Computer Engineering Commons^™

Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul

Master's Projects

Malware detection and classification remain critical challenges in cybersecurity, especially as malicious software becomes increasingly sophisticated and prevalent. While much of the work involving embeddings has traditionally relied on supervised learning approaches, there is significant potential in leveraging unsupervised learning techniques to discern hidden structures in malware data. By employing embedding techniques to convert malware samples into high-dimensional vector representations, we can capture the subtle and complex patterns inherent in malicious code without relying on pre-labeled data. This unsupervised approach helps categorize malware into predefined malware families, greatly aiding in developing cybersecurity solutions. In contrast to traditional supervised models that …

Gen Ai For Malicious Network Data, Aneesh Maturu

Master's Projects

Though botnet attacks are on the rise, they also have become sophisticated and difficult to detect. Such a rising threat demands more and more sophisticated cybersecurity that leverages machine learning technology. Nevertheless, one of the biggest bottlenecks remains the unavailability of large and well-balanced datasets, particularly for malicious traffic, which hampers the efficacy of detection models. In an attempt to address this issue, our research utilizes Generative Adversarial Networks (GANs) to produce synthetic samples of botnet traffic from the CTU-13 dataset. While the majority of generative models have been targeting image data, we use GANs for a new application: generating …

Enhancing Code Review Automation With Large Language Models Using Qlora Fine-Tuning And Rags, Sumukh Naveen Aradhya

Master's Projects

In this technological era where Artificial Intelligence and Machine Learning are revolutionizing various domains, Large Language Models (LLMs) are emerging as a very powerful tool. In the software development lifecycle, it is extremely important to have reliable code reviews to ensure security and maintain code quality. This project aims to survey various existing methodologies to aid creation of efficient code review automation agents and also research on ways to make this process more efficient. Parameter Efficient Fine-Tuning (PEFT) methodologies such as LoRA and QLoRA have been explored with an additional focus on a hybrid model that combines adaptive QLoRA with …

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

Advanced Knowledge Extraction With Biomedical Data Using Llms, Akshat Krishna

Master's Projects

The rapid growth of biomedical research has led to an overwhelming volume of unstructured textual data in the scientific literature. This has necessitated the development of an automated approach for knowledge extraction and integration. In

this project, we present a comprehensive pipeline for constructing a unified biomed- ical knowledge graph by combining two well-known datasets: CHEMPROT [1],

which captures chemical–protein interactions, and EU-ADR [2], which annotates drug–gene–disease relationships. In order to identify important biomedical entities and interactions from CHEMPROT dataset, we perform Named Entity Recognition (NER) and relation Extraction (RE) using state-of-the-art biomedical models like BioBERT [3], BioGPT [4] and …

A Framework For Biomimetic Robot Design Applied To The Development Of A Robotic Model Of Drosophila Melanogaster, Clarissa A. Goldsmith

Graduate Theses, Dissertations, and Problem Reports (ETD)

For decades, the field of biologically inspired robotics has leveraged insights from animal locomotion to improve the walking ability of legged robots. Recently, “biomimetic” robots have been developed to model how specific animals walk. By prioritizing biological accuracy to the target organism rather than the application of general principles from biology, these robots can be used to develop detailed biological hypotheses for animal experiments, ultimately improving our understanding of the biological control of legs while improving technical solutions. Much of this work involves biologically inspired walking controllers informed by the morphology and dynamics of the insect nervous system, which necessitate …

Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun

College of Graduate Studies: Theses & Dissertations

Contextual understanding is a significant challenge of Large Language Models (LLMs), which are typically trained on general-purpose datasets. Due to this, LLMs fail to capture nuanced or domain-specific information and may struggle to interpret user queries accurately. Consequently, prompt engineering can become complex in automating, and LLMs are prone to “hallucinating”—generating random or irrelevant texts—when they lack sufficient context. This undermines their ability to provide focused, accurate responses. Accordingly, this thesis seeks to enhance the contextual understanding capabilities of Artificial Intelligence systems to facilitate more precise and relevant answer generation. Study A looks into a new approach to combating misinformation …

Insider Threat Agent: A Behavioral Based Zero Trust Access Control Using Machine Learning Agent, Michael Fojude

College of Graduate Studies: Theses & Dissertations

Hybrid work, cloud adoption, and freely available AI‑enabled attack tools have exposed critical weaknesses in perimeter‑centric security. Current breach reports attribute more than one‑third of incidents to insider misuse or credential compromise, yet many organizations still depend on static Role‑ or Attribute‑Based Access Control that neither verifies intent continuously nor adapts to subtle behavioral change. This research addresses that gap by designing and validating a behavioral based Zero Trust Access Control (ZTAC) Agent. A five‑year enterprise log Dataset was extracted and cleansed to establish a high‑fidelity baseline of normal user behavior. Feature engineering captured temporal regularity (login sequence, session duration), …

Ecu-Pmu-Fdi/Tsa, Taylah Griffiths, Mohiuddin Ahmed, Chadni Islam

Research Datasets

Cyberattacks are now targeting the electrical grid due to its inclusion of smart devices, e.g., smart meters, phasor measurement units. The need to understand and review the impacts of attacks is vital. ECU-PMU-FDI/TSA encompasses communications between a phasor measurement unit and a phasor data concentrator, using the IEEE C37.118 protocol. Benign traffic was captured as control, and for attacks false data injection and time synchronization attack traffic were captured.

Fast-Sparse-Spanner: A Practical Algorithm For Constructing Low-Stretch Sparse Geometric Graphs, Fnu Shariful

UNF Graduate Theses and Dissertations

When constructing geometric graphs (vertices are points and edges are line segments connecting point pairs) on pointsets, stretch-factor (worst-case detour between any point pair) is often considered a quality metric. A low stretch-factor (a quantity that is usually > 1) guarantees short paths between all vertex pairs. A geometric graph having a stretch-factor of t is known as a t-spanner. Creating low stretch-factor geometric graphs for large pointsets with a low number of edges is an open problem in computational geometry.

In this work, we have designed and engineered a new simple and practical (fast and memory-efficient) algorithm named Fast-Sparse-Spanner algorithm …

Rodcast Interaction: A Novel Technique For Dense Virtual Reality Environments, Nevzat U. Demirseren

UNF Graduate Theses and Dissertations

Virtual Reality (VR) technologies continue to grow in popularity and application versatility, yet effective interaction within complex dense environments remains as a critical challenge. In particular, users with low level of VR experience often face decreased accuracy and dissatisfaction selecting occluded objects. A variety of interaction techniques to select and manipulate objects exist, but there is a research gap in understanding what kinds of techniques support users in dense environments. This study evaluates the user performance and preference in such environments. Three interaction techniques are examined in this study: Go-Go Hand, Flower Cone, and a proposed technique called RodCast Interaction. …

Solar Energy Prediction Using Advanced Hybrid Machine Learning Models, Jelawi A. Alqhtani

UNF Graduate Theses and Dissertations

Accurate short-term forecasting of solar power generation is critical for the reliable and cost-effective operation of renewable-based microgrids, where sudden weather-induced variability can compromise grid stability, battery scheduling, and energy trading decisions. Traditional physical and statistical models struggle to capture the complex non-linear relationships and localized weather effects, while individual deep learning architectures often exhibit systematic biases such as chronic under-prediction of peak generation. This thesis proposes a novel Cross-Feedback Ensemble framework that combines the complementary strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1D-CNN) models through an iterative cross-feedback mechanism and a …

Quantum-Resilient Architectures For Enterprise And Cloud Information Systems, Implications Of Quantum Computing For Enterprise Cybersecurity And Data Integrity, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

The emergence of quantum computing presents unprecedented challenges to contemporary enterprise cybersecurity frameworks. Current cryptographic systems that protect sensitive data and secure communications will become vulnerable to quantum attacks within the next decade. This research examines the implications of quantum computing advancement for enterprise and cloud information systems, proposing quantum-resilient architectural frameworks that can withstand both classical and quantum threats. We analyze the timeline of quantum computing development, assess vulnerabilities in existing enterprise security infrastructures, and evaluate post-quantum cryptographic approaches suitable for organizational implementation. Through comparative analysis of quantum-resistant algorithms and architectural patterns, this study demonstrates that enterprises must begin …

Blockchain-Enabled Trust Frameworks For Enterprise Information Systems, Establishing Verifiable Trust In Distributed Organizational Environments, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Enterprise information systems increasingly operate in distributed environments where traditional trust mechanisms based on centralized authority prove insufficient. This research develops a comprehensive blockchain-enabled trust framework that establishes verifiable, decentralized trust mechanisms for enterprise systems operating across organizational boundaries. The study addresses critical gaps in current enterprise architectures where trust depends on centralized intermediaries, creating single points of failure and limiting inter-organizational collaboration. Through examination of existing trust models and blockchain capabilities, we propose an integrated framework that combines cryptographic verification, distributed consensus, and smart contract automation to establish trust without centralized control. Our framework enables organizations to verify data …

Bot Detection In Social Media Using Graphsage And Bert, Abhishek Deshmukh

Master's Projects

This project details a novel bot detection system developed to battle the ever- changing challenge of disinformation, misinformation, and other bot-generated content.

The methodology employed in this project combines the text-based analytical strength of BERT (Bidirectional Encoder Representations from Transformers) with the strength of GraphSage (Graph Sample and Aggregation) for analyzing network structures. The project concatenates BERT and GraphSage vectors to create an 896-size feature embedding with a rich blend of network and text features. This project employs a Support Vector Machine to process the concatenated embeddings, as SVM works well with high-dimensional data. This project was evaluated on two …

Ai-Enabled Anticipatory Handover Predictions In 5g Networks, Ojas Ankush Naik

Master's Projects

As users move across network cells in 5G, it is critical to maintain seamless connectivity through efficient and fast handovers. However, as 5G networks have a very dense deployment of cells and higher carrier frequencies, handovers are often more frequent and challenging, leading to failures or the ping-pong effect. In this research, we are going to use Artificial Intelligence (AI) techniques to enable predicting handover(HO) events proactively as opposed to reactively, aiming to reduce HO failures and unnecessary handovers. We develop Long-Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models to forecast future signal measurements, predict handover trigger points, and compare …

Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-Based Feature Selection And Ml Models, Mohith Sai Venkat Ankem

Master's Projects

Landslides pose significant risks to human life, the community, and the environment, yet their prediction remains a complex and unexplored challenge. Existing prediction models often rely on surface measurements and satellite images, neglecting the critical role, in providing deeper insights into landslide analysis. The literature review highlights a lack of research in time series decomposition techniques, despite their potential to improve prediction accuracy. Similarly, feature selection methods that enhance model robustness and precision have not been adequately addressed. This study presents a novel approach to predicting landslide displacement by combining feature selection through reinforcement learning techniques with advanced time-series machine …

Immersive Extended Reality For Lower Limb Rehabilitation: Design, Deployment, And Pilot Study, Jeremy Varghese

Electronic Theses & Dissertations (2024 - present)

This thesis presents the design, deployment, and pilot study of an immersive extended-reality (XR) rehabilitation system integrated with a ceiling-mounted dynamic body-weight support device (Vector Gait and Safety System), aimed at improving lower-limb rehabilitation out- comes. The implemented system combined immersive virtual tasks—such as Touch Wall, Ball Launcher, Obstacle Dodge, and Stepping Stones—with real-time movement tracking, enabling detailed kinematic analysis and personalized therapy. A pilot study conducted at Sunnyview Rehabilitation Hospital involved seven patients with various mobility impairments, providing quantitative performance metrics and quali- tative user feedback. Results demonstrated consistent patient engagement, measurable im- provements in gait speed and task …

Movie Genre Classification Using Script Texts, Michael Roman Cuomo

Electronic Theses and Dissertations

Genres are used to classify movies so that they can be grouped with others that have similar themes and structures. These classifications are categories created by humans. In the process of creating a movie, a script is often the first creation to write and share ideas about a topic. The script contains large amounts of text that is used to describe the dialog, setting and direction of the film. Although the script contains important information for the film, the amount of text can present a challenge for machine learning algorithms. Often in studies on film classification, if text is used, …

Scalable Mental Health Analysis Using Big Data: A Demographic And Geographic Study Of Depressive Symptoms, Saikrishna Katamneni, Anitha Bommana, Naman Pandya, Gokul Kareti, Sangwhan Cha

Harrisburg University Other Works

This project explores the application of Big Data technologies for large-scale mental health analysis, focusing on the prevalence of depressive disorder symptoms across diverse demographic and geographic subgroups. Utilizing Apache Spark on Google Cloud Dataproc, the system efficiently processed millions of survey records stored in Hadoop Distributed File System (HDFS). Through comprehensive data preprocessing, aggregation, and visualization, the analysis revealed critical trends and disparities in mental health outcomes related to age, race, education level, gender, and state. Seasonal variations and subgroup-specific confidence intervals were also examined to identify high-risk populations and areas of measurement uncertainty. The results offer actionable insights …

Implication And Applications Of Machine Learning On Biomedical Images, Jason Hagerty

Doctoral Dissertations

Medical imaging ranges in modality including computer tomography imaging, x-ray imaging, digital microscopy, and macro-focus dermoscopy images. The latter two modalities are the focus of the presented work.

To perform a diagnostic evaluation on the captured dermoscopy image, it begins with what is usually a labor-intensive operation that requires an expert to perform the initial segmentation for localizing a region of interest (ROI). Once that ROI is obtained, a physician with years of training and experience will observe biological markers that can be used to visually differentiate whether a lesion is benign or malignant. A similar process is used for …

Safe Data-Enabled Control Of Human-In-The-Loop Robotic Manipulator Systems, Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan

Publications

Safe control of human-in-the-loop (HIL) robotic manipulators is critical for applications such as assistive robotics, teleoperation in hazardous environments, and collaborative manufacturing. However, this remains challenging due to the lack of a unified framework that simultaneously addresses safety constraints, external disturbances, unmodeled dynamics, and dynamic role switching in the HIL setting. In this paper, we propose a novel NN-driven HIL control framework in which human–robot dyadic interaction occurs through the haptic channel. Using Lyapunov stability analysis, we theoretically show that the proposed NN-based controller ensures accurate joint trajectory tracking, compensates for system uncertainties, and adapts to human inputs modeled as …

Collaborative Online Interactive Laboratory On Software Defined Radio Fundamentals, Otilia Popescu, Dimitrie C. Popescu, Emanuel Puschita

Engineering Technology Faculty Publications

Teaching of fundamentals of communication systems varies widely across programs in US and abroad, mainly due to the type of undergraduate engineering programs and the depth of the communications field within the curricula. The variety is spread across electrical engineering and electrical engineering technology programs, and programs with focus on telecommunications or which only offer core or elective courses in communications. Adding to the variety, some programs include hands-on laboratory courses, others include simulation-based laboratories most of the time using Matlab, while others may only include lecture courses with no labs. The accessibility of the new software defined radio (SDR) …

Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one …

Machine Learning-Driven Optimization For Utility-Scale Quantum Optimization, Bao Tran

Theses and Dissertations

Hard combinatorial optimization problems, often mapped to Ising models, promise potential solutions with quantum advantage but are constrained by limited qubit counts in near-term devices. We present an innovative quantum-inspired framework that dynamically compresses large Ising models to fit available quantum hardware of different sizes. Thus, we aim to bridge the gap between large-scale optimization and current hardware capabilities. Our method leverages a physics-inspired GNN architecture to capture complex interactions in Ising models and accurately predict alignments among neighboring spins (aka qubits) at ground states. By progressively merging such aligned spins, we can reduce the model size while preserving the …

Swarming Segregation: Leveraging Swarm Intelligence And Regionalization As Instruments For School District Desegregation, Jeffrey Wooten

Theses and Dissertations

Even after Brown led to the South briefly having the most diverse schools in the nation, schools throughout the Northeast have remained the most segregated in the nation for decades. While federal jurisprudence has made compelling desegregation pursuant to the Equal Protection Clause more challenging, New Jersey has a particularly favorable landscape to address severe segregation. With a highly diverse, densely populated public enrollment, favorable state constitutional precedent, and a history of successfully compelling desegregation, New Jersey is fertile ground exploring regional desegregation. Scholars, judges, and even plaintiffs in ongoing litigation (Latino Action Network v. N.J.) have called for New …

Implementation Of Quantized Artificial Neural Networks With Spintronic Stochastic Computing, Saadi Sabyasachi Mr.

Theses and Dissertations

Artificial intelligence or machine learning is going through a rapid expansion. It also incurs significant costs for power and device footprints. Various approaches are being explored to design energy and hardware efficient machine learning models. Stochastic computing has been proposed for efficient machine learning implementation. It requires a source of random number generation which poses some practical challenges. So spintronic solutions such as magnetic tunnel junction has been used for random number generation. Again, spintronic random number generation to implement high precision circuit is prone to device-to-device variations. Hence we designed quantized artificial neural network with spintronic stochastic computing which …

Hypnosis And Mindfulness Audio Recordings For Reducing Fatigue In Individuals With Multiple Sclerosis: A Randomized Controlled Study, Mark P. Jensen, Susan Robles, Michael G. Nash, Susanne May, Dwan M. Ehde, Melissa A. Day, Owen Gottlieb, Laurence I. Sugarman, Kevin N. Alschuler

Articles

Background

Fatigue is a common problem in individuals with multiple sclerosis (MS).

Objective

The objective was to evaluate the effects on fatigue of having 4 weeks of

access to audio recordings of therapeutic hypnosis (HYP) and mindfulness meditation

(MM) practices.

Methods

A total of 333 individuals with MS and fatigue were randomly assigned to

one of the three treatment conditions for 28 weeks: (1) access to therapeutic HYP audio

recordings, (2) access to MM audio recordings, or (3) no access to recordings

(treatment as usual or TAU). Fatigue impact (primary outcome) and other outcomes

were assessed at 4, 16, and …

An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency …

Computer Vision-Based Framework For Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach To Unlocking Financial Insights, Iftakhar Ali Khandokar, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and …