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Automatic Weather Observing System, Amielia Beam Apr 2025

Automatic Weather Observing System, Amielia Beam

Undergraduate Research Conference

No abstract provided.

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Building Compassion And Connection: A Case Study Of Humane Education Through Middle Level Book Club Experiences, Kennedy Barnhouse Apr 2025

Building Compassion And Connection: A Case Study Of Humane Education Through Middle Level Book Club Experiences, Kennedy Barnhouse

Undergraduate Research Conference

This case study explores the impact of a humane education-focused book club on middle level students' understanding of humane education principles and solutionary thinking. The study found that participation in the book club increased students' understanding of humane education and boosted their confidence in addressing societal challenges, suggesting that book clubs can effectively foster empathy, ethical reasoning, and social responsibility in middle level students.

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The Effects Of Leadership On College Student Well-Being, Kate O. Middleton Apr 2025

The Effects Of Leadership On College Student Well-Being, Kate O. Middleton

Undergraduate Research Conference

• Student leaders play an important role on college campuses • However, they can also face increased pressures that may impact their psychological well-being • The purpose of this study was threefold: 1. A purposive sample of student leaders was examined and their investment and satisfaction with their positions were assessed 2. Students' leadership investment was correlated with a variety of psychological variables (i.e., life satisfaction, impostorism) 3. Student leaders were compared to a sample of non-leaders to examine the specific psychological variables on which leaders and non-leaders differ

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The Impact Of Ut System Branding On Stephen F. Austin State University, Kynlie Foit, Isabel Jedmears, Ian Lahera Apr 2025

The Impact Of Ut System Branding On Stephen F. Austin State University, Kynlie Foit, Isabel Jedmears, Ian Lahera

Undergraduate Research Conference

The research will assist SFA in assessing the impact of joining the UT System and provide insight into how SFA can better align with the UT x SFA brand among the community and students.

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The Ethics Of Ai In Wealth Management, Vivian Bivens, Kailey Fischer Apr 2025

The Ethics Of Ai In Wealth Management, Vivian Bivens, Kailey Fischer

Undergraduate Research Conference

This paper examines the ethical implications of AI in wealth management by analyzing regulations from international and national securities bodies and evaluating how firms implement AI responsibly. While current regulations are not perfect, they generally promote ethical AI use, and firms are actively working to reduce biases and enhance data security.

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Astronaut And Bubbles: Representing Speech Disability Through Art, Susana De La Paz Apr 2025

Astronaut And Bubbles: Representing Speech Disability Through Art, Susana De La Paz

Undergraduate Research Conference

Purpose: The Astronaut's dialogue was intentionally written to highlight speech impairments, a challenge I have personally faced in my life. Growing up, I struggled with articulating certain speech sounds and found it disheartening that children's books rarely featured characters with speech disabilities. This book aims to provide comfort and visibility to children who face similar challenges.

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Opposites: Poetry/Design Collaboration, Susana De La Paz, Rebekah Glasgow Apr 2025

Opposites: Poetry/Design Collaboration, Susana De La Paz, Rebekah Glasgow

Undergraduate Research Conference

No abstract provided.

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Digital Comic Book Creation, Austin Pena Apr 2025

Digital Comic Book Creation, Austin Pena

Undergraduate Research Conference

No abstract provided.

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Neotropical Catfish Diversity In Rivers Of The Guiana Shield: A Combined Approach Using Taxonomic, Functional, And Isotopic Tools, Joseph Sheffield Apr 2025

Neotropical Catfish Diversity In Rivers Of The Guiana Shield: A Combined Approach Using Taxonomic, Functional, And Isotopic Tools, Joseph Sheffield

Undergraduate Research Conference

Rivers of the Guiana Shield in South America are very heterogenous, containing a mosaic of habitats and environmental characteristics ideal for maintaining high freshwater biodiversity. The connection between the Essequibo and Amazon rivers via the Rupununi River during the wet season plays an important role for the great diversity of Neotropical ichthyofauna. Neotropical catfishes (Siluriformes) are the second most diverse group of freshwater fishes, after the Characiformes. They span a wide range of body sizes and exhibit diverse trophic specializations, making them ideal organisms for studying the influence of body size variation on community organization and ecosystem function. We explored …

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Grp-082 Ready Cluster One: Optimizing Film Success With Data Science, Mohsin Md Abdul Karim, Joseph Richardson Apr 2025

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 …

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Gc-074 Real-Time Object Detection, Rohit Malik Apr 2025

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 …

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Gc-079 Nibbleai, Ryan Deem, Zeynep Birgili, Austin Cook Apr 2025

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.

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Grm-076 Assessing The Performance Of Intelligent Agents In Visual Food Recognition Relative To Manual Data Entry, El Arbi Belfarsi, Henry Ekeocha Apr 2025

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 …

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Grm-109 Quantum Machine Learning For Science And Engineering Research, Andrew Polisetty Apr 2025

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, …

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Grp-021 Shap-Explainable Image-To-Topology Regression, Charles Fanning Apr 2025

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.

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Uc-040 Security Lookup Interface Project, Alhasan Mohsen, Alejandro Albarran, Ethan Lan, Jerrat Jester Apr 2025

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 …

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Uc-049 From Forecast To Fortune: Portfolio Optimization And Prediction, Nia Taylor Apr 2025

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 …

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Ur-001 Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad Apr 2025

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 …

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Uc-116 Robot Tactics, John Anderson Apr 2025

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

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Ur-094 Aistudy: Using Ai To Study Ai, Mason Pederson Apr 2025

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) …

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Gc-008 Medivault: An Ai-Powered Secure Medical Image Sharing Platform, Henry Ekeocha, Reginald Calixte, Dhruv Patel Apr 2025

Gc-008 Medivault: An Ai-Powered Secure Medical Image Sharing Platform, Henry Ekeocha, Reginald Calixte, Dhruv Patel

C-Day Computing Showcase

MediVault is a secure, cloud-native platform that empowers patients and healthcare providers to upload, view, and share medical images like X-rays and MRIs with confidence. Built using Next.js and Node.js, and deployed on AWS Free Tier (S3, RDS, KMS), the system implements role-based access, two-factor authentication, and end-to-end encryption to ensure privacy and HIPAA compliance. MediVault features an intuitive interface and integrates AI-enhanced automation to streamline metadata tagging and detect potential anomalies in scans. Designed for scalability, usability, and compliance, this project showcases real-world expertise in full-stack cloud development and healthcare cybersecurity.

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Gc-025 Secure Medical Image Sharing Platform – Medshare, Toyese Adenuga, Richard Lateu Apr 2025

Gc-025 Secure Medical Image Sharing Platform – Medshare, Toyese Adenuga, Richard Lateu

C-Day Computing Showcase

The Secure Medical Image Sharing Platform is a cloud-based solution that ensures secure upload, management, and sharing of medical images, adhering to HIPAA and GDPR. It utilizes advanced encryption protocols, role-based access control (RBAC), and audit trails to safeguard patient data. The platform's user-friendly interface facilitates seamless interaction between patients and healthcare providers, enabling the use of AI tools for diagnostic support.

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Gc-058 Personalized Wellness Recommendations, Varshini Yaganti Apr 2025

Gc-058 Personalized Wellness Recommendations, Varshini Yaganti

C-Day Computing Showcase

A health recommendation system using machine learning, built on Hadoop, Spark, and HDFS, represents a significant advancement in personalized healthcare. This project aims to leverage big data technologies to process and analyze vast amounts of medical data across a distributed computing environment, utilizing at least three virtual machines. The background of this project lies in the increasing prevalence of chronic diseases and the growing volume of health-related data collected by healthcare providers. The motivation for this project stems from several key factors. Firstly, traditional healthcare systems often struggle to provide personalized recommendations due to the sheer volume and complexity of …

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Gc-123 Deep Learning-Based Skin Cancer Detection, Andrew Polisetty, Akshay Krishna Varma Buddharaju, Siri Yellu Apr 2025

Gc-123 Deep Learning-Based Skin Cancer Detection, Andrew Polisetty, Akshay Krishna Varma Buddharaju, Siri Yellu

C-Day Computing Showcase

Skin cancer is increasingly becoming a severe health problem globally today, but early detection is essential to enhance survival rates. Nonetheless, conventional diagnosis relies largely on visual examinations by dermatologists, which can be subjective and time-consuming. This research examines the application of deep learning for the automation of skin cancer detection based on dermoscopic images from the HAM10000 dataset. The models VGG19, DenseNet121 and ResNet152 will be trained and evaluated, with class mbalance addressed using data augmentation strategies. The outputs will demonstrate the applicability of deep learning to improve skin cancer diagnosis. Classification optimization using ensemble modeling and its improved …

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Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi Apr 2025

Grm-011 Non-Invasive Convolution-Based Coronary Artery Blood Pressure Prediction, Rene Lisasi

C-Day Computing Showcase

The primary objective of this proposal is to develop an innovative technique for determining the functional significance of coronary artery lesions in patients with coronary artery disease (CAD) and evaluate its utility for clinical decision-making using coronary computed tomography angiography (CCTA).

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Grm-022 Exploring Coronavirus 2019 Datasets With Convolutional Neural Networks, Ryan Deem, Sandeep Kumar Vupputuri, Tyler Hood, Droan Patel, Stephen Jacobs Apr 2025

Grm-022 Exploring Coronavirus 2019 Datasets With Convolutional Neural Networks, Ryan Deem, Sandeep Kumar Vupputuri, Tyler Hood, Droan Patel, Stephen Jacobs

C-Day Computing Showcase

Over the past several decades, the healthcare sector has increased its data creation velocity at an astonishing rate. More doctors and patients have access to real-time imaging technology, which leads to earlier detection and diagnosis for a variety of diseases. In this project, we will explore several datasets that were gathered by various health organizations during the Coronavirus 2019 (COVID-19) pandemic. We will leverage big data analytics techniques and neural network modeling to gain deeper insights into the differentiated diagnosis of COVID-19.

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Grm-041 Ai-Driven Analysis Of Openalg Curriculum: Mapping Ai Competencies Across Georgia’S Higher Education Landscape, Michelle Ilechukwu, Iveren Leghemo, Timothy Ngeny Apr 2025

Grm-041 Ai-Driven Analysis Of Openalg Curriculum: Mapping Ai Competencies Across Georgia’S Higher Education Landscape, Michelle Ilechukwu, Iveren Leghemo, Timothy Ngeny

C-Day Computing Showcase

This project investigates the presence of artificial intelligence (AI) competencies across Georgia’s higher education curriculum using university course catalogs as the primary data source, supplemented by OpenALG materials. We applied large language models, including OpenAI’s ChatGPT and embedding APIs, to analyze over 34,000 courses summarizing content, classifying AI relevance, and mapping to global frameworks (AI4K12 and UNESCO). Techniques such as topic clustering, semantic similarity analysis, and geographic distribution mapping were used to uncover patterns in AI integration. Findings reveal that AI content is concentrated in computing disciplines and research universities, with limited coverage in community colleges, MSIs, and non-technical fields. …

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Grm-043 Performance Assessment Of Deepseek Versus Bard And Chatgpt In Detecting Alzheimer’S Dementia, Muhammad Awais Arshad Apr 2025

Grm-043 Performance Assessment Of Deepseek Versus Bard And Chatgpt In Detecting Alzheimer’S Dementia, Muhammad Awais Arshad

C-Day Computing Showcase

Alzheimer’s disease is a growing public health issue due to its progressive nature and increasing prevalence. Large language models (LLMs) offer promising avenues for non-invasive cognitive assessment through natural language understanding. In this study, we evaluate DeepSeek’s general-purpose model V3 and reasoning-enhanced R1 variant—for identifying Alzheimer’s dementia (AD) and Cognitively Normal (CN) individuals using transcripts derived from spontaneous speech. Two baseline prompting strategies (zero-shot, chain-of-thought ) were applied to both model types and an additional query (self-consistency prompting) was applied to assess better predictions. Accuracy was the primary performance metric. When positively identifying AD, the general-purpose DeepSeek V3 model produced …

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Grm-050 Context-Aware Misinformation Detection Using Fine-Tuned Bert And Bilstm With Attention, Rakshak Gurung, Nino Tkabladze Apr 2025

Grm-050 Context-Aware Misinformation Detection Using Fine-Tuned Bert And Bilstm With Attention, Rakshak Gurung, Nino Tkabladze

C-Day Computing Showcase

Misinformation spreads fast, and 60% of consumers now question media reliability (Redline Digital, 2023). Manual verification is slow, and most systems still rely on binary real/fake classification, which overlooks nuanced types of misinformation. We propose a multi-class deep learning approach using a fine-tuned BERT model and a custom BiLSTM with attention to better detect categories like satire, conspiracy, and bias. Our models were trained on a balanced subset of the Fake News Corpus using nine distinct misinformation classes. By addressing both class imbalance and linguistic ambiguity, this system enhances contextual understanding and improves detection across varied news content. Our approach …

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Grm-060 Abstractive Summarization Of Informal Text: Fine-Tuning Transformers On Reddit Discussions, Nong Ming, Arpana Challa, Sharon Edward John Apr 2025

Grm-060 Abstractive Summarization Of Informal Text: Fine-Tuning Transformers On Reddit Discussions, Nong Ming, Arpana Challa, Sharon Edward John

C-Day Computing Showcase

In recent years, the rapid growth of social media platforms has led to an information overload, as a result, the ability to compress long and complex texts into short and precise summaries is essential, especially in online discussions and comment sections. Summarizing such content is difficult due to inconsistencies in sentence structure, slang, abbreviations, and the lack of formal grammar. State-of-the-art models such as BART and PEGASUS have shown promising results, but their performance on informal datasets remains lower compared to structured text benchmark. To address these challenges, we fine-tune BART and PEGASUS on the Reddit TIFU dataset, leveraging their …

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