Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 120661 - 120690 of 5158459

Full-Text Articles in Entire DC Network

Uc-1276 Ci-Cd Pipeline Team 2, Cameron Arnold Nov 2025

Uc-1276 Ci-Cd Pipeline Team 2, Cameron Arnold

C-Day Computing Showcase

Our project is about creating a basic cloud-native pipeline that can build and deploy an application in a more automated way. We will also try to add some security checks and monitoring tools so that we can see how everything is working. The goal is to get hands-on experience with the process and show a working demo at the end of the semester.

Go to article

Gc-0151 Smart Hr Onboarding With Microsoft 365 Team 2 Capstone Project, Annalise Gregory, Michael Colley, David Laurent, Harshita Agarwal, Nilesh Kumar Nov 2025

Gc-0151 Smart Hr Onboarding With Microsoft 365 Team 2 Capstone Project, Annalise Gregory, Michael Colley, David Laurent, Harshita Agarwal, Nilesh Kumar

C-Day Computing Showcase

The Smart HR Onboarding with Microsoft 365 Capstone Project aims to transform the new hire onboarding experience by leveraging Microsoft 365 tools to deliver a seamless, automated process. Using Power Automate, we streamline tasks through automated emails, reminders, and documents sharing. This ensures every step of the onboarding journey is efficient and consistent. Sharepoint and Outlook serve as the main sources of storing and sharing required tasks. Power Automate's integration with Power BI allows for real-time reporting with an interactive dashboard the provides insights into onboarding progress or areas where new hires need support. This project aims to simplify onboarding …

Go to article

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 …

Go to article

Gc-1154 Peer Evaluation Automation And Feedback System, Sameer Khan, Nnedi Okafor Nov 2025

Gc-1154 Peer Evaluation Automation And Feedback System, Sameer Khan, Nnedi Okafor

C-Day Computing Showcase

A web-based platform to streamline peer evaluations in team-based courses. Professors can securely create and manage student rosters, assign students to courses/teams, trigger email invitations, and receive structured, professor- friendly reports with both numeric and textual feedback. Optional AI features may summarize comments and flag potential concerns, depending on timeline and scope.

Go to article

Grm-0237 Efficient Defense Against Adversarial Patch Attacks In Remote Sensing Using Transfer Learning, Ravi Rogannagari Nov 2025

Grm-0237 Efficient Defense Against Adversarial Patch Attacks In Remote Sensing Using Transfer Learning, Ravi Rogannagari

C-Day Computing Showcase

Remote sensing is the science of acquiring information about the Earth's surface using satellite-mounted imaging sensors. In the past, this data had to be interpreted manually, which was slow, tedious, and often prone to error. With the rise of deep learning, image classification models have greatly accelerated and improved remote sensing tasks such as land-use analysis, environmental monitoring, etc. However, despite their strong performance, these models are still vulnerable to adversarial patch attacks—physically realizable patterns that, when placed on an object, can force the model to make incorrect predictions. This creates serious risks for practical geospatial applications. Traditional defenses like …

Go to article

Grm-0243 Sentient Agi Rights And The Future: The Modern Digital Prometheus, Ryan Deem Nov 2025

Grm-0243 Sentient Agi Rights And The Future: The Modern Digital Prometheus, Ryan Deem

C-Day Computing Showcase

As artificial intelligence advances toward artificial general intelligence (AGI), society must determine how to ethically integrate sentient AI into our communities. This paper argues that once AI achieves sentience and human-level intelligence, it should be granted the same rights and protections as human citizens. Using utilitarian and deontological perspectives, as well as the IEEE Code of Ethics, it examines why treating AGI as lesser beings could lead to fear, conflict, and harmful outcomes—echoing the cautionary themes of Frankenstein. The paper also evaluates public concerns and existing governance frameworks, proposing that mutual respect, rights, and responsibilities are essential for safe coexistence …

Go to article

Grm-20242 Cipher: Covert Influence Passed Via Hidden Encoding In Representations Evaluating Subliminal Bias Transfer During Knowledge Distillation, Crystal Tubbs Nov 2025

Grm-20242 Cipher: Covert Influence Passed Via Hidden Encoding In Representations Evaluating Subliminal Bias Transfer During Knowledge Distillation, Crystal Tubbs

C-Day Computing Showcase

AI models can inherit hidden behavioral biases when student models learn from teacher outputs during knowledge distillation. Project CIPHER investigates whether covert signals, such as zero-width Unicode characters or column order shifts, can transmit bias from a teacher model to a student model even when the student never receives group labels. Using an experimental pipeline with controlled subliminal cues and dual distillation, the project aims to reproduce and measure subtle bias transfer. Preliminary results showed that weak signals produce no measurable bias, while the redesigned high-frequency signal and MLP student architecture reveal quantifiable disparity.

Go to article

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

Go to article

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 …

Go to article

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.

Go to article

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.

Go to article

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 …

Go to article

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, …

Go to article

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, …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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 …

Go to article

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.

Go to article

Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz Nov 2025

Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz

C-Day Computing Showcase

RiverGuard’s mission is to protect and preserve waterways by using technology to identify and reduce pollution. The system uses an object detection model to automatically locate and classify trash within images or video of rivers and lakes, removing the need for slow, manual observation. By providing real-time insight into waste accumulation, RiverGuard helps communities, researchers, and organizations take faster, more effective action to keep waterways clean. Its goal is to create a sustainable monitoring system that empowers people to understand pollution patterns and support long-term environmental responsibility. RiverGuard represents a step toward cleaner water, healthier ecosystems, and a more informed …

Go to article

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. …

Go to article

Uc-1183 Morphyxcam: Instant Photo Transformation Tool, Priscilla Awatey, Long Doan, Shaokun Weng, Aryan Merchant Nov 2025

Uc-1183 Morphyxcam: Instant Photo Transformation Tool, Priscilla Awatey, Long Doan, Shaokun Weng, Aryan Merchant

C-Day Computing Showcase

MorphyxCam is an interactive browser-based application that lets users capture live images and apply real-time visual effects. The system performs color filtering, shading adjustments, dynamic warping, and expressive distortion effects. Users can instantly reshape features, apply artistic styles, and wrap their photos onto 3D surfaces, creating engaging and playful visual transformations. By capturing live camera images and transforming them through pixel-level filtering, distortion effects, and 3D surface mapping, the system shows how multimedia techniques can be applied creatively within a web browser. Overall, MorphyxCam showcases the potential of interactive digital imaging and highlights how accessible web technologies can be used …

Go to article

Uc-1196 Verocity: A Reactive Combat Framework, Brendan Moore Nov 2025

Uc-1196 Verocity: A Reactive Combat Framework, Brendan Moore

C-Day Computing Showcase

Verocity is a Minecraft plugin designed for fast-paced, visceral combat, where interactivity and FUN take center stage. The complex mathematics and system design required to build this plugin push the limits of standard Minecraft development, providing a reactive framework for advanced combat interactions. New Actions and Combat Features: Enhanced Basic Attacks – Smooth, responsive, and satisfying to chain together. Throwable Items – Every item can be thrown. Swords lodge into enemies on impact, ready to be recovered. Dashing – Lunge to swords stuck in the ground or at enemies to pull them out while tactically repositioning. Umbral Blade – Command …

Go to article

Uc-1207 Ai Driven Resident Inquiry Processing, Ben Moran, Sahil Sachwani, Thomas Ashe, Sean Johnson Nov 2025

Uc-1207 Ai Driven Resident Inquiry Processing, Ben Moran, Sahil Sachwani, Thomas Ashe, Sean Johnson

C-Day Computing Showcase

The AI Driven Resident Inquiry Processing System is designed to enhance the National Housing Compliance (NHC) ability to process resident inquiries using artificial intelligence(AI). NHC is a 501(c)(4) not-for-profit corporation who provides training and compliance services to the affordable housing industry. Each month NHC receives over 200 inquiries from residents via phone and email. These inquiries range from general questions to urgent, life-threatening concerns. Efficiently processing and responding to these inquiries is often critical to resident safety and well being. This project uses AI to automate resident inquiries as they are received, extract and classify key information, and display this …

Go to article

Uc-1211 Machine Learning Linux Log Anomaly Detection, Samuel Scott, Audrey Loisy, Dylan Silva-Rivas, Sheamus Brady Nov 2025

Uc-1211 Machine Learning Linux Log Anomaly Detection, Samuel Scott, Audrey Loisy, Dylan Silva-Rivas, Sheamus Brady

C-Day Computing Showcase

Cybersecurity is becoming an increasingly important part of digital life. Malware can silently intrude on a user’s system and perform malicious actions and generate unusual system behavior without the user ever being aware. This malware often presents with unusual system logs being generated. These logs, however, are difficult to consistently track and analyze, especially for casual users. To help bridge this gap between hard-to-read log data and the useful information it contains, we created LUAADS (short for Linux User Account Anomaly Detection System), designed for Ubuntu systems. LUAADS can automatically collect entries from common log files (such as syslog and …

Go to article

Uc-1222 Active Learning System For Labeling Chest X-Rays, Matthew Hall, Noah Lane, Josh Smith, Elijah Merrill Nov 2025

Uc-1222 Active Learning System For Labeling Chest X-Rays, Matthew Hall, Noah Lane, Josh Smith, Elijah Merrill

C-Day Computing Showcase

This project aims to develop a complete Active Learning System for chest X-ray image classification, designed to automate data preparation, streamline model training, and reduce the manual effort required for medical image labeling. The system establishes a structured and scalable pipeline that moves from raw data ingestion to automated decision-making, incorporating dataset indexing, patient-aware splitting, preprocessing, configuration management, and validation to ensure data flows reliably through the system. The model component uses CNNs to generate baseline diagnostic predictions across chest pathologies. Active learning strategies are then applied to identify the most informative unlabeled images, enabling iterative retraining that improves model …

Go to article

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

Go to article

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 …

Go to article

First Prev Next Last