Computer Sciences Commons^™

Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo

C-Day Computing Showcase

Dehydration is a common and preventable complication for oncology patients, especially those undergoing chemotherapy and radiation. Side effects such as nausea, fatigue, and loss of appetite make it difficult for patients to maintain adequate fluid intake, contributing to avoidable discomfort and potential treatment disruptions. This capstone project presents Onco-Boost, a mobile hydration monitoring application designed to help adult oncology patients track daily fluid intake, recognize their intake patterns, and stay engaged in daily self-care between clinic visits. Built with React Native and Expo, and backed by Firebase for authentication and cloud data storage. Onco-Boost translates clinical hydration guidance and research …

Gc-1146 Student Engagement Portal: Enhancing Student Success Through Milestone Tracking, Antonio Brewer, Taylor Bolinger, Tyler Dawkins, Ricartho Franck, Moises Valles

C-Day Computing Showcase

The Student Engagement Portal, also known as the Milestone Map, is a platform developed to help students within KSU’s College of Computing and Software Engineering monitor their academic and professional growth. The system enables students to log milestones, check in at events, and view progress toward personal and departmental goals. Built with a full-stack architecture using NestJS, React, and MongoDB, the portal also includes an administrative dashboard for event management and analytics. The project demonstrates how progress tracking and clear visualization of achievements can improve communication, organization, and engagement between students and the college.

Gc-1198 Onboarding Tool For New Smartphone Users, Namita Velagapudi, Bhagya Surekha Dasari, Yaswanth Maddineni, Rohith Venkata Sai Chekka, Balachandar Pinninti

C-Day Computing Showcase

The Smartphone Onboarding Tool is an interactive web platform created to help seniors and new smartphone users become comfortable with mobile technology. It offers a realistic, simulated smartphone interface, guided walkthroughs, and an easy-to-use design that builds confidence in performing everyday tasks. Caregivers can monitor user progress, while learners can practice safely without affecting an actual device. The solution is developed with a React frontend, a Node.js/Express backend, and an SQLite database, all built with a strong focus on mobile-first accessibility.

Gc-1215 Clinicalrag: A Scalable Benchmark Of Privacy, Relevance, And Speed In Semantic Retrieval For Clinical Transcriptions​ ​, Pradyumna Kumar, Sai Sruti Dandibhatla, Srinivasan Subramanian, Purna Chandu Anukula, Pranitha Athukuri

C-Day Computing Showcase

Traditional keyword search struggles with the scale, complexity, and contextual depth of clinical data. This project develops and evaluates semantic search systems that better understand medical language, enabling physicians and researchers to retrieve contextually relevant information through a Retrieval Augmented Generation (RAG) framework. We integrate privacy-preserving methods, including differential privacy and homomorphic encryption to protect sensitive clinical transcriptions. For improved speed and accuracy, we enhance the baseline RAG architecture with Hierarchical Navigable Small World (HNSW) indexing and Maximal Marginal Relevance (MMR) based reranking. To ensure scalability, clinical documents are ingested using PySpark and stored in a vector database optimized for …

Gc-1233 An Ai-Powered Convolutional Neural Network System For Multi-Class Image Classification Of Rice Plant Leaf Diseases, Akshay Krishna Varma Buddharaju, Siri Yellu, Pranay Kumar Peddi

C-Day Computing Showcase

This project focuses on building an intelligent system that can automatically identify common diseases found on rice leaves by analyzing simple images. Using a deep convolutional neural network, the model learns to recognize visual patterns associated with three major diseases: Bacterial Blight, Brown Spot, and Leaf Smut. These diseases often show subtle differences in color, texture, and leaf damage, and the model is trained to distinguish them accurately. The goal of this work is to show how artificial intelligence can support modern agriculture by helping farmers detect problems early, even without expert knowledge. By processing images through careful preprocessing, augmentation, …

Grm-0204 Unpacking Early Burnout Through Predictive Risk Boundaries, Soarov Chakra Borty

C-Day Computing Showcase

Caregiver burnout is a significant issue in healthcare delivery and management, as it directly impacts caregivers' health and compromises the standard of care, often leading to negligence, health deterioration, or withdrawal from caregiving duties. Caregivers play a crucial role in supporting the health, well-being, and quality of life of care recipients by providing both personal and professional services. However, the continuous needs and stress associated with caregiving duties can affect their health and everyday life, leading to caregiver burnout. This study applied data analytics and machine learning by merging several feature selection methods on the NHATS dataset, including LightGBM, XGBoost, …

Grm-0210 Distance Measures For Multi-Target Tracking, Rakshak Gurung

C-Day Computing Showcase

Multi-object tracking (MOT) supports applications such as radar monitoring and autonomous perception, where multiple objects move, appear, or disappear over time. A central challenge is resolving which detections correspond to which tracks. The Hungarian algorithm is often used to solve this assignment problem. For ambiguous scenes, Murty’s algorithm extends this approach by generating multiple top-k association hypotheses. In this work, we study an alternative search-space formulation for top-k enumeration. Our results show that it can provide strong speedups over Murty’s method on small matrices. We also reviewed identity-focused MOT evaluation metrics such as HOTA and created a visualization tool to …

Grm-0254 Unified Robust Optimal Transport For Outlier-Resilient Learning, Rohan Jonnalagadda

C-Day Computing Showcase

Classical Optimal Transport (OT) is particularly sensitive to outliers. The existing robust variant, ROBOT, mitigates this through hard truncation, but its rigidity often compromises stability. We propose WROT-r, a unified r-power framework for weighted robust OT that combines rigorous hard-clipping and smooth cost compression through a single parameter r. WROT-r offers a continuous robustness spectrum, enabling adaptive control over how strongly transport costs are down-weighted for outliers. Experiments on synthetic mean estimation and resilient GANs show clear patterns: larger r performs best under weak contamination by preserving more inliers, while smaller r (≈1.5) is more effective under moderate and strong …

Grm-1150 Investigating Spatial Patterns Of Tumor And Stroma In Gastric And Colorectal Cancer For Survival Prediction, Siri Yellu

C-Day Computing Showcase

The spatial organization of tumor cells, stroma, and tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment plays a critical role in cancer progression and is strongly associated with clinical outcomes. However, quantifying the significance and statistical impact of these spatial patterns remains challenging due to the complex interactions among these components. In this study, we analyze spatial patterns associated with patient survival in gastric and colorectal cancer by integrating four predictive classifiers with spatial image statistics across four large patient cohorts. U-Net was used for semantic segmentation of tumor, stroma, and TILs on digitized Hematoxylin and Eosin–stained FFPE whole-slide images, while …

Grm-1153 National Energy And Emission Modeling And Analysis Tool, Swetha Kakaraparthi, S M Tanvir Faysal Alam Chowdhoury

C-Day Computing Showcase

NEEMAT is a web-based decision-support tool that predicts vehicle and power-plant emissions plus fuel/energy consumption under rising EV adoption for Atlanta, Los Angeles, New York, and Seattle. A feedforward neural network trained on MOVES estimates tract-level vehicle energy use and CO2/NOx/PM2.5 by speed, vehicle type, fuel, and age, while a macroscopic traffic model captures flow effects. Grid-side CO2/CH4/N2O from EV charging are forecast with a Meta-Prophet model trained on Cambium. Users can explore 24-hour profiles and five-year outlooks, compare scenarios, and export results. Findings show that despite substantial EV uptake, mixed fleets and grid responses can raise total emissions, underscoring …

Grm-1249 Ai-Assisted Diabetic Retinopathy Screening From Fundus Images, Mohan Krishna Thiriveedhi, Tarun Teja Pokala

C-Day Computing Showcase

Diabetic Retinopathy (DR) is a major cause of avoidable blindness among diabetic patients worldwide. Early screening is critical, but manual diagnosis is time-consuming and requires specialists. This paper presents a deep learning system to automatically analyze retinal fundus images and perform a focused, binary classification to distinguish between 'No DR' (Healthy) and 'Severe-Stage DR' (Severe/Proliferative). We benchmark three prominent architectures: a ResNet-50, an EfficientNet-B0, and a Vision Transformer (ViT-B/16). The models are trained and evaluated on a custom-balanced, binary dataset derived from the APTOS 2019 collection. We conduct two experiments, one with a small dataset (N=500) and one with a …

Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

Uc-0253 Stock Price Predictions Using Lstm & Technical Indicators, Kendal Elison, Allen Smith, Dylan Quinn

C-Day Computing Showcase

Stock price predictions using traditional statistical methods remains challenging due to market volatility and nonlinear dynamics. Long Short-Term Memory (LTSM) networks may model temporal dependencies in stock data more effectively than traditional statistical methods. Historical data for several companies’ stocks was obtained from Yahoo Finance, where it was then enriched with various technical indicators such as momentum and volatility. Preliminary analysis through Scala programming language suggests that incorporating these technical indicators can enhance short-term price prediction accuracy. Future works may seek to integrate additional trend and volume based indications in another, more robust, programming language like Python.

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 …