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Uc-030 Heartspeak​ Ai, Nabeel Faridi, Shammah Charles, Isha Minocha, Ethan Barnes, Aravind Iyer Apr 2025

Uc-030 Heartspeak​ Ai, Nabeel Faridi, Shammah Charles, Isha Minocha, Ethan Barnes, Aravind Iyer

C-Day Computing Showcase

This project is Sentiment Analysis AI for comprehensive text review analysis and more. The system leverages a fine-tuned BERT-based models to classify overall sentiment, detect emotions, identify sarcasm, and extract aspect-level opinions. Evaluations show robust performance across tasks, with sentiment accuracy around 69%, aspect analysis. Emotion and sarcasm. The pipeline provides actionable insights, empowering businesses to refine products and improve customer satisfaction with OpenAI Integration.

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Uc-037 Dynamic Requirements For A Software Training Environment​, Cassidie Grogan, Jesus Valdez, Dawson White Apr 2025

Uc-037 Dynamic Requirements For A Software Training Environment​, Cassidie Grogan, Jesus Valdez, Dawson White

C-Day Computing Showcase

STEDR outlines the development of a software training environment for Warner Robins Air Base (Robins) to enhance employee coding skills to foster innovative solutions for Air Force projects. STEDR is a proof of concept serving as a dynamic requirements document represented by a user interface, to be delivered to a development team. STEDR involved two phases: (1) requirements gathering via interviews; and (2) interactive user interface development for feedback. The resulting proof of concept, includes an interactive UI and refined requirements, and serves as the foundation for a collaborative project with KSU, enabling the computing colleges to contribute to military …

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Uc-048 Dinengo - Ai Genie, John Sheffield, Juwon Atunnise, Jaz Ankrah, Alex Colas, Kayla Grant Apr 2025

Uc-048 Dinengo - Ai Genie, John Sheffield, Juwon Atunnise, Jaz Ankrah, Alex Colas, Kayla Grant

C-Day Computing Showcase

This projects aims to enhance the flagship product from Driven Software Solutions called DineNGo by implementing a new chatbot to help users with technical troubleshooting. This will allow for instant technical support for common issues and reduces the number of support tickets being created. It provides informed and brief response in a quick manner to walk users through whatever technical issues they are currently having with the DineNGo software. It was built with an Angular frontend and a Node.JS backend as well as a MongoDB database for querying information.

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Uc-101 Sight-Singing Feedback, Terah Dann, Sandy Giroux, Nathanael Johnson, Amber Scarbro Apr 2025

Uc-101 Sight-Singing Feedback, Terah Dann, Sandy Giroux, Nathanael Johnson, Amber Scarbro

C-Day Computing Showcase

This project creates an engaging and interactive music-learning experience. Users start by selecting a tempo and melody number. The app then displays sheet music to guide them through the exercise. While singing, performers receive real-time visual feedback on pitch accuracy and tempo progression, allowing for dynamic adjustments and improved performance precision. The system continuously updates the music staff based on user performance, ensuring seamless interaction. This approach integrates technology with musical education, enhancing skill development through intuitive, data-driven feedback. By combining user-driven selections, interactive visualization, and real-time analysis, the application provides a structured, engaging platform for improving musical skills.

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Uc-111 Accessible Interactive Map​, Justin Connick, Megan Ingram, Derrick Novak, Spencer Williams, Emily Zhu Apr 2025

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.

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Uc-136 Foster Ai Interview And Biography Generation, Joshua Crawford, Katlin Lahr, Mikayla Haigh, Brittany Payne, Daria Morhun Apr 2025

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 …

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Uc-125 Database Masking Tool - Project 04 - Team 1, Lleyton Callison, Josh Tettey-Enyo, Reda Salimi, Stephen Sigmon, Alec Quillen Apr 2025

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. …

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Ur-002 Fedda-Tsformer: Federated Domain Adaptation With Vision Timesformer For Left Ventricle Segmentation On Gated Myocardial Perfusion Spect Image, Yehong Huang Apr 2025

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 …

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Ur-044 Quantum Machine Learning For Science And Engineering, Caleb Dow, Daniel Tebor, Dante Lewis Apr 2025

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.

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Ur-031 Impact Of Motor Skill On Learning Experiences And Outcomes Using Note-Taking In Vr, Sawyer Strickland Apr 2025

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.

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Ur-115 Mobinav: Accessible Campus Navigation, Eric Legostaev, Damien Castro, Dom Evans Apr 2025

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.

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Ur-126 Multimodal Neuroimaging Meets Ai: Enhancing Alzheimer's Diagnosis With Pyradiomics, Dina Xu Callaway, Maya Castillo, Richard Haynes Apr 2025

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 …

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Uc-107 Draw The Night Sky, Dominic Ho, Richard Deas, Monica Phillips, Gabe Strong, Conner Hartsfield Apr 2025

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 …

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Gc-033 Oncoclarify – Ai Powered Cancer Report Simplifier, Sai Chandana Koganti Apr 2025

Gc-033 Oncoclarify – Ai Powered Cancer Report Simplifier, Sai Chandana Koganti

C-Day Computing Showcase

Cancer pathology reports are important for diagnosis and treatment planning, yet their complex language poses a significant challenge for patients and nurses to understand. This communication barrier often results in confusion, anxiety, delayed decisions, and reduced care quality. To address this, OncoClarify, an AI-powered tool, has been developed to simplify cancer pathology reports and provide role-specific explanations tailored to doctors, nurses, and patients.

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Gc-039 Clinicpix: Secure Medical Image Sharing Web Application, Michael Harris, Selorm Kumi, Zhi Ern Tan, James Hodgson Apr 2025

Gc-039 Clinicpix: Secure Medical Image Sharing Web Application, Michael Harris, Selorm Kumi, Zhi Ern Tan, James Hodgson

C-Day Computing Showcase

ClinicPix is a cloud-based system designed to streamline the management of medical images such as X-rays and MRIs. It offers healthcare providers and patients a secure, intuitive interface to upload, view, and share medical images across institutions and devices. The platform ensures full compliance with HIPAA through robust security measures, including role-based access control, end-to-end encryption, and comprehensive audit trails. Its scalable architecture supports growing data needs while maintaining high performance and reliability. By enhancing accessibility and safeguarding sensitive health information, the platform aims to improve clinical workflows, patient engagement, and collaborative care.

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Gc-059 Large-Scale Cybersecurity Threat Detection, Pavan Chowdary Chilukuri, Mohan Krishna Kandimalla Triveni Thiriveedhi, Raghava Sammeta, Venkata Basanth Challapalli, Triveni Kandimalla Apr 2025

Gc-059 Large-Scale Cybersecurity Threat Detection, Pavan Chowdary Chilukuri, Mohan Krishna Kandimalla Triveni Thiriveedhi, Raghava Sammeta, Venkata Basanth Challapalli, Triveni Kandimalla

C-Day Computing Showcase

Cybersecurity threats are becoming more sophisticated, posing serious risks to critical systems. Traditional intrusion detection systems often fail to manage the scale and complexity of network traffic. This study investigates large-scale threat detection using machine learning in PySpark, utilizing the UNSW-NB15 dataset. It focuses on building scalable models through preprocessing, feature selection, and implementing algorithms like Decision Trees, Naïve Bayes, Random Forest, and Gradient Boosting. Evaluation metrics include accuracy, precision, recall, F1-score, and ROC-AUC, with emphasis on hyperparameter tuning and minimizing false positives. Leveraging PySpark’s distributed computing, the system ensures efficient real-time analysis of vast network data. The research supports …

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Gc-089 Safecircle:​ Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd​, Awan-Ur Rahman, Shakib Quddus, Soarov Chakra Borty Apr 2025

Gc-089 Safecircle:​ Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd​, Awan-Ur Rahman, Shakib Quddus, Soarov Chakra Borty

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) is an irreversible and degenerative neurological condition that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

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Gc-128 Multi-Label Commit Message Classification Using P-Tuning, Tanvi Mistry Apr 2025

Gc-128 Multi-Label Commit Message Classification Using P-Tuning, Tanvi Mistry

C-Day Computing Showcase

Version control systems (VCS) play a crucial role by enabling developers to record changes, revert to previous versions, and coordinate work across distributed teams. In version control systems (e.g., GitHub), commit message serves as concise descriptions of code changes made during development. In our project, we propose to evaluate the performance of multi-label commit message classification using p-tuning (learnable prompt templates) through pre-trained models such as BERT and DistilBERT. The initial results show that p-tuning can provide similar results by designing various flexible templates that are not restricted by fixed templates.

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Grm-012 (Tcc) Transformer Embedded Synthetic Source Code Multiclass Classification, Rene Lisasi, Patrick Wu Apr 2025

Grm-012 (Tcc) Transformer Embedded Synthetic Source Code Multiclass Classification, Rene Lisasi, Patrick Wu

C-Day Computing Showcase

Recent advances in large language models have significantly increased their capability to write code. While tools such as ChatGPT are useful and represent increased efficiency for many programmers, they represent a major issue when used in academically dishonest ways. To solve the problem of identifying code written by language models, we offer a novel, light-weight classification solution based on a transformer architecture. We compare the performance of three separate transformer models (GraphCodeBERT, PLBART, and CodeBERT) for tokenization and processing and then perform classification using a random forest classifier. Preliminary results indicate that the GraphCodeBERT-based model has a 100% test and …

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Grm-038 Optimizing Prompts For Alzheimer's Speech Classification Using Llm, Imaan Shahid Apr 2025

Grm-038 Optimizing Prompts For Alzheimer's Speech Classification Using Llm, Imaan Shahid

C-Day Computing Showcase

Large Language Models (LLMs) are widely used in Alzheimer's disease research to classify speech patterns. However, there is no standardized framework to ensure the reliability of prompts used in these classifications. This study investigates the sensitivity of Alzheimer’s disease classification prompts to small variations and finds that these prompts are indeed sensitive, leading to inconsistencies in model performance. To address this, we implement an automatic prompt optimization framework to refine the base prompt. Experimental results demonstrate that the optimized prompt improves classification accuracy by 12.83% compared to the baseline, underscoring the significance of systematic prompt engineering in enhancing the reliability …

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Grm-042 Ihelp: A Care Partner Activation Program Mhealth System For Ad/Adrd Caregivers, Trisha Bhowmick Apr 2025

Grm-042 Ihelp: A Care Partner Activation Program Mhealth System For Ad/Adrd Caregivers, Trisha Bhowmick

C-Day Computing Showcase

The iHelpCare platform is designed to offer a seamless and supportive experience for patients and caregivers through a clear and user-friendly interface. Users begin at the login page, where they can either sign in or create a new account. Once logged in, the home page provides access to essential services such as a 24/7 helpline, emergency visit coordination, emergency support, and a service directory. It also includes engagement tools like discussion forums, learning modules, and resource materials, along with community-focused features such as events, activities, and support groups. The personalized dashboard allows users to monitor health conditions, review patient history, …

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Grm-081 Evaluation Of Hand-Crafted Features With Mask Images Obtained From Pannuke Dataset Using Bayesian Optimization And Machine Learning Models, Siri Yellu Apr 2025

Grm-081 Evaluation Of Hand-Crafted Features With Mask Images Obtained From Pannuke Dataset Using Bayesian Optimization And Machine Learning Models, Siri Yellu

C-Day Computing Showcase

Semantic image segmentation enables computing systems to understand the semantic patterns of image pixels by using deep learning models to classify the pixels into specific labels. The deep-learning models’ performance in image classification has been evaluated by comparing the predicted images using deep-learned features with human-labeled images or mask images. However, there remains a substantial need to investigate the performance of machine learning models that do not use deep learned features but use hand-crafted features. In this project, we perform a comprehensive evaluation of the performance of the eight machine learning models using 46 hand-crafted features extracted from the PanNuke …

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Grp-087 Empowering Alzheimer’S Caregivers: Designing Explainable And Personalized Ai For Mental Health Support​, Syeda Umme Salma, Chandra Rekha Renduchintala, Isa Siddique Apr 2025

Grp-087 Empowering Alzheimer’S Caregivers: Designing Explainable And Personalized Ai For Mental Health Support​, Syeda Umme Salma, Chandra Rekha Renduchintala, Isa Siddique

C-Day Computing Showcase

This study presents the design of an AI-powered caregiver support app aimed at personalized mental health and burden management for individuals caring for Alzheimer’s patients. The design is grounded in insights drawn from a comprehensive analysis of 28 recent studies on AI-driven mental health interventions. These findings informed the implementation of key features, including machine learning models such as Random Forest, clustering, and supervised learning to create adaptive care plans tailored to patient and caregiver profiles. The system dynamically adjusts task schedules based on engagement data and provides interpretable recommendations through SHAP. With built-in emotional check-ins, mood tracking, and caregiver-centric …

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Grp-077 Mulisa: Mhealth-Enabled User-Friendly Light-Based Stroke Screening And Assessment In Pediatric Sickle Cell Disease In Uganda, Nursat Jahan, Ayushi Bharath Raj Apr 2025

Grp-077 Mulisa: Mhealth-Enabled User-Friendly Light-Based Stroke Screening And Assessment In Pediatric Sickle Cell Disease In Uganda, Nursat Jahan, Ayushi Bharath Raj

C-Day Computing Showcase

This research presents a novel mHealth-enabled solution for stroke screening of sickle cell disease children in LMICs using light-based stroke screening technologies. We conducted a systematic literature review to identify key barriers and used these insights to develop a conceptual framework guiding the design of an integrated system. Our prototype includes a SWIR SCOS device and a wearable oximeter, combined with an AI-enhanced mHealth platform. The proposed mHealth framework aims to improve screening accessibility and adoption in low-resource settings.

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Grp-088 Nutrilyzer: A Vision-Based App For Macronutrient Estimation And Blood Glucose Response Prediction, El Arbi Belfarsi Apr 2025

Grp-088 Nutrilyzer: A Vision-Based App For Macronutrient Estimation And Blood Glucose Response Prediction, El Arbi Belfarsi

C-Day Computing Showcase

This study predicts postprandial glucose peaks and spike durations using 10-day multimodal data from 10 participants. Glucose, meals, workouts, and insulin doses were logged via the Nutrilyzer web app. Macronutrient content carbs, fats, and proteins was extracted using GPT-Vision, a highly accurate food analysis tool. These tuples were normalized to baseline glucose and aligned with a 3-hour window post-meal. Three models were tested: LSTM, Time Series Transformer, and ARIMA. LSTM performed best with 83.78% accuracy, followed by Transformer (71.43%) and ARIMA (62.41%). Results show the promise of AI-based food logging and time series modeling for personalized glucose forecasting.

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Uc-023 Bathtub Racing Game, Sasha Melbourne, Sulaiman Bah, Damari Brown, Deylin Ealy, Adam Calo Apr 2025

Uc-023 Bathtub Racing Game, Sasha Melbourne, Sulaiman Bah, Damari Brown, Deylin Ealy, Adam Calo

C-Day Computing Showcase

The Virtual Bathtub Racing Game is a capstone project that uses an interactive 3D digital experience to preserve and modernize the long-standing bathtub racing tradition at Southern Polytechnic State University (SPSU). With real-world physics, adjustable features, and multiplayer capabilities, the Unity-developed game recreates the famous event where students raced imaginatively designed bathtub carts. Since stakeholder input influences the creation of tracks, sound profiles, and gameplay elements that replicate the original races, alumni involvement is crucial in determining the authenticity of the game. In order to provide a captivating user experience for both new players and past SPSU students, the project …

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Uc-026 Pet Matchmaker, Victoria Davis, Colton Baldwin, Edward Sadler Apr 2025

Uc-026 Pet Matchmaker, Victoria Davis, Colton Baldwin, Edward Sadler

C-Day Computing Showcase

Angel Among Us is a non-profit organization that saves animals from high-killing rate shelters in Georgia. Their goal is to find homes for homeless pets. To increase their efforts, they are developing a web-based application to help improve adoption processes. The goal is to increase adoption rates and be able to provide adopters with information about pets and overall reduce the number of pet returns. This will be accomplished by using adopters’ information and preferences from the web-based application to find long-term compatibility with their recommended pets. The objective is to create a web application interface that includes many core …

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Uc-028 Intelligent Arm Meets Machine Vision, Alvaro Esteche, Sergio Sanchez-Alvares Apr 2025

Uc-028 Intelligent Arm Meets Machine Vision, Alvaro Esteche, Sergio Sanchez-Alvares

C-Day Computing Showcase

Most AI and robots have been used to make mundane tasks easier for humans however intricate tasks, such as monitoring have not been tackled. Using the OpenMANIPULATOR-X, Robot Operating System (ROS2), and machine vision, we planned on having AI tracking monitor with 4 degrees of freedom.

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Uc-029 Graphbat: Subterranean Data Visualizer, Caroline Roberson, Abdalla Ugas, Nathan Karg, Hayden Harper Apr 2025

Uc-029 Graphbat: Subterranean Data Visualizer, Caroline Roberson, Abdalla Ugas, Nathan Karg, Hayden Harper

C-Day Computing Showcase

GraphBat is a desktop data visualization application designed for speleology and similar fields that bundles common graph types with a unique heatmap tool which few comparable apps provide. It was developed in Python and is intended as an open-source tool available for use and extension by the scientific community. The heatmaps offer two data interpolation methods—inverse distance weighting and linear interpolation—to visualize the spread of data across a space using a real-world map and sensor data relative to the space. GraphBat aims to expediate scientific analysis and facilitate the presentation of results across many fields of subterranean study.

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Uc-046 Cat Classification Of 20 Distinct Breeds, Tabitha Washington, Ashley Nestor Apr 2025

Uc-046 Cat Classification Of 20 Distinct Breeds, Tabitha Washington, Ashley Nestor

C-Day Computing Showcase

Cat breed classification algorithms have been made time and time before due to cats being such a popular and beloved animal. As such, classification algorithms aim to identify their breeds for veterinary pursuits and wildlife tracking which necessitates accurate classification. Our classification algorithm identifies 20 different CFA-recognized pedigreed cat breeds utilizing TensorFlow with the MobileNetV3 Large model as the base for training. Our preliminary results over 25 initial epochs and 25 fine tuning epochs resulted in a model with a test accuracy of 65%. In the future, we plan to add more techniques to prevent overfitting and experimenting with a …

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