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2025

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Articles 361 - 390 of 3495

Full-Text Articles in Computer Sciences

Uc-1257 Integrating A Pressure Profiling Dock Into An Open-Source Digital Art Framework, Luke Miller, Noah Enyart, Jonah Smith, Jaeden Jones Nov 2025

Uc-1257 Integrating A Pressure Profiling Dock Into An Open-Source Digital Art Framework, Luke Miller, Noah Enyart, Jonah Smith, Jaeden Jones

C-Day Computing Showcase

The effectiveness and expressiveness of digital drawing are heavily dependent on an artist’s control over their medium; pen pressure dynamics, which enables fine-tuned variation in line weight and opacity. While the use of this tool improves an artist's control, the open-source editor GIMP currently offers only limited means to visualize or adjust these pressure behaviors in real time. GIMP does support pressure-based input, however, it lacks a dedicated interface for configuring a personalized pressure profile, creating a barrier for users seeking precision comparable to commercial tools. This project addresses that gap by designing and implementing a Stylus Pressure Profiling Dock …

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Uc-1260 Digital Micromouse Maze Simulation, Chase Bell Nov 2025

Uc-1260 Digital Micromouse Maze Simulation, Chase Bell

C-Day Computing Showcase

We construct a digital simulation of the Micromouse competition and analyze popular algorithms for searching (A*, Dijkstra’s, and Flood-fill). From our analyses, we design an algorithm with the goal of achieving the shortest possible run time to the end of the maze.

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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-1269 Website Makeover - Loving Arms Cancer Outreach, Yuliana Pacheco, Alicia Cook, Taylor Tompkins, Schuyler Bridges, Dalton Roberts Nov 2025

Uc-1269 Website Makeover - Loving Arms Cancer Outreach, Yuliana Pacheco, Alicia Cook, Taylor Tompkins, Schuyler Bridges, Dalton Roberts

C-Day Computing Showcase

Loving Arms Cancer Outreach (LACO) provides financial, emotional, and community support to individuals affected by cancer, making an accessible and reliable website essential to its mission. Our team conducted a quality assurance audit using tools such as Google Lighthouse and axe DevTools, identifying issues with accessibility, navigation, readability, and mobile responsiveness. Using these findings, we redesigned key sections of the site, improved layouts and forms, and recommended updated plugins to enhance usability and long-term performance. We also developed a Website Architecture and Maintenance Guide to support sustainability. This project establishes the foundation for a modern, user-friendly website that strengthens LACO’s …

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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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Gc-1144 Meetless: The Operating System For Business In The Ai Age, An Pham Nov 2025

Gc-1144 Meetless: The Operating System For Business In The Ai Age, An Pham

C-Day Computing Showcase

Meetless functions as an OS for modern organizations: a secure, multi-agent runtime that turns scattered inputs (docs, emails, tickets, chats) into asynchronous, outcome-driven discussions with decisions, owners, and due dates—so teams ship without meetings. System architecture (OS metaphor).- Kernel (Orchestrator): schedules “processes” across specialized agents using routing policies, guardrails, and retry semantics. - Process I/O: unified connectors for Google/Microsoft suites, Slack, Jira/Linear, and web sources, normalized into a document graph with vector embeddings. - Memory & FS: temporal knowledge graph (Neo4j) + document store (PostgreSQL) + vector index (Weaviate) with time-aware retrieval. - Syscalls/APIs: /discussions.create, /decisions.propose, /actions.sync, /summaries.latest; all idempotent …

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Gc-1147 Enhancing Mass Casualty Triage Training Through Human–Ai Collaboration In Virtual Reality (Vr), Rishi Kiran Aiyatham Prabakar Nov 2025

Gc-1147 Enhancing Mass Casualty Triage Training Through Human–Ai Collaboration In Virtual Reality (Vr), Rishi Kiran Aiyatham Prabakar

C-Day Computing Showcase

Mass casualty triage requires quick, accurate decisions under pressure. Live training is costly and time-intensive. Virtual Reality (VR) trainings approach has shown comparable learning effectiveness compared to live trainings, motivating the use of VR simulations. This study explores how collaboration with an AI robot partner can enhance the triage training effectiveness. The Findings will contribute in understanding how human-AI collaboration enhances trainings.

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Gc-1162 Present Panic Game, Adetunji Adegeye, Kendrick Bryant, Catherine Ayeronwi Nov 2025

Gc-1162 Present Panic Game, Adetunji Adegeye, Kendrick Bryant, Catherine Ayeronwi

C-Day Computing Showcase

Present Panic is a festive top-down 2D arcade game where players control an elf inside Santa’s workshop and must collect presents while avoiding Christmas-themed enemies. During Sprint 2 in our SWE class, our team transitioned from a functional prototype to a fully developed Beta, expanding assets, implementing UI systems, building levels, and integrating polished game mechanics following the MDA framework. We developed new artwork, sound effects, animations, and user interface elements including a main menu, HUD, pause menu, and scene transitions. A survey and both manual and automated testing were prepared to gather user feedback. This Beta version forms the …

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Grm-0216 Towards Analyzing The Bridge Dataset With Quantum Machine Learning, Gayathri Kolavennu Nov 2025

Grm-0216 Towards Analyzing The Bridge Dataset With Quantum Machine Learning, Gayathri Kolavennu

C-Day Computing Showcase

This research presents a comparative evaluation of classical and quantum machine learning models applied to the Bridge dataset. Classical algorithms like Support Vector Machine, Random Forest, and Neural Networks are benchmarked against Quantum SVM, Quantum Random Forest, and Quantum Neural Networks using identical preprocessing and training conditions. Results indicate a consistent quantum advantage, with quantum models achieving higher accuracy, stronger nonlinear feature separation, and improved minority-class detection. QSVM and QNN exhibit the most significant performance gains. Although quantum models require greater computational resources, the findings underscore the emerging effectiveness of quantum-enhanced learning for structural classification tasks in the NISQ era.

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Grm-1145 Autumn Lite Llm, Michael Knighten Nov 2025

Grm-1145 Autumn Lite Llm, Michael Knighten

C-Day Computing Showcase

Autumn Lite is an inspectable, small-footprint language modeling pipeline for reproducible experimentation and practical integration into video-game non-player character (NPC) systems. It comprises four components: (1) a regex-aware tokenizer/normalizer for vocabulary construction and mixed prose–code handling; (2) a classical evaluation track that reports perplexity to quantify predictive quality; (3) a compact neural language model (decoder-only Transformer) targeted at low latency and controllable outputs; and (4) a lightweight sentiment classifier (logistic regression) that assigns positive/neutral/negative tags to steer text-to-speech (TTS) prosody during NPC dialogue. By combining transparent preprocessing with baseline metrics and a small, deployable decoder, Autumn Lite aims to deliver …

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Grm-1245 A Synthetic Data Engine For Explainable Injection-Area Perception, Yukang Shen Nov 2025

Grm-1245 A Synthetic Data Engine For Explainable Injection-Area Perception, Yukang Shen

C-Day Computing Showcase

Vision-Language-Action (VLA) systems are beginning to support everyday clinical workflows. Deltoid intramuscular injection is a representative task, but progress is limited by data scarcity, privacy constraints, and the cost of expert annotation. Recent text-to-image (T2I) models make large-scale data synthesis possible, yet ensuring anatomical correctness, diversity, and label quality remains difficult. To address this gap, we propose a Synthetic Data Engine tailored for medical perception, integrating cold-start filtering, controlled T2I generation, CLIP-based quality checks, and iterative segmentation training. We further introduce an anthropometry-grounded formulation of injection safety that produces interpretable safe-zone guidance. Experiments show that synthetic data can effectively bootstrap …

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Grm-1252 How Humans Perceive Mobile Robots: Anxiety, Environment, And Behavior Analysis, Rohan Jonnalagadda, Reed Tumlin, Roderick Powell Nov 2025

Grm-1252 How Humans Perceive Mobile Robots: Anxiety, Environment, And Behavior Analysis, Rohan Jonnalagadda, Reed Tumlin, Roderick Powell

C-Day Computing Showcase

We conducted 6,400 physics-based simulations to examine how environmental and behavioral factors shape human anxiety during interactions with mobile robots. The model incorporated robot behavior, environmental density, visibility, indoor/outdoor settings, and human age. Anxiety was driven primarily by context: levels were highest indoors, during daytime, and in sparse environments, while nighttime and outdoor interactions consistently reduced anxiety. Robot behavior produced smaller effects, with erratic and non-avoidance strategies yielding slightly higher responses. Older adults showed marginally greater anxiety across all conditions. These findings suggest that environmental design and deployment context matter more than avoidance strategy, offering guidance for improving the safety …

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Grm-20169 Proxy Recognition And Inclusive Scoring Method (Prism): Evaluating Context-Dependent Bias In Large Language Models For Resume Screening, Crystal Tubbs, Destiny Raburnel Nov 2025

Grm-20169 Proxy Recognition And Inclusive Scoring Method (Prism): Evaluating Context-Dependent Bias In Large Language Models For Resume Screening, Crystal Tubbs, Destiny Raburnel

C-Day Computing Showcase

AI-driven hiring tools are reshaping recruitment but often mirror biases in their training data. PRISM examines how large language models express or reduce demographic bias during resume evaluation and how linguistic context within prompts shapes these outcomes. Using a controlled dataset of 324 synthetic resumes with racially neutral surnames, differing only by first name as the demographic proxy, we compared GPT 3.5 turbo with a Sentence BERT similarity model. Under neutral prompts, no stable bias was observed across demographic groups, yet contextual shifts in the prompt changed how the model responded to proxy cues. These findings show that LLM bias …

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Grm-20188 Intelligent Book Recommendation And Rating Prediction System, Destiny Raburnel Nov 2025

Grm-20188 Intelligent Book Recommendation And Rating Prediction System, Destiny Raburnel

C-Day Computing Showcase

When selecting a book, readers often rely on surface level information such as the title, author, synopsis, and keywords to determine whether a story matches their interests. These features contain important cues related to genre, tone, and narrative elements that help set expectations before reading. The Intelligent Book Recommendation and Rating Prediction System works to automate this process by using natural language processing and machine learning techniques. It takes in readers’ personalized reading data such as book titles, author, subjects, synopsis, and personal ratings to learn semantic patterns using TF-IDF vectorization. A supervised Linear Regression model was then trained to …

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Gc-0157 Ai Graduate Admissions Assistant, Katherine Hyatt, Ginger Wright, Pablo Edgar, Oluwatosin Akinwusi, Alan Johnson Nov 2025

Gc-0157 Ai Graduate Admissions Assistant, Katherine Hyatt, Ginger Wright, Pablo Edgar, Oluwatosin Akinwusi, Alan Johnson

C-Day Computing Showcase

Project Overview: Create a "Proof of Concept" for an AI Graduate Admissions Assistant chatbot that will: • Autonomously reference KSU website information in real-time • Direct users efficiently to specific, relevant content • Reduce questions for admissions personnel • Self-update its knowledge base when website content changes •Provide accurate, instant responses to common admissions questions and allow for response correction in an admin dashboard

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Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo Nov 2025

Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo

C-Day Computing Showcase

Dehydration is a common and preventable complication for oncology patients, especially those undergoing chemotherapy and radiation. Side effects such as nausea, fatigue, and loss of appetite make it difficult for patients to maintain adequate fluid intake, contributing to avoidable discomfort and potential treatment disruptions. This capstone project presents Onco-Boost, a mobile hydration monitoring application designed to help adult oncology patients track daily fluid intake, recognize their intake patterns, and stay engaged in daily self-care between clinic visits. Built with React Native and Expo, and backed by Firebase for authentication and cloud data storage. Onco-Boost translates clinical hydration guidance and research …

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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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Gc-1233 An Ai-Powered Convolutional Neural Network System For Multi-Class Image Classification Of Rice Plant Leaf Diseases, Akshay Krishna Varma Buddharaju, Siri Yellu, Pranay Kumar Peddi Nov 2025

Gc-1233 An Ai-Powered Convolutional Neural Network System For Multi-Class Image Classification Of Rice Plant Leaf Diseases, Akshay Krishna Varma Buddharaju, Siri Yellu, Pranay Kumar Peddi

C-Day Computing Showcase

This project focuses on building an intelligent system that can automatically identify common diseases found on rice leaves by analyzing simple images. Using a deep convolutional neural network, the model learns to recognize visual patterns associated with three major diseases: Bacterial Blight, Brown Spot, and Leaf Smut. These diseases often show subtle differences in color, texture, and leaf damage, and the model is trained to distinguish them accurately. The goal of this work is to show how artificial intelligence can support modern agriculture by helping farmers detect problems early, even without expert knowledge. By processing images through careful preprocessing, augmentation, …

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Grm-0204 Unpacking Early Burnout Through Predictive Risk Boundaries, Soarov Chakra Borty Nov 2025

Grm-0204 Unpacking Early Burnout Through Predictive Risk Boundaries, Soarov Chakra Borty

C-Day Computing Showcase

Caregiver burnout is a significant issue in healthcare delivery and management, as it directly impacts caregivers' health and compromises the standard of care, often leading to negligence, health deterioration, or withdrawal from caregiving duties. Caregivers play a crucial role in supporting the health, well-being, and quality of life of care recipients by providing both personal and professional services. However, the continuous needs and stress associated with caregiving duties can affect their health and everyday life, leading to caregiver burnout. This study applied data analytics and machine learning by merging several feature selection methods on the NHATS dataset, including LightGBM, XGBoost, …

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Grm-0210 Distance Measures For Multi-Target Tracking, Rakshak Gurung Nov 2025

Grm-0210 Distance Measures For Multi-Target Tracking, Rakshak Gurung

C-Day Computing Showcase

Multi-object tracking (MOT) supports applications such as radar monitoring and autonomous perception, where multiple objects move, appear, or disappear over time. A central challenge is resolving which detections correspond to which tracks. The Hungarian algorithm is often used to solve this assignment problem. For ambiguous scenes, Murty’s algorithm extends this approach by generating multiple top-k association hypotheses. In this work, we study an alternative search-space formulation for top-k enumeration. Our results show that it can provide strong speedups over Murty’s method on small matrices. We also reviewed identity-focused MOT evaluation metrics such as HOTA and created a visualization tool to …

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Grm-0254 Unified Robust Optimal Transport For Outlier-Resilient Learning, Rohan Jonnalagadda Nov 2025

Grm-0254 Unified Robust Optimal Transport For Outlier-Resilient Learning, Rohan Jonnalagadda

C-Day Computing Showcase

Classical Optimal Transport (OT) is particularly sensitive to outliers. The existing robust variant, ROBOT, mitigates this through hard truncation, but its rigidity often compromises stability. We propose WROT-r, a unified r-power framework for weighted robust OT that combines rigorous hard-clipping and smooth cost compression through a single parameter r. WROT-r offers a continuous robustness spectrum, enabling adaptive control over how strongly transport costs are down-weighted for outliers. Experiments on synthetic mean estimation and resilient GANs show clear patterns: larger r performs best under weak contamination by preserving more inliers, while smaller r (≈1.5) is more effective under moderate and strong …

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Grm-1150 Investigating Spatial Patterns Of Tumor And Stroma In Gastric And Colorectal Cancer For Survival Prediction, Siri Yellu Nov 2025

Grm-1150 Investigating Spatial Patterns Of Tumor And Stroma In Gastric And Colorectal Cancer For Survival Prediction, Siri Yellu

C-Day Computing Showcase

The spatial organization of tumor cells, stroma, and tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment plays a critical role in cancer progression and is strongly associated with clinical outcomes. However, quantifying the significance and statistical impact of these spatial patterns remains challenging due to the complex interactions among these components. In this study, we analyze spatial patterns associated with patient survival in gastric and colorectal cancer by integrating four predictive classifiers with spatial image statistics across four large patient cohorts. U-Net was used for semantic segmentation of tumor, stroma, and TILs on digitized Hematoxylin and Eosin–stained FFPE whole-slide images, while …

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Grm-1153 National Energy And Emission Modeling And Analysis Tool, Swetha Kakaraparthi, S M Tanvir Faysal Alam Chowdhoury Nov 2025

Grm-1153 National Energy And Emission Modeling And Analysis Tool, Swetha Kakaraparthi, S M Tanvir Faysal Alam Chowdhoury

C-Day Computing Showcase

NEEMAT is a web-based decision-support tool that predicts vehicle and power-plant emissions plus fuel/energy consumption under rising EV adoption for Atlanta, Los Angeles, New York, and Seattle. A feedforward neural network trained on MOVES estimates tract-level vehicle energy use and CO2/NOx/PM2.5 by speed, vehicle type, fuel, and age, while a macroscopic traffic model captures flow effects. Grid-side CO2/CH4/N2O from EV charging are forecast with a Meta-Prophet model trained on Cambium. Users can explore 24-hour profiles and five-year outlooks, compare scenarios, and export results. Findings show that despite substantial EV uptake, mixed fleets and grid responses can raise total emissions, underscoring …

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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-0253 Stock Price Predictions Using Lstm & Technical Indicators, Kendal Elison, Allen Smith, Dylan Quinn Nov 2025

Uc-0253 Stock Price Predictions Using Lstm & Technical Indicators, Kendal Elison, Allen Smith, Dylan Quinn

C-Day Computing Showcase

Stock price predictions using traditional statistical methods remains challenging due to market volatility and nonlinear dynamics. Long Short-Term Memory (LTSM) networks may model temporal dependencies in stock data more effectively than traditional statistical methods. Historical data for several companies’ stocks was obtained from Yahoo Finance, where it was then enriched with various technical indicators such as momentum and volatility. Preliminary analysis through Scala programming language suggests that incorporating these technical indicators can enhance short-term price prediction accuracy. Future works may seek to integrate additional trend and volume based indications in another, more robust, programming language like Python.

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