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Ecotoxicological Assessment Of A Sunscreen-Derived Pollutant, Benzophenone-2, On Freshwater Fish (Labeo Rohita), Mina Jamil, Sajid Abdullah, Maham Riaz, Sana Aziz Nov 2025

Ecotoxicological Assessment Of A Sunscreen-Derived Pollutant, Benzophenone-2, On Freshwater Fish (Labeo Rohita), Mina Jamil, Sajid Abdullah, Maham Riaz, Sana Aziz

Turkish Journal of Zoology

Ultraviolet filters (UVFs) are constituents of skincare products that protect skin against UV ray damage, such as suntan, sunburn, and skin tumors. Due to their ability to biotransform, UVFs are considered emerging pollutants. Benzophenone-2 (BP-2), with a yellowish crystalline appearance, is one of the widely used UVFs in skincare products. It enters the aquatic environment by washing off the skin during swimming and recreational activities and becomes hazardous to aquatic life due to its lipophilicity and bioaccumulative potential. This study was designed to evaluate the acute and chronic toxicity of BP-2 for the freshwater fish Labeo rohita. For acute toxicity, …

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Morphological Variability And Systematics Of Ponto-Caspian Cladocerans Of The Genus Cercopagis Sars, 1897 (Branchiopoda: Onychopoda: Cercopagidae), Larisa F. Litvinchuk, Spartak N. Litvinchuk Nov 2025

Morphological Variability And Systematics Of Ponto-Caspian Cladocerans Of The Genus Cercopagis Sars, 1897 (Branchiopoda: Onychopoda: Cercopagidae), Larisa F. Litvinchuk, Spartak N. Litvinchuk

Turkish Journal of Zoology

In recent decades, significant changes have occurred in the composition of zooplankton, especially in the Ponto-Caspian Basin. An important component of zooplankton in this region is cladocerans of the genus Cercopagis. However, the introduction of invasive species means that many species of this genus have probably become extinct. Because their disappearance molecular study of these organisms is no longer possible. Therefore, a comparative analysis of morphological variability has become more important. We studied the morphological variability of all 12 currently known species of the genus. Samples from the Caspian, Azov, Black, and Baltic Seas were analyzed. The number of species …

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An Intelligent Vision System For Metal Surface Defect Detection, Sida Zhang Nov 2025

An Intelligent Vision System For Metal Surface Defect Detection, Sida Zhang

Dissertations (1934 -)

With the continuous growth in the need for artificial intelligence driven manufacturing, automatic detection of metal surface quality has become a key issue in forging production. Traditional manual detection methods are not only inefficient and subjective but also have low accuracy when analyzing workpieces with complex surfaces and diverse colors. To solve this problem, this dissertation proposes two methods for metal surface defect detection and classification. This dissertation begins with the background of forging and summarizes existing literature on industrial vision systems and surface defect detection methods. Basic image processing techniques, machine learning algorithms, and deep learning foundations—including artificial neural …

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Comparative Analysis Of 2d And 3d Gait Metrics From Video Using Deep Learning Techniques, Akhilesh Singh Kushwaha Nov 2025

Comparative Analysis Of 2d And 3d Gait Metrics From Video Using Deep Learning Techniques, Akhilesh Singh Kushwaha

Electronic Theses and Dissertations 2020 - Present

This thesis presents a comparative evaluation of two deep learning-based methods for extracting 2D and 3D gait metrics from monocular video: a 2D-to-3D lifting approach combining AlphaPose for keypoint detection with MotionBERT for temporal 3D reconstruction, and a direct 3D estimation method using MeTRAbs. Videos of two healthy adults walking across a ProtoKinetics Zeno™ Walkway were captured laterally at 4K/60 fps using an iPhone 16. Ground-truth spatiotemporal gait parameters were obtained from the pressure-sensitive mat, enabling quantitative validation via Mean Absolute Error (MAE) and Pearson correlation. MeTRAbs consistently outperformed the lifting pipeline, achieving MAE below 2% for temporal metrics (e.g., …

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Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha Nov 2025

Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha

C-Day Computing Showcase

Virtualization technologies form the backbone of modern cloud and serverless platforms, but the balance between performance efficiency and strong isolation remains a key research challenge. Using a comparative literature based methodology, benchmark data from empirical studies are synthesized and normalized across four metrics: startup latency, memory footprint, isolation strength, and scalability. The findings show that traditional VMs such as KVM and Xen provide robust, formally verified isolation but incur higher boot times and memory usage, whereas Micro VMs like Firecracker and Kata Containers achieve lower latency (often < 125 ms) and smaller memory footprints (5–30 MB) while preserving VM-level isolation. Overall, Micro VMs deliver near container responsiveness with VM-grade security, making them suited for serverless and edge environments. As a future direction, this study highlights the need for scalable, formally verified Micro VM architectures for next-generation cloud systems.

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Grp-0165 Reinforcement Learning For Latency-Aware Priority Boosting In Linux Completely Fair Scheduler, Joseph Natter Nov 2025

Grp-0165 Reinforcement Learning For Latency-Aware Priority Boosting In Linux Completely Fair Scheduler, Joseph Natter

C-Day Computing Showcase

Tail latency remains a persistent challenge in Linux’s Completely Fair Scheduler (CFS), particularly when short, latency-sensitive jobs compete with longer ones. Traditional boosting heuristics improve tail latency but require manual tuning and generalize poorly across workloads. This project evaluates whether a reinforcement-learning (RL) controller can dynamically apply priority boosts more effectively than fixed heuristics. Using a discrete-event Python simulator modeled after CFS, this project compares baseline CFS, heuristic boosting, and PPO-based learned boosting under mixed workloads. Results show that while heuristics achieve the lowest absolute latency, RL achieves competitive tail-latency reduction with significantly better fairness and adaptability. A state-space study …

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Grp-21186 A Safe Vision-Guided Robotic Injection Control Framework Based On Reinforcement Learning, Zhiguo Liu Nov 2025

Grp-21186 A Safe Vision-Guided Robotic Injection Control Framework Based On Reinforcement Learning, Zhiguo Liu

C-Day Computing Showcase

This project presents a modular vision-guided robotic framework for safe deltoid intramuscular injection. An external YOLO-based detector localizes the deltoid region and outputs a 3D injection point in the robot base frame. In NVIDIA Isaac Sim, a simulated myCobot 280 arm receives this point, and a deep reinforcement learning policy generates a safe approach pose under kinematic and safety constraints, while a deterministic controller executes the final straight-line insertion and withdrawal. A safety supervisor monitors target validity, joint limits and distance to a simplified arm model, triggering immediate stop and retraction when unsafe conditions arise.

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Grp-20236 Zero-Day Host-Based Intrusion Detection Via Hybrid Deep Sequence Modeling Of Systemcalls​ ​, Syeda Umme Salma Nov 2025

Grp-20236 Zero-Day Host-Based Intrusion Detection Via Hybrid Deep Sequence Modeling Of Systemcalls​ ​, Syeda Umme Salma

C-Day Computing Showcase

Protecting endpoints has become increasingly challenging, as adversaries have been effective in bypassing defenses. Traditional signature-based Host-based IDS performs quite well at recognizing known patterns but often struggles with previously unseen activity. This study incorporates deep neural sequence modeling with classic OS telemetry to flag novel behavior from Linux system-cell traces. This study design is paired with a sequence encoder over syscall streams, incorporating lightweight statistical signals that are derived from process activity. We believe that our hybrid neural network approach will outperform conventional baselines and boost recall on unknown attacks while maintaining low false-positive rates, providing a practical and …

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Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan Nov 2025

Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) is one of the leading causes of global health concern, but current risk assessments are limited to episodic clinical visits. Most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of …

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Uc-1152 Sustainsync, Youssef El-Shaer, Zaid Khan Nov 2025

Uc-1152 Sustainsync, Youssef El-Shaer, Zaid Khan

C-Day Computing Showcase

Sustain Sync investigates how organizations can standardize sustainability tracking and how AI can convert that data into actionable insights. The platform normalizes utility data into a consistent schema aligned with industry frameworks. It applies machine learning forecasting and a retrieval-augmented co-benefit engine to relate sustainability goals across domains such as CO₂, water, and biodiversity. Through a simple dashboard, Sustain Sync demonstrates an end-to-end data-driven approach for goal tracking, trend analysis, and AI-driven sustainability recommendations for an organization.

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Ur-1256 Realistic Paint-Like Color Mixing In Gimp, Calvin Crose, Cassidie Grogan, Dawson White, Joey Montalvo Nov 2025

Ur-1256 Realistic Paint-Like Color Mixing In Gimp, Calvin Crose, Cassidie Grogan, Dawson White, Joey Montalvo

C-Day Computing Showcase

Gimp updated its support for the MyPaint brushes, from version 1.0 to version 2.0. However, despite the added support, the application still uses legacy blending from version 1.0 of MyPaint brushes and lacks the new tools for spectral blending offered in version 2.0. A request has been made to implement the new blending so users can produce art using this feature that mirrors real-world blending.

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Ur-1163 Editing Films And Movies On Gimp, Ahmed Al Saad, William Grace, Brian Robinson Nov 2025

Ur-1163 Editing Films And Movies On Gimp, Ahmed Al Saad, William Grace, Brian Robinson

C-Day Computing Showcase

This project is to develop an import feature for Digital Picture Exchange (.DPX) files with GIMP. The DPX format is popularly used in the film and visual effects industry. DPX stores uncompressed high bit depth images. Although the project is still in progress, a working prototype is provided.

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Ur-0247 Lyric Prediction Model, Evan Gideon, Garrett Dasher, Ulrich Batanado, Drew Claerbout, Andrew Henshaw Nov 2025

Ur-0247 Lyric Prediction Model, Evan Gideon, Garrett Dasher, Ulrich Batanado, Drew Claerbout, Andrew Henshaw

C-Day Computing Showcase

Word prediction plays a central role in the development and refinement of large language models, supporting applications such as search optimization, dialect identification, and conversational AI systems like Siri as AI text generation becomes increasingly widespread, the demand for precise and contextually aware predictive capabilities continues to grow. This project presents lyric prediction model designed to generate the next lyric based on preceding words, with the ability to identity line breaks and sequential structure. Ultimately, this work aims to advance lyrical text generation by enabling the model to emulate the stylistic characteristics of specific artists or musical genre.

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Ur-0246 Quantum Ml For Science & Engineering, Dharani Shakthivel, Haoxian Tan, Justin Martin Nov 2025

Ur-0246 Quantum Ml For Science & Engineering, Dharani Shakthivel, Haoxian Tan, Justin Martin

C-Day Computing Showcase

Classical machine learning methods - including CNNs, SVMs, PCA, Logistic Regression, and Random Forests - have achieved strong performance across fields such as computer vision, malware detection, and drug discovery. However, these models face scalability limits when trained on large or high-dimensional datasets. Quantum computing introduces superposition, interference, and entanglement, enabling quantum kernels, quantum feature maps, and hybrid quantum-classical architectures that may reduce computational cost or enhance data representation. This project implements classical versions of these algorithms alongside their quantum counterparts to evaluate differences in accuracy, efficiency, and resource demands. By comparing performance across diverse scientific and engineering datasets, the …

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Grp-0214 User-Level Gpu Right-Sizing In Hpc: A Framework For Predicting Training Runtime, Yinning Zhang, S M Tanvir Faysal Alam Chowdhoury Nov 2025

Grp-0214 User-Level Gpu Right-Sizing In Hpc: A Framework For Predicting Training Runtime, Yinning Zhang, S M Tanvir Faysal Alam Chowdhoury

C-Day Computing Showcase

Graphics Processing Unit (GPU) resources in High-Performance Computing (HPC) systems are frequently underutilized due to inaccurate user-provided run time estimates. This research develops a machine learning framework for predicting neural network training time from architectural features, dataset size, and other hyperparameters. This approach can be implemented on any HPC systems without requiring hardware access or runtime profiling as other preceding methods do. We sampled neural network models from the NATS-Bench benchmark and used 3 benchmark datasets to generate 400 training configurations. We used these 400 data points to build regression models and found that the best model, Gradient Boosting Regressor, …

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Gc-0251 Reproducing Extended Isolation Forests With Star-Cast, Drew Patrick, Ram Sai Sivakoti, Rohit Malik, Vardhineedi Surya Kamal, Venkata Sasidhar Reddy Palagundla Nov 2025

Gc-0251 Reproducing Extended Isolation Forests With Star-Cast, Drew Patrick, Ram Sai Sivakoti, Rohit Malik, Vardhineedi Surya Kamal, Venkata Sasidhar Reddy Palagundla

C-Day Computing Showcase

Fraud models routinely flag suspicious transactions but rarely explain why, which slows investigations and erodes trust. In this work we study Extended Isolation Forest (EIF) for unsupervised fraud detection and propose STAR-CAST, a lightweight framework that turns raw anomaly scores into threshold-aligned IF–THEN rule cards with explicit reliability measures. Using the public credit-card fraud dataset (284,807 transactions, 492 frauds; ~0.17% prevalence), we apply a time-aware 70/15/15 Train/Validation/Test split and fit-on-train preprocessing (Amount log1p→z; Time z; V1–V28 retained). We train IF, EIF, an EIF ensemble, a Mahalanobis baseline, and density models (HBOS, COPOD, ECOD) fully unsupervised, evaluate them as rankers first …

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Gc-1146 Student Engagement Portal: Enhancing Student Success Through Milestone Tracking, Antonio Brewer, Taylor Bolinger, Tyler Dawkins, Ricartho Franck, Moises Valles Nov 2025

Gc-1146 Student Engagement Portal: Enhancing Student Success Through Milestone Tracking, Antonio Brewer, Taylor Bolinger, Tyler Dawkins, Ricartho Franck, Moises Valles

C-Day Computing Showcase

The Student Engagement Portal, also known as the Milestone Map, is a platform developed to help students within KSU’s College of Computing and Software Engineering monitor their academic and professional growth. The system enables students to log milestones, check in at events, and view progress toward personal and departmental goals. Built with a full-stack architecture using NestJS, React, and MongoDB, the portal also includes an administrative dashboard for event management and analytics. The project demonstrates how progress tracking and clear visualization of achievements can improve communication, organization, and engagement between students and the college.

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Gc-1198 Onboarding Tool For New Smartphone Users, Namita Velagapudi, Bhagya Surekha Dasari, Yaswanth Maddineni, Rohith Venkata Sai Chekka, Balachandar Pinninti Nov 2025

Gc-1198 Onboarding Tool For New Smartphone Users, Namita Velagapudi, Bhagya Surekha Dasari, Yaswanth Maddineni, Rohith Venkata Sai Chekka, Balachandar Pinninti

C-Day Computing Showcase

The Smartphone Onboarding Tool is an interactive web platform created to help seniors and new smartphone users become comfortable with mobile technology. It offers a realistic, simulated smartphone interface, guided walkthroughs, and an easy-to-use design that builds confidence in performing everyday tasks. Caregivers can monitor user progress, while learners can practice safely without affecting an actual device. The solution is developed with a React frontend, a Node.js/Express backend, and an SQLite database, all built with a strong focus on mobile-first accessibility.

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Gc-1215 Clinicalrag: A Scalable Benchmark Of Privacy, Relevance, And Speed In Semantic Retrieval For Clinical Transcriptions​ ​, Pradyumna Kumar, Sai Sruti Dandibhatla, Srinivasan Subramanian, Purna Chandu Anukula, Pranitha Athukuri Nov 2025

Gc-1215 Clinicalrag: A Scalable Benchmark Of Privacy, Relevance, And Speed In Semantic Retrieval For Clinical Transcriptions​ ​, Pradyumna Kumar, Sai Sruti Dandibhatla, Srinivasan Subramanian, Purna Chandu Anukula, Pranitha Athukuri

C-Day Computing Showcase

Traditional keyword search struggles with the scale, complexity, and contextual depth of clinical data. This project develops and evaluates semantic search systems that better understand medical language, enabling physicians and researchers to retrieve contextually relevant information through a Retrieval Augmented Generation (RAG) framework. We integrate privacy-preserving methods, including differential privacy and homomorphic encryption to protect sensitive clinical transcriptions. For improved speed and accuracy, we enhance the baseline RAG architecture with Hierarchical Navigable Small World (HNSW) indexing and Maximal Marginal Relevance (MMR) based reranking. To ensure scalability, clinical documents are ingested using PySpark and stored in a vector database optimized for …

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Grm-1249 Ai-Assisted Diabetic Retinopathy Screening From Fundus Images, Mohan Krishna Thiriveedhi, Tarun Teja Pokala Nov 2025

Grm-1249 Ai-Assisted Diabetic Retinopathy Screening From Fundus Images, Mohan Krishna Thiriveedhi, Tarun Teja Pokala

C-Day Computing Showcase

Diabetic Retinopathy (DR) is a major cause of avoidable blindness among diabetic patients worldwide. Early screening is critical, but manual diagnosis is time-consuming and requires specialists. This paper presents a deep learning system to automatically analyze retinal fundus images and perform a focused, binary classification to distinguish between 'No DR' (Healthy) and 'Severe-Stage DR' (Severe/Proliferative). We benchmark three prominent architectures: a ResNet-50, an EfficientNet-B0, and a Vision Transformer (ViT-B/16). The models are trained and evaluated on a custom-balanced, binary dataset derived from the APTOS 2019 collection. We conduct two experiments, one with a small dataset (N=500) and one with a …

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Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa Nov 2025

Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

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Uc-1168 Shepherd's Sin - A Visual Novel Hybrid Game Made With Unity, Ara Randolph, Rin Egl, Everett Joiner, Jonah Swerdlow Nov 2025

Uc-1168 Shepherd's Sin - A Visual Novel Hybrid Game Made With Unity, Ara Randolph, Rin Egl, Everett Joiner, Jonah Swerdlow

C-Day Computing Showcase

By day, the grand old house shifts and shudders as if though alive. The six other residents gather in its lounges and parlors, sipping tea, squabbling over rooms, and faking civility. They laugh, they bicker, and they carry on as though nothing festers within these walls. When night falls, their facades rot away. They twist into monstrous embodiments of malice, each one a reflection of the seven deadly sins. By morning, they forget. You do not. Armed with a worn-out Monster Hunter’s Guidebook, you must reclaim its missing pages to learn who these people truly are, what they truly are. …

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Uc-1226 Iknowit: Multilingual Smartphone Tutorial Platform, Jacqueline Juarez, David Bazan, Julissa Rivera Nov 2025

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

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Uc-1244 Agentic Ai For Intelligent Customer Communication, Lucas Papadopoulos, Jeremy Hopkins, Munir Gargour, Weston Dease Nov 2025

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 …

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Uc-1259 Light'em Up, Collin Sutton, Ronnie Jones, Max Anderson Nov 2025

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 …

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Uc-1261 Ai-Powered Gre Vocabulary App, Michael Verde, Ellyan Landeta, Cynthia Onuorah, David Tran, Bereket Binchamo Nov 2025

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.

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Uc-1262 Georgia Watch - Georgia Hospital Accountability Score, Robert Straiton, Patrick Cox, Constant Nortey, Sankalp Amaravadi, Kahmin Keller Nov 2025

Uc-1262 Georgia Watch - Georgia Hospital Accountability Score, Robert Straiton, Patrick Cox, Constant Nortey, Sankalp Amaravadi, Kahmin Keller

C-Day Computing Showcase

Georgia Watch and the members of this team have partnered to change how Georgia residents understand their healthcare by providing a source of objective metrics which affect their care. This project represents the quintessential React Project produced with the purpose of a reactive interface for the end user, built for maintainability for any subsequent developers. The hospital data is maintained in JSON format for its ease of parsing and adjustment pending any changes. The interactive map was developed through the MAPBOX library, and the team is maintaining a deployment via an independent repository with necessary control over the website.

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Uc-1268 Falling Debris, Julian Duarte, Adam Martin, Rylan Collins, Hugh Haggard, Chance Boecker Nov 2025

Uc-1268 Falling Debris, Julian Duarte, Adam Martin, Rylan Collins, Hugh Haggard, Chance Boecker

C-Day Computing Showcase

Falling Debris is a student-developed party 2D platformer game in which players have to survive falling blocks by grappling upward to outlive the others. The game is played in rounds, and between them, players can purchase items to improve their odds of survival.

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Uc-1274 Cloud-Native Ci/Cd Pipeline, Enitan Meduteni, Rami Elmostafa, Matt Crowley, Kade Fleming, Cecily Graffree Nov 2025

Uc-1274 Cloud-Native Ci/Cd Pipeline, Enitan Meduteni, Rami Elmostafa, Matt Crowley, Kade Fleming, Cecily Graffree

C-Day Computing Showcase

This project documents a 12-week capstone implementing a cloud-native CI/CD pipeline using industry-standard DevOps tools. The system integrates Jenkins for continuous integration, Kubernetes for container orchestration, GitOps (ArgoCD) for automated deployments, DevSecOps practices including RBAC and vulnerability scanning, and comprehensive monitoring using Prometheus and Grafana. Mentored by Sudheer Amgothu, Principal Cloud Operations Engineer.

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Gc-0241 Using Dog Breed Classification Uncertainty Estimation To Inform Mixed Breed Ancestry​, Brandon Mackey, Maryam Koya, Theodore King, Scott Hutchison Nov 2025

Gc-0241 Using Dog Breed Classification Uncertainty Estimation To Inform Mixed Breed Ancestry​, Brandon Mackey, Maryam Koya, Theodore King, Scott Hutchison

C-Day Computing Showcase

This study investigates whether Monte Carlo uncertainty estimation and probability distributions can be used to identify ancestral composition of mixed-breed dogs. A dataset containing images of purebred dogs was used to train a Monte Carlo Dropout model. The trained model will next be tested on images of mixed breed dogs. Our hypothesis is that the model can be used to provide informative probability distribution for breed ancestry classification, offering a potentially valuable tool for analyzing the genetics of dogs.

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