Digital Commons Network^™

Resilient Power System Load Frequency Control, Ghadeer Ahmed Almahri

Theses

Standard frequency and voltage levels need to be maintained when the electric power system is running in order to keep it in a satisfactory state of operation. Between AC power generation and use, there should be a balance between active and reactive power. However, due to variations in active and reactive power requirements, the frequency and voltage provided may fluctuate from their rated values. Any unwelcome change in frequency or voltage can have an immediate effect on how the power system operates and may even cause harm to linked devices. These parts are intended to operate at certain rated frequencies …

Domain-Specific Customization For Improving Speech To Text, Zubin Mario Tobias

Theses

The advent of transformer-based models has revolutionized natural language processing, bringing remarkable improvements in tasks like automatic speech recognition (ASR). Inspired by these advancements, this thesis explores the optimization of a transformer-based ASR model to improve transcription accuracy in educational settings, particularly for lecture content. The goal of this research is to provide real-time, high-accuracy captions that enhance accessibility for all students, while offering a cost-effective solution for educators. To assess the potential of domain-specific fine-tuning, Whisper-small underwent two phases of fine-tuning. In the first phase, it was finetuned on care- fully selected, publicly available datasets: SpeechColab’s Gigaspeech-XS [39], AMI …

Advancing Prediction Of Stimulant Medication Misuse Through Graph Representation Learning, Hamid Razavi

Theses

The misuse of stimulant prescription medications poses a significant and escalating public health concern in the United States, particularly among young adults. Addressing this issue requires sophisticated methodologies capable of uncovering complex patterns and relationships in data. Geometric Deep Learning, a paradigm designed to analyze data with non-Euclidean structures, has achieved remarkable success across various domains, offering a powerful framework for tackling complex graph structure data challenges.

This study leverages Graph Convolutional Networks (GCNs) to predict the likelihood of stimulant medication misuse using data from the National Survey on Drug Use and Health (NSDUH). Individuals are represented as nodes in …

Surveying The Role Of Visual Analytics In Human-Machine Teaming, Naga Datha Saikiran Battula

Theses

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Utilitarian And Self-Representational: Player-To-Player Character Relationships In Final Fantasy Xiv, Zu Er Guan

Theses

This thesis studies the relationships between video game players and their player characters, the characters that they control, through the lens of player purpose and intent by conducting research using an online survey developed and analyzed using an interdisciplinary methodology based on existing studies in the field regarding avatar identification, player psychology, and queer and feminist theory. This thesis posits that are two types of player relationships with their player characters based on player intent—player characters as the personal avatar and representation of the self, and player characters as a tool for the execution of player control and gameplay experience. …

Modeling Stormwater Driven Plastic Debris In The Lake Ontario Watershed, Jayson A. Kucharek

Theses

Stormwater-driven plastic debris has emerged as a significant contributor to environmental pollution, particularly in urbanized watersheds like the Lake Ontario basin. This study focuses on developing a comprehensive framework to quantify and predict anthropogenic debris (AD) inputs and transport pathways across Monroe County, NY, using stormwater systems as a key vector. Over two years, empirical data on plastic debris were collected from storm drains retrofitted with LittaTrap™ devices across urban and suburban areas. Debris was weighed, categorized by material and use, and paired with spatial and temporal variables such as land use, rainfall, temperature, and wind events. A Support Vector …

Leveraging Machine Learning For Advanced Malware Detection, Khalifa Al Habtoor

Theses

The detection of malware is an important challenge in the field of cybersecurity, which is demanding efficient and accurate solutions for making threats easier. This study is focused on the detection of malware by integrating a framework that uses a Mamdani fuzzy interference system (FIS) and diffusion techniques based on three different deep learning algorithms, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) for malware detection by using recent dataset. This was done by using their outputs through a voting and routing mechanism. The results of the study show that the fuzzy logic …

Social Media Intelligence: Responding To Crime And Violence In The Digital World, Chloe Lynn Sitton

Theses

The integration of social media into everyday life has transformed how crime is committed, communicated, and investigated. This capstone project examines the emerging field of social media intelligence and its growing influence on modern policing, surveillance, and the community. Drawing from sociological theory, law enforcement practice, legal analysis, and community-based research, this project offers a far-reaching interdisciplinary view of how social media is shaping crime and justice in the United States.

Machine-Learning And Game-Theoretic Inspired Strategies For Adversarial Freeze-Tag, Matthew Giacovelli

Theses

In this thesis, we explore three machine-learning and game-theoretic inspired strategies to address the Freeze-Tag Problem with adversarial agents (FTPA). We develop a custom Gymnasium envi- ronment where unfrozen robots collaborate to “tag” and unfreeze immobile frozen robots while avoiding being tagged by the adversaries. The FTPA models scenarios such as restoring disabled relay towers after a cyberattack where different towers vary in priority, repair difficulty, and ad- versarial threats. We evaluate three strategies: a baseline heuristic where unfrozen robots target the nearest untargeted frozen robot, a hierarchical approach that incorporates adversary interac- tions and likelihood of success into decision-making, …

Predicting Students At Risk Of Dropping Out In The College Of Medicine At Mohammed Bin Rashid University Of Medicine And Health Sciences. A Machine Learning Approach To Understand Attrition Factors Over The Past Seven Years, Moza Sulaiman Al-Zaabi

Theses

This master’s thesis investigates student attrition within the College of Medicine at Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) by applying the CRISP-DM methodology to analyse a dataset of 19 variables and around 2,000 records from Student Admission and Academic Performance sources. Although the dataset was relatively small, several machine learning models were developed to predict students at risk of dropping out, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Of these models, the ANN model, particularly when combined with Principal Component Analysis (PCA), achieved the highest performance with an accuracy …

Applications Of Neural Networks In Parkinson’S Disease Diagnosis, Saladin Minhaaj

Theses

Parkinson's disease (PD) is a complex and debilitating neurodegenerative disorder that affects millions of people worldwide. Early and accurate diagnosis is crucial for effective treatment and management of PD. This thesis explores the application of neural networks in PD diagnosis, leveraging their ability to learn patterns from large datasets and make accurate predictions.

Thesis provides an overview of PD, including its symptoms, diagnosis, and current challenges in diagnosis. We then delve into the fundamentals of neural networks, including supervised learning, mathematical interpretations, and parametric models. This research focuses on the development of neural network models that can accurately diagnose PD …

Integrative Hyperspectral Approaches For Advanced Soil Property Analysis And Environmental Monitoring, Nayma Binte Nur

Theses

Hyperspectral imaging has emerged as a powerful tool for enhancing the analysis of diverse soil properties and environmental monitoring. This study refines the precision of soil biogeophysical analyses by improving the estimation of soil moisture content (SMC), soil organic matter (SOM), total carbon (C), and nitrogen (N) through hyperspectral remote sensing techniques. The research examines the capabilities of two prominent moisture retrieval models: the multilayer radiative transfer model (MARMIT) and the modified soil water parametric (SWAP)-Hapke model. These models are evaluated using hyperspectral imagery derived from unmanned aerial systems (UAS) and goniometric data collected across various environmental settings. The findings …

Near Real-Time Center Of Gravity Estimation Of An Unmanned Aerial Vehicle, Carola Vasquez

Theses

As UAV technology advances and finds broader applications across industries, real-time estimation of the center of gravity (CG) becomes crucial for ensuring safe and efficient flight. Accurate CG estimation is vital not only for UAVs but also for manned aircraft, as it significantly influences stability and control by indicating the point where the entire weight is evenly distributed. Variations in CG, resulting from changes in altitude, attitude, speed, fuel consumption, and payload, directly impact stability and may lead to compromised performance and increased risk of accidents if not monitored. Existing methods focus on regression analysis, multi-accelerometer measurements, and pitching moment …

In Vitro Investigation Of The Potential Therapeutic Implications Of Safranal On Gastric Cancer, Aisha Juma Al Otaibi

Theses

Despite the fact that gastric cancer patients are indispensable to conventional radiation and chemotherapy, the detrimental side effects of those approaches prompt cancer research to seek new integrative treatments. In this respect, safranal (SAF) stands out as a complementary phytotherapeutic drug with exceptional anticancer properties. Concurring studies have shown that SAF is negatively associated with several cancer types, yet its potential therapeutic impact on gastric cancer remains vastly obscure. Thus, this dissertation sought to investigate SAF's prospective tumoricidal ability and its underlying mechanisms of action on gastric cancer in vitro. Herein, the study evaluated the cytotoxicity of SAF on …

Second- And Third-Party Punishment In Common-Pool Resource Management, Jacquelyn Spence

Theses

Common-pool resources play a significant role in many economies, especially in developing countries where they are a critical source of food, income, and employment for communities. Long term management of common-pool resources can be challenging because the continued existence of the resource depends upon balancing the rate of resource depletion with the rate of repletion. Effective management of these resources is vital for protecting the environment and preserving the resources for future generations as overuse and degradation can have serious impacts on ecosystems and biodiversity. Elinor Ostrom’s design principles for common-pool resource (CPR) management provide valuable insight into human interaction …

Integration Of Blockchain Technology In Industrial Applications, Sanjana Sakinala

Theses

This research explores the integration of Hyperledger Fabric., a framework of blockchain in industrial applications, particularly focusing on supply chain management. By addressing the limitations of traditional blockchains - such as high transaction latency, scalability challenges, and privacy concerns - this research demonstrates how Hyperledger fabrics overcomes them. Through simulation, we show significant enhancements in transaction speed, data privacy and scalability, making the Fabric a viable solution for industrial systems.

Investigating The Effect Of Metal Site Of The Leaf And Branch Compost Cutinase Through Molecular Dynamics Simulations, Ryan James Molitor

Theses

The petroleum-based polyethylene terephthalate (PET) is a versatile and synthetic polymer that plays a pivotal role in the economy, including packaging, textiles, 3D printers, and even medical-related applications. There is a need for plastic waste management. Biological up-cycling of PET is a promising method for a clean and sustainable future. In 2012, a group of researchers, Tournier et. al., performed experiments and molecular dynamics simulations on the Leaf-branch Compost Cutinase, LCC (PDB Code: 4EB0), which outperformed the PET hydrolysis activity compared to three other PET hydrolyzing enzymes. The LCC and PET depolymerization rate of wildtype LCC was reported to be …

Parametric Analysis Of Wire Arc Additively Manufactured Low-Carbon Steel Using Response Surface Methodology, Mirzagha Halilov

Theses

As the manufacturing sector continues on its path towards sustainability, additive manufacturing (AM) remains as one of its most promising streams, aiming to reduce material waste while also allowing for increased design complexity. In the extensive area of metal 3D printing, one method that is steadily gaining traction is wire-arc additive manufacturing (WAAM). This method of AM utilizes the energy created by an electric arc formed between a continuous consumable metal wire and a metal base, fusing the two together and ultimately creating a 3D component layer by layer. WAAM has several notable advantages over other metal 3D printing methods …

Modeling Impact Of Simulated Myocardial Perfusion On Cardiac Ablation For Arrhythmia, Brendan Cappon

Theses

Bioheat transfer is the study of heat transfer applied in anatomy and physiology, which is relevant to maintaining homeostatic conditions from metabolic activity, thermal exposures, and various treatments and diagnostic methods. Blood flow significantly impacts heat transfer throughout the body, facilitating the diffusion of heat. Models have been developed to quantify bioheat transfer and the effect of blood flow through tissue for many biological functions and medical procedures, one of which is radiofrequency ablation for cardiac arrhythmia. Significant improvements to models and assumptions are still required as success rates for this procedure remain low for various arrhythmia types. The research …

Application Programming Interface For Embedded Protocol Management System, Mathew David Barrera

Theses

This paper defines an architecture and software structure which implements a polymorphic system to manage hardware devices. The architecture implements a system to manage integration of hardware devices by providing the constructs required to implement communication and data transport protocols with a method that manages system state independence of each hardware component integrated. The architecture components provide a system which maintains the independence of each device by defining abstractions between each component of the system providing the software constructs required to integrate hardware components which implement differing connections methods and communication protocols. The software architecture provides the means of Integration …

An Automated Approach To Detect Cyber Threats With Smart Contracts In Blockchain Systems., Alia Mohammad Ahli

Theses

Blockchain is becoming one of the main components of the modern world due to its rapid development. Considering its main characteristics as a collaborative distributed database that provides transparency and scalability, blockchain development and application have grown dramatically over the past decade. While the blockchain is a key feature and is used in the industry, it is subject to many risk factors and attacks. Advanced technologies that can successfully detect and mitigate vulnerabilities must be developed for solving these security challenges. This thesis presents the development of a machine learning based approach to discover vulnerabilities in blockchain-based systems. It begins …

Small Angle X-Ray Scattering And Form Factor Modeling Of Β Crystallins - A Physiologically Significant Eye Lens Cytoplasm Protein, Srijan Banerjee

Theses

This thesis provides in depth analysis of small angle x-ray scattering (SAXS) experiments conducted on the beta-high crystallin eye lens cytoplasm protein. In particular, radius of gyration and molecular weight measurements are extracted and compared to measurements from existing work. Furthermore, an approach to math ematically modeling the scattering ”form-factor”, which describes the size and shape of individual particles, is also developed and applied to the SAXS data. The model acheived in the thesis appears to agree closely with scattering data at the low q range. Several obstacles which impede the resulting modeling from perfectly characterizing the data at higher …

Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy

Theses

Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute …

Predicting Air Passenger Traffic At Busy International Airports, Ratana Fraihat

Theses

This thesis presents the development and validation of a machine learning model using Long Short-Term Memory (LSTM) networks to forecast air traffic passenger volumes at international, high-traffic airports. By leveraging historical passenger data, the study seeks to provide accurate predictions that can enhance operational planning and resource allocation at the airport. The implementation was done in Python, using historical passenger data from San Francisco International Airport (SFO) from 2005-2018. Data preprocessing steps involved grouping and summing passenger counts for each activity period. The data was normalized to a range of 0 to 1, which is crucial for preventing instability and …

Resolution Enhancement For High Numerical Aperture Extreme Ultraviolet Lithography Using Aperiodic Multilayer Masks, W Ethan Maguire

Theses

As extreme ultraviolet lithography tools with higher image numerical apertures are introduced, the range of angles at the mask multilayer are increased. This represents a challenge for maintaining through-angle mask reflection using the current periodic mask multilayer structures. In addition to reflectance amplitude, the phase of reflected light, which plays an important role in imaging, is also strongly influenced by increasing angles. The propagation of light through each bilayer in the stack imparts a phase shift based on incidence angle. This accumulates over many layers, inducing phase effects which are unique to each illumination point. While adjustments to multilayer period …

Ai-Powered Pedagogy: Revolutionizing Students’ Learning Experiences Through Integration Of Ai Technologies, Amna Awad Alsaedi

Theses

This research examines the integration of Artificial Intelligence (AI) within the educational sector with the aim of enhancing student learning outcomes. AI offers tailored learning experiences, interactive educational content, prompt feedback, and access to diverse learning resources. Nonetheless, challenges including addiction, costliness, privacy infringement, bias, ethical dilemmas, market competition, and moral considerations require resolution. Research particularly delves into the utilization of chatbots, and algorithms designed to simulate human interactions and generate text resembling human speech. Educational applications powered by AI have the potential to heighten student engagement, comprehension, and academic performance by substituting traditional assignments with concise, information-rich lessons. Furthermore, …

Exclusion Tests For Approximating All Solutions Of Nonlinear Equations, Yasmeen Omar Hamida

Theses

The exclusion test is a mathematical technique used to isolate and approximate the solutions of nonlinear equations within a specified interval. This thesis aims to construct a new test leveraging the principles of the exclusion test to achieve faster convergence to the zeros of any given interval within a compact domain. By selecting an alternative objective function and employing nonlinear equations, the proposed method significantly reduces the number of subintervals required for accurate approximation. The key finding of this research is the development of a more efficient algorithm that enhances the speed of convergence. The implications of these findings are …

Analysis, Design, And Experimental Validation Of The Thermal Subsystem Of Alainsat-1 Cubesat, Ameereh Seyedzadeh

Theses

This thesis investigates the thermal management techniques of AlAinSat-1 during extremely harsh space conditions. It presents a comprehensive analysis of its thermal behavior using numerical techniques and validates the CubeSat's survivability in space via experimental testing. AlAinSat-1 is a nanocube-shaped satellite carrying Earth observation payloads, including RITA (Remote sensing and Interference detector with radiome-Try and vegetation Analysis) and Locana. Understanding how the CubeSat behaves in harsh space conditions and maintains thermal stability during its mission is critical to its success. The main aim of this study is to employ finite element analysis (FEA) techniques to simulate the thermal behavior of …

Design And Implementation Of Attitude Control System For Gnssas 6u Cubesat, Anoud Nasser Alkatheeri

Theses

This thesis presents the design and simulation of the Attitude Control System (ACS) for the GNSSaS 6U CubeSat, developed by the National Space Science and Technology Center (NSSTC) at UAE University. The CubeSat's mission is to enhance GNSS signal accuracy, which requires precise attitude control to achieve this objective. The ACS utilizes magnetorquers and reaction wheels for actuation. The primary objective of this thesis is to design an ACS that fulfills the mission’s performance requirements, including maintaining pointing accuracy in both fine and medium pointing modes. To achieve this, a comprehensive disturbance analysis was conducted, leading to the selection of …