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Grp-1203 Market-Driven Endurance Credits For Page Retention (Mecr) In Hybrid Dram–Nvm Systems, A E M Ridwan Nov 2025

Grp-1203 Market-Driven Endurance Credits For Page Retention (Mecr) In Hybrid Dram–Nvm Systems, A E M Ridwan

C-Day Computing Showcase

This work proposes MECR, a lightweight market-driven page-retention model for hybrid DRAM–NVM memory systems. MECR integrates a contextual bandit learner with endurance-aware credit bidding to optimize hit rate, fairness, and NVM lifetime. A deep verification layer ensures safe eviction decisions under wear-sensitive workloads. Experimental evaluation using synthetic memory traces shows stable accuracy, improved fairness, and adaptive DRAM allocation.

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Grp-1190 Hybrid Virtualization Performance Modeling Using Monte Carlo Simulation, Amatul Akhi Nov 2025

Grp-1190 Hybrid Virtualization Performance Modeling Using Monte Carlo Simulation, Amatul Akhi

C-Day Computing Showcase

This project presents a performance analysis of virtualization, containerization, and hybrid container-in-VM architectures in modern operating systems. Virtual machines provide strong isolation and system stability but incur higher CPU, memory, and startup costs. Containers offer lightweight and fast execution but rely on weaker isolation. To evaluate a balanced alternative, performance data was extracted from recent research and modeled using a Monte Carlo simulation with 1,000 randomized workloads. A unified Hybrid Efficiency Score (HES) was introduced to compare systems consistently, weighting efficiency at 70% and isolation at 30%. Simulation results demonstrate that hybrid systems achieve the highest efficiency–isolation balance, with an …

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Grp-1184 Edge-Llm Anomaly Detection On Raspberry Pi: Syscall Dataset Collection And Prototype Llm Explanation Layer, Shiva Shrestha, Shiva Shrestha Nov 2025

Grp-1184 Edge-Llm Anomaly Detection On Raspberry Pi: Syscall Dataset Collection And Prototype Llm Explanation Layer, Shiva Shrestha, Shiva Shrestha

C-Day Computing Showcase

This research work offers a light-weight, end-to-end, syscall-level anomaly detection approach for the Raspberry Pi platform. The proposal involves the collection of around 2000 NORMAL and 200 ANOMALY syscall observation groups using the Linux Auditd safe synthetic generators. The work also utilizes a prototype LLM Explanation Layer, allowing the provision of human-friendly explanations pertaining to identified anomalies leveraging small LLM models like the Gemma-3 1B, Phi-3 Mini, or other sub 1B LLMs employing the Ollama platform. The LLM inference layer in this research work has partial implementations, as the fine-tuning of the model remains to be done.

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Grp-1141 Decentralized Scheduling And Memory Management In A Simulated Multikernel Os Environment, Md Jahirul Islam Nov 2025

Grp-1141 Decentralized Scheduling And Memory Management In A Simulated Multikernel Os Environment, Md Jahirul Islam

C-Day Computing Showcase

Multikernel operating systems treat each processor core as an independent computing node rather than shared memory. This experiments presents the design and implementation of a multikernel OS simulator that models both decentralized and global scheduling architectures across multiple simulated cores. Each core executes tasks using either Round Robin (RR) or Shortest Job First (SJF) scheduling. The simulator incorporates memory management, task tracking, visualization, generating performance metrics including turnaround time, waiting time, CPU utilization, and heatmap. Experimental results demonstrate that scheduler architecture significantly influences system performance. Decentralized scheduling favors RR where in global scheduling SJF performs better through balanced workload distribution.

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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-0227 Compilation Of Binary Neurons To Obdds, Aidan Boyce Nov 2025

Ur-0227 Compilation Of Binary Neurons To Obdds, Aidan Boyce

C-Day Computing Showcase

A neuron with binary inputs & outputs corresponds to a Boolean function. To explain and verify the behavior a neuron (and by extension, a neural network), we can explain and verify its Boolean function. There has been recent interest in representing the Boolean function of such a neuron as an Ordered Binary Decision Diagram (OBDD), which facilitates such analyses. We propose an algorithm for compiling a binary neuron into an OBDD using a compiler that decomposes a Boolean function into a decision graph. We augment this compiler so that it outputs an OBDD instead. Our augmented compiler produces intermediate OBDDs …

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Ur-0199 Blood Flow Simulation From Coronary Computed Tomography Angiography Using Vnet, Pedro Pinto Nov 2025

Ur-0199 Blood Flow Simulation From Coronary Computed Tomography Angiography Using Vnet, Pedro Pinto

C-Day Computing Showcase

Coronary artery disease (CAD) is one of the leading causes of death worldwide, making the assessment of blood flow and pressure distribution within coronary vessels essential for diagnosis and treatment planning. Fractional Flow Reserve (FFR) is a key measure used to determine the severity of arterial blockages, but traditional methods, such as invasive measurements or computational fluid dynamics (CFD) simulations, are not only time-consuming and costly but also invasive. This project explores the use of deep learning to predict blood pressure distribution in coronary arteries using a 3D convolutional neural network. The dataset consists of Coronary Computed Tomography Angiography (CCTA) …

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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-1202 Generative Ai & Cybersecurity​, Emmanuel Okafor, David Nguyen, Alex Lu Nov 2025

Ur-1202 Generative Ai & Cybersecurity​, Emmanuel Okafor, David Nguyen, Alex Lu

C-Day Computing Showcase

Generative AI both strengthens and threatens cybersecurity. This project develops six reproducible, Google-Colab modules that demonstrate real attack paths — direct and indirect prompt injection, deepfake phishing, phishing-URL classification, insecure code/hallucination risks, and a malware reconstruction (Honor) challenge — and evaluates practical defenses including prompt wrapping, input sanitization, output redaction, and LLM-based guard chains. Using open-weights models and transparent data handling, we provide runnable notebooks, a public Google Site, and an IEEE-style paper to support education and defense-in-depth design.

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Ur-1195 Using Machine Learning To Diagnose Alzheimer's, Julia Johnson, Jordan Rainford Nov 2025

Ur-1195 Using Machine Learning To Diagnose Alzheimer's, Julia Johnson, Jordan Rainford

C-Day Computing Showcase

Early detection of Alzheimer’s disease (AD) remains a significant clinical challenge, as the changes associated with cognitive decline are often subtle and difficult to identify through visual assessment alone. This study investigates modern machine learning methodologies to improve the prediction of cognitive impairment using volumetric MRI–derived region-of-interest (ROI) features. We constructed three binary classifiers [NC vs. AD, MCI vs. AD, and NC vs. MCI] and evaluated various algorithms, including logistic regression, random forests, neural networks, and support vector machines (SVMs). Using measurements generated from eight anatomical brain templates, our models learned patterns indicative of normal cognition, mild cognitive impairment, and …

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Ur-1167 Owlexchange, Laura Chaplin, Caitlin Johnson, Alexin D'Haiti, Jamaul Gordon, Leonardo Barranco Nov 2025

Ur-1167 Owlexchange, Laura Chaplin, Caitlin Johnson, Alexin D'Haiti, Jamaul Gordon, Leonardo Barranco

C-Day Computing Showcase

OwlExchange is a secure, role-based campus marketplace designed to help Kennesaw State University students buy, sell, exchange, and donate items in a trusted digital environment. The platform replaces unorganized and unsafe exchanges occurring across group chats and social media by providing a centralized system with authenticated user accounts, item listings, and transparent communication. Built with a Flask (Python) MVC architecture, Auth0 for secure identity management, and MySQL for persistent storage, OwlExchange supports modular dashboards for buyers, sellers, and administrators, enabling item management, interest requests, and platform oversight. By promoting reuse and donation of textbooks, furniture, electronics, and other student items, …

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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-0248 Shell Commands Used In Cybersecurity Training, Nathan Kourk, Jacob Suda, Ming Butler, Yonnas Alemu Nov 2025

Ur-0248 Shell Commands Used In Cybersecurity Training, Nathan Kourk, Jacob Suda, Ming Butler, Yonnas Alemu

C-Day Computing Showcase

This project explores patterns in shell command usage during cybersecurity training programs. We will analyze syntax frequency and selection of shell commands across multiple datasets looking for patterns in user behavior. The goal of this study is to identify differences between programs, and to provide insight into the trends within the command line environment.

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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-0275 Graph Attention Network Based Downlink Channel Prediction Using In Frequency Division Duplexed Nextgen Networks, Jui Mhatre Nov 2025

Grp-0275 Graph Attention Network Based Downlink Channel Prediction Using In Frequency Division Duplexed Nextgen Networks, Jui Mhatre

C-Day Computing Showcase

In Frequency Division Duplex (FDD) 5G networks, downlink channel state information (CSI) must be estimated at the user equipment (UE) and fed back to the base station, a process that requires frequent CSI-RS transmission and uplink feedback, resulting in high overhead and energy consumption. This research proposes a novel base-station–centric framework that predicts the downlink channel matrix directly at the gNB, eliminating the need for continuous CSI-RS–based estimation at the UE. By leveraging uplink channel observations, geometric environment features, and learned mappings between uplink and downlink channel relationships, our model reconstructs the downlink MIMO channel with high fidelity. The system …

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Grp-0217 Comparative Analysis Of Os-Level Security Vulnerabilities And Isolation Mechanisms In Hypervisors And Containers, Jiban Krisna Das Nov 2025

Grp-0217 Comparative Analysis Of Os-Level Security Vulnerabilities And Isolation Mechanisms In Hypervisors And Containers, Jiban Krisna Das

C-Day Computing Showcase

This project investigates the operating-system-level performance and isolation mechanism of Virtual Machines and Docker container. The experiment includes CPU/memory microbenchmarks, disk throughput tests, web-server latency measurements, multi-process scheduling stress and controlled security checks. We aim to quantify each benchmark under identical conditions. The study findings reveal that Docker consistently provides lower overhead and faster I/O due to its shared-kernel architecture, while VirtualBox maintains stronger isolation but introduces more scheduling and disk latency. The findings provide practical insights for the OS system designers to find better execution environments for security critical and performance-sensitive workloads.

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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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Ur-0225 Carbonyl Detection In Ir Using Deep Learning, Dharani Shakthivel Nov 2025

Ur-0225 Carbonyl Detection In Ir Using Deep Learning, Dharani Shakthivel

C-Day Computing Showcase

The goal of this project is to train a Convolutional Neural Network (CNN) to recognize carbonyl groups in infrared (IR) spectra. A carbonyl group is defined by a characteristic C=O double bond, which produces a strong, easily recognizable absorption peak near 1700 cm⁻¹. To develop and evaluate the model, I am using spectra prepared through three common techniques: KBr disc, nujol mull, and liquid film. Among these, liquid-film spectra provide the cleanest signal and most closely resemble what a chemist visually relies on when identifying carbonyls. In contrast, both the KBr disc and nujol mull methods require mixing the target …

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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.

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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 …

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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-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.

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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 …

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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 …

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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.

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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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