Other Computer Engineering Commons^™

Hyperchaotic Noise Generation For Adversarial Encryption In Privacy-Preserving Image Classification, Neeraja Beesetti

Master's Theses

Artificial intelligence systems make useful predictions by taking in data and returning a classification, recommendation, or decision. Obtaining that prediction, however, requires sharing the data first. This creates a fundamental privacy challenge in machine learning: users must expose their data to receive a valuable prediction. Machine learning systems increasingly rely on cloud-based image classification for this reason, transmitting images from edge devices to remote servers rather than running large models locally. This creates a conflict between the accuracy a classifier requires and the privacy a data owner wants. Traditional encryption destroys the image structure on which a classifier depends, while …

Assessing Computer Vision Based Conflict Detection In Uas Traffic Monitoring Under Secure Communication Constraints, Fadjimata Issoufou Anaroua

Doctoral Dissertations and Master's Theses

The rapid growth of Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) is creating a new low-altitude airspace ecosystem where drones, air taxis, service suppliers, communication networks, sensors, and ground-based monitoring systems must work together safely. Within this ecosystem, UAS Traffic Management (UTM) is expected to provide a digital framework for coordinating operations beyond traditional air traffic control. However, reliable integration also requires resilient monitoring methods that can detect non-cooperative aircraft, protect communication links, and maintain timely situational awareness under real-world constraints.

This dissertation examines how computer vision can support cooperative monitoring systems such as Remote ID and ADS-B …

A Cross-Dataset Vision Transformer Study For Brain Tumor Mri Image Classification, Sharon Kawira Mungania

Masters Theses

Brain tumor MRI classification is an important medical-imaging task because MRI scans contain complex anatomical patterns that can be time consuming to interpret manually. This study evaluates whether a pre-trained Vision Transformer can classify brain tumor MRI images consistently across datasets with different class structures. Three publicly available Kaggle datasets were used: Nickparvar, Br35H, and Figshare. Nickparvar and Figshare were treated as multi-class classification tasks, while Br35H was treated as a binary tumor/no-tumor task. Images were converted to three-channel format, resized to 384 × 384 pixels, normalized using ImageNet statistics, and augmented during training. The selected model was ViT-Base Patch …

Reproducing And Evaluating Charger Surfing: Robustness Of Smartphone Charging-Line Side-Channels, Colby M. Watts

Master's Theses

Smartphones are frequently connected to external, untrusted charging hardware, creating opportunities for side-channel attacks that do not require malware or direct access to device data. Charger Surfing, a recently proposed charging-line power analysis side-channel attack, reported high accuracy in inferring touchscreen input from voltage measurements collected from a smartphone’s charging cable; however, the reproducibility and robustness of these results under different conditions remain unclear. This thesis presents an independent replication and evaluation of Charger Surfing, including the development of an end-to-end data collection pipeline consisting of a modified charging cable, oscilloscope-based recordings, custom Android app, automated trace processing, and convolutional …

Polysaber: A Custom Reactive Lightsaber Soundboard, Pedro B. Medeiros

Computer Engineering

The PolySaber project was developed as a custom reactive lightsaber control system built as a fully custom PCB design. The purpose of the project was to create a lower-cost and more customizable alternative to commercially available lightsaber soundboards while simultaneously providing hands-on experience in PCB design, embedded systems development, and hardware integration. Commercial lightsaber soundboards are expensive, proprietary, and difficult for hobbyists to customize. The PolySaber project addresses this by creating a modifiable hardware platform built around the ESP32 microcontroller. The system supports programmable firmware, RGB NeoPixel blade control, motion sensing, reactive swing and clash effects, onboard audio amplification, and …

Design And Parametric Study Of A Mems-Based Reservoir Computer For Reinforcement Learning, Andrew P. Carr

Master's Theses

Single-node reservoir computing (RC) is a hardware-efficient approach to machine learning, leveraging the dynamics of physical systems. In this work, two reinforcement learning algorithms, Q-learning and Proximal Policy Optimization (PPO), are applied to a simulated micro-electro-mechanical system (MEMS)-based reservoir computer to solve both discrete and continuous control tasks. MEMS-based reservoirs are low-power, compact, and their natural frequencies (kHz to MHz) pair well with real-time control loops. To explore the relationship between reservoir dynamics and learning performance, a parametric study is conducted on two reservoir hyperparameters, reservoir size and neuron separation, using CartPole-v1 and MountainCar-v0. The RC successfully learns multiple tasks …

Trustworthy Intelligence Fusion For Search And Rescue: Designing Grounded And Secure Multi-Agent Ai For Mission Decision Support, Yayun Tan

Master's Theses

Search and rescue (SAR) operations require teams to integrate uncertain information under severe time pressure. Large language model (LLM)-based multi-agent systems (MAS) can support decision-making, but they also risk producing hallucinated outputs and processing unreliable or malicious data. This thesis addresses these risks through three linked studies. First, it proposes a modular eight-agent SAR MAS architecture that reflects the structure of real SAR missions by assigning specialized roles to different agents. Second, it introduces a post-hoc probabilistic verification framework that checks LLM agent outputs against a probabilistic knowledge graph built from historical SAR incidents.  Third, the thesis examines indirect adversarial …

Omama-Db: The Oregon-Massachusetts Mammography Database, Avanih Kanamarlapudi

Graduate Masters Theses

Public datasets for training AI models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive publicly available collection of 2D mammograms and 3D tomosynthesis volumes. Starting from 967,991 images, we created a curated set of 231,080 images us ing a multi-stage filtering process that removes missing labels, uncommon dimensions, rare scanner types, duplicate studies, and invalid DICOM files. All 2D images then undergo additional outlier detection using histogram filtering and a variational autoen coder to remove low-quality outliers. OMAMA-DB includes pathology-based cancer labels and automated lesion annotations …

Evaluating Soil Health And Crop Yield In Louisiana Agricultural Systems: Impacts Of Best Management Practices And Prediction Models, Hector J. Mendoza Lagos

LSU Doctoral Dissertations

The adoption of conservation management practices is critical for improving soil health, enhancing nutrient use efficiency, and sustaining crop productivity in row crop systems in Louisiana. This study evaluated the role of conservation agronomic practices, soil biochemical indicators, and machine learning predictive models to improve soil nutrient dynamics, soil health indicators, microbial communities (MC), and crop productivity on a corn (Zea mays L.) research plot scale and in a commercial forty-hectare cotton (Gassypium hirsutum L.)-corn-soybean (Glycine max L.) rotation system in northeast Louisiana. The objectives of the study were to evaluate soil nutrient dynamics and MCs under …

Power Consumption Prediction And Energy Forecasting Using Machine Learning Models, Sheik Mohideen Shah S Mr

Theses and Dissertations

Power consumption trends are essential to be identified in the energy grid areas to analyze the utilization, deficiency, and the measures to be taken for an effective and comfortable usage of energy. There are two scenarios in which the power consumption can be analyzed, namely identification and prediction. Identification deals with the post-utilization analysis of energy trends, whereas prediction deals with prior analysis of various factors of energy utilization, including the cost, supply details, shortages, and the need for new energy resources. In the existing models, the power consumption-related data are collected through smart meters, and the energy forecasting methods …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Effectiveness Of Quiz-Based Interventions In Virtual Reality Lectures On Learning, Mason T. Prather

Master's Theses

This thesis examined whether quiz-based interventions affect learning during an immersive virtual reality (VR) lecture and whether quiz timing strategy matters when prompts are delivered on a fixed timer or through gaze/Region of Interest (ROI)-based attention timing. The study used an eye-tracking virtual reality headset and a game engine-based classroom application. The final completed cohort included 29 participants assigned to three between-subjects conditions: No Intervention (n = 10), Timer-Based Intervention (n = 9), and Attention-Based Intervention (n = 10). All participants viewed the same virtual reality lecture and completed the same 15-item post-lecture assessment. Mean learning scores were 44.00% for …

Parallelism In Java, Matt Vierow

Departmental Honors & Graduate Capstone Projects

Parallelism and multithreading have become an important facet of programming in recent years. Much work has been done in programming languages such as C and C++ with libraries such as OpenMP. However, less effort has been made to create parallelism tools in higher level languages such as Java. This is important because most programmers learn the basics in languages such as Python and Java that lack parallel tools like OpenMP. This means they must directly manage threads which requires knowledge of object-oriented programming and design.

This project sought to incorporate some elements of OpenMP into Java to allow newer programmers …

Robust Hardware Trojan Detection Leveraging Dual‑Domain Features And Stacked Ensemble Learning, Sefatun-Noor Puspa

All Theses

In Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under …

Hdra-Fusion: Hybrid Detection With Routed Architecture For Manipulation-Aware Ai Face Forgery Detection, Omar Ebeid

Theses and Dissertations

With the rapid advancements in artificial intelligence-based image generation and manipulation tools, it is extremely difficult to detect if an image is genuine or artificially crafted. Despite extensive research in this area, existing image detection systems suffer from three major problems: suboptimal cross-dataset generalization due to shortcut learning of dataset-specific patterns, unreliable probability estimates due to domain shift, particularly in cross-manipulation evaluation settings, and an inability to detect images manipulated by multiple types of manipulations within a single detection framework. To address these limitations, we propose HDRA-Fusion (Hybrid Detection with Routed Architecture), a framework built on the conclusion that different …

Generative Ai Of Breast Cancer Progression In Gene Expression Space, Xusheng Ai

All Dissertations

Breast cancer is one of the most common and deadly cancers in the world. Although doctors can use gene activity data to better understand different types of breast cancer, it is still difficult to identify the most important genes and to track how the disease changes over time. This is partly because gene data are very large and complex.

This dissertation develops a computer-based framework to study breast cancer using gene expression data. The work focuses on three goals: creating realistic synthetic gene data, identifying important genes linked to cancer, and modeling how breast cancer changes from normal tissue to …

Culturally Inclusive Usability Engineering (Ciue): A Framework For Evaluating Cultural Inclusion In Study Abroad Platforms, Louis Muhammad

Master's Theses

Study abroad programs provide students with opportunities to develop global com- petence and intercultural understanding. However, the digital platforms that support these programs often fail to communicate information that reflects the cultural and faith-based needs of diverse student populations. While prior research in Human-Computer Interac- tion (HCI) and usability engineering has explored cross-cultural and accessible design, cultural and religious considerations are rarely integrated into usability engineering pro- cesses. This thesis introduces the Culturally Inclusive Usability Engineering (CIUE) frame- work, which extends traditional usability engineering by integrating culturally informed con- siderations into the system development lifecycle. CIUE begins by identifying the …

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Development Of Hypergraph Based Deep Neural Framework For Precise Cancer Subtyping And Meta Visualization, Pooja G Ms

Theses and Dissertations

Accurate Cancer Subtyping is a cornerstone of modern oncology essential for effective diagnosis and guiding personalized treatment. Histopathological Images (HIs) which capture the microscopic structure of tissues are widely used for cancer detection and subtyping. Even though deep learning has made significant advances, existing HI based subtyping methods often focus on specific cancer types, lacking a generic framework.

A unified framework that can classify multiple cancers with high specificity is desperately needed. In response to these limitations, this thesis proposes a robust multi-cancer, multi-class subtyping framework called DSHGNet (Depthwise Separable Hypergraph Convolutional Neural Network) which integrates Depthwise Separable Convolutional Neural …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Cognivault: Enabling Privacy-Aware Cognitive Distortion Detection In Intelligent Mental Health Applications, Mariam Dawoud

Theses and Dissertations

Mental health applications are increasingly leveraging intelligent systems to sup- port psychological well-being, yet preserving user privacy remains a major concern. This thesis presents CogniVault, a secure ecosystem for cognitive distortion data. The framework includes Cognify, a mobile journaling application that detects cog- nitive distortions in user-written journal entries using a locally deployed machine learning model. Cognitive distortions are maladaptive thought patterns such as catastrophizing or personalization, which the app identifies to provide therapeu- tic insights. To ensure privacy-preserving data analytics, CogniVault includes the design and implementation of a hybrid security architecture, PRISM-HDI, that combines Paillier Homomorphic Encryption (HE), Differential …

Comparative Evaluation Of Deep Learning Models: Resnet18, Minivgg, And Yolov8 For Five-Class Blood Cell Classification, Keita Sakurai

Master's Theses or Doctor of Nursing Practice

Accurate classification of blood cell types is a critical task in automated hematological analysis. This study presents a comparative evaluation of three deep learning architectures, ResNet18, MiniVGG, and YOLOv8, for five-class blood cell image classification. To ensure a fair comparison, all models were trained under standardized conditions, including a consistent 90:10 training–validation split, controlled dataset size, and fixed training epochs. ResNet18 was trained to establish a baseline using residual learning. MiniVGG employed a compact VGG-inspired design with regularization to balance efficiency and accuracy, while YOLOv8 leveraged a lightweight, pretrained classification backbone with integrated data augmentation. Experimental results demonstrate a clear …

Advancing Task-Oriented Dialog Systems: Scalability, Generalization, And Evaluation, Adib Mosharrof

Theses and Dissertations--Computer Science

Task-oriented dialog (TOD) systems enable conversational interfaces for complex tasks like flight booking and restaurant reservations. However, deploying TOD systems at scale faces three critical barriers: scalability, generalization, and evaluation. Scalability is primarily restricted by the human-annotation bottleneck, as current systems depend on vast quantities of manually labeled data for every new domain, making deployment prohibitively expensive. Generalization remains a persistent challenge, as systems optimized for known domains often suffer significant performance degradation when encountering new, unseen ones. Existing evaluation metrics measure response quality and fluency, but fail to measure functional task success. As TOD systems are deployed across diverse …

Evaluating The Robustness Of Gnn-Based Vulnerability Detectors Under Semantics-Preserving Code Obfuscation, Jesse Ks Chumo

Computer Science and Engineering Theses

Graph neural network–based vulnerability detectors are typically evaluated on clean benchmark datasets, yet real-world code frequently undergoes semantics-preserving transformations such as identifier renaming, dead-code insertion, and control-flow restructuring. The extent to which such transformations affect detector reliability remains insufficiently understood. We evaluate ten vulnerability detectors from four architectural families across the Devign, Big-Vul, and DiverseVul datasets. To quantify robustness, we evaluate each model at three transformation budgets: one transform, two transforms combined, and all three together, finding that token-based models degrade under identifier renaming and compound transformations, while models that read only code structure are largely unaffected. We further evaluate …

Videoscoop: A Non-Traditional, Domain-Independent Framework For Video Analysis, Umme Hafsa Billah

Computer Science and Engineering Dissertations

Due to the proliferation of cameras in handheld devices and the widespread use of CCTV, images and videos have become a preferred alternative for capturing and disseminating information. Automated analysis for understanding image or video contents (e.g., objects, activities, backgrounds, situations of interest, etc.) is critical for many applications such as Civic Monitoring, Surveillance (in general), monitoring activities in Assisted Living environments, and many more. Image and Video Analysis (IVA) research has been ongoing for several decades, resulting in numerous techniques for algorithmically analyzing and understanding image and video contents.

Image Analysis (IA) has advanced in several areas, including object …

Non-Invasive Digital Restoration Of Damaged Photographic Film Negatives, Ankan Bhattacharyya

University of Kentucky Doctoral Dissertations

Physical restoration of damaged photographic film causes more damage. Also, existing non-invasive digital restoration of film negatives does not produce print-quality optical images. Instead, they produce X-ray projections, which are not the same as optical projections. This thesis addresses these problems and establishes a framework that can digitally restore old, damaged films without the need to open them physically. Virtual Unwrapping is an existing pipeline that has proven itself over the last couple of decades to work on unopenable papyrus scrolls, like the Herculaneum Scrolls. This thesis utilizes the concept of virtual unwrapping to restore damaged photographic film negatives. Due …

Security Onion Ids Case Study: Detecting And Investigating Threat Traffic Using Suricata, Zeek, And Pcap Evidence, Daryna Myroniuk

Williams Honors College, Honors Research Projects

A key component of cybersecurity is network intrusion detection, which is used to examine network traffic for any malicious activity. Many organizations deploy intrusion detection systems (IDS) but still face challenges such as validating detections, investigating alerts in a timely manner, and producing clear and repeatable evidence of what has occurred, especially when live traffic capture may be limited to risks, permissions, or privacy concerns. The goal of this project was to use Security Onion, which includes Suricata and Zeek to create and demonstrate a controllable and manageable evidence-based IDS investigation workflow. This project is focused on deploying and validating …

Analyzing Network Traffic And Data Exfiltration Via Smb In Post-Vm Escape Scenarios, Noah M. Disanza

Williams Honors College, Honors Research Projects

Virtual machines (VMs) play a crucial role in modern IT infrastructure environments by providing isolation and enhanced security, among other things, for both personal and corporate systems. VMs are heavily rely upon to safely test malware, manage infrastructure, and reduce risk to host systems. This reliance is so substantial that the idea of reducing risk to the host system is believed to be erasing risk entirely. However, this mindset has shown to be challenged time and time again by the emergence of exploits known as virtual machine escapes. These exploits allow malicious actors to break out of the virtualized environment …

Performance Analysis Of Sparse Neural Networks In Brain Abnormality Detection, Megan Danh

Honors Undergraduate Theses

Neuroimages have held the capability of revealing to medical professionals patterns for brain abnormalities since their development. However, more recently, these professionals and researchers are looking to use neural networks to identify these brain abnormalities through neuroimages for early detection that would allow more effective treatment. Neuroimage datasets, specifically functional magnetic resonance imaging (fMRI), are extremely large in size. This would result in their processing and training to be computationally expensive, even with smaller neural networks. Fortunately, recent pruning methods have recently emerged, where network weights and neurons are pruned to reduce computational cost without compromising too much accuracy. By …