Computer Sciences Commons^™

Uc-111 Accessible Interactive Map​, Justin Connick, Megan Ingram, Derrick Novak, Spencer Williams, Emily Zhu

C-Day Computing Showcase

Finding that walking campus gets you out of breath? We did too! Using React and Flask, we are building a web application that directs KSU students to the path with the lowest elevation and shows the shifts in between. It also displays accessible doors. The purpose of this app is to develop a more inclusive application so people with asthma, cardiovascular issues, and wheelchairs at KSU can safely traverse campus.

Uc-136 Foster Ai Interview And Biography Generation, Joshua Crawford, Katlin Lahr, Mikayla Haigh, Brittany Payne, Daria Morhun

C-Day Computing Showcase

This capstone project presents a proof of concept for a mobile and web-based application designed to streamline communication between foster caregivers and the Angels Among Us Pet Rescue team. The application addresses critical inefficiencies in generating pet biographies and coordinating photography efforts, which are essential components in increasing adoption rates. Leveraging cutting-edge technologies such as Twilio, Retell AI, and OpenAI, the app implements a bio generation workflow that conducts foster interviews via phone calls, transcribes responses using AI-powered voice-to-text, analyzes sentiment, and produces structured, engaging pet bios for platforms like Petfinder. Additionally, the system automates email workflows to coordinate photography …

Uc-125 Database Masking Tool - Project 04 - Team 1, Lleyton Callison, Josh Tettey-Enyo, Reda Salimi, Stephen Sigmon, Alec Quillen

C-Day Computing Showcase

The Database Masking Tool for Gwinnett County Public Schools secures sensitive data while preserving its analytical utility. Developed alongside an in-depth research paper, this web-based solution enables real-time masking of information in SQL Server and MySQL databases. Utilizing automated field recognition, it applies three masking techniques—Faker-based masking, hash masking, and pseudonymization through generalized masking—to protect personally identifiable information. Key features include an intuitive interface for configuring masking rules, real-time data previews, and an export function for generating masked datasets in multiple formats. Built with a React-Flask stack and containerized for consistency, the system supports compliance with GDPR, HIPAA, and FERPA. …

Ur-002 Fedda-Tsformer: Federated Domain Adaptation With Vision Timesformer For Left Ventricle Segmentation On Gated Myocardial Perfusion Spect Image, Yehong Huang

C-Day Computing Showcase

This study presents FedDA-TSformer, an approach for accurate left ventricle segmentation in gated myocardial perfusion single-photon emission computed tomography (MPS) images, designed to ensure both high segmentation quality and patient data privacy. By integrating federated learning with domain adaptation techniques, the proposed model leverages a novel Divide-Space-Time-Attention mechanism that effectively captures spatio-temporal correlations inherent in multi-centered MPS datasets. Domain discrepancies among data from three different hospitals are mitigated using a local maximum mean discrepancy (LMMD) loss, enabling robust performance across various clinical settings. Evaluated on a dataset comprising 150 subjects with eight distinct cardiac cycle phases, FedDA-TSformer achieved Dice Similarity …

Ur-044 Quantum Machine Learning For Science And Engineering, Caleb Dow, Daniel Tebor, Dante Lewis

C-Day Computing Showcase

This research explores the comparative effectiveness of traditional machine learning algorithms and their quantum counterparts. Traditional and quantum implementations of algorithms including Support Vector Machines (SVM), logistic regression, Principal Component Analysis (PCA), random forest classifiers, neural networks, and convolutional neural networks (CNN) are evaluated and contrasted. Findings highlight that quantum algorithms can provide certain clear advantages in some models and data while exhibiting inferior performance in others. By assessing these nuances, this research helps contribute to the understanding of quantum machine learning algorithms and their potential applications for science, engineering, and industrial tasks.

Ur-031 Impact Of Motor Skill On Learning Experiences And Outcomes Using Note-Taking In Vr, Sawyer Strickland

C-Day Computing Showcase

Immersive learning experiences have been proposed to offer rich immersion and interaction, effectively addressing the distractions and low engagement commonly found in typical online learning environments. Research in neuroscience and psychology suggests that motor skills, such as note-taking, help students improve their learning by enhancing cognitive abilities and decision-making, ultimately leading to better performance. This study aims to investigate the impact of motor skills, specifically note-taking with a physical VR stylus, on learning experiences, outcomes, and retention in our VR classroom environment.

Ur-115 Mobinav: Accessible Campus Navigation, Eric Legostaev, Damien Castro, Dom Evans

C-Day Computing Showcase

MobiNav addresses the gap in campus navigation by providing personalized route planning for individuals with diverse mobility requirements. The system uses dual-layer routing (Google Maps API and custom OSRM routing), real-time obstacle reporting, and detailed accessibility feature mapping. It creates custom routes considering wheelchair access, elevation changes, building entrances, and temporary obstacles. Initially scoped for Kennesaw State University's Marietta campus, it is designed for scalability to other locations.

Ur-126 Multimodal Neuroimaging Meets Ai: Enhancing Alzheimer's Diagnosis With Pyradiomics, Dina Xu Callaway, Maya Castillo, Richard Haynes

C-Day Computing Showcase

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that requires early and accurate diagnosis for effective intervention. This research explores how multi-modal data integration can enhance Alzheimer’s disease staging prediction by developing an AI model that classifies patients into normal, mild cognitive impairment (MCI), or AD stages. Unlike traditional methods that rely on clinical assessment to make diagnoses, this study develops an AI-driven approach that integrates clinical and imaging data to improve classification accuracy. The research utilizes the Australian Imaging, Biomarkers & Lifestyle (AIBL) dataset, importing patient clinical data along with PET and MRI scans. First, image features were extracted …

Uc-107 Draw The Night Sky, Dominic Ho, Richard Deas, Monica Phillips, Gabe Strong, Conner Hartsfield

C-Day Computing Showcase

Draw The Night Sky is a game project made in collaboration with Carter’s Lake to make their constellation viewing program more accessible. The stars in the sky are quite difficult to see without the perfect conditions, so an alternative would assist with this greatly. By creating a fun and interactive experience through a game, it should teach the visitors of the nature center to be able to search for stars even outside of the game. Utilizing an accurate star map based on the Yale Bright Star catalogue, we have an accurate star map that mirrors the real world which adds …

Analysis Of Cybersecurity Threats And Mitigation Strategies: Theory In To Practice, Project Proposal, Fransly Dutervil

Student Academic Conference

Cybersecurity threats pose significant risks to individuals and organizations, leading to data breaches, financial losses, and operational disruptions. This presentation explores key threats such as malware, phishing, DDoS attacks, insider threats, and zero-day exploits. It also discusses mitigation strategies, including network security measures, multi-factor authentication, encryption, and incident response planning. Through case studies of real-world cyber incidents, we highlight lessons learned and best practices to strengthen security defenses. The goal is to enhance awareness and promote proactive cybersecurity measures in an increasingly digital world.

Ai Assistance In Legal Analysis: An Empirical Study, Johnathan H. Choi, Daniel Schwarcz

Journal of Legal Education

No abstract provided.

Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu

Cybersecurity Undergraduate Research Showcase

Financial fraud, particularly credit card fraud, continues to pose substantial challenges to financial institutions due to its increasing frequency and impact on consumer trust. While traditional rule-based methods have provided foundational defenses, their limitations in scalability and adaptability have accelerated the adoption of machine learning (ML) techniques. Concurrently, Benford’s Law—a statistical principle often used in forensic accounting—has demonstrated efficacy in detecting anomalies within naturally occurring numerical datasets. This study explores a hybrid fraud detection approach that integrates Benford’s Law with supervised machine learning algorithms, including Logistic Regression, Random Forest, and k-Nearest Neighbors. Using the publicly available European credit card fraud …

Disentanglement In Representation Learning: Interpretability In Dimension Reduction With Vae, Minh Hong Vu

LSU Doctoral Dissertations

This research explores both theoretical and practical aspects of disentangled representation learning by extending the VAE framework. We address the core challenge of extracting independent generative factors from observed data while preserving high reconstruction fidelity. To this end, we propose two novel VAE variants: (i) the $\lambda\beta$-VAE, which incorporates an additional $\ell^2$-norm reconstruction loss to improve accuracy, and (ii) the $\gamma\beta$-VAE, which introduces a mutual information regularization term to encourage independence across latent dimensions.

Our theoretical analysis is conducted in a linear Gaussian setting, where we derive optimal solutions for these VAE-based models. We further examine how varying levels of …

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 …

A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor

Thesis/ Dissertation Defenses

The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a data-driven recommendation framework that predicts Course Learning Outcome (CLO) achievement and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics.

This study analyzed 100 undergraduate and postgraduate courses from the College of Information Technology (CIT) at …

The National Cybersecurity Teaching Coalition: Expanding Cybersecurity Education Opportunities, Paul Wagner, Melissa Dark, Robert Honomichl, Filipo Sharevski, Sandra Leiterman

Journal of Cybersecurity Education, Research and Practice

The increasing prevalence of cybersecurity threats and the shortage of qualified professionals necessitate innovative solutions for cybersecurity education at all levels. Despite the expansion of post-secondary cybersecurity programs, employer dissatisfaction with graduates and a lack of standardized introductory curricula highlights the need for structured secondary education pathways. The National Cybersecurity Teaching Coalition (NCTC) and its National Cybersecurity Teaching Academy (NCTA) address this gap by equipping high school educators with the necessary knowledge and credentials to teach cybersecurity effectively. NCTA offers an 18-credit cybersecurity graduate certificate program to ensure teachers are competent and confident to develop and teach cybersecurity curriculum with …

The Impact Of Ai Use In Programming Courses On Critical Thinking Skills, Christian Jay St Francis Clarke, Abdullah Konak

Journal of Cybersecurity Education, Research and Practice

Proficiency in computer programming extends far beyond memorizing syntax; it depends on the cultivation of critical thinking. Computer programming requires multiple interconnected competencies, including systematic problem analysis, algorithmic reasoning, mastery of programming languages, debugging capabilities, a comprehensive understanding of software development methodologies, rigorous testing practices, and systematic troubleshooting approaches. These skills are also essential for cybersecurity experts; cybersecurity programs require several programming courses to enhance students’ technical and critical thinking skills. Generative AI (GenAI) technologies have fundamentally changed the process of developing applications and approaches to teaching coding. The growing use of GenAI technologies by students in writing computer code …

Ai & Xr Explorations To Support Social Interactions: Speculative Design For Ubiquitous Workplace Space By And For Neurodivergent Employees, Dominique Michaud, Alejandro Reyes, Jonathan Proulx Guimond, Fafadzi Akpene Agbe, Valérie Payen, Diane Gabrielle Tremblay, Marie Claude Leblanc, Claude Vincent, Geoffreyjen Edwards, Caroline Brassard, Marie Helene Parizeau, Valéry Psyche, Martin Caouette, James Hutson, Piper Hutson, Julie Ruel, Julien Voisin, Jocelyne Kiss

Faculty Scholarship

This study explores the potential of interdisciplinary theories and advanced technologies, such as augmented realities and artificial intelligence, to address the socio-professional integration challenges faced by neurodivergent individuals, particularly those on the autism spectrum. It investigates the design of personalized, functional spaces that integrate interconnected living environments and intelligent systems tailored to support communication needs. Using speculative design methodology, the research adopts an experiential framework to examine alternative solutions, starting with a central hypothesis and testing it through debates with researchers, experts, neurodivergent individuals, and knowledge users. The premise is rooted in the recognition that neurodivergent individuals encounter significant barriers …

Integrating Ai-Driven Neurofeedback With Brain-Computer Interfaces: A Paradigm For Effortless Learning And Workforce Transformation, James Hutson

Faculty Scholarship

This editorial discusses the merging of AI-driven neurofeedback with brain-computer interfaces (BCIs) to create a new model for effortless, unconscious learning. By interpreting and reinforcing specific neural patterns, these technologies can enable users to acquire skills without traditional instruction, making them especially valuable in fast-evolving industries. They also offer powerful tools for individuals with physical impairments by enabling control through thought alone. However, the author emphasizes the importance of ethical oversight, particularly around cognitive autonomy, data privacy, and consent. As the field matures, ongoing research and regulation will be essential to ensure responsible development and widespread, beneficial use.

Assessing Readiness For Transformation From Rule-Based To Ai-Based Chatbot In Uae Healthcare: A Case Study Of A Rehabilitation Hospital In Abu Dhabi, Mubarak Alketbi

Thesis/ Dissertation Defenses

This research investigates the readiness of UAE healthcare institutions to transition from rule-based chatbot systems to AI-powered alternatives, focusing on a rehabilitation hospital in Abu Dhabi. Through a structured quantitative study involving 96 healthcare professionals, the research explores technology acceptance, service quality, usability, and implementation readiness. Findings highlight strong correlations between perceived usefulness and behavioral intention to adopt AI, emphasizing the importance of integration, staff training, and service reliability. The study proposes a practical implementation framework for healthcare transformation, offering insights for institutions seeking to improve operational efficiency through AI integration.

Brushstroke Island, Yifan Luo, Mariel Domnenko, Gillian Donley

24th Annual A. Paul and Carol C. Schaap Celebration of Undergraduate Research and Creative Activity (2025)

We developed an innovative Android application using the Unity engine, a premier platform renowned for crafting immersive 2D and 3D experiences. Our project expanded upon an existing app by integrating a collection of fresh engaging mini-games designed to enhance visual creativity and interactively. These additions were designed to introduce new opportunities for users to explore their artistic potential.

Pardcuhre: A Scalable Parallel Solution For Multivariate Integration With Cuda, Tobias Shaw, Peter Worden

24th Annual A. Paul and Carol C. Schaap Celebration of Undergraduate Research and Creative Activity (2025)

Mathematical integration is a common process in a wide array of fields such as medical imaging, statistical analysis, orbital mechanics, and simulation modeling. Since analytical solutions to many integration problems are impossible to obtain, they are instead numerically approximated. There exists a variety of numerical integration software used to estimate such integrals; one such software is DCUHRE which employs the global adaptive algorithm over a hyperrectangular region to estimate a given function. However, as the integral dimension increases, the number of region evaluation points increases exponentially. While DCUHRE was written to accommodate parallel execution on multiple processors (subregion-level parallelization), this …

Climate Data Imputation And Quality Improvement Using Satellite Data, Kadhim Hayawi, Sakib Shahriar, Hakim Hacid

All Works

Combating climate change has emerged as a global concern recently, and meteorological data remain an important measure for analyzing and predicting climate trends. However, ground weather stations and sensors can be impacted by faults due to accidents and unreliability, often resulting in, for example, missing data and lowering the overall quality of the data. This paper explores the impact of using satellite data as an input feature for machine learning algorithms. In particular, temperature, pressure, wind speed, and global horizontal radiation data are imputed using various machine learning algorithms to overcome potential data quality issues resulting from the ground stations. …

Phishing Attacks And Prevention, Ruth Johnson

Cybersecurity Undergraduate Research Showcase

Phishing attacks have been the number one leading cause of identity theft and financial theft since 1990. The internet is one of the leading places where individuals’ identity is stolen. Many businesses and government agencies have been at risk of cyber phishing attacks from foreign countries. Attacks on Several U.S. federal government agencies have been hit in a global cyberattack by Russian cybercriminals that exploit a vulnerability in widely used software, according to a top us cybersecurity agency (Lyngaas, Government hit cyber-attacks, 2023).

Phishing attacks are cybercrimes where one or many individuals steal sensitive information like passwords, credit card information, …

How Can We Improve Our Security Measures To Safeguard Against Cyber Threats?, Mattea Trotter-Hicks

Cybersecurity Undergraduate Research Showcase

Many people face the issue of having their information stolen without their knowledge of what is happening. I understand that some people don’t pay attention to everything when it comes to making sure their information is being secured properly. There must be set stages for those who don’t know technology as well and will need help with knowing what to do. There are many stories of people going through issues with getting hacked or scammed out of their money or important information. We are going to dive into finding ways to fix the outcome of others knowing what to do …

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

Data-Driven Strategy For Contact Angle Prediction In Underground Hydrogen Storage Using Machine Learning, Mehdi Nassabeh, Zhenjiang You, Alireza Keshavarz, Stefan Iglauer

Research outputs 2022 to 2026

In response to the surging global demand for clean energy solutions and sustainability, hydrogen is increasingly recognized as a key player in the transition towards a low-carbon future, necessitating efficient storage and transportation methods. The utilization of natural geological formations for underground storage solutions is gaining prominence, ensuring continuous energy supply and enhancing safety measures. However, this approach presents challenges in understanding gas-rock interactions. To bridge the gap, this study proposes a data-driven strategy for contact angle prediction using machine learning techniques. The research leverages a comprehensive dataset compiled from diverse literature sources, comprising 1045 rows and over 5200 data …

Perceptions Of Ai Skills In Resumes, Brandy Whitford, Patrick J. Cooper

Faculty and Staff Publications & Presentations

No abstract provided.

An Ai-Driven Framework For Assessing Intersection Infrastructure And Accessibility, Logan Liddiard, Joel Pierson, Brent Chamberlain, Keith Christensen, Xiaojun Qi

Computer Science Student Research

Background: Intersections serve as vital nodes within transportation networks, facilitating the movement of pedestrians. However, their effectiveness often hinges on the quality of their infrastructure and accessibility. Furthermore, it is expensive and time-consuming to collect infrastructure data, yet is crucial for creating a safe and efficient urban landscape.

Objective: Develop an AI-driven model that leverages Google Street View to provide detailed information about intersections, helping city planners identify infrastructure and accessibility issues and supporting improvements to create safer, more accessible intersections for people of all abilities.

Ai-Based Accessibility Widget (Aibaw) Shortcomings For Blind Web Users, Joshua A. Rovira

LSU Master's Theses

With legal, ethical, and financial motivations to make their websites more accessible, many businesses and sites have begun to employ the use of artificial intelligence (AI) based widgets to automatically make necessary modifications to their sites' pages and subdomains. In this work, we conduct a qualitative case study to determine the efficacy of these AI tools as they pertain specifically to blind users. We analyze pages from twelve websites using accessiBe's AI accessibility widget and provide a taxonomy of their violations against the Web Content Accessibility Guidelines (WCAG) 2.1 level AA compliance. We found each website to bear numerous violations …