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Uc-1222 Active Learning System For Labeling Chest X-Rays, Matthew Hall, Noah Lane, Josh Smith, Elijah Merrill
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
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
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
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
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
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
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.
Grp-082 Ready Cluster One: Optimizing Film Success With Data Science, Mohsin Md Abdul Karim, Joseph Richardson
Grp-082 Ready Cluster One: Optimizing Film Success With Data Science, Mohsin Md Abdul Karim, Joseph Richardson
C-Day Computing Showcase
This study presents a novel approach to predicting and optimizing screenplay investments by combining graph theory and finite mixture modeling (FMM) techniques. We construct a k-partite graph representing movies, genres, subgenres, production companies, directors, actors, and directors of photography, to explore the interconnectedness between these entities. Using FMM, we identify clusters within budget tiers, enabling a deeper understanding of how similar films perform based on their creative team and production characteristics. By balancing profit potential with risk-adjusted profit, the model suggests the most viable budget tiers for unproduced screenplays. This approach incorporates confidence intervals and evaluates the accuracy of budget …
Gc-074 Real-Time Object Detection, Rohit Malik
Gc-074 Real-Time Object Detection, Rohit Malik
C-Day Computing Showcase
This project explores the implementation of real-time object detection using the You Only Look Once (YOLO) architecture. Leveraging its speed and accuracy, we developed a system capable of identifying and localizing multiple objects within live video streams. Our implementation focused on optimizing YOLO's performance for real-time applications, specifically addressing the trade-off between speed and accuracy. We employed a pre-trained YOLO model and fine-tuned it on a custom dataset tailored to specific object classes. This fine-tuning process aimed to enhance the model's ability to recognize objects in our target environment. The system was implemented using Python and the OpenCV library, enabling …
Gc-079 Nibbleai, Ryan Deem, Zeynep Birgili, Austin Cook
Gc-079 Nibbleai, Ryan Deem, Zeynep Birgili, Austin Cook
C-Day Computing Showcase
Ever looked into your fridge or pantry and wondered, “What can I make with this?” NibbleAI is a mobile app designed to solve exactly that. Using artificial intelligence, the app identifies ingredients from user-uploaded images and suggests recipes based on what’s available. Built with React Native and powered by a DenseNet169 model for image recognition, NibbleAI seamlessly analyzes photos and returns curated recipe ideas — all within a few taps. This intuitive approach helps users reduce food waste, save time, and get creative with the ingredients they already have.
Grm-076 Assessing The Performance Of Intelligent Agents In Visual Food Recognition Relative To Manual Data Entry, El Arbi Belfarsi, Henry Ekeocha
Grm-076 Assessing The Performance Of Intelligent Agents In Visual Food Recognition Relative To Manual Data Entry, El Arbi Belfarsi, Henry Ekeocha
C-Day Computing Showcase
Accurate dietary assessment remains a critical yet time-consuming task in health and nutrition monitoring. This study benchmarks the macronutrient estimation capabilities of three intelligent vision agents: GPT Vision, Claude, and Gemini against manually logged food data. We unify two distinct datasets: MenuMatch, annotated by a professional nutritionist, and CGMacros, populated through user entries on MyFitnessPal. After flattening and cleaning both datasets, we first assess each model’s performance in calorie estimation. GPT Vision outperforms the others with the lowest percentage error 13.83% and is subsequently used to benchmark the macro estimations of Claude and Gemini. While Claude shows higher carbohydrate and …
Grm-109 Quantum Machine Learning For Science And Engineering Research, Andrew Polisetty
Grm-109 Quantum Machine Learning For Science And Engineering Research, Andrew Polisetty
C-Day Computing Showcase
This research project aims to understand and explore the practical applications of Quantum Machine Learning (QML) in solving real-world challenges. By comparing classical machine learning models such as Support Vector Machines (SVM), Neural Networks, Logistic Regression, and Naive Bayes, with their quantum counterparts. Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Quantum Logistic Regression (QLR), Quantum Deep Neural Networks (QDNN), and Hybrid Quantum Models, we gain hands-on experience in advanced machine learning techniques. The project cover diverse domains including cybersecurity, healthcare, industrial engineering, energy management, and supply chain optimization. Each part of project involves working with real-world datasets, preprocessing, …
Grp-021 Shap-Explainable Image-To-Topology Regression, Charles Fanning
Grp-021 Shap-Explainable Image-To-Topology Regression, Charles Fanning
C-Day Computing Showcase
We evaluated whether deep regression models predicting vectorized topological features (in the form of persistence landscapes) actually learn the underlying persistent homology of the image. A DenseNet-121 is trained to regress 300-dimensional persistence landscapes from grayscale scene images. Using SHAP, we evaluate the contribution of pixels in the original images to the persistence landscapes. Across all six classes, SHAP-feature overlap is consistently lower than the baseline, implying that DenseNet may not be truly learning the underlying persistent homology.
Uc-040 Security Lookup Interface Project, Alhasan Mohsen, Alejandro Albarran, Ethan Lan, Jerrat Jester
Uc-040 Security Lookup Interface Project, Alhasan Mohsen, Alejandro Albarran, Ethan Lan, Jerrat Jester
C-Day Computing Showcase
The "Security Lookup Interface" capstone project aims to create a streamlined tool for COX's cybersecurity team, enabling analysts to efficiently perform IP address and hostname lookups while providing actionable, data-driven insights to enhance security investigations. The project will develop a user-friendly interface that simplifies the lookup process, allowing cybersecurity analysts to quickly retrieve relevant data and make informed decisions during security investigations. One of the key features of the tool is its seamless integration with both internal APIs and external resources. This integration will ensure that analysts have quick and easy access to valuable information, minimizing manual effort and enabling …
Uc-049 From Forecast To Fortune: Portfolio Optimization And Prediction, Nia Taylor
Uc-049 From Forecast To Fortune: Portfolio Optimization And Prediction, Nia Taylor
C-Day Computing Showcase
This project explores the intersection of time series forecasting and portfolio optimization to support data-driven investment strategies. Historical price data from 30 individual stocks was analyzed using two forecasting models: ARIMA and Prophet. Each model’s performance was evaluated using key accuracy metrics, including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Directional Accuracy (MDA). Results showed that ARIMA performed better on error-based metrics, while Prophet excelled at predicting directional trends. In parallel, historical return data was used to construct optimized portfolios using Modern Portfolio Theory. Two strategies were implemented: one minimizing overall volatility and another maximizing …
Ur-001 Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad
Ur-001 Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad
C-Day Computing Showcase
The increasing use of large language models (LLMs) in mental health support neces-sitates detailed evaluation of their recommendation capabilities. This study compares four modern LLMs—GPT-4o, Claude 3.5 Sonnet, dataset-enhanced Gemma 2, and dataset-enhanced GPT-3.5-Turbo—in recommending mental health applications. We constructed a structured dataset of 55 mental health apps using RoBERTa-based sentiment analysis and keyword similarity scoring, focusing on depression, anxiety, ADHD, and insomnia. Standard LLMs emonstrated inconsistent accuracy and often relied on outdated or generic information. In contrast, our retrieval-augmented generation (RAG) pipeline enabled lower-cost models to achieve 100% accuracy, compared to baseline models (GPT-4o at 45% and Claude at …
Uc-116 Robot Tactics, John Anderson
Uc-116 Robot Tactics, John Anderson
C-Day Computing Showcase
Robot Tactics is a first person strategic shooter made in Unity where the player takes control of an agent who fights off bodyguards who are chasing him while using Robots to detour them
Ur-094 Aistudy: Using Ai To Study Ai, Mason Pederson
Ur-094 Aistudy: Using Ai To Study Ai, Mason Pederson
C-Day Computing Showcase
Interactive AI studying tool or AIStudy is a flask-based web-app which enables users to quickly search, save, and study scientific papers. AIStudy streamlines the literature review process by utilizing large language models (LLMs) allowing for users to engage with research in a creative and interactive way. To begin with a user searches up papers using the arXiv API and PyMuPDF for scraping the contents. These are saved to a user database managed by SQL Alchemy. The user can then ask a chatbot about one or more papers at a time through Ollama’s API in order to produce Retrieval-Augmented Generated (RAG) …
Gc-033 Oncoclarify – Ai Powered Cancer Report Simplifier, Sai Chandana Koganti
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.
Gc-039 Clinicpix: Secure Medical Image Sharing Web Application, Michael Harris, Selorm Kumi, Zhi Ern Tan, James Hodgson
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.
Gc-059 Large-Scale Cybersecurity Threat Detection, Pavan Chowdary Chilukuri, Mohan Krishna Kandimalla Triveni Thiriveedhi, Raghava Sammeta, Venkata Basanth Challapalli, Triveni Kandimalla
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 …
Gc-089 Safecircle: Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd, Awan-Ur Rahman, Shakib Quddus, Soarov Chakra Borty
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.
Gc-128 Multi-Label Commit Message Classification Using P-Tuning, Tanvi Mistry
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.
Grm-012 (Tcc) Transformer Embedded Synthetic Source Code Multiclass Classification, Rene Lisasi, Patrick Wu
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 …
Grm-038 Optimizing Prompts For Alzheimer's Speech Classification Using Llm, Imaan Shahid
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 …
Grm-042 Ihelp: A Care Partner Activation Program Mhealth System For Ad/Adrd Caregivers, Trisha Bhowmick
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, …
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 …
Grp-087 Empowering Alzheimer’S Caregivers: Designing Explainable And Personalized Ai For Mental Health Support, Syeda Umme Salma, Chandra Rekha Renduchintala, Isa Siddique
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 …
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
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.
Grp-088 Nutrilyzer: A Vision-Based App For Macronutrient Estimation And Blood Glucose Response Prediction, El Arbi Belfarsi
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.