Digital Commons Network^™

Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta

2025 Spring Honors Capstone Projects - Archive

Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …

Light Without Memory., John Day

Electronic Theses and Dissertations

This collective work and thesis sought to remedy a fragmented sense of self in the wake of addressing trauma (Personal and generational) through therapy and diagnosis. Visualizing the experience of isolation and loss through deterioration of gelatin and pixels, it exposes the fragility of memory and our tools of preservation in the presence of time. While the images of my family, past and present, cannot speak, they can activate memory evoking a desire to foster relationships with those closest to us. As I explore my identity through family photographs, I find myself embodied within them. Fragments of a whole, bound …

Resonating Patterns: Adaptive Resonance Theory And Self-Organizing Maps, Meeti Dixit

Theses

Adaptive Resonance Theory (ART) represents a powerful neural network architecture designed to address the stability-plasticity dilemma. Its primary objective is to enable rapid learning without compromising retention. ART embodies characteristics of self-organization and self-stabilization, distinguishing it from traditional neural networks. Unlike error-based learning prevalent in conventional models, ART employs competitive learning mechanisms. One of the distinguishing features of ART is its involvement in hypothesis testing, a departure from the approach of deep learning. This capability allows ART to learn dynamically in real-time environments. Over time, various ART models have emerged that cater to different types of data, such as binary …

Hybrid Fea And Meta-Modeling For De Optimization Of A Highly Saturated Spoke Ipm, Oluwaseun A. Badewa, Marcelo Silva, Rosemary E. Alden, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces a novel approach for high performance electric motor design that combines machine learning (ML)-based meta-modeling with a differential evolution (DE) optimization algorithm. The method leverages finite element analysis (FEA) results to train the ML meta-model, enabling efficient design optimization for high-power density cored machines, such as spoke interior permanent magnet motors (IPM), which exhibit complex nonlinearities and saturation effects. This hybrid ML-DE framework seeks to provide an alternative for physics-based electric motor design and optimization, offering significant reductions in computational effort while maintaining accuracy. The meta-model’s accuracy in capturing the nonlinear relationships between design parameters, core losses, …

A Random Forest Regression Approach For Prediction Of Specific Flux For Ultrafiltration Membranes, Isabel Medeiros

Master's Theses and Capstones

Ultrafiltration (UF) membrane operation is strongly affected by fouling, where dissolved and suspended water quality constituents interact with each other and the membrane surface, leading to pore blockage. As a result, a fouled membrane loses filtration efficiency, reducing the volume of treated water produced and increasing operation and maintenance costs associated with membrane cleaning. At a Dutch full-scale facility in North Holland, fouling is indicated by both an increase in the amount of pressure required across the membrane, known as transmembrane pressure (TMP), and a decrease in permeability, represented by specific flux (SF). The facility performs hydraulic backwashing - both …

Advancing Snow Cover Observations In Glacierized And Alpine Environments Using Optical Remote Sensing, Rainey Aberle

Boise State University Theses and Dissertations

Warming air temperatures in recent years have led to declines in seasonal snow pack across western North America. These changes have profoundly impacted communities that depend on seasonal snow melt for water resources, as well as glaciers that act as critical long-term reservoirs in the region. While global snow cover extent products are available, they often fail to reliably distinguish between snow, glacier ice, and clouds, which are prevalent in western North America. Additionally, water managers in the region typically rely on point-based observations of snow mass for decision-making, despite the pressing need for comprehensive, watershed-scale estimates. To address these …

Flowermd: A Flexible Library Of Organic Workflows And Extensible Recipes For Molecular Dynamics And Machine-Learned Coarse-Grained Simulations Of Isotropic And Anisotropic Systems, Marjan Albooyeh

Boise State University Theses and Dissertations

Molecular dynamics (MD) simulations are essential tools for understanding and predicting material behavior at the atomic and molecular scale. While numerous open-source software packages exist for different stages of MD simulations, assembling a seamless, end-to-end workflow for complex multi-step simulations remains a significant challenge. In this work, we develop FlowerMD, a flexible and extensible Python-based software package that automates molecular simulation workflows, improving both reproducibility and ease of use.

FlowerMD provides modular components that simplify the end-to-end execution of MD workflows, from system initialization and force field application to simulation execution. It also automates multi-step simulation workflows through Recipes—predefined, modular …

Phoneme Recognition For Pronunciation Improvement, Matthew Heywood

Theses/Capstones/Creative Projects

This project aims to improve English pronunciation by investigating speech errors and developing a tool to provide precise feedback. The study focuses on creating a new pronunciation tool that offers localized feedback, identifies specific errors, and suggests corrective measures. By addressing the shortcomings of current methods, this research seeks to enhance pronunciation refinement.

Utilizing cutting-edge technology, the tool leverages speech-to-phoneme AI models and modified lazy string matching algorithms to compare the user's spoken input with the intended pronunciation. This allows for a detailed analysis of discrepancies, providing users actionable insights into their phonetic errors. The speech-to-phoneme AI models mark a …

Enhancing Product Image Classification: Utilizing Machine Learning Models For Retail Applications, Avery A. Thompson

Data Science Undergraduate Honors Theses

The expansion of e-commerce has continued at a blinding pace since the COVID-19 pandemic, and retailers are constantly looking for new ways to retain customers. Ensuring that diverse and well-classified images are on product pages has been a paramount method for retailers to ensure retention as they increase product engagement and sales and enhance user experience. Managing and labeling these vast catalogs of images by hand is becoming increasingly infeasible, so some online retailers have started to turn to automated classification models to assist them. Accuracy in these classification models is integral, as a good image classification model can improve …

Analyzing Unmanned Aircraft System (Uas) Incidents From Nasa Asrs Data Using Unsupervised Machine Learning, Kacey Haws

Electrical Engineering and Computer Science Undergraduate Honors Theses

The NASA Aviation Safety Reporting System (ASRS) assembles voluntarily submitted aviation safety incident reports in their database to act on the information provided. This database allows the government, companies, and citizens to submit incident or situational reports to its database to discern recurring issues in the National Aviation System (NAS) so that the proper officials can act [1]. The narratives provided in these reports are text-based, resulting in large amounts of data to process. Previous work in the University of Arkansas Aerospace Systems Engineering and Transportation Laboratory (ASYST) lab involved parsing unmanned aircraft system (UAS) incident reports manually. While these …

Criminal Activity Monitoring System: Targeting Africans And East Asian Communities In Dubai, Hamdan Kalantar

Theses

This dissertation investigates crime patterns among African and East Asian communities in Dubai, focusing on geographic hotspots, temporal trends, victim demographics, and the use of machine learning for predictive crime modeling. The research addresses the challenges faced by these communities, including labor exploitation, financial fraud, and human trafficking, within the socio-economic context of a rapidly growing urban city. Building on existing studies of urban crime, this research aimed to explore the spatial, temporal, and demographic dynamics of crimes and assess the potential of predictive models to assist law enforcement in crime prevention and resource allocation. A mixed-methods approach was employed, …

Roads Of Tomorrow: Augmented Reality For Enhanced Road Maintenance, Nasser Humaid Alshamsi

Theses

The rapid deterioration of roads creates major obstacles that affect public security, urban movement capacity, and economic durability. Standardized road maintenance operations depend heavily on human inspections that prove time-consuming while being labour-intensive and subject to human mistakes. Implementing Augmented Reality (AR) with Point Cloud Data represents a smart approach that improves delivery efficiency in road maintenance procedures for modern cities. AR enables real-time visual content and interactive displays through this research, yet Point Cloud Data delivers detailed, precise road inspections through high-definition three-dimensional mapping. These technologies are evaluated for their usability, effectiveness, and adoption barriers in research that advance …

Beyond The Badge: Predicting Military Employee Attrition In The Police Force, Alia Abdulla Alhajji

Theses

This study explores the issue of employee attrition within the military police force, with a focus on predicting which employees are at risk of leaving. Attrition in law enforcement is costly and disruptive, especially in roles that require long training periods and operational readiness. The research specifically targets the internal organizational factors influencing turnover in military police environments and aims to leverage machine learning models to support the early identification of high-risk individuals. Guided by the CRISP-DM framework, the study followed a structured process across six stages: business understanding, data exploration, preparation, modeling, evaluation, and insight generation. A simulated dataset …

Innovating Criminal Justice: Predictive Analytics For Effective Recidivism Management, Dina Ali Haidar

Theses

Recidivism—the tendency of previously convicted individuals to reoffend—poses significant challenges to criminal justice systems worldwide. In the United States, a study by the Bureau of Justice Statistics revealed that approximately 68% of released prisoners were rearrested within three years, and 83% within nine years (Bureau of Justice Statistics, 2018). These high rates underscore systemic deficiencies in rehabilitation processes and resource allocation. Traditional risk assessment tools often rely on static factors, failing to account for the dynamic and individualized nature of reoffending risks. This limitation highlights the need for innovative, data-driven methodologies to enhance offender management strategies. This study proposes a …

Predicting And Forecasting University Rankings In The Uae Using Machine Learning, Fatma Ibrahim Ahmad

Theses

Higher Education Institutions (HEIs) play a vital role in advancing knowledge economies, and institutional rankings are increasingly used as global benchmarks of academic performance and reputation. In the context of the United Arab Emirates (UAE), enhancing institutional competitiveness in global rankings aligns with national strategies such as UAE Vision 2030 and the Centennial Plan 2071. This research applies machine learning (ML) techniques to develop predictive models that forecast institutional ranking outcomes, enabling data-driven planning and continuous academic improvement. The study utilizes a dataset compiled from the QS World University Rankings (2020–2024), cross- referenced against the institutional listings maintained by the …

Advancing Multi-Agent Robotics Simulations Through Heterogeneous Reinforcement Learning In Isaaclab, Jacob R. Haight

All Graduate Theses and Dissertations, Fall 2023 to Present

Robots increasingly operate in collaborative teams across domains such as search-and- rescue, warehouse automation, and autonomous driving—scenarios that demand advanced coordination strategies enabled by multi-agent reinforcement learning (MARL). However, existing simulation frameworks often struggle to balance realism, speed, and scalability, especially when supporting diverse, heterogeneous robot teams. This research extends Isaac Lab, a high-performance robotics simulator, by integrating heterogeneous-agent reinforcement learning (HARL) capabilities. The result is a flexible and GPU-accelerated platform for training both homogeneous and heterogeneous robot teams in complex, physics-based environments. These enhancements significantly narrow the gap between simulation and real-world deployment for multi-robot systems.

Modeling Language And Vision At Human Scales, Clayton Fields

Boise State University Theses and Dissertations

The impressive results that have recently been achieved in natural language processing and artificial intelligence have been primarily driven by the introduction of the transformer deep learning architecture, increasingly large models with many parameters and using enormous datasets. The size of models and their training data requirements present costly demands that freeze many researchers out of training with cutting edge models. Beyond these practical implications, current methods learn from text alone, without the rich array of sensory information that human beings use in learning language. This means that language models are often incapable of reasoning about the concrete world that …

Leveraging Machine Learning And Deep Learning Techniques For Voter Registration Fraud Detection, Nahid Anwar

Boise State University Theses and Dissertations

The primary objective of this research is to develop an advanced framework for detecting voter registration anomalies, with a specific focus on fraud detection, using the Idaho Voter Registration Election Dataset. The data set contains both anonymized real voter data and synthetically generated fraudulent instances, allowing for a comprehensive examination of potential vulnerabilities in voter registration systems. The real data was obtained from the Idaho Secretary of State's office. The initial part of the research involved data analysis and identification of misinformation and potential disinformation using statistical analysis and approximate string matching algorithms. Subsequently, we have created the aforementioned anonymized …

Advancing Snow Water Equivalent Monitoring With Machine Learning And L-Band Interferometric Synthetic Aperture Radar (Insar) Data, Ibrahim Olalekan Alabi

Boise State University Theses and Dissertations

Seasonal snow is a critical freshwater resource for an estimated 2 billion people worldwide. Yet, accurately measuring the amount of water sitting in a snowpack, referred to as snow water equivalent (SWE), over large, often mountainous regions has posed a long-standing challenge. Ground-based measurements of SWE are precise but sparse, while remote sensing techniques like passive microwave sensors struggle with coarse resolution and signal saturation in deep snow. Due to the challenges of direct SWE measurement, snow depth has emerged as an alternative pathway to SWE estimation. SWE is strongly correlated with snow depth, and by leveraging this relationship, we …

Implementing And Evaluating An Ai-Powered Visual Decision Support System To Improve Antibiotic Usage Among Physicians With A Built-In Early Warning System, Akua Sekyiwaa Osei-Nkwantabisa

Theses and Dissertations

The widespread misuse and excessive prescription of antibiotics have played a pivotal role in the emergence and proliferation of antibiotic-resistant bacteria, posing a critical global public health crisis. Addressing this challenge necessitates innovative solutions that enhance antimicrobial stewardship. This study presents the development and implementation of a visual decision support system designed to monitor and optimize antibiotic usage among healthcare providers. The proposed system integrates advanced machine learning algorithms with real-time data analytics to provide a dynamic, evidence-based decision support tool. Specifically, a neural network model was developed after evaluating multiple machine learning approaches, including Random Forest, Logistic Regression and …

Beyond Regex – Heuristic-Based Secret Detection, Jesse Burdick-Pless

Theses

Accidental token or credential leakage presents a significant concern within digital environments. Current detection methods of such secrets employ ruleset-based techniques to identify secret information. These methods use pattern recognition within strings (i.e., regex rules) to pinpoint characteristics that resemble various types of secrets. However, regex does not allow for detection of secrets that lack specific patterns, such as passwords. This research addresses the possibility of using heuristics and machine learning to develop a reliable and accurate method for determining if a given string is merely a piece of inconsequential data or a leaked secret requiring timely attention, without the …

Towards Advancing Streamflow And Peak Flow Prediction With Machine Learning: Identifying Infrastructure At Risk, Sudan Pokharel

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Due to climate change and its impact, the need for adaptive strategies for natural disaster mitigation and resource management has never been more urgent. Central to this is water resource management, which is essential for sustainable human activities, ecological balance, and the mitigation of natural hazards like floods. Streamflow is a crucial element of water resource management and plays a vital role in planning and building water infrastructure, implementing emergency response plans, supporting flood mitigation initiatives, and regulating agricultural and industrial use. However, accurate prediction of streamflow still remains a challenge due to the complex non-linear and non-stationary interaction between …

A Machine Learning Framework For Packet Anomaly Detection In Smartgrid Substation Networks, Sowmya Bandari

School of Computing: Dissertations, Theses, and Student Research

The increasing reliance on Smart Grid Substation Networks for efficient electricity distribution has amplified cybersecurity vulnerabilities, particularly within Supervisory Control and Data Acquisition (SCADA) systems. The IEC 60870-5-104 (IEC-104) protocol, widely adopted for communication between Remote Terminal Units (RTUs) and Human-Machine Interfaces (HMIs), lacks inherent encryption and authentication mechanisms, rendering it susceptible to sophisticated cyberattacks. Threats such as False Data Injection Attacks (FDIAs), command injection, covert attacks and replay attacks pose significant risks by manipulating grid control signals, potentially leading to undetected operational disruptions, cascading failures, or system-wide instability. Conventional signature-based Intrusion Detection Systems (IDS) often fail to identify zero-day …

Computation-Driven Design And Synthesis Of Molecular Sensors For Surface-Enhanced Raman Spectroscopy In Liquids, Junhu Zhou

Dartmouth College Ph.D Dissertations

The reliable detection of molecules in liquid environments is a continuing challenge in chemical sensing and biological diagnostics. While surface-enhanced Raman spectroscopy (SERS) is well-recognized for its high sensitivity and molecular specificity, conventional platforms – based on colloidal nanoparticles or patterned substrates – have shortcomings. These conventional configurations struggle with signal reproducibility, non-uniform hotspot distribution, and, critically, restricted accessibility for large targets such as proteins and exosomes.

This thesis develops a computation-guided approach to developing next-generation SERS sensors capable of overcoming these persistent obstacles. At the heart of the study is the design and fabrication of metal-insulator-metal (MIM) nanoparticles, where …

Using Machine Learning To Detect Vault (Anti-Forensic) Apps, Michael N. Johnstone, Wencheng Yang, Mohiuddin Ahmed

Research outputs 2022 to 2026

Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static or dynamic, prove inadequate when devices—typically smartphones—cannot be modified. Additionally, these methods frequently require prior knowledge of which apps are classified as vault apps. This research decisively demonstrates that a non-invasive method of app analysis, combined with machine learning, can effectively identify vault apps. Our findings reveal that it is entirely possible to detect an Android vault …

Developing End-To-End Imitation Learning For Asteroid Proximity Operations, Patrick David Quinn

Theses and Dissertations

Asteroid exploration remains a popular topic in the scientific community, however hurdles still exist for controlling spacecraft within the asteroid environment. Communication delays often require the usage of limited onboard computing hardware for navigation. Additionally, long mission timelines must be accommodated with highly efficient fuel use. Considering these issues, it is apparent that any guidance, navigation, and control (GNC) system in these spacecraft should emphasize both computational and fuel efficiency in its design. Furthermore, the integration of a robust state estimation system is necessary for the successful deployment of such systems. The development of a controller aiming to address these …

Comparing Regression Methods To Interpretable Machine Learning In The Prediction Of Infrequent Events In The Social And Psychological Sciences, Brian Lieberman

Theses, Dissertations and Culminating Projects

The following study seeks to answer the question of whether traditional regression models and more complex machine learning models can predict rare and infrequent events in the social and psychological sciences. Part of the study sought to compare the performance of regression models to more complex models, in an effort to determine whether the use of more complex models (which are harder to interpret and configure) is even necessary. This study explored this question via two studies. The first, being a study on workplace misconduct, in which 363 participants in the United States were surveyed as to their workplace experiences …

Using Deep Learning And Two-Photon Excitation Fluorescence Microscopy To Predict Breast Cancer Recurrence And Response To Chemotherapy, Nicholas Powell

Graduate Theses and Dissertations

Accurate prediction of breast cancer recurrence plays a critical role in guiding treatment decisions, particularly regarding the use of systemic therapy for patients. While genomic assays such as the Oncotype DX Recurrence Score offer valuable prognostic information, they are limited in accessibility and application. The work presented in this thesis explores an imaging-based approach for the prediction of breast cancer recurrence that combines multiphoton microscopy (MPM) with deep learning to classify individual breast cancer biopsy samples by risk of recurrence. Genomic differences in tumors with different recurrence potentials may translate to distinct optical and structural information that can be captured …

Creating Talking Points For Client Advisers At Banks To Promote Sustainable Investing, Wewe Zi Yi, Pradeep Varakantham, Alan Megargel

Research Collection School Of Computing and Information Systems

Environmental, social and governance (ESG) factors have become key nonfinancial factors for investors to evaluate companies with respect to understanding material risks and growth opportunities. While not mandatory, companies are providing ESG reports that outline progress in different ESG metrics (six broad metrics and 15 specific ones). Client advisers (CAs) read these reports to identify key metrics of interest to investors. Given the number of companies and investment products, however, it is not feasible for CAs to read all the reports, which can sometimes run into tens or hundreds of pages). The authors have developed multiple frameworks building on leading …

Deep Learning Algorithms For Traffic Flow Predictions, Adegoke Ojeniyi, Prince Pal Singh, Ankita Vashisht, Swati Kumari, Karan Karan

AUIQ Technical Engineering Science

Given the growing complexity of urban transportation systems, precise traffic flow forecasting is essential for reducing not only issues of congestion but also, for boosting road safety and enhancing mobility management. This study integrates Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) to present a hybrid deep learning framework for traffic prediction. Of these, the CNN-LSTM model is a reliable option for real-time traffic forecasting since it successfully captures both spatial and temporal dependencies, resulting in superior predictive performance. The dataset used to assess the framework includes 48,120 records from a traffic monitoring …