Categorical Data Analysis Commons^™

Factors Predictive Of The Development Of Surgical Site Infection In Thyroidectomy, A Replication Study Of Myssiorek (2018), Kaitlyn M. Kenig

Capstone Experience: Master of Public Health

The original study aimed to show that thyroidectomy does not result in surgical site infection (SSI) in most cases, and thus routine prescription of antibiotics is not necessary. The study looked to see what risk factors could predict the incidence of SSI. This would highlight those individuals who were at most risk of developing SSI, and then antibiotics would only be prescribed to these individuals instead of all or most individuals who undergo thyroidectomy.

This study used NSQIP data to look at incidence of SSI and look for risk factors that may be predictive of SSI. Only surgeries that were …

Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Concrete cracks and structural steel corrosion are two of the most common defects in bridges. Quantifying and classifying these defects provide bridge inspectors and engineers with valuable data for assessing deterioration levels. However, the bridge inspection process is typically a subjective, time intensive, and tedious task, as defects can be overlooked or in locations not easily accessible. Previous studies have investigated deep learning-based inspection methods, implementing popular models such as Mask R-CNN and U-Net. The architectures of these models offer certain advantages depending on the required task. This thesis aims to evaluate and compare Mask R-CNN and U-Net regarding their …

A Survey Of The Murray State University Csis Department Of Student And Instructor Attitudes In Relation To Earlier Introduction Of Version Control Systems, Gavin Johnson

Honors College Theses

Over the previous 20 years, the software development industry has overseen an evolution in application of Version Control Systems (VCS) from a Centralized Version Control System (CVCS) format to a Decentralized Version Control Format (DVCS). Examples of the former include Perforce and Subversion whilst the latter of the two include Github and BitBucket. As DVCS models allow software contributors to maintain their respective local repositories of relevant code bases, developers are able to work offline and maintain their work with relative fault tolerance. This contrasts to CVCS models, which require software contributors to be connected online to a main server. …

Latent Classification Of Multi-Model Non-Homogeneous Continuous-Time Markov Chains, Joonha Chang

Dissertations and Theses (Open Access)

The continuous-time Markov chain (CTMC) model and latent clustering models are commonly used to study longitudinal measures of categorical outcomes. Because of its simple but powerful Markovian property, CTMC models have been widely used in medical and public health researches. Due to limitations in the standard CTMC model, there have been some studies on non-homogeneous continuous-time Markov chain (NH-CTMC) that utilized time-dependent rates, but the progresses have been limited. NH-CTMC can be more powerful than CTMC by its default nature of time-dependent rate that can be fitted to a wider range of applications in medical studies. In this study, we …

Health And Healthcare: Designing For The Social Determinants Of Health And Blue Zones In North Nashville, Rebecca Tonguis, Honor Thomas, Olivia Hobbs

[Archive] Belmont University Research Symposium (BURS)

Owned by North Nashville’s First Community Church, a now empty site in the Osage-North Fisk neighborhood of North Nashville has been identified as a potential site for a new location of The Store, in addition to a community-centric architectural development based on the social determinants of health and informed by the principles behind Blue Zones, the locations with the highest lifespans in the world. Opened by Brad Paisley and Kimberly Williams-Paisley, The Store is a free grocery store that “allow[s] people to shop for their basic needs in a way that protects dignity and fosters hope”, for which North Nashville …

The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals

Faculty Publications

Given the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was …

Making Sense Of Making Parole In New York, Alexandra Mcglinchy

Dissertations, Theses, and Capstone Projects

For many individuals incarcerated in New York, the initial step toward freedom begins with an interview with the Board of Parole. This process, however, is frequently a complex and challenging one, characterized by repeated denials and extended incarcerations. The disparity in outcomes – where one individual may receive over 20 denials and another is granted parole on their first attempt – highlights the ambiguity and inconsistency in the parole decision-making process. This project aims to clarify the factors that influence parole decisions by concentrating on measurable variables. These include age, race, duration of sentence served, proportion of sentence served, type …

Using Ai For Qualitative Labeling: Consistency And Comparisons, James Mcintyre

Honors Program Theses

This paper details a research study evaluating AI's ability to perform qualitative deductive coding. Multiple AI models were utilized and compared against three human coders and one expert coder. A series of 107 statements were sourced from a group discussion for a qualitative impact assessment of an organization. The AI models were provided these statements and directed to code them using the Community Capitals Framework. Two generations of AI models were evaluated. Overall, the AI achieved a fair level of agreement with the human annotators, but the alignment was far from perfect. Newer AI models did not increase agreement with …

Imputation Strategies For Different Categories Of Missing Data, Karthik Chalumuri

Honors Theses and Capstones

Addressing missing data in research is crucial for ensuring the reliability and validity of study findings, yet it remains a significant challenge. This study investigates the impact of missing data on research outcomes and explores the underutilization of existing tools for managing missingness, potentially leading to gaps in critical information with tangible implications for decision-making processes (Dziura et al.).

Focusing on the different categories of missing data—Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR)—this research examines various imputation strategies tailored to each category. Specifically, we compare the efficacy of several model-based imputation methods, …

Intelligent Capabilities Of Traditional Knowledge Organization Methods, Xinning Su

Journal of Scientific Information Research

[Purpose/significance]By analyzing the system and rules of traditional knowledge organization methods, the intelligent capabilities of traditional knowledge organization methods are refined and integrated into artificial intelligence（AI） technology, to enhance the precision and efficiency of AI in information processing. [Method/process]This paper reviews the development of knowledge organization and analyses the inherit structure and mechanisms of traditional knowledge organization methods. [Result/conclusion]Research suggests that over centuries of development and evolution, knowledge organization has gained the ability to reflect knowledge systems and disciplinary systems across different disciplines from diverse perspectives, establish semantic relations from diverse knowledge associations, and associate and integrate knowledge of different …

Bayesian Variable Selection With Shrinkage Priors And Generative Adversarial Networks For Fraud Detection, Amina Issoufou Anaroua

Graduate Thesis and Dissertation 2023-2024

This research paper focuses on fraud detection in the financial industry using Generative Adversarial Networks (GANs) in conjunction with Uni and Multi Variate Bayesian Model with Shrinkage Priors (BMSP). The problem addressed is the need for accurate and advanced fraud detection techniques due to the increasing sophistication of fraudulent activities. The methodology involves the implementation of GANs and the application of BMSP for variable selection to generate synthetic fraud samples for fraud detection using the augmented dataset. Experimental results demonstrate the effectiveness of the BMSP GAN approach in detecting fraud with improved performance compared to other methods. The conclusions drawn …

Deep Learning One-Class Classification With Support Vector Methods, Hayden D. Hampton

Graduate Thesis and Dissertation 2023-2024

Through the specialized lens of one-class classification, anomalies–irregular observations that uncharacteristically diverge from normative data patterns–are comprehensively studied. This dissertation focuses on advancing boundary-based methods in one-class classification, a critical approach to anomaly detection. These methodologies delineate optimal decision boundaries, thereby facilitating a distinct separation between normal and anomalous observations. Encompassing traditional approaches such as One-Class Support Vector Machine and Support Vector Data Description, recent adaptations in deep learning offer a rich ground for innovation in anomaly detection. This dissertation proposes three novel deep learning methods for one-class classification, aiming to enhance the efficacy and accuracy of anomaly detection in …

Ensemble Classification: An Analysis Of The Random Forest Model, Jarod Korn

Williams Honors College, Honors Research Projects

The random forest model proposed by Dr. Leo Breiman in 2001 is an ensemble machine learning method for classification prediction and regression. In the following paper, we will conduct an analysis on the random forest model with a focus on how the model works, how it is applied in software, and how it performs on a set of data. To fully understand the model, we will introduce the concept of decision trees, give a summary of the CART model, explain in detail how the random forest model operates, discuss how the model is implemented in software, demonstrate the model by …

Tropical Fish Study In Tahiti, French Polynesia, Miranda Brainard, Caitlyn Swango, Paityn Houglan, Richard Londraville

Williams Honors College, Honors Research Projects

In May of 2023, I embarked on an exciting research journey to Moorea, French Polynesia, alongside fellow students and faculty members from the University of Akron and Syracuse University. This expedition was part of the university-sponsored Tropical Vertebrate Biology course, where we delved into the exploration of various tropical species inhabiting the island, including sea urchins, geckos, and my primary focus, the blackspotted rockskipper.

My research team, composed of my co-authors and me, was particularly intrigued by the unique refuge-seeking behavior displayed by blackspotted rockskippers. These amphibious fish are renowned for their remarkable ability to inhabit tide pools and rocky …

Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Cui, Lucia Tabacu, Andrew Leroux

Mathematics & Statistics Faculty Publications

Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of this sort requires novel methodologic work at the intersection of survey statistics and functional data analysis. Here, we address this crucial gap in the literature by proposing an estimation and inferential framework for generalizable scalar-on-function regression models for data collected under a complex survey design. We propose to: …

Optimizing Sports Outcome Prediction Through Feature Engineering And Machine Learning, Vitor S. Freitas

Graduate Theses/Dissertations

The challenge of predicting the outcome of a team game lies in the high complexity and dynamics of the sports data. This thesis focuses on the aspect of using feature engineering and the genetic algorithm to predict the winner and the score of various sports events. Generally, it deals with how machine learning algorithms are combined with state-of-the-art feature engineering techniques in sports datasets derived from various sports disciplines. In this thesis, five different machine learning models have been applied, classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine …

Redesign, Evaluation, And Validation Of A Commercially Viable High-Resolution Melt Based Mixture Screening Tool, Chastyn Smith

Theses and Dissertations

Analysis of evidentiary samples containing DNA from multiple contributors (“mixtures”) is a time intensive process for a forensic analyst and one where the contributor nature of a sample is not revealed until the end of the traditional forensic workflow. Often, at this stage, retesting or additional testing of mixture samples may not be possible, particularly if the DNA collection device did not preserve the DNA well enough; consequently leaving only trace amounts of a contributor’s DNA present. Thus, a new collection device that would allow for the increased preservation/integrity of evidentiary samples as well as a method that would allow …

Multiscale Modelling Of Brain Networks And The Analysis Of Dynamic Processes In Neurodegenerative Disorders, Hina Shaheen

Theses and Dissertations (Comprehensive)

The complex nature of the human brain, with its intricate organic structure and multiscale spatio-temporal characteristics ranging from synapses to the entire brain, presents a major obstacle in brain modelling. Capturing this complexity poses a significant challenge for researchers. The complex interplay of coupled multiphysics and biochemical activities within this intricate system shapes the brain's capacity, functioning within a structure-function relationship that necessitates a specific mathematical framework. Advanced mathematical modelling approaches that incorporate the coupling of brain networks and the analysis of dynamic processes are essential for advancing therapeutic strategies aimed at treating neurodegenerative diseases (NDDs), which afflict millions of …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Exploration And Statistical Modeling Of Profit, Caleb Gibson

Undergraduate Honors Theses

For any company involved in sales, maximization of profit is the driving force that guides all decision-making. Many factors can influence how profitable a company can be, including external factors like changes in inflation or consumer demand or internal factors like pricing and product cost. Understanding specific trends in one's own internal data, a company can readily identify problem areas or potential growth opportunities to help increase profitability.

In this discussion, we use an extensive data set to examine how a company might analyze their own data to identify potential changes the company might investigate to drive better performance. Based …