Computer Sciences Commons^™

Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz

C-Day Computing Showcase

RiverGuard’s mission is to protect and preserve waterways by using technology to identify and reduce pollution. The system uses an object detection model to automatically locate and classify trash within images or video of rivers and lakes, removing the need for slow, manual observation. By providing real-time insight into waste accumulation, RiverGuard helps communities, researchers, and organizations take faster, more effective action to keep waterways clean. Its goal is to create a sustainable monitoring system that empowers people to understand pollution patterns and support long-term environmental responsibility. RiverGuard represents a step toward cleaner water, healthier ecosystems, and a more informed …

Uc-1168 Shepherd's Sin - A Visual Novel Hybrid Game Made With Unity, Ara Randolph, Rin Egl, Everett Joiner, Jonah Swerdlow

C-Day Computing Showcase

By day, the grand old house shifts and shudders as if though alive. The six other residents gather in its lounges and parlors, sipping tea, squabbling over rooms, and faking civility. They laugh, they bicker, and they carry on as though nothing festers within these walls. When night falls, their facades rot away. They twist into monstrous embodiments of malice, each one a reflection of the seven deadly sins. By morning, they forget. You do not. Armed with a worn-out Monster Hunter’s Guidebook, you must reclaim its missing pages to learn who these people truly are, what they truly are. …

Uc-1183 Morphyxcam: Instant Photo Transformation Tool, Priscilla Awatey, Long Doan, Shaokun Weng, Aryan Merchant

C-Day Computing Showcase

MorphyxCam is an interactive browser-based application that lets users capture live images and apply real-time visual effects. The system performs color filtering, shading adjustments, dynamic warping, and expressive distortion effects. Users can instantly reshape features, apply artistic styles, and wrap their photos onto 3D surfaces, creating engaging and playful visual transformations. By capturing live camera images and transforming them through pixel-level filtering, distortion effects, and 3D surface mapping, the system shows how multimedia techniques can be applied creatively within a web browser. Overall, MorphyxCam showcases the potential of interactive digital imaging and highlights how accessible web technologies can be used …

Uc-1196 Verocity: A Reactive Combat Framework, Brendan Moore

C-Day Computing Showcase

Verocity is a Minecraft plugin designed for fast-paced, visceral combat, where interactivity and FUN take center stage. The complex mathematics and system design required to build this plugin push the limits of standard Minecraft development, providing a reactive framework for advanced combat interactions. New Actions and Combat Features: Enhanced Basic Attacks – Smooth, responsive, and satisfying to chain together. Throwable Items – Every item can be thrown. Swords lodge into enemies on impact, ready to be recovered. Dashing – Lunge to swords stuck in the ground or at enemies to pull them out while tactically repositioning. Umbral Blade – Command …

Uc-1207 Ai Driven Resident Inquiry Processing, Ben Moran, Sahil Sachwani, Thomas Ashe, Sean Johnson

C-Day Computing Showcase

The AI Driven Resident Inquiry Processing System is designed to enhance the National Housing Compliance (NHC) ability to process resident inquiries using artificial intelligence(AI). NHC is a 501(c)(4) not-for-profit corporation who provides training and compliance services to the affordable housing industry. Each month NHC receives over 200 inquiries from residents via phone and email. These inquiries range from general questions to urgent, life-threatening concerns. Efficiently processing and responding to these inquiries is often critical to resident safety and well being. This project uses AI to automate resident inquiries as they are received, extract and classify key information, and display this …

Uc-1211 Machine Learning Linux Log Anomaly Detection, Samuel Scott, Audrey Loisy, Dylan Silva-Rivas, Sheamus Brady

C-Day Computing Showcase

Cybersecurity is becoming an increasingly important part of digital life. Malware can silently intrude on a user’s system and perform malicious actions and generate unusual system behavior without the user ever being aware. This malware often presents with unusual system logs being generated. These logs, however, are difficult to consistently track and analyze, especially for casual users. To help bridge this gap between hard-to-read log data and the useful information it contains, we created LUAADS (short for Linux User Account Anomaly Detection System), designed for Ubuntu systems. LUAADS can automatically collect entries from common log files (such as syslog and …

Uc-1222 Active Learning System For Labeling Chest X-Rays, Matthew Hall, Noah Lane, Josh Smith, Elijah Merrill

C-Day Computing Showcase

This project aims to develop a complete Active Learning System for chest X-ray image classification, designed to automate data preparation, streamline model training, and reduce the manual effort required for medical image labeling. The system establishes a structured and scalable pipeline that moves from raw data ingestion to automated decision-making, incorporating dataset indexing, patient-aware splitting, preprocessing, configuration management, and validation to ensure data flows reliably through the system. The model component uses CNNs to generate baseline diagnostic predictions across chest pathologies. Active learning strategies are then applied to identify the most informative unlabeled images, enabling iterative retraining that improves model …

Uc-1226 Iknowit: Multilingual Smartphone Tutorial Platform, Jacqueline Juarez, David Bazan, Julissa Rivera

C-Day Computing Showcase

Digital literacy challenges affect millions of adults who struggle with basic smartphone use due to rapidly changing technology and limited support. iKnowIT is a dynamic, web-based learning platform designed to provide clear, visual, and multilingual tutorials that guide users through essential device functions. The goal of iKnowIT is to bridge the digital divide and empower users to engage confidently with modern technology

Uc-1244 Agentic Ai For Intelligent Customer Communication, Lucas Papadopoulos, Jeremy Hopkins, Munir Gargour, Weston Dease

C-Day Computing Showcase

E-commerce web shoppers need fast, reliable responses to a variety of requests: account modifications, order tracking, or policy inquiries. Businesses must address user queries in a fast and efficient manner, or else lose customers. Multi-agent AI models boast the ability to answer customer questions and act upon consumer queries without outside intervention. However, research is sparse as to how agentic models can transfer benefit to large commercial software stacks under realistic commercial load. We sought to ask whether a multi-agent AI architecture can effectively handle commercial-scale e-commerce customer service tasks. Moreover, we investigated how a multi-agent AI architecture compares to …

Uc-1259 Light'em Up, Collin Sutton, Ronnie Jones, Max Anderson

C-Day Computing Showcase

The primary goal of “Light’em Up” is to create engaging and intelligent AI that can operate within three degrees of freedom and against forces of gravity. Enemies will track the player, predict their movement, and collaborate to set traps and outflank them. All of this takes place in space, at high speeds, and at a scale where gravity has a real effect on navigation. We have four distinct AI enemies at play: Homing missiles - single agent system that follows the player’s movement at a slightly faster speed Tracking missiles - single agent system that moves at a constant speed …

Uc-1261 Ai-Powered Gre Vocabulary App, Michael Verde, Ellyan Landeta, Cynthia Onuorah, David Tran, Bereket Binchamo

C-Day Computing Showcase

Our project develops a client-side React application for GRE vocabulary practice using structured JSON word data. The site supports filtering, search, audio output, and randomized quizzes. A reinforcement-learning hint system, inspired by prior research on adaptive learning, guides users toward difficult vocabulary. We aimed to create an interface that demonstrates how lightweight front-end tools can support personalized study without requiring a backend.

Uc-1263 Budgetwise - The College Friendly Budgeting App, Taylor Thompson, Yasmeen Issa, Sameer Khan, John Nguyen, Reynaldo Lechuga

C-Day Computing Showcase

For our senior project, we developed BudgetWise, a budgeting app designed to help college students manage their finances with confidence. BudgetWise has an emphasis on ease of use and accessibility, with features such as dark mode for improved visibility. Bank accounts and credit cards can be securely linked to the user’s account where they can track their recent purchases, create budgets based on their personalized needs, and track their spending with a dynamic progress bar that changes colors the closer they get to their budget limit. By combining financial tools with accessibility, BudgetWise empowers students to make informed financial decisions …

Uc-1273 V.A.P.R. Rush, Rylan Collins, Jullian Duarte, Oliver Hugh, Ethan Mcmillian

C-Day Computing Showcase

A 3D platformer where you can transform from a cube to a boat and a plane. The game is on mobile and features the player traversing through a vapor wave inspired level with techno music in the background. They must perform jumps and lane switches to the beat of the song, and survive to the end of the level to win.

Predicting Simulation Times For Multiphase Thermal-Hydraulic Models, Andrew Yule, Andrew Taylor

SMU Data Science Review

Addressing the challenge of computationally intensive OLGA

simulations in the oil and gas industry, a machine learning framework is

developed for accurate runtime prediction. A specialized feature extraction

pipeline identifies key parameters—such as simulation time, time step,

number of branches, and section count—from OLGA input files that serve as

high-impact predictors. Multiple predictive models, including regression,

tree-based ensembles, and neural networks, are implemented to validate

accuracy and robustness. Results reveal that prioritizing simulations based on

predicted runtimes optimizes licensing resources and reduces operational

costs, making real-time scheduling more efficient. This research demonstrates

the effectiveness of data-driven runtime prediction in enhancing …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

Deconstructing The Black Box: An Explainability Analysis Of Deep Learning Architectures In Cytopathology, Chase A. Garrett

Master's Theses or Doctor of Nursing Practice

Deep learning shows strong potential in medical-image analysis, yet adoption in cyptopathology remains limited. Cytopathology could benefit from deep learning applications by improving diagnostic efficiency and accuracy. However deep learning comes with a notorious “black box” that keeps the models from being transparent and trustworthy for widespread clinical adoption. We conducted a comprehensive and comparative analysis of several deep learning architectures for multi-class classification of acute leukemia types, ALL, AML, and normal healthy cells from peripheral blood smear images. The models in this research include a Vision Transformer (ViT) and a diverse selection of Convolutional Neural Network (CNN) models. The …

Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh

All Works

Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs …

Privacy In Flux: A 35-Year Review Of Trends, Legal Evolution, And Emerging Challenges, Kong Phang, Jihene Kaabi

Research & Publications

Privacy harms have expanded alongside rapid technological change, challenging the adequacy of existing regulatory frameworks. This systematic review (1990–2025) systematically maps documented privacy harms to specific legal mechanisms and observed enforcement outcomes across jurisdictions, using PRISMA-guided methods and ROBIS risk-of-bias assessment. We synthesize evidence on major regimes (e.g., GDPR, COPPA, CCPA, HIPAA, GLBA) and conduct comparative legal analysis across the U.S., E.U., and underexplored regions in Asia, Latin America, and Africa. Key findings indicate increased recognition of data subject rights, persistent gaps in cross-border data governance, and emerging risks from AI/ML/LLMs, IoT, and blockchain, including data breaches, algorithmic discrimination, and …

High Frequency Dc-Dc Converter Design Optimization, Modeling And Control Based-Wbg Technology., Salah Ahmed Abdullah Eltief

Electronic Theses and Dissertations

The performance of DC-DC power converters is a cornerstone of modern electric vehicle (EV) powertrains, directly governing overall system efficiency, size, cost, and reliability. This dissertation presents a comprehensive performance analysis and optimization of DC-DC converter topologies to determine the most suitable design for high voltage EV applications. The evaluation rigorously compares efficiency, power losses, and physical size under a range of harsh operating conditions. A primary objective is to leverage Wide Bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), to push the performance boundaries of power conversion. While SiC devices are known for their superior material properties, a clear understanding …

From Vulnerability To Robustness: A Survey Of Patch Attacks And Defenses In Computer Vision, Xinyun Liu, Ronghua Xu

Michigan Tech Publications

Adversarial patch attacks have emerged as a powerful and practical threat to machine learning models in vision-based tasks. Unlike traditional perturbation-based adversarial attacks, which often require imperceptible changes to the entire input, patch attacks introduce localized and visible modifications that can consistently mislead deep neural networks across varying conditions. Their physical realizability makes them particularly concerning for real-world security-critical applications. In response, a growing body of research has proposed diverse defense strategies, including input preprocessing, robust model training, detection-based approaches, and certified defense mechanisms. In this paper, we provide a comprehensive review of patch-based adversarial attacks and corresponding defense techniques. …

Archaeological Predictive Meta-Modeling In Pre-Columbian Mexico, Peter Stamm

Electronic Theses and Dissertations

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to …

Non-Invasive Infection Detection Framework: Smartphone-Based Hematologic And Thermal Biomarker Monitoring, Nafi Us Sabbir Sabith

Dissertations (1934 -)

Infectious diseases remain one of the leading global causes of illness and death in both community and hospital settings. Infections lead to lasting health issues, such as life-threatening chronic conditions or even cancer. Early detection is crucial for timely intervention, such as isolation and treatment. Hematology originated from the Greek word haima, meaning blood. It is a unique discipline in medicine that involves the study of blood and its contents. White blood cells (WBC) are at the forefront of the body’s immune system. WBCs are categorized into five main subtypes: neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Monitoring the total WBC …

Enhancing Recommendation Performance Via Stacking Ensemble And Synthetic Data Augmentation, Zahraa Yaareb Hani, Mohsin Hasan Hussein

Karbala International Journal of Modern Science

Recommendation systems are essential tools that primarily aim to help users navigate through a large volume of information. They simplify the decision-making process by suggesting relevant items based on users’ historical behaviour. However, their performance is often affected by common problems such as data sparsity. This work proposes a stacking-based ensemble recommendation system that integrates multiple machine learning models to enhance the model’s predictive performance. A new synthetic data augmentation technique is introduced to address the sparsity issue in the user–item rating matrix. This method uses the Naïve Bayes algorithm to predict additional ratings for each user. These are then …

Novel Eco-Friendly Synthesis Of Coo Nps: Evaluation Of Their Diode Laser-Enhanced Inhibitory Activity Through Combined In Silico And In Vitro Approaches., Arshad Mahdi Hamad, Sahar Naji Rashid

Karbala International Journal of Modern Science

In our study, for the first time worldwide, to the best of our knowledge, cobalt oxide nanoparticles (CoO NPs) were synthesized using naringin (Nar) extracted from citrus peels. The properties of the prepared Nar-CoO NPs were studied using field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX), ultraviolet-visible (UV-Vis), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. All the test results demonstrated the efficiency and success of the nanofabrication process. The solution was then exposed to three types of diode lasers: red (650 nm), green (532 nm), and blue (405 nm). The purpose of this study was …

Insider Threat: A Case Study Of The Maroochy Water Services Attack, Samuel Rector

Cybersecurity Undergraduate Research Showcase

In 2000, a former employee at Hunter Watertech went rogue and caused a spill of 800,000 liters of sewage. He leveraged his insider knowledge and access to stolen equipment in order to seek retribution for the company that wronged him. After three months of torment police arrested him and an investigation and many studies were done on the incident. A consensus remains that much of this chaos was preventable with simple cybersecurity implementations.

Behavioral Detection Methods For Automated Mcp Server Vulnerability Assessment, Christian Coleman

Cybersecurity Undergraduate Research Showcase

The Model Context Protocol (MCP) has emerged as a critical standard for connecting AI agents to external data sources and tools. Still, its adoption has introduced significant security vulnerabilities across multiple attack surfaces. While recent research has catalogued extensive vulnerability taxonomies and attack implementations, automated detection methodologies remain limited. Current detection tools primarily employ static code analysis, which fails to identify behavioral vulnerabilities that only manifest during runtime server interactions. This study explores behavioral detection approaches for identifying MCP server vulnerabilities through systematic query-based testing, with particular emphasis on context manipulation techniques. Preliminary analysis of existing vulnerability research reveals 48 …

Unless Ai Washes The Dishes, Can We Really Call It Intelligent?, Essraa Nawar

Library Articles and Research

"A few weeks ago, I found myself sitting with a question that keeps resurfacing as AI becomes louder, faster, and everywhere. What happens when the models know everything about our lives except the one thing that matters most in the moment. It is remarkable how much of human decision making is driven not by external information but by internal states. A tightening in the chest. A sudden clarity. A quiet discomfort that redirects us before we can explain why. These signals guide our choices in ways computation cannot replicate."

Creating An Iot User Centric Security And Privacy Label, Luke Joseph Gleba

Theses and Dissertations

Concerns for cybersecurity awareness and training in the public have been rising at astronomical rates over the past few years. People globally have entered a critical intersection with computers. Computers are being used in everyday life, and users do not have an easy way to understand their own cyber risk. Consumers deserve to know how secure their new Internet of Things (IoT) system is in a quick, efficient way before they purchase the device and while they own it. The following research looks to improve on ideas for the criteria and the development of a security label. Few prototypes currently …

Mimar-Net: Multiscale Inception-Based Manhattan Attention Residual Network And Its Application To Underwater Image Super-Resolution, Nusrat Zahan, Sidike Paheding, Ashraf Saleem, Timothy C. Havens, Peter C. Esselman

Michigan Tech Publications

In recent years, Single-Image Super-Resolution (SISR) has gained significant attention in the geoscience and remote sensing community for its potential to improve the resolution of low-quality underwater imagery. This paper introduces MIMAR-Net (Multiscale Inception-based Manhattan Attention Residual Network), a new deep learning architecture designed to increase the spatial resolution of input color images. MIMAR-Net integrates a multiscale inception module, cascaded residue learning, and advanced attention mechanisms, such as the MaSA layer, to capture both local and global contextual information effectively. By utilizing multiscale processing and advanced attention strategies, MIMAR-Net allows us to handle the complexities of underwater environments with precision …

Game Hacking & Anti-Cheat Analysis, Quang Hoang

Cybersecurity Undergraduate Research Showcase

Reverse engineering and analyzing game hacking and anti-cheat mechanisms is a complex and evolving field. This research document explores the history of game hacking, various techniques used in game hacking, and the countermeasures implemented by anti-cheat systems. Through case studies, we illustrate the strategies involved in creating cheats. Specifically, we demonstrate how to hack the open-source game AssaultCube using memory editing and code injection techniques available in Cheat Engine in a step-by-step manner so that the reader can theoretically reproduce the results shown in this paper. We also dissect the game’s anti-cheat mechanisms, identifying their strengths and weaknesses. This document …