Data Science Commons^™

Estimation Methods For Bayesian Exponential Random Graph Models Under The Horseshoe Prior., Pamela Linares

Electronic Theses and Dissertations

Networks are powerful tools for modeling the complexity of social interactions, biological systems, and information spread. A leading statistical frameworks for analyzing network data are Exponential Random Graph Models (ERGMs), which provide a principled approach to capturing structural dependencies. However, ERGMs remain challenging to estimate, especially in sparse or high-dimensional settings where models suffer from degeneracy and unstable parameter inference. This paper proposes a penalized Bayesian approach to ERGMs that utilizes the horseshoe prior, a sparsity-inducing global-local shrinkage prior. This prior offers robust regularization while preserving important signals, improving estimation by shrinking irrelevant parameters and reducing the impact of extreme …

Application Of Artificial Neural Network Algorithms For Irrigation Scheduling, Lisa Umutoni

All Dissertations

Neural networks have been extensively used in predicting soil water tension for improved irrigation scheduling and management. However, their lack of interpretability constrains their efficacy in grasping the nuanced patterns prevalent in soil water tension time series data. The first goal of this research was to develop interpretable deep neural network models for soil water tension prediction across multiple soil depths (0.15m, 0.3m, 0.46m and 0.6m) and prediction horizons (1h, 6h, and 12h). The Neural Hierarchical Interpolation for Time Series (N-HiTS) and Neural Basis Expansion Analysis Time Series (N-BEATS) models were used in this research. Historical soil water tension data …

Graph-Based Machine Learning: Higher-Order Interactions, Guided Generation, And Knowledge-Graph Tools, Thomas J. Kerby

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation brings the power of graph thinking to three key challenges in modern AI, making complex data more transparent, generative design more controllable, and scholarly exploration more intuitive. First, we introduce Local CorEx, a new machine learning technique that uncovers hidden relationships among variables, making it easier to understand complex datasets without heavy computation. Next, we show how to guide the creation of new molecules by viewing the generation process itself as a walk through a "state graph," letting researchers steer outcomes toward desired chemical properties—without any extra model training. Finally, we deliver an open-source toolkit that builds interactive …

Non-Blackbox Robust Design Of Machine Learning In Networks, Venkat Sai Suman Lamba Karanam

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

Networked systems have become increasingly complex, with newer communication technologies and standards being added every day. Machine Learning (ML) and Artificial Intelligence (AI) paradigms have been adopted in networks to not only solve many fundamental problems, but also to allow seamless integration of components comprising them. The saying “let’s not reinvent the wheel” in ML/AI adoption implies that model architecture design be left for pure ML/AI researchers, while network researchers focus on input preprocessing (e.g. formatting the packet data to be fed to a model), hyperparameter fine-tuning and a trial-and-error approach to find the “best” result. …

Authentication And Message Integrity Verification For Emerging Wireless Networks, Ebuka Philip Oguchi

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

This dissertation presents a comprehensive body of research on authentication and message integrity verification for emerging wireless networks, focusing on secret-free and physical layer security techniques across diverse, challenging, and unconventional environments.

It comprises four first-author contributions that span underground wireless systems, over-the-air (OTA) channels, vehicular communications, and nanoscale molecular networks.

The first contribution, Soil-Assisted Trust Establishment for Underground Wireless Networks (STUN), introduces a physical-layer trust bootstrapping protocol that achieves authentication and message integrity without pre-shared secrets. Leveraging underground-to-air propagation laws and trusted relay nodes, STUN resists active signal injection attacks and demonstrates security comparable to the unbalanced oil and …

Tackling Data Quality Challenges In Remote Sensing: Solutions For Reliable Urban Heat Island Analysis, Wei Xia, Aqil Tariq, Hesham El-Askary, Rana Waqar Aslam, Elgar Barboza, Dmitry E. Kucher, Youssef M. Youssef, Habib Kraiem

Mathematics, Physics, and Computer Science Faculty Articles and Research

Urban heat islands (UHIs) pose critical challenges to public health, energy demand, and environmental sustainability, particularly in rapidly expanding urban regions. This study examines the complex relationship between building configurations and integrated green spaces, as well as their combined impact on thermal regulation. It focuses on addressing data quality issues commonly encountered in remote sensing applications. Using high-resolution multispectral and thermal imagery, we developed an integrated modeling approach that captures the collective influence of built form and green infrastructure on urban microclimates. A key finding is the significant linear inverse relationship between green space coverage and land surface temperature, underscoring …

Discovering And Designing Novel Perovskite Photovoltaic Materials Via Machine Learning, Junyeong Ahn

Discovery Undergraduate Interdisciplinary Research Internship

Perovskite semiconductors are promising materials for high-efficiency photovoltaics due to their outstanding optoelectronic properties, emerging as a sustainable energy source through solar cell applications. Perovskites with the ABX₃ composition (A, B = metal or organic cations with varying oxidation states; X = chalcogen or halogen anions) have gained interest for their excellent phase stability and compositional tunability. However, combinatorial possibilities arising from the many choices of A, B, and X site species, and their respective mixing fractions, a large number of possible ABX₃ perovskites remain undiscovered. In this work, we used machine learning (ML) methods to design new stable and …

Multiclass Cyberbullying Detection Using Advanced Neural Network Architectures: A Comparative Study Amidst The Covid-19 Pandemic, Mahyar Alinejad

Data Science and Data Mining

Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance …

Multiclass Cyberbullying Detection Using Advanced Neural Network Architectures: A Comparative Study Amidst The Covid-19 Pandemic, Mahyar Alinejad

Data Science and Data Mining

Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance …

Towards Scalable Taxi Demand Prediction, Yifei Shen

Lingnan Theses (MPhil & PhD)

Accurate taxi demand prediction is essential for optimizing urban mobility systems across varying spatial-temporal resolutions and data conditions. Scalable taxi demand prediction refers to the capability of forecasting models to adapt to different granularities of spatial and temporal data while maintaining prediction accuracy, a critical requirement for practical urban applications ranging from fleet management to transportation planning. However, two fundamental challenges impede this scalability: data sparsity and multi-resolution forecasting requirements. Data sparsity, particularly pronounced in high-resolution predictions where numerous regions exhibit minimal activity, significantly compromises model performance. Concurrently, different urban applications necessitate predictions at varying temporal and spatial granularities, requiring …

Policy-Based Redactable Set Signatures, Zachary A. Kissel

Computer and Data Science Faculty Publications

A redactable set signature scheme is a signature scheme that allows a redactor, without possessing the signing key, to convert a signature on set S to a signature on set S' if S' ⊂ S. This paper introduces a new form of redactable set signature scheme called a policy-based redactable set signature scheme. These redactable set signatures allow for a signer to provide a redaction policy at signing time that limits the possible redactions that can be made by a redactor. In particular, a signature on set S can only be redacted to a signature on if S' ⊂ …

Three-Stage Latent Dynamics Forecasting (T-Ldf) Framework For Shenzhen Metro Passenger Flow Prediction, Tianze Zhang

Lingnan Theses (MPhil & PhD)

Accurate forecasting of metro passenger flow is vital for efficient urban transportation management and optimal resource allocation in modern cities. Traditional ARIMA-based models effectively capture regular, cyclical patterns but struggle with sudden, nonlinear fluctuations caused by random events such as weather disruptions, special events, or service interruptions. Moreover, existing research predominantly focuses on individual stations, overlooking the complex cross-station interactions inherent in networked metro systems where passenger flows are interconnected across the entire network.

To address these critical limitations, we propose the Three-Stage Latent Dynamics Forecasting (T-LDF) Framework, a novel approach that systematically integrates temporal decomposition, latent dynamics extraction, and …

A Cancer Education Needs Assessment: Informing Middle-Aged Female Patients About The Relationships Between Obesity And Women’S Health Concerns In The Reproductive System, Breast, And Endometrial Health, Batul Mirza

MUSC Theses and Dissertations

Obesity significantly impacts women’s health, particularly among middle-aged women, by increasing the risk of hormone-sensitive cancers such as breast, endometrial, and reproductive system cancers. This study examines the educational needs of this demographic group regarding obesity-related cancer risks and explores effective intervention strategies. Obesity-induced mechanisms – hormonal imbalances, chronic inflammation, and insulin resistance – drive cancer susceptibility, emphasizing the need for targeted health education. The study employs a qualitative design, which includes interviews with subject matter experts (SMEs) and surveys of middle-aged women. The goal is to assess awareness, perceived barriers, and preferred learning methods. Findings suggest that with many …

Ai Project Facilitation Guidance For Research Computing And Data (Rcd) Professionals, Anna Alber, Laura Briggs, Paul Brunk, Manasvita Joshi, Atish P. Kamble, Amira Kefi, Timothy Middelkoop, Semir Sarajlic, Ana Maria Sokovic, Jeffrey N. Valdez, Ying Zhang

Administration and Staff Articles and Research

The role of Artificial Intelligence (AI) in research and education continues to rapidly grow, resulting in increased collaboration between researchers in AI and Research Computing and Data (RCD) professionals to meet the research and teaching demands. RCD professionals bridge the gap between research and technology by guiding and collaborating with researchers and educators through the process of selecting the hardware, software, and services best suited for executing their AI projects. This includes ensuring compliance with funding and regulatory requirements across the entire lifecycle of the project. In this paper, we present an overview of the AI project lifecycle and how …

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …

A Study Of Machine Learning Techniques In Solving Biochemical And Chemical Problems, Kenneth Micheal Plackowski

Chemistry and Chemical Biology ETDs

Data-driven approaches to solving problems in biology and chemistry require utilization of reliable techniques and machine learning algorithms are the modern reliable approach. This work presents three problems that involve use of supervised learning techniques when classification is the goal and unsupervised learning techniques when global data representation is the goal.

In the first problem, we demonstrate the use of unsupervised clustering techniques, self-organizing maps and K-means, to ascertain analyte detection capabilities of carbon nitride dots. In the second problem, we add scalability features to a functional group classification model applied to infrared data and evaluate its ability to inform …

Seeking Structure In Complex Systems: From Feature Analysis To Space-Time Causal Discovery With Earth Science Applications, Jeffrey J. Nichol

Computer Science ETDs

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that …

Modifications To The Spiral Array: A Computational Approach To Music Analysis, Rose Bittle

DePaul Discoveries

The Spiral Array is a geometric model of musical tonality and exists as a tool in computer-aided music analysis. The model was first published in 2000 by Elaine Chew, PhD, in her thesis, Towards a Mathematical Model of Tonality. This project aimed to restructure the Spiral Array, limiting user ambiguity and optimizing the application of musical key finding. The existing model defines pitch, chord, and key location to form a series of spirals dependent on a set of flexible weights. In our research we were able to identify potential issues with the pitch definitions themselves and experiment with methods …

Modeling, Analysis, And Prediction Of Covid-19 Dynamics With Interacting Subpopulations And Implicit Behavior Using Physics-Informed Neural Networks, Naima Aubry-Romero, Alonso Ogueda-Oliva, Padmanabhan Seshaiyer

Spora: A Journal of Biomathematics

In this paper, we consider an extended SEIR compartmental model that incorporates young and old interacting subpopulations, allowing for cross-group transmission dynamics. Implicit behavioral changes are included to determine the influence of social behavior on coronavirus transmission dynamics. The basic reproduction number, the average number of secondary cases of infection produced by a single primary case, is derived for both the explicit and implicit model using the next-generation matrix method. We solve the associated differential equation systems and estimate useful parameters in the explicit model using physics-informed neural networks (PINNs). Our results point to how the PINNs approach offers an …

3d Solid Models, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

3D solid models for model vehicles, target panels, objects, and the desert terrain model in STL file format.

Ground Truth Images, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Laboratory and scenario ground truth images for the Model Desert Terrain Monochromatic DoT dataset.

Polarimetric Data: Scenario 01, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric data Scenario 01 collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

Dataset Description, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Polarimetric dataset containing data collected within the Automated Remote Sensing Solar Simulation Lab at the University of Dayton. The data were collected using a visible monochromatic division-of-time imaging polarimeter. A model desert terrain model was constructed and imaged for eight different scenarios consisting of different model panel and vehicle targets. The dataset is parameterized across different sensor, scene, and illumination geometries that mimic outdoor solar irradiance conditions.

From Disruption To Integration: Cryptocurrency Prices, Financial Fluctuations, And Macroeconomy, Zhengyang Chen

Faculty Publications

This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency's deep financial system …

Measuring How Much Judges Matter For Case Outcomes, Ryan W. Copus, Ryan Hübert

Faculty Works

A large empirical literature examines how judges’ traits affect how cases get resolved. This literature has led many to conclude that judges matter for case outcomes. But how much do they matter? Existing empirical findings understate the true extent of judicial influence over case outcomes since standard estimation techniques hide some disagreement among judges. We devise a machine learning method to reveal additional sources of disagreement. Applying this method to the Ninth Circuit, we estimate that at least 38% of cases could be decided differently based solely on the panel they were assigned to.

Use Of High-Throughput Phenomics With And Without Fungicide As A Wheat Breeding Tool, Gerardo Ivan Rivera Collazo

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

Wheat (Triticum aestivum) is an important food staple for many countries around the world and current consumption and production data demonstrate a production deficit. Breeders are challenged to help close this gap in production by selecting better cultivars with improved yields. Several methods aim to accelerate the breeding process to reduce the time required for releasing an elite variety. One of the main breeding bottlenecks is the lack of fast and reliable phenotypic data acquisition that could dissect physiological and morphological plant data. High-throughput remote sensing could have the potential to reduce this bottleneck by streamlining data acquisition, …

Data Annotations, Bradley M. Ratliff

Model Desert Terrain Monochromatic DoT Dataset

Pixel-wise object masks for each polarimetric scene in ASL file format for the Model Desert Terrain Monochromatic DoT data.

Quantum Analysis Of Protein–Ligand Binding By Integrating Structural Resolution, Sequence Homology, And Ligand Properties, Don Roosan, Samira Samrose, Rubayat Khan, Saif Nirzhor, Brian Provencher

Computer and Data Science Faculty Publications

Predicting protein–ligand binding affinity is a fundamental challenge in computational biology and drug discovery, complicated by diverse factors including protein sequence variability, ligand chemical diversity, and structural resolution. Here, we present an integrative study that combines classical machine learning and quantum-enhanced modeling to investigate how crystal structure resolution, sequence similarity, and ligand properties jointly influence binding affinity. Using a curated “refined” dataset from PDBbind and an expanded general dataset, we first conduct correlation and regression analyses to quantify the relationships among binding affinity, ligand descriptors (e.g., molecular weight, logP), and protein structural metrics (resolution, R-factor). We observe moderate positive correlations …

Comparing Methods For Glomerular Filtration Rate Estimation, Xiaoqian Zhu, Tariq Shafi, Keith Norris, Jeannette Simino, Srishti Shrestha, Thomas H. Mosley, Michael E. Griswold, Seth T. Lirette

Data Science Publications

Background: The glomerular filtration rate (GFR), estimated from serum creatinine (SCr), is widely used in clinical practice for kidney function assessment, but SCr-based equations are limited by non-GFR determinants and may introduce inaccuracies across racial groups. Few studies have evaluated whether advanced modeling techniques enhance their performance. Methods: Using multivariable fractional polynomials (MFP), generalized additive models (GAM), random forests (RF), and gradient boosted machines (GBM), we developed four SCr-based GFR-estimating equations in a pooled data set from four cohorts (n = 4665). Their performance was compared to that of the refitted linear regression-based 2021 CKD-EPI SCr equation using bias (median …

Enhancing Biosecurity In Tamper-Resistant Large Language Models With Quantum Gradient Descent, Fahmida Hai, Saif Nirzhor, Rubayat Khan, Don Roosan

Computer and Data Science Faculty Publications

This paper introduces a tamper-resistant framework for large language models (LLMs) in medical applications, utilizing quantum gradient descent (QGD) to detect malicious parameter modifications in real time. Integrated into a LLaMA-based model, QGD monitors weight amplitude distributions, identifying adversarial fine-tuning anomalies. Tests on the MIMIC and eICU datasets show minimal performance impact (accuracy: 89.1 to 88.3 on MIMIC) while robustly detecting tampering. PubMedQA evaluations confirm preserved biomedical question-answering capabilities. Compared to baselines like selective unlearning and cryptographic fingerprinting, QGD offers superior sensitivity to subtle weight changes. This quantum-inspired approach ensures secure, reliable medical AI, extensible to other high-stakes domains.