Computer Sciences Commons^™

Analyzing Musical Emotions: A Multi-Dataset Approach To Sentiment And Mood Classification In Songs, Mahad Syed

SPARK Symposium Presentations

Music evokes a wide range of emotions, yet most music recommendation systems focus on sound and listening patterns rather than the meaning of lyrics. This project enhances lyric-based emotion recognition by applying Natural Language Processing (NLP) and Machine Learning (ML) to classify song lyrics into emotional categories.

I used eight datasets from Kaggle, including collections of lyrics, emotion labels, and audio features, providing a strong foundation for analysis. Our approach combines traditional NLP techniques (like TF-IDF and Word2Vec) with advanced deep learning models (such as BERT and XLNet) to classify lyrics into categories like happy, sad, angry, calm, romantic, and …

Knowledge Distillation For Efficient Object Detection: Toward Scalable And Deployable Vision Models, Qizhen Lan

All ETDs from UAB

Object detection is a critical component of autonomous driving, requiring real-time, robust perception to ensure safety. However, state-of-the-art deep neural network object detectors typically incur high computational cost and memory footprint, hindering their deployment in resource-constrained environments such as self-driving vehicles. This dissertation addresses the need for efficient yet accurate detectors by leveraging knowledge distillation (KD), a model compression technique that transfers knowledge from a high-capacity teacher model to a lightweight student model. While KD has seen success in image classification, its application to object detection poses unique challenges due to multiple instances per image and complex output structures. To …

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

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

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

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

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

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

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

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

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

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

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-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.

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.

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 …

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 …

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).

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.

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. …

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 …

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 …

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 …

Grm-080 Leveraging Data Science For Resilience: Improving Trauma-Informed Care Practice For Adverse Childhood Experience With Ai & Data Science Application, Mohmmad Arif Shaik

C-Day Computing Showcase

Adverse Childhood Experiences (ACEs) have long-lasting effects on physical health, mental well-being, education, and socioeconomic outcomes. Resilient Georgia (RG), a statewide initiative, seeks to address ACEs through trauma-informed care and data-driven strategies. However, challenges in data collection, analysis, and tracking hinder the effectiveness of these efforts. This study explores the role of data science and interactive visualization tools in improving outcomes for individuals and communities affected by ACEs. A key focus of this research is the development of a data science management application designed to enhance data collection and facilitate real-time decision-making. The application features interactive dashboards that allow stakeholders …

Grm-083 Leveraging Graph Attention Networks And Bert For Robotic Surgery Report Generation, Akshay Krishna Varma Buddharaju

C-Day Computing Showcase

This project focuses on generating surgical reports from robotic surgery videos by leveraging graph-based representations of instrument-tissue interactions. We utilize Graph Attention Networks (GAT) to model these interactions, which are then integrated into a BERT-based language model for caption generation. Our approach enhances the accuracy of automated surgical reporting by capturing spatial and relational dependencies within surgical scenes. The model is evaluated on the Robotic Instrument Segmentation dataset from the 2018 MICCAI Endoscopic Vision Challenge(Endovis-18) and TORS surgery dataset, achieving high performance across multiple metrics, including BLEU-n, Cider, and ROUGE scores. By automating report generation, this study aims to assist …

Grm-093 Advances In Non-Invasive Glucose Sensing: A Comprehensive In Vitro Analysis, El Arbi Belfarsi, Henry Flores

C-Day Computing Showcase

This study explores non-invasive glucose sensing using infrared (IR) imaging and electrical measurements in an in-vitro setup. Glucose samples (70–200 mg/dL) were prepared by diluting concentrated solutions (700–2000 mg/dL) 1:10 in synthetic blood concentrate, with 2 mg/dL increments. A custom 3D-printed black cuvette holder ensured consistent alignment of components, including either an IR camera or a 1550 nm photodiode, light sources (850 nm LED/laser, 808 nm, 650 nm, or 1600 nm), and a 3 mm skin-mimicking silicone layer. A Region Based Convolutional Neural Network (RCNN) trained on IR images achieved the lowest RMSE of 10.98 mg/dL at 850 nm LED. …

Grm-102 Analysis Of Climate Change Effects On Bird Migration​​ Patterns Using Long-Term Data​​ ​, Aadil Syed, Mary Beyioku, Osi Aizebeokhai, Felix Dogbe

C-Day Computing Showcase

To investigate how climate change has affected bird migration patterns over the past decades, focusing on changes in migration timing, routes, and population trends. This project will aim to identify correlations between climate variables and observed changes in bird behavior, contributing to conservation efforts and climate change research.

Grm-118 Analysis Of Climate Change Effects On Bird Migration Patterns Using Long-Term Data, Saikiran Banneni, Naga Sreeja Arabu, Bhavana Modepu, Abilash Kanduri, Leelakarthik Devisetty

C-Day Computing Showcase

This project examines the impact of climate change on bird migration patterns by integrating bird observation data from eBird with climate data from the NOAA Global Historical Climate Network (GHCN). Focusing on species such as the Arctic Tern, the study analyzes changes in migration timing, routes, and population trends over recent decades. Migration paths were visualized using QGIS, while MODIS land cover data helped assess habitat changes along these routes. Temporal analysis revealed noticeable shifts in migration timing, with earlier arrivals in some regions correlating with rising temperatures and changing precipitation patterns. For future predictions, CHELSA climate data was combined …

Grm-120 Coding Neurodivergent, Veronica Bramlett

C-Day Computing Showcase

This literary research provides a look at neurodivergent individuals learning coding, specifically Python, and the struggles and benefits that come from the way their brains are wired. This literature research was conducted to look at the benefits and struggles of learning to code as a neurodivergent individual. One area that has not been studied extensively is the learning of Python, or coding in general, by neurodivergent populations. This includes the benefits a neurodivergent learner may glean from learning Python as well as the challenges they may encounter - anything which makes their experience different from that of a neurotypical Python …

Grm-131 Xr Agent (A Mllm Powered Xr System), Yukang Shen

C-Day Computing Showcase

This project proposes “XR Agent”, a uncoupled and efficient framework for developing AI-powered extended reality (XR) applications on head-mounted displays (HMDs). Leveraging multimodal artificial intelligence—including MediaPipe(Google open-source CV Model) for computer vision (object segmentation, recognition, pose estimation), multimodal large language models (MLLMs) like Gemini, and Unity’s cross-platform XR development ecosystem—the framework aims to create an extensible base system that enables rapid prototyping and deployment of intelligent XR applications. Currently, it was deployed on the Meta Quest 3 platform, XR Agent explores novel HCI(Human Computer Interaction) paradigms, combining real-time sensor data processing, immersive visualization, and adaptive AI-driven logic. This work addresses …