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 46531 - 46560 of 5149677

Full-Text Articles in Entire DC Network

Uc-167-222 Secu Horizon, Julian Duarte, Chance Boecker, Adam Martin, Rylan Collins, Oliver Haggard Apr 2026

Uc-167-222 Secu Horizon, Julian Duarte, Chance Boecker, Adam Martin, Rylan Collins, Oliver Haggard

C-Day Computing Showcase

Secu Horizon is a fast-paced, stealth-based 3d action platformer, where you run around in a dystopian city. The main mechanic of the game revolves around a knife projectile that the player throws and teleports to. The game will have a strong mix of smooth platforming, soft stealth sections, and bullet time combat, as the player throws around the knife to defeat enemies. The feeling that this game will invoke is that of being an unstoppable rebel ninja, in the cold dead of the night.

Go to article

Uc-151-197 Nest: An Ai-Powered Transition Navigator For Aging-Out Foster Youth In Georgia, Stephen Sookra, Tylin Delaney, Brenden Bryant Apr 2026

Uc-151-197 Nest: An Ai-Powered Transition Navigator For Aging-Out Foster Youth In Georgia, Stephen Sookra, Tylin Delaney, Brenden Bryant

C-Day Computing Showcase

Each year, approximately 600–700 young people age out of the Georgia foster care system with no permanent family, no housing plan, and no clear guide beyond a 250‑page state transition PDF. The outcomes are severe: high rates of homelessness, low college completion, and unstable employment. Nest is an AI‑powered, mobile‑first web application that turns this overwhelming bureaucracy into a personalized 90‑day transition plan generated in under 60 seconds. Through a short conversational intake, the system collects a youth’s age, county, housing status, and education or work goals, then uses a deterministic rules engine to determine likely eligibility for key programs …

Go to article

Uc-149-185 Ai-Assisted Media Organization And Intake System, Meilun Wu, Nevaeh Branham, Isaiah Higgins, Claude Kangni, Fatima Ahmed Apr 2026

Uc-149-185 Ai-Assisted Media Organization And Intake System, Meilun Wu, Nevaeh Branham, Isaiah Higgins, Claude Kangni, Fatima Ahmed

C-Day Computing Showcase

This project includes the design and validation of a metadata-driven media intake and organization system implemented for the Office of the District Attorney, Cobb Judicial Circuit. The institution produces a considerable volume of photographs and video content via outreach programs and community interaction initiatives. Yet, the organization does not have an organized framework for managing media files, which results in inefficient file retrieval and data loss over time due to a lack of standardized organizational practices. In this regard, this project aims to develop a workflow-based system for the efficient organization of media files that will be powered by a …

Go to article

Uc-139-175 Agentic Debugger And Documenter, Olaoluwa Omodemi, Jade Le, Preston Dietz Apr 2026

Uc-139-175 Agentic Debugger And Documenter, Olaoluwa Omodemi, Jade Le, Preston Dietz

C-Day Computing Showcase

Modern software development teams routinely introduce subtle bugs — mutable default arguments, bare exception handlers, insecure eval()/exec() calls, resource leaks, hardcoded secrets — that escape manual review but accumulate into technical debt and security risk. Existing linters identify problems but leave remediation to the developer. This project investigates whether a coordinated multi-agent system, combining rule-based static analysis with generative LLM reasoning, can autonomously detect, fix, and document such issues with no developer involvement beyond providing the input file.

Go to article

Ur-160-200 Tortured Artist, Caitlin Tigani, Ben Scholl, Adam Tucker, Anaiya Tucker Apr 2026

Ur-160-200 Tortured Artist, Caitlin Tigani, Ben Scholl, Adam Tucker, Anaiya Tucker

C-Day Computing Showcase

You are a photographer that wants to move out, so you take pictures of your house to give to your real- estate agent. However, as you are developing the photos you hear a noise that makes you turn on the lights, ruining your photos. Now you must retake the photos before morning, but something around the house has changed. Rooms are no longer in the right place, items are moved around, doors are locked, and an entity is watching you. Will you find the secrets within the puzzles or be left tortured?

Go to article

Ur-147-188 Staged Multi-Modal Alzheimer Classification Using Uncertainty Quantification, Branden Chen, Ethan Litton, Long Doan Apr 2026

Ur-147-188 Staged Multi-Modal Alzheimer Classification Using Uncertainty Quantification, Branden Chen, Ethan Litton, Long Doan

C-Day Computing Showcase

Diagnosing Alzheimer’s disease often depends on costly neuroimaging techniques such as MRIs and PET scans, which are not always accessible and can place a significant financial burden on healthcare systems. Existing clinical workflows lack a reliable way to determine which patients truly require these advanced tests, resulting in either unnecessary imaging or delayed and inaccurate diagnoses. To address this challenge, we propose the Uncertainty-Driven Dual-view (UDD) model, a multi-stage framework that integrates low-cost clinical and structural data with uncertainty-aware learning. The model first generates predictions using accessible data and quantifies its confidence, referring only high-uncertainty cases for further evaluation with …

Go to article

Ur-084-219 Towards Bounding The Behavior Of Neural Networks, Emmanuel Nwankwo Apr 2026

Ur-084-219 Towards Bounding The Behavior Of Neural Networks, Emmanuel Nwankwo

C-Day Computing Showcase

Modern neural networks are typically considered black-box systems: while they are able to achieve state-of-the-art performance in many domains, it is difficult to elicit the reasons behind their decisions. From this, a sub-field of artificial intelligence called eXplainable Artificial Intelligence (XAI) arose to fill this gap. One approach to XAI is based on the symbolic compilation of a neural network's behavior to a logical formula. However, such approaches are limited in scalability, due to the fundamental difficulty of the problem. This research instead proposes an incremental and anytime approach to explaining the behavior of a neural network, for image recognition. …

Go to article

Grp-125-144 Mapping The Affordances Of Human-Ai Interaction: A Large-Scale Text Mining And Statistical Analysis Of Llm Usage Patterns, Anil Vallepu Apr 2026

Grp-125-144 Mapping The Affordances Of Human-Ai Interaction: A Large-Scale Text Mining And Statistical Analysis Of Llm Usage Patterns, Anil Vallepu

C-Day Computing Showcase

People increasingly communicate with AI for schoolwork, office tasks, and daily needs. This study investigates the affordances of human-AI interaction using modern text mining and statistical analysis on the WildChat-1M dataset of over 1.1 million real-world ChatGPT user conversation logs. We apply BERTopic to extract latent interaction topics, compute a probabilistic topic-document matrix P(T|D), and perform rigorous statistical testing including Welch’s T-test and ANOVA to compare affordance patterns between GPT-3.5 and GPT-4.0 Results reveal that creative writing, Coding, message drafting and many interesting topics are the dominant affordances, while a spatio-temporal trend analysis maps how interaction patterns evolve globally over …

Go to article

Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu Apr 2026

Grm-134-128 Neurovision: Mapping Brain Signals To Language And Visual Meaning, Siri Yellu

C-Day Computing Showcase

NeuroVision presents a unified framework for mapping electroencephalography (EEG) signals to language and visual meaning. Extracting semantic information from EEG remains a fundamental challenge due to its low signal-to-noise ratio, high dimensionality, and inter-subject variability. To address these challenges, we propose a multimodal representation learning framework that aligns EEG signals with both textual and visual embeddings through temporal modeling, spatial brain-region decomposition, and contrastive learning. The framework integrates self-supervised pretraining with supervised multimodal alignment to learn robust and transferable representations. Experimental results demonstrate BLEU-1 of 0.1106 and ROUGE-1 of 0.1493 for EEG-to-text generation, alongside a 52% improvement in retrieval performance …

Go to article

Grp-148-223 Uncertainty-Guided Conservative Propagation For Robust Coronary Artery Segmentation, Huan Huang, Chen Zhao Apr 2026

Grp-148-223 Uncertainty-Guided Conservative Propagation For Robust Coronary Artery Segmentation, Huan Huang, Chen Zhao

C-Day Computing Showcase

Coronary artery segmentation plays a key role in cardiovascular disease analysis, yet existing methods often produce fragmented and structurally inconsistent vessels in challenging regions. We propose an uncertainty-guided conservative propagation (UGCP) framework that improves segmentation reliability by allowing high-confidence regions to guide uncertain ones through controlled information propagation under a conservation principle. This mechanism enhances structural continuity while preventing unstable updates. Experiments on Coronary CT Angiography (CCTA) and Invasive Coronary Angiography (ICA) datasets demonstrate improved segmentation accuracy and topology preservation. Additional evaluations on other vascular datasets further suggest the generalizability of the proposed approach.

Go to article

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg Apr 2026

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg

C-Day Computing Showcase

This project evaluates automated SMS spam classification by comparing traditional machine learning against modern transformer architectures. We built a Bidirectional LSTM (BiLSTM) baseline using TF-IDF feature extraction and NearMiss-1 undersampling to handle severe class imbalances. We then compared this against a fine-tuned Hugging Face Sentence-BERT model. Preliminary results show Sentence-BERT significantly outperformed the BiLSTM baseline (99.01% vs. 95.65% accuracy). These findings demonstrate that transformer-based embeddings offer a highly accurate, scalable solution for spam mitigation without relying on aggressive data undersampling.

Go to article

Gc-168-220 Active Directory To Cloud Security Data Pipeline, Michael Butler, Shahiba Shamshad, Mounia Touil, Koko Afantchao Apr 2026

Gc-168-220 Active Directory To Cloud Security Data Pipeline, Michael Butler, Shahiba Shamshad, Mounia Touil, Koko Afantchao

C-Day Computing Showcase

This project builds an automated pipeline that extracts identity and asset data from on-prem Active Directory, stages it in Google BigQuery, and securely sends normalized data to Lucid through Google Cloud Run. Using PowerShell scripts, the system collects users, groups, computers, DNS, DHCP, and related metadata without changing the source environment. BigQuery serves as the staging layer for validation and processing, while Cloud Run transforms and transfers the latest data to Lucid for visualization. The goal is to provide a repeatable, traceable, and reliable workflow that improves visibility into identity relationships for security investigations, auditing, and validation in a controlled …

Go to article

Grp-120-138 Wall-E: Wide-Area Aerial Live Learning For Emergency Disaster Evaluation, Shiva Shrestha Apr 2026

Grp-120-138 Wall-E: Wide-Area Aerial Live Learning For Emergency Disaster Evaluation, Shiva Shrestha

C-Day Computing Showcase

WALL-E is an AI-powered, custom-built quadcopter that surveys disaster zones in real time, classifying building damage and generating a GPS-tagged damage map with no internet required. The system uses an RGB camera for live AI inference and a FLIR thermal camera for additional situational awareness. A custom YOLO based model runs entirely onboard the Jetson Orin Nano, logging every detection via GPS for immediate command use. Future work will expand detection capabilities to include human presence identification.

Go to article

Gc-173-232 A Surrogate Accountability Framework For Agentic Ai Systems, Crystal Tubbs Apr 2026

Gc-173-232 A Surrogate Accountability Framework For Agentic Ai Systems, Crystal Tubbs

C-Day Computing Showcase

Agentic AI systems introduce new accountability challenges because autonomous agents can act, adapt, and execute decisions without continuous human oversight. This research develops the Surrogate Accountability Framework (SAF), an architectural approach for embedding external oversight, traceability, and control directly within agentic workflows. To operationalize SAF, a working system, Chrysalis, was designed and implemented as a real time governance layer that monitors agent behavior, evaluates decision pressure, and enforces constraints through validation and intervention mechanisms. By shifting accountability from post hoc evaluation to continuous system level enforcement, this approach reduces the risk of compounding errors and enables interpretable, actionable control signals. …

Go to article

Grm-06-150 Comparative Analysis Of Deep Learning Architectures For Inpatient Mortality Prediction Using Time-Series Vital Signs, Jonathan Meurer, Andrew Philip John, Pranay Udhaya, Nyah Robinson, Dhruv Shrivastva Apr 2026

Grm-06-150 Comparative Analysis Of Deep Learning Architectures For Inpatient Mortality Prediction Using Time-Series Vital Signs, Jonathan Meurer, Andrew Philip John, Pranay Udhaya, Nyah Robinson, Dhruv Shrivastva

C-Day Computing Showcase

This project evaluates deep learning architectures for predicting inpatient mortality using time-series vital signs derived from the MIMIC-III dataset. A baseline Long Short-Term Memory (LSTM) model was reproduced and extended with Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and Transformer architectures. Vital signs including heart rate, respiratory rate, temperature, and systolic blood pressure were processed into fixed-length time windows for model input. Results indicate that GRU achieved the highest performance, while Transformer models underperformed due to dataset limitations. This study demonstrates the impact of model architecture on clinical time-series prediction tasks.

Go to article

Grm-05-158 Spark Mllib Vs Python Frameworks, Austin D Klein Apr 2026

Grm-05-158 Spark Mllib Vs Python Frameworks, Austin D Klein

C-Day Computing Showcase

The original study evaluated Apache Spark MLlib on large datasets showing that it was able to outperform Weka in speed while also achieving similar accuracy scores. Our research builds upon this work by extending the analysis to compare Apache Spark MLlib with PyTorch. We will measure training time, speed, and accuracy to compare the results of each approach on similar hardware specifications as the original test. Original tests show promise, though we have only implemented one of the datasets. We will continue to implement the following datasets and improve metrics.

Go to article

Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra Apr 2026

Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra

C-Day Computing Showcase

This project develops and evaluates a question-answering (QA) system designed to address theological and interpretive questions about the New Testament. It uses a Retrieval-Augmented Generation (RAG) framework that integrates a pretrained large language model with a structured knowledge base consisting of public-domain Berean Standard Bible (BSB) New Testament and New Testament commentaries. User queries are embedded to retrieve semantically relevant passages, which are then supplied as contextual input for answer generation. The system is evaluated based on retrieval quality, answer faithfulness, and comparison to ground truth. Performance is benchmarked against a baseline BM25 keyword retrieval system without commentary, demonstrating that …

Go to article

Grp-07-163 Smishguard: An Ai-Powered Framework For Sms Phishing Detection And Alert System For Vulnerable Users, Jiban Krisna Das Apr 2026

Grp-07-163 Smishguard: An Ai-Powered Framework For Sms Phishing Detection And Alert System For Vulnerable Users, Jiban Krisna Das

C-Day Computing Showcase

This research introduces SMISH-GUARD, a multi-layer framework for adaptive SMS phishing (smishing) detection that integrates language-aware semantic modeling, graph-theoretic campaign reasoning, and cost-sensitive decision calibration within a unified architecture. The framework integrates dual transformer encoders for multilingual semantic understanding with a heterogeneous temporal graph layer that captures relational attack signals such as shared URLs, sender reuse, and campaign propagation patterns. A cost-sensitive decision optimization module is further incorporated to translate probabilistic model outputs into risk-aware alert policies that explicitly balance false-positive inconvenience against the higher societal and financial cost of missed smishing attacks. The study evaluates four integrated datasets comprising …

Go to article

Gc-140-126 Machine Learning Models For Solar Power Output Prediction: A Comparative Study With Adaptive Pso-Based Random Forest Tuning, Hasitha Mahabaduge Apr 2026

Gc-140-126 Machine Learning Models For Solar Power Output Prediction: A Comparative Study With Adaptive Pso-Based Random Forest Tuning, Hasitha Mahabaduge

C-Day Computing Showcase

Accurate prediction of solar power output is essential for energy scheduling, grid reliability, and efficient integration of renewable resources. Because photovoltaic generation is governed by changing atmospheric conditions, forecasting output is inherently a nonlinear learning problem. This study evaluates four machine learning models — Linear Regression, Random Forest, Multi-Layer Perceptron (MLP), and an Adaptive Particle Swarm Optimization-tuned Random Forest (Adaptive PSO-RF) — using irradiance, temperature, humidity, wind speed, cloud cover, and time-derived features drawn from a dataset of 6,738 observations. Random Forest achieved the strongest overall performance, with an RMSE of 1,816.24 and an R² of 0.9533. The Adaptive PSO-RF …

Go to article

Gc-155-130 Multimodal Speech-Based Dementia Detection, Tyler Hood, Carlos Blanco, Prajwal Shetty Apr 2026

Gc-155-130 Multimodal Speech-Based Dementia Detection, Tyler Hood, Carlos Blanco, Prajwal Shetty

C-Day Computing Showcase

Early detection of dementia is important for timely intervention, but traditional diagnostic procedures remain costly, time-consuming, and difficult to scale. Speech-based analysis offers a promising non-invasive alternative because cognitive decline often affects fluency, articulation, and other acoustic properties of speech. In this work, we present a multimodal dementia detection framework that combines self-supervised speech representations from wav2vec2 with demographic metadata, including age, gender, and ethnicity. We first compare the multimodal approach against a strong audio-only baseline under a controlled experimental setup. We then extend the analysis with a systematic ablation study and repeated-run statistical evaluation to measure the contribution of …

Go to article

Gc-154-201 Evaluation Of Generative Ai Responses To Pharmacy Prompts, Abrar Syed Apr 2026

Gc-154-201 Evaluation Of Generative Ai Responses To Pharmacy Prompts, Abrar Syed

C-Day Computing Showcase

Generative AI (GenAI) is increasingly used in pharmacy for drug information and decision support, yet accuracy remains variable. We systematically reviewed studies that reported full prompts and model responses to evaluate correctness across pharmacy‑relevant tasks. GenAI performed well on basic drug facts but was inconsistent for patient-specific recommendations and interaction checking, with occasional hallucinations. Findings support cautious, supplementary use in pharmacy practice and education.

Go to article

Grp-093-174 Transforming Everyday Smartwatch Data Into Clinical Early Warnings, Nursat Jahan Apr 2026

Grp-093-174 Transforming Everyday Smartwatch Data Into Clinical Early Warnings, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) related 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 derived CVD risk profile groups. Our study introduces a non-invasive continuous health monitoring framework, demonstrating that passively collected daily …

Go to article

Grm-175-233 From Leakage To Reliability In Dementia Detection, Crystal Tubbs Apr 2026

Grm-175-233 From Leakage To Reliability In Dementia Detection, Crystal Tubbs

C-Day Computing Showcase

Automated dementia detection from speech offers a scalable approach to cognitive screening, but its reliability depends on rigorous experimental design. In this work, we reconstructed a Wav2Vec2-based dementia classification pipeline and identified critical methodological flaws, including speaker leakage, nondeterministic preprocessing, and invalid test partitions. We rebuilt the dataset using strict speaker-level separation, deterministic segmentation, and validation checks to ensure reproducibility. The corrected baseline achieved an accuracy of 44.74 percent and macro F1 score of 0.4439, reflecting a more realistic performance estimate than prior inflated results. This work establishes a scientifically valid foundation for evaluating augmentation strategies such as SpecAugment in …

Go to article

Grp-0100-193 Are Tgnn-Based Intrusion Detection Results Trustworthy? A Dataset Audit And Evaluation Framework, Faysal Chowdhoury, Sait Suer, Yinning Zhang Apr 2026

Grp-0100-193 Are Tgnn-Based Intrusion Detection Results Trustworthy? A Dataset Audit And Evaluation Framework, Faysal Chowdhoury, Sait Suer, Yinning Zhang

C-Day Computing Showcase

Temporal Graph Neural Networks (TGNNs) have reported near-perfect accuracy in Network Intrusion Detection (NID). However, this research reveals these results are often artifacts of dataset flaws rather than genuine model capability. Through a systematic audit of five benchmark datasets, we identify critical issues: node identity leakage, feature extraction artifacts, train/test contamination, and temporal sparsity. We demonstrate that models often learn to recognize specific attacker IP addresses instead of generalizing attack behavior. We propose a standardized evaluation framework featuring leakage-aware relabeling and attack-aware chronological splitting to provide a more reliable basis for future TGNN-NID research.

Go to article

Grp-09-169 Using Logic To Explain And Formally Verify The Behavior Of Relu Neural Networks, Nguyen Thi Binh Nguyen Apr 2026

Grp-09-169 Using Logic To Explain And Formally Verify The Behavior Of Relu Neural Networks, Nguyen Thi Binh Nguyen

C-Day Computing Showcase

Homeschooling in the United States has expanded rapidly, reaching about 3.4 million students (6.26% of K-12 school-age population) in 2024-2025, with accelerated growth following COVID-19. Understanding the reasons behind these decisions is important for informing education policy, resource allocation, and the design of schooling systems that better meet families’ needs. Most of the research on homeschooling relies on statistical methods such as logistic regression or probit models to identify significant factors associated with homeschooling decisions. In this work, we approach this research question from a different perspective by leveraging Explainable AI techniques to provide deeper insights into the homeschooling decisions.

Go to article

Grp-165-217 Evaluation Of Multi-Platform Simulation Environments For Diverse Robotic Manipulation Tasks, Zhiguo Liu Apr 2026

Grp-165-217 Evaluation Of Multi-Platform Simulation Environments For Diverse Robotic Manipulation Tasks, Zhiguo Liu

C-Day Computing Showcase

Robotic development often requires transitioning between different simulation environments to meet specific task requirements. This project presents a comparative evaluation of four major simulation platforms—Gazebo, MuJoCo, CoppeliaSim, and Isaac Sim—through the successful reproduction of diverse manipulation tasks. By implementing system integration, dual-arm coordination, sequential logic, and reinforcement learning across these engines, this study identifies the functional strengths and practical engineering constraints of each environment. The results provide a qualitative guide for selecting simulation tools based on task-specific needs, such as middleware compatibility versus physical fidelity.

Go to article

Uc-097-186 Nudox - Compiler Based Information Retrieval, Mikita Slabysh Apr 2026

Uc-097-186 Nudox - Compiler Based Information Retrieval, Mikita Slabysh

C-Day Computing Showcase

Nudox is a language-agnostic, version-aware documentation and search platform backed by compiler-level analysis. By lowering source code to intermediate representations, Nudox extracts structural metadata, like function signatures, types, and modules independent of the source language. At the core of the platform is a custom search engine built around versioned knowledge: queries resolve to graph nodes and expand outward along structural edges using semantic heuristics, surfacing contextually relevant symbols rather than flat text matches. The result is a canonical, automatically generated source of truth that tracks how a codebase evolves across commits.

Go to article

Uc-115-161 Wayward Stray: Selix, Arly Tinoco, Tyler Ercole, Ivy Stansel, Austin Lothman, Jeremi Charland-Martin Apr 2026

Uc-115-161 Wayward Stray: Selix, Arly Tinoco, Tyler Ercole, Ivy Stansel, Austin Lothman, Jeremi Charland-Martin

C-Day Computing Showcase

Wayward Stray:Selix is a 3rd person platformer which places importance on exploration and discovery. Players will take the role of Selix as they explore an arid desert, fighting off enemies and discovering items hidden around the map, which reveal more about the game world and its characters. Selix, a young dragon, is exiled from the only home he’s known, forced into a strange land in search of a new place to call his own. Along the way, he finds a companion, a small dove that aids and guides his way. Exploring these uncharted areas, Selix discovers there’s more to the …

Go to article

Uc-123-140 P15-T2 Boating Safety Game Us Army Corps | Boating Mvp, Maryam Hamza, Saleh Hamza, Will Vanwinkle, Trevor Caffrey, Tobi Akinsunmi Apr 2026

Uc-123-140 P15-T2 Boating Safety Game Us Army Corps | Boating Mvp, Maryam Hamza, Saleh Hamza, Will Vanwinkle, Trevor Caffrey, Tobi Akinsunmi

C-Day Computing Showcase

This project is an interactive 2D educational boating safety game developed in Unity to teach students essential water navigation and life jacket safety practices in an engaging and immersive format. Designed in collaboration with a real-world sponsor, the game simulates a dynamic boating environment where players navigate obstacles, identify hazards, and make safety decisions under time constraints. The experience integrates instructional modules, guided character narration, and a final quiz phase that reinforces knowledge through immediate feedback, scoring, and achievement-based rewards. Players learn critical concepts such as proper life jacket fit, safe boating procedures, hazard identification, and shallow water awareness. The …

Go to article

Uc-128-145 Ksu Esports Discord Server Bot​, Austin Gammill, Foster Thomas, Lam Truong, Jeffrey Olubajo, Ismail Ahmed Apr 2026

Uc-128-145 Ksu Esports Discord Server Bot​, Austin Gammill, Foster Thomas, Lam Truong, Jeffrey Olubajo, Ismail Ahmed

C-Day Computing Showcase

The KSU Esports program has requested us to develop further upon the Discord Server Bot that they are currently using. This prior implementation was developed by Capstone students last year. Our project’s goal was to build upon their work, polish existing features, commands, UI/UX, and fix known bugs. We have worked on improving the bot’s matchmaking algorithms, tournament seeding logic, API integration, database persistence layer, stability, and statistics tracking. Furthermore, we have developed the UI/UX to be more user-friendly, added support for additional games, and overhauled the bot’s database logic.

Go to article

First Prev Next Last