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Articles 14461 - 14490 of 291660

Full-Text Articles in Physical Sciences and Mathematics

Profiting On The Kentucky Derby, Bailey Korfhage Apr 2025

Profiting On The Kentucky Derby, Bailey Korfhage

Undergraduate Theses

This paper analyzes the quantitative data of horses that ran in the Kentucky Derby to recognize statistically significant variables to predict the horse that comes in first or in-the-money. This analysis is specific to the post-implementation of the points system that began for the 2013 Kentucky Derby. Churchill Downs, the host of the Kentucky Derby, changed the methodology of qualification for a horse to enter the race; instead of qualifying with highest earnings in lifetime starts, the institution implemented a points system that awarded different proportions of points depending on the value of various prep races leading up to the …

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Identifying The Factors Affecting The Survival Of Trauma Patients Using Logistic Regression Analysis, Maggie Smith Apr 2025

Identifying The Factors Affecting The Survival Of Trauma Patients Using Logistic Regression Analysis, Maggie Smith

Honors College Theses

There is a broad interest among researchers and clinicians in identifying factors affecting clinical outcomes of patients with physical trauma. Numerous factors affect Hospital Discharge Status (HDS), one of the main binary outcome variables of trauma patients. Logistic regression is one of the widely used methods to analyze relationships between a set of predictors with a binary outcome. In this study, a logistic regression model is built for HDS. Predictors include arrival time, age, trauma level, injury severity score, arrival heart rate, arrival blood pressure, length of hospital stay, time from injury to arrival at Billings Clinic (BC), patient transfer …

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Algebraic Topics For Future Middle School Teachers, Leonard Van Wyk Apr 2025

Algebraic Topics For Future Middle School Teachers, Leonard Van Wyk

Department of Mathematics and Statistics - Faculty Scholarship

This text contains algebraic concepts relevant to the middle school mathematics curriculum. Topics include the basics of number theory, functions, linear systems, matrices, and polynomials.

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Development Scenarios And Wildfire Risk In Central Oregon, Samantha Hall Apr 2025

Development Scenarios And Wildfire Risk In Central Oregon, Samantha Hall

Dissertations and Theses

Risk from natural hazards occurs where areas vulnerable to natural hazards and development intersect. Development in vulnerable areas is a societal choice that has a lasting impact as our built environment is relatively permanent. Development patterns that contribute to increased wildfire risk mostly occur within the wildland urban interface, a land use type where development is at the fringes or intercept of wildland areas and is more flammable due to surrounding vegetation, slope, local climate, and other factors. To understand how past and future development potentials may impact fire risk in wildfire prone areas, a case study of Deschutes County, …

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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning With Biosensor Signals, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul Apr 2025

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning With Biosensor Signals, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

School of Public Health Faculty Publications

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to …

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Human Capital And Lifetime Income Gains Of Scaling-Up Small-Quantity Lipid Nutrient Supplements Among Children Under 2 Years: A Modelling Analysis, Nandita Perumal Phd, Goodrarz Danaei, Günther Fink, Mark Lambiris, Christopher R. Sudfeld Apr 2025

Human Capital And Lifetime Income Gains Of Scaling-Up Small-Quantity Lipid Nutrient Supplements Among Children Under 2 Years: A Modelling Analysis, Nandita Perumal Phd, Goodrarz Danaei, Günther Fink, Mark Lambiris, Christopher R. Sudfeld

Faculty Publications

Undernutrition in early childhood is associated with adverse health and developmental outcomes later in life and remains a persistent global public health problem. Providing small-quantity lipid nutrient supplements (SQ-LNS) to children aged 6-24 months improves child growth and neurodevelopmental outcomes, but the potential long-term benefits to human capital have not been previously estimated. We estimated the potential returns to schooling and lifetime income attributable to increasing coverage of SQ-LNS for children < 2 years of age from 0% to 50% or 90% per five-year birth cohort in five countries (Bangladesh, Burkina Faso, Ethiopia, Pakistan, and Uganda) with a high burden of undernutrition. Random-effects meta-analyses were used to estimate the effect of SQ-LNS on child development using evidence from randomized controlled trials, and to estimate the returns to lifetime income as a function of change in development based on a de novo meta-analysis of observational economic studies. Gains in school years attributable to scaling-up SQ-LNS to 90% coverage ranged from 0.14 million school years (95% uncertainty interval [UI]: 0.064, …

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Knowledge Distillation For Efficient Object Detection: Toward Scalable And Deployable Vision Models, Qizhen Lan Apr 2025

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 …

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On The Determinant Of Up On Mk(P,Χ), Xingyu Huang, Timothy J. Huber, Dongxi Ye Apr 2025

On The Determinant Of Up On Mk(P,Χ), Xingyu Huang, Timothy J. Huber, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

In this work, for p a prime, we compute the absolute value of the determinant of the UpUp-operator on the vector space Mk(p,χ)Mk(p,χ) of holomorphic modular forms of weight k and level Γ0(p)Γ0(p) with character χχ. As an implication, we confirm a number of conjectures of the second author.

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Exploring Graphs And Chromatic Symmetric Functions, Olivia Payne Apr 2025

Exploring Graphs And Chromatic Symmetric Functions, Olivia Payne

Undergraduate Research Conference

We consider graphs with a small number of vertices and analyze the coefficients of hook partitions of their chromatic symmetric function, which is a generalization of the chromatic polynomial of a graph. Some of these coefficients can hold information about the graphs themselves and, in this research, we find a combinatorial formula for coefficients of hook partitions. This research is motivated by Stanley's Tree Conjecture.

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Antioxidant Properties Of Phytochemicals In Watercress And Mint Extracts In Serum Albumin, Perla Tovar Apr 2025

Antioxidant Properties Of Phytochemicals In Watercress And Mint Extracts In Serum Albumin, Perla Tovar

Undergraduate Research Conference

Hypothesis: Phytochemicals present in mint and watercress help stabilize protein under oxidative stress. Main Objective: Analyze the interaction of HSA with different phytochemicals present in mint extract.

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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-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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