rui Chen, Degui Zhi

Faculty, Staff and Student Publications

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes …

Development And Validation Of A Rule-Based Algorithm To Identify Periodontal Diagnosis Using Structured Electronic Health Record Data, Bunmi Tokede, Ryan Brandon, Chun-Teh Lee, Guo-Hao Lin, Joel White, Alfa Yansane, Xiaoqian Jiang, Elsbeth Kalenderian, Muhammad Walji

Faculty, Staff and Student Publications

AIM: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions.

MATERIALS AND METHODS: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm's ability to accurately extract clinical data from the EHR, and then …

Predicting True Attributes Of Retailer Data, Abby Willard

Data Science Undergraduate Honors Theses

In the rapidly evolving landscape of consumer-packaged goods (CPG) retail, understanding the true values of various factors influencing sales performance is paramount for strategic decision-making and effective resource allocation. In ensuring accuracy of data points, the CatBoost model is utilized, a state-of-the-art gradient boosting technique, to predict the true attribution values of datasets sourced from CPG industry retailers.

By leveraging CatBoost’s inherent capabilities to handle categorical data and its robustness against overfitting, the models are optimized to accurately predict the true attribution values for various items. The performance of the CatBoost models is evaluated through rigorous cross-validation techniques and compared …

Concurrent Processing Of Retail Data In Python To Optimize Runtime, Bobby Slavin

Data Science Undergraduate Honors Theses

This thesis explores the application of multiprocessing and multithreading techniques in Python to optimize runtime efficiency on the analysis of retail data. As the retail data processed by a program increases, so does the runtime of the program. If you are performing this processing using only a single core, even a gigabyte of data can potentially take upwards to half an hour to finish processing, while larger datasets of 100 GB or more could take days, heavily limiting the amount of retail data that can be processed in a reasonable amount of time. By employing multithreading and multiprocessing architectures in …

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 …