Other Computer Engineering Commons^™

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 …

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. …

Computational Pangenomics And Machine Learning For Genotype-Phenotype Analysis, Tejaswi Vemuri

UNF Graduate Theses and Dissertations

Phenotypes are the observable characteristics of an individual organism. Predicting quantitative phenotypes from genomic variation remains challenging when causal signals span both local motifs and distal regulatory contexts. Building on Frequented Regions (FRs)—subsequences conserved across genomes and extracted from a pangenome graph generated from a large collection of closely related species—we compare several modeling strategies across 35 Saccharomyces cerevisiae growth phenotypes: Random Forest (RF) on FR counts (called RFCounts), RF on FR sequences, 1D convolutional neural networks (CNN) on FR sequences, Long Short-Term Memory (LSTM) networks on FR sequences, a Genomewide Association Study (GWAS) baseline, and a sequence-based transformer model, …

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 …

Robotic Gas Source Localization And Distribution Mapping Via Deep Reinforcement Learning, Iliya Kulbaka

UNF Graduate Theses and Dissertations

This research aims to advance the fields of Gas Source Localization (GSL) and Gas Distribution Mapping (GDM) by developing deep reinforcement learning (DRL) methodologies suitable for complex, real-world environments. GSL and GDM are crucial for applications such as environmental monitoring, hazardous material detection, and search-and-rescue missions, where safe and efficient exploration is essential. Traditional methods often fall short in dynamic settings influenced by factors like wind and obstacles. To address these limitations, this study proposes novel neural network architectures and learning frameworks for adaptive exploration and mapping, integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) layers, and Deep Q-Networks …

A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance, Jeremy A. Cantor

UNF Graduate Theses and Dissertations

Previous literature demonstrates that autonomous UAVs (unmanned aerial vehicles) have the po- tential to be utilized for wildfire surveillance. This advanced technology empowers firefighters by providing them with critical information, thereby facilitating more informed decision-making processes. This thesis applies deep Q-learning techniques to the problem of control policy design under the objective that the UAVs collectively identify the maximum number of locations that are under fire, assuming the UAVs can share their observations. The prohibitively large state space underlying the control policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from …

Explainable Automated Inconsistency Detection In Biomedical And Health Literature, Prajwol Lamichhane

UNF Graduate Theses and Dissertations

Given the exponential growth of scientific information online, researchers often face the daunting task of detecting contradictory statements on crucial health topics. This work develops a comprehensive pipeline for automated contradiction detection that integrates an Information Retrieval (IR) system, machine learning classifiers, and Explainable AI (XAI). The Information Retrieval system is tailored for biomedical data and comprises a datastore, syntactic, and semantic components. Users can input queries, initiating a pipeline that identifies top documents through syntactic analysis and refines results via semantic examination for relevant research claims. Employing a diverse range of Large Language Models such as pre-trained Distil-BERT, BioBERT, …

Energy-Efficient Hmac For Wireless Communications, Cesar Enrique Castellon Escobar

UNF Graduate Theses and Dissertations

This thesis introduces the Farming Lightweight Protocol (FLP) optimized for energy-restricted environments that depend upon secure communication, such as multi-robot information gathering systems within the vision of ``smart'' agriculture. FLP uses a hash-based message authentication code (HMAC) to achieve data integrity. HMAC implementations, resting upon repeated use of the SHA256 hashing operator, impose additional resource requirements and thus also impact system availability. We address this particular integrity/availability trade-off by proposing an energy-saving algorithmic engineering method on the internal SHA256 hashing operator. The energy-efficient hash is designed to maintain the original security benefits yet reduce the negative effects on system availability. …

Energy Considerations In Blockchain-Enabled Applications, Cesar Enrique Castellon Escobar

UNF Graduate Theses and Dissertations

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while mitigating significant cybersecurity risks pertaining to the main application. Associated tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory, computation) required by each blockchain-enabled host. This thesis applies an energy-reducing algorithmic engineering technique for Merkle Tree root and Proof of Work calculations, two principal elements of blockchain computations, as a means to preserve the promised security benefits but with less compromise to system availability. Using pyRAPL, a python library to measure computational energy, we experiment with both the …

Sorting By Strip Moves And Strip Swaps, Chandrika Pandurang Rao

UNF Graduate Theses and Dissertations

Genome rearrangement problems in computational biology [19, 29, 27] and zoning algorithms in optical character recognition [14, 4] have been modeled as combinatorial optimization problems related to the familiar problem of sorting, namely transforming arbitrary permutations to the identity permutation. The term permutation is used for an arbitrary arrangement of the integers 1, 2,···, n, and the term identity permutation for the arrangement of 1, 2,···, n in increasing order. When a permutation is viewed as the string of integers from 1 through n, any substring in it that is also a substring in the identity permutation will …

Trapping Aco Applied To Mri Of The Heart, Shannon Lloyd Birchell

UNF Graduate Theses and Dissertations

The research presented here supports the ongoing need for automatic heart volume calculation through the identification of the left and right ventricles in MRI images. The need for automated heart volume calculation stems from the amount of time it takes to manually processes MRI images and required esoteric skill set. There are several methods for region detection such as Deep Neural Networks, Support Vector Machines and Ant Colony Optimization. In this research Ant Colony Optimization (ACO) will be the method of choice due to its efficiency and flexibility. There are many types of ACO algorithms using a variety of heuristics …

On Learning And Visualizing Lexicographic Preference Trees, Ahmed S. Moussa

UNF Graduate Theses and Dissertations

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of …

Mobile Cloud Computing: A Comparison Study Of Cuckoo And Aiolos Offloading Frameworks, Inan Kaddour

UNF Graduate Theses and Dissertations

Currently, smart mobile devices are used for more than just calling and texting. They can run complex applications such as GPS, antivirus, and photo editor applications. Smart devices today offer mobility, flexibility, and portability, but they have limited resources and a relatively weak battery. As companies began creating mobile resource intensive and power intensive applications, they have realized that cloud computing was one of the solutions that they could utilize to overcome smart device constraints. Cloud computing helps decrease memory usage and improve battery life. Mobile cloud computing is a current and expanding research area focusing on methods that allow …

Applied Deep Learning: Automated Segmentation Of White Matter Hyperintensities (Wmh) On Brain Mr Images, Matt Berseth

DHI Digital Projects Showcase

Small vessel disease plays a crucial role in stroke, dementia, and ageing. White matter hyperintensities (WMH) of vascular origin are one of the main consequences of small vessel disease and well visible on brain MR images. Quantification of WMH volume, location, and shape is of key importance in clinical research studies and likely to find its way into clinical practice; supporting diagnosis, prognosis, and monitoring of treatment for dementia and other neurodegenerative diseases. It has been noted that visual rating of WMH has important limitations and hence a more detailed segmentation of WMH is preferred. Various automated WMH segmentation techniques …

Generating A Normalized Database Using Class Normalization, Daniel Sushil Sudhindaran

UNF Graduate Theses and Dissertations

Relational databases are the most popular databases used by enterprise applications to store persistent data to this day. It gives a lot of flexibility and efficiency. A process called database normalization helps make sure that the database is free from redundancies and update anomalies. In a Database-First approach to software development, the database is designed first, and then an Object-Relational Mapping (ORM) tool is used to generate the programming classes (data layer) to interact with the database. Finally, the business logic code is written to interact with the data layer to persist the business data to the database. However, in …

An Empirical Investigation Of Collaborative Web Search Tool On Novice's Query Behavior, Mareh Fakhir Al-Sammarraie

UNF Graduate Theses and Dissertations

In the past decade, research efforts dedicated to studying the process of collaborative web search have been on the rise. Yet, a limited number of studies have examined the impact of collaborative information search processes on novices’ query behaviors. Studying and analyzing factors that influence web search behaviors, specifically users’ patterns of queries when using collaborative search systems can help with making query suggestions for group users. Improvements in user query behaviors and system query suggestions help in reducing search time and increasing query success rates for novices.

This thesis investigates the influence of collaboration between experts and novices as …

Challenging The Efficient Market Hypothesis With Dynamically Trained Artificial Neural Networks, Kevin M. Harper

UNF Graduate Theses and Dissertations

A review of the literature applying Multilayer Perceptron (MLP) based Artificial Neural Networks (ANNs) to market forecasting leads to three observations: 1) It is clear that simple ANNs, like other nonlinear machine learning techniques, are capable of approximating general market trends 2) It is not clear to what extent such forecasted trends are reliably exploitable in terms of profits obtained via trading activity 3) Most research with ANNs reporting profitable trading activity relies on ANN models trained over one fixed interval which is then tested on a separate out-of-sample fixed interval, and it is not clear to what extent these …

Hadoop Based Data Intensive Computation On Iaas Cloud Platforms, Sruthi Vijayakumar

UNF Graduate Theses and Dissertations

Cloud computing is a relatively new form of computing which uses virtualized resources. It is dynamically scalable and is often provided as pay for use service over the Internet or Intranet or both. With increasing demand for data storage in the cloud, the study of data-intensive applications is becoming a primary focus. Data intensive applications are those which involve high CPU usage, processing large volumes of data typically in size of hundreds of gigabytes, terabytes or petabytes. The research in this thesis is focused on the Amazon’s Elastic Cloud Compute (EC2) and Amazon Elastic Map Reduce (EMR) using HiBench Hadoop …

Towards Designing Energy-Efficient Secure Hashes, Priyanka Dhoopa Harish

UNF Graduate Theses and Dissertations

In computer security, cryptographic algorithms and protocols are required to ensure security of data and applications. This research investigates techniques to reduce the energy consumed by cryptographic hash functions. The specific hash functions considered are Message Digest-2 (MD2), Message Digest-5 (MD5), Secure Hash Algorithm-1 (SHA-1) and Secure Hash Algorithm-2 (SHA-2).

The discussion around energy conservation in handheld devices like laptops and mobile devices is gaining momentum. Research has been done at the hardware and operating system levels to reduce the energy consumed by these devices. However, research on conserving energy at the application level is a new approach. This research …

Unknown Exception Handling Tool Using Humans As Agents, Mwaka Mahanga

UNF Graduate Theses and Dissertations

In a typical workflow process, exceptions are the norm. Exceptions are defined as deviations from the normal sequence of activities and events. Exceptions can be divided into two broad categories: known exceptions (i.e., expected and predefined deviations) and unknown exceptions (i.e., unexpected and undefined deviations). Business Process Execution Language (BPEL) has become the de facto standard for executing business workflows with the use of web services. BPEL includes exception handling methods that are sufficient for known exception scenarios. Depending on the exception and the specifics of the exception handling tools, processes may either halt or move to completion. Instances of …

An Empirical Performance Analysis Of Iaas Clouds With Cloudstone Web 2.0 Benchmarking Tool, Neha Soni

UNF Graduate Theses and Dissertations

Web 2.0 applications have become ubiquitous over the past few years because they provide useful features such as a rich, responsive graphical user interface that supports interactive and dynamic content. Social networking websites, blogs, auctions, online banking, online shopping and video sharing websites are noteworthy examples of Web 2.0 applications. The market for public cloud service providers is growing rapidly, and cloud providers offer an ever-growing list of services. As a result, developers and researchers find it challenging when deciding which public cloud service to use for deploying, experimenting or testing Web 2.0 applications. This study compares the scalability and …

Use Of Ibm Collaborative Lifecycle Management Solution To Demonstrate Traceability For Small, Real-World Software Development Project, Lovelesh Chawla

UNF Graduate Theses and Dissertations

The Standish Group Study of 1994 showed that 53 percent of software projects failed outright and another 31 percent were challenged by extreme budget and/or time overrun. Since then different responses to the high rate of software project failures have been proposed. SEI’s CMMI, the ISO’s 9001:2000 for software development, and the IEEE’s JSTD-016 are some examples of such responses. Traceability is the one common feature that these software development standards impose.

Over the last decade, software and system engineering communities have been researching subjects such as developing more sophisticated tooling, applying information retrieval techniques capable of semi-automating the trace …

Analysis Of Bfsa Based Anti-Collision Protocol In Lf, Hf, And Uhf Rfid Environments, Varun Bhogal

UNF Graduate Theses and Dissertations

Over the years, RFID (radio frequency identification) technology has gained popularity in a number of applications. The decreased cost of hardware components along with the recognition and implementation of international RFID standards have led to the rise of this technology.

One of the major factors associated with the implementation of RFID infrastructure is the cost of tags. Low frequency (LF) RFID tags are widely used because they are the least expensive. The drawbacks of LF RFID tags include low data rate and low range. Most studies that have been carried out focus on one frequency band only. This thesis presents …

Performance Evaluation Of Data Intensive Computing In The Cloud, Bhagavathi Kaza

UNF Graduate Theses and Dissertations

Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, …

Performance Evaluation And Comparison Of Distributed Messaging Using Message Oriented Middleware, Naveen Mupparaju

UNF Graduate Theses and Dissertations

Message Oriented Middleware (MOM) is an enabling technology for modern event- driven applications that are typically based on publish/subscribe communication [Eugster03]. Enterprises typically contain hundreds of applications operating in environments with diverse databases and operating systems. Integration of these applications is required to coordinate the business process. Unfortunately, this is no easy task. Enterprise Integration, according to Brosey et al. (2001), "aims to connect and combines people, processes, systems, and technologies to ensure that the right people and the right processes have the right information and the right resources at the right time"[Brosey01]. Communication between different applications can be achieved …

Performance Evaluation Of Linq To Hpc And Hadoop For Big Data, Ravishankar Sivasubramaniam

UNF Graduate Theses and Dissertations

There is currently considerable enthusiasm around the MapReduce paradigm, and the distributed computing paradigm for analysis of large volumes of data. The Apache Hadoop is the most popular open source implementation of MapReduce model and LINQ to HPC is Microsoft's alternative to open source Hadoop. In this thesis, the performance of LINQ to HPC and Hadoop are compared using different benchmarks.

To this end, we identified four benchmarks (Grep, Word Count, Read and Write) that we have run on LINQ to HPC as well as on Hadoop. For each benchmark, we measured each system’s performance metrics (Execution Time, Average CPU …

Empirical Performance Analysis Of High Performance Computing Benchmarks Across Variations In Cloud Computing, Sindhu Mani

UNF Graduate Theses and Dissertations

High Performance Computing (HPC) applications are data-intensive scientific software requiring significant CPU and data storage capabilities. Researchers have examined the performance of Amazon Elastic Compute Cloud (EC2) environment across several HPC benchmarks; however, an extensive HPC benchmark study and a comparison between Amazon EC2 and Windows Azure (Microsoft’s cloud computing platform), with metrics such as memory bandwidth, Input/Output (I/O) performance, and communication computational performance, are largely absent. The purpose of this study is to perform an exhaustive HPC benchmark comparison on EC2 and Windows Azure platforms.

We implement existing benchmarks to evaluate and analyze performance of two public clouds spanning …