Digital Commons Network^™

Stability Analysis Of Thermohaline Convection With A Time-Varying Shear Flow Using The Lyapunov Method, Kalin Kochnev

Honors Scholar Theses

This work applies the Lyapunov method to identify instabilities and compute the growth rate of a linear time-varying system. The linear system studied describes cold fresh water on top of hot salty water with a periodically time-varying background shear flow. A time-dependent weighting matrix is employed to construct a Lyapunov function candidate. The resulting linear matrix inequalities are discretized in time using the forward Euler method. As the number of temporal discretization points increases, the growth rate predicted by the Lyapunov method or Floquet theory, used for comparison, will converge to the same value obtained from numerical simulations. Furthermore, the …

Achieving More Accurate And Interpretable Fraud Detection With Double Machine Learning, Jeremy Andrew Berry

Theses and Dissertations

Fraud detection remains a critical challenge across industries such as insurance, healthcare, finance, and government. Global losses from fraud and financial crime are estimated in the trillions annually, including billions in healthcare and insurance fraud alone. While effective for prediction, traditional machine learning methods often lack causal interpretability and struggle to adapt to evolving fraud tactics. This dissertation investigates the application of Double Machine Learning (DML), an emerging causal inference technique, to enhance both the accuracy and interpretability of fraud analytics. The research compares DML against established causal inference approaches, leveraging a meta-learning framework to evaluate model performance on accuracy, …

Cybercamp: An Experience Report On The Transformations Of An Intensive Cybersecurity Summer Camp For High School Students, Jose R. Ortiz Ubarri, Kariluz Dávila Diaz Ph.D., Rafael A. Arce Nazario

Journal of Cybersecurity Education, Research and Practice

The Cybercamp is a Cybersecurity summer camp for high school students that has been held for the last nine years at a Hispanic Serving Institution. Since its inception in 2016 the Cybercamp has undergone several transformations in response to budget reductions and the COVID pandemic, to finally become its current version: a rich, hands-on learning experience that we believe is easily replicable even in resource-challenged environments.

In this paper, we document the transformations of the Cybercamp and discuss the developed curriculum and materials in hopes that others will reuse, adapt, and improve upon them. In the Cybercamp, we apply active …

Patterns Of Llm Weaponization: A Comparative Analysis Of Exploitation Incidents Across Commercial Ai Systems, George Antoniou

Faculty and Staff Publications & Presentations

This comparative study examines patterns of Large Language Model (LLM) weaponization through systematic analysis of four major exploitation incidents spanning 2023-2025. While existing research focuses on isolated incidents or theoretical vulnerabilities, this study provides the first comprehensive comparative framework analyzing exploitation patterns across state-sponsored cyber-espionage (Anthropic Claude incident), academic security research (GPT-4 autonomous privilege escalation), social engineering platforms (SpearBot phishing framework), and underground criminal commoditization (WormGPT/FraudGPT ecosystem). Through comparative analysis across eight dimensions—adversary sophistication, target selection, exploitation techniques, autonomy levels, detection evasion, attribution challenges, defensive gaps, and capability democratization—this research identifies critical cross-case patterns informing defensive prioritization. Findings reveal three …

Developing An Ai-Assisted Grading System Using Large Language Models, Andrei Modiga

MS in Computer Science Project Reports

We present a grading system that accelerates evaluation of open-ended student work across scanned and digital workflows. The system crops answer regions from PDFs, assigns submissions via OCR on identity regions only, and groups answers by visual semantics using a vision LLM. Instructors review and edit groups, apply rubric items once per group, and export grades from an on-screen table. The solution integrates Ghostscript rasterization, PdfPig page orchestration, SkiaSharp region extraction, Tesseract identity OCR, and GPT-4o Vision for grouping. We detail the architecture, token-budgeted batching strategy, and persistence design, then describe testing results for grouping quality, time-on-task, and usability. The …

Application Of Augmented Reality Technology As A Dietary Monitoring And Control Measure Among Adults: A Systematic Review, Gabrielle Victoria Gonzalez, Bingjing Mao, Ruxin Wang, Wen Liu, Chen Wang, Tung Sung Tseng

School of Public Health Faculty Publications

Background/Objectives: Traditional dietary monitoring methods such as 24 h recalls rely on self-report, leading to recall bias and underreporting. Similarly, dietary control approaches, including portion control and calorie restriction, depend on user accuracy and consistency. Augmented reality (AR) offers a promising alternative for improving dietary monitoring and control by enhancing engagement, feedback accuracy, and user learning. This systematic review aimed to examine how AR technologies are implemented to support dietary monitoring and control and to evaluate their usability and effectiveness among adults. Methods: A systematic search of PubMed, CINAHL, and Embase identified studies published between 2000 and 2025 that evaluated …

Impacts Of Upgrading Gcc On The Srtuner Compiler Autotuning Algorithm, Jonathan D. Butterfield

Theses and Dissertations

An often-overlooked research gap is the applicability of research over time. The impact of updating the GNU Compiler Collection version was investigated for the SRTuner compiler optimization research. Versions 7 through 14 were tested using documented flags and recommended flag dependencies. Quality controls measured and enforced consistency across test runs and benchmarks. The performance of SRTuner was compared to a random control while the impact of enforcing flag dependencies was evaluated. The most influential flags were investigated by turning them off in isolation, but results were mixed. It was determined that enforcing flag dependencies resulted in improved performance, but the …

Polyminhash: Efficient Area-Based Minhashing Of Polygons For Approximate Nearest Neighbor Search, Alima Subedi, Sankalpa Pokharel, Satish Puri

Computer Science Faculty Research & Creative Works

Similarity searches are a critical task in data mining. As datasets grow larger, exact nearest neighbor searches quickly become unfeasible, leading to the adoption of approximate nearest neighbor (ANN) searches. ANN has been studied for text data, images, and trajectories. However, there has been little effort to develop ANN systems for polygons in spatial database systems and geographic information systems. We present PolyMinHash, a system for approximate polygon similarity search that adapts MinHashing into a novel 2D polygon-hashing scheme to generate short, similarity-preserving signatures of input polygons. Minhash is generated by counting the number of randomly sampled points needed before …

A Trigger For The Autonomous Decommissioning Of Smart Devices, Ravindra Mangar, Jared Chandler, Jingyu Qian, Carl A. Gunter, Timothy J. Pierson, David Kotz

Dartmouth Scholarship

Smart devices are ubiquitous in modern environments, yet their decommissioning phase remains poorly studied and often overlooked in system design. We define secure decommissioning as the process by which a smart device securely disconnects from its environment and makes sensitive data inaccessible. If not decommissioned, devices may retain sensitive information – such as security credentials or user-behavior data that could be recovered by an adversary. Unfortunately, some users may forget to decommission a device when they dispose or sell it, and cannot decommission a device that is lost or stolen. This paper investigates a trigger mechanism for individual wireless smart …

Modern Technology Addiction: Developer Duty Of Care, Jonah Hampton

Honors College Theses

Technology addiction includes any frequent use of technology which interferes in the user’s life. The subject continues growth as an epidemic and research field, yet prior literature does not often analyze the role of technology developers. This study performs a literature and legal synthesis to evaluate user and company responsibility, implications of responsibility, and promising solutions. Post 2020 literature was selected for coverage on context, addictive features, effects, solutions, or perspectives on law. Legal examples from different addiction industries were also selected for analysis to understand previous precedents. The study found a pattern of addictive traits, persuasive design, and recurring …

Causal Structure Discovery For Explaining Important Features In Machine Learning, Yina Hou

Tennessee State University Alumni Theses and Dissertations

Understanding causal relationships between input variables and outcomes is critical for scientific advancement. The famous adage in medicine, "correlation does not imply causation," holds profound significance for understanding disease etiology. Although explainable machine learning (ML) has shown major advances in predictive modeling, it remains unclear how ML-derived important variables relate to causal variables. This thesis investigates the association between causal variables and those identified as correlated and important for ML-based prediction to bridge a critical knowledge gap in data science. Specifically, the thesis explores two research questions: when and to what extent (1) statistically correlated variables are also causal? and …

Cross-Domain Transfer Learning Of Tabular Data With Heterogeneous Feature Spaces, Kazi Fuad Bin Akhter

Tennessee State University Alumni Theses and Dissertations

Transfer learning is a cornerstone of artificial intelligence (AI), enabling the learning of new domains with limited labeled data by reusing knowledge from large-scale foundation models. Transfer learning has achieved remarkable success with vision and language models due to the homogeneity in image and text data. In contrast, a heterogeneous feature space, structured in rows and columns as tabular data, remains underexplored in the transfer-learning literature. The inductive bias of tabular data with heterogeneous feature spaces from disparate application domains is very different from image and text data, which complicates transfer learning. Several recent studies have attempted within-domain and limited …

Parameter Informed Reinforcement Learning For Vehicle System Identification, Nathan Schaff

Doctoral Dissertations and Master's Theses

Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control …

Challenges In Engineering Machine Learning (Software) Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides on the software engineering challenges unique to machine learning systems, for an undergraduate software engineering course. After contrasting traditional programming with machine learning, the deck examines why the usual tools for managing complexity—abstraction, reuse, and composition—are harder to apply to ML, given the lack of clear specifications and modularity. It covers concept drift, feedback loops (illustrated with crime-prediction and recommendation examples), and the accumulation of technical debt in ML systems, including the role and pitfalls of notebooks in moving from experimentation to production. Based on "Machine Learning in Production/AI Engineering" by Christian Kaestner and Eunsuk Kang (Carnegie Mellon …

Implementing Dataops: A Scalable Framework For Modern Data Warehousing, Dmytro Valiaiev

Theses and Dissertations

DataOps has been coined as a novel term that emerged as a synthesis of data management practices with software engineering concepts, such as DevOps and Agile, with the goal of improving data quality and governance in enterprises. The proliferation of scratch table use and transformation tools, such as dbt, has led to an exponential increase in the number of data models, which complicates standardization efforts and increases maintenance overhead. Although the market is saturated with various flavors of text-to-SQL engines that promote increased productivity and self-service use in organizations, there are limited tools available to optimize individual queries, enforce consistency, …

Artificial Intelligence In Insurance Fraud Detection: Applications And Implications For Internal Audit, Maria Davis

Honors Projects

Artificial Intelligence (AI) is being used in accounting and the insurance industry to assist with auditing and fraud detection. The Big Four public accounting firms have invested heavily in AI implementation efforts. Across these firms, AI has been used to reallocate auditors’ time from mundane tasks to more complex tasks that require human judgement. Within the insurance industry, machine learning, deep learning, and natural language processing, among other AI tools, have proven helpful in fraud detection efforts. While the positive impacts of AI usage are clear, concerns surrounding the replacement of human jobs, a lack of transparency in auditing, heavy …

Bcser: Learning Analytics For Process-Driven Computer Programming Assignments, Hamid Karimi

Funded Research Records

No abstract provided.

Predicting Skin Concern Severity From Genetic And Lifestyle Factors: A Comparative Multi-Output Machine Learning Framework, Yassine Benachour, Lina Maloukh, Sadok Bouamama, Barbara Geusens

All Works

Personalized dermatology increasingly leverages both genetic predispositions and lifestyle behaviors to model individual skin health outcomes. This study proposes a multi-output machine learning framework to predict the severity of six dermatological phenotypes—acne, redness, dryness, sensitivity, scarring, and pigmentation—using a multimodal dataset of 5,254 individuals. Input features include mutation profiles for six skin-related genes (FLG, MMP1, MMP3, AQP3, SOD2, GPX) and 22 lifestyle variables such as sun exposure, stress, and hydration. We train and evaluate LightGBM models under independent, multi-output, and chained configurations. Performance is assessed using Mean Absolute Error (MAE) and average Quadratic Weighted Kappa (QWK). The proposed ordinal-aware independent …

Career: End-To-End Active Region-Based Heliospheric Forecasting System Using Multi-Spacecraft Data And Machine Learning, Soukaina Filali Boubrahimi

Funded Research Records

No abstract provided.

Shine: Understanding The Relationships Of Photospheric Vector Magnetic Field Parameters In Solar Flare Occurrences Using Graph-Based Machine Learning Models, Shah Hamdi

Funded Research Records

No abstract provided.

Dynamicslab: Interactive Physics Simulations For Intermediate Classical Mechanics, John M. Edwards, Boyd Farrell Edwards, Hillary L. Swanson

Funded Research Records

No abstract provided.

How Novices Write Code: Discovering Best Practices And How They Can Be Adopted, John Martin Edwards

Funded Research Records

No abstract provided.

Examining The Intersectional And Structural Issues Of Routine Healthcare Utilization And Access Inequities For Lgb People With Chronic Diseases, Shiya Cao, Mehreen Mirza, Sophia Silovsky, Nicole Tresvalles, Lucia Qin, Sarah Susnea

Statistical and Data Sciences: Faculty Publications

In the United States, although the gaps in health insurance coverage by sexual orientation have been closing since the implementation of the Affordable Care Act and legalization of same-sex marriage, the LGB group (i.e., lesbian, gay, bisexual) continues to report healthcare utilization and access inequities such as more delayed or unmet care. The extant research has often examined healthcare utilization and access inequities due to affordability (e.g., out-of-pocket costs). However, healthcare utilization and access inequities are only partially explained by cost reasons; there are non-cost reasons that have not been adequately empirically examined. The present study innovatively includes discrimination structural …

A Predictive Model For Multi- Week Respiratory Risk From Red Tide On Florida’S Gulf Coast., Elmer S. Ochaeta

Computer Science and Engineering Faculty Publications

Florida’s Gulf Coast red tide (Karenia brevis) can put toxins into the air, making people cough, irritating the throat, and worsening asthma or other breathing problems especially when winds blow from the ocean toward the beach. Right now, most public updates don’t really help with the question people actually ask when planning a weekend or vacation: “Will going to or close to the beach be risky in the next few weeks?”.

In this project, I build a weekly early warning system that estimates respiratory risk for specific beaches and predicts that risk 2 to 4 weeks ahead. The study covers …

Enhancing Breast Cancer Detection In Mammographic Imaging Using Explainable Clinical Decision Support System And Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

All Works

Breast cancer remains one of the leading causes of mortality among women worldwide, where early and precise detection plays a vital role in improving survival rates and treatment outcomes. However, conventional deep learning approaches often encounter challenges in handling dense mammographic tissues and lack transparency in decision-making, limiting their clinical reliability. To address these limitations, this study introduces TransYOLO-GJO, an explainable and optimized detection framework that integrates transformer-based attention mechanisms into the YOLOv9 architecture and leverages the Golden Jackal Optimization (GJO) algorithm for hyperparameter tuning. The transformer encoder enhances contextual feature extraction, particularly in dense breast regions, while GJO dynamically …

Gamification Of A Bimanual Coordination Task, Robby Johnson, Caeden Kidd, Johnathan Oestringer, Jaylin Pigeon, Brandon Woolman

Infinite Loop

Video games are a promising future for research and development. A new way to measure motor skills is to use robots to test individuals. This technology is incredible and has helped the medical field, but there must be a way to allow individuals to use this technology in a similar and affordable manner. Space Trash is a game that was developed with the intent to gamify the object hit detection that is used in the Kin Arm Robot with the intent to see if a person will show signs of Alzheimer’s. The goal is to not only be able to …

Business Financial Information Systems, Lirie Koraqi, James Jolovski Prof.

International Journal of Business and Technology

An information system is a combination of software, hardware, and telecommunication networks to collect useful data, especially in an organisation. Many businesses use information technology to complete and manage their operations, interact with their consumers, and stay ahead of their competition. Some companies today are completely built on information technology.

Well designed and implemented business information systems should provide the information management and outside parties need to make informed and timely decisions about the operating health of the company. Business considers the need to have information available to assess the profitability of a new product they are selling or their …

Digital Transformation Management And Organizational Performance: The Case Of Albanian Healthcare Sector, Oltjan Hamza

International Journal of Business and Technology

Purpose - The integration of digital technologies into the healthcare system is essential for improving both operational efficiency and quality of services. This study examines the relationship between digital transformation management and organizational performance in the healthcare sector in Albania, with particular focus on the mediating role of leadership in digital transformation and the digital skills of the healthcare staff. Methodology - The study employ a quantitative method through a structured questionnaire using a Likert scale, which was distributed to 93 healthcare sector employees in public and private institutions in Albania. Findings - Data collected through the survey were analyzed …

Applied Cryptography With Python, Hizer Leka, Albiona Leka

International Journal of Business and Technology

Cryptography is a key aspect of information security and provides data security. This paper aims to provide a better understanding of cryptography and its application with Python through real life examples. It covers the basics of cryptography, containing information about symmetric and asymmetric encryption methods. Throughout the paper we dive into different encryption methods, from simple to more complex, starting with the Caesar Cipher that has been used by people ever since ancient times, the Reverse Cipher which is one of the simplest encryption methods, all the way to implementing a RSA Algorithm using Python’s cryptography library, always providing knowledge …

Supply Chain Attacks Through Open Source Software: A Comprehensive Analysis Of Npm, Pypi, And Docker Hub Vulnerabilities, Thomas Pham

Cybersecurity Undergraduate Research Showcase

Open-source software ecosystems have become critical infrastructure for modern software development, yet they remain vulnerable to sophisticated supply chain attacks. This paper presents a comprehensive empirical analysis of supply chain attacks targeting npm, PyPI, and Docker Hub, examining 23 documented campaigns affecting over 2.6 billion weekly downloads. Through systematic analysis of attack vectors including typosquatting, dependency confusion, and maintainer account compromise, we identify recurring patterns and structural vulnerabilities across package registries. Our analysis reveals that 86.1% of detected typosquatted packages contained malware, with cryptocurrency theft emerging as the predominant attack objective. We document the September 2025 npm compromise affecting 18 …