Digital Commons Network^™

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

Master's Theses

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

Patent Analyses And Technology Forecasting: Advances Into Artificial Intelligence Technologies, Sarah Over, Emma Jiren Wang, M. Shehryar Khan

Journal of the Patent and Trademark Resource Center Association

For several decades, patents have been utilized in analyzing technology and forecasting future trends. Historically, this has been done via keywords, citations, and some bibliometric approaches. The efficiency that is promised by artificial intelligence (AI), machine learning, natural language processing, and related technologies has also heavily entered the domain of intellectual property. There is a growing body of research that is using AI technologies to analyze, learn, and make predictions for patents. Some of the most common applications in practice are those that assist searchers and examiners in review of patents. Journals such as World Patent Information have even developed …

Chemical Structure Representation Standardization Is Needed To Generalize Metabolite-Pathway Involvement Prediction Across Kegg, Reactome, And Metacyc Knowledgebases, Erik D. Huckvale, Hunter N.B. Moseley

Markey Cancer Center Faculty Publications

Background/Objectives: Due to the utility of knowing the pathway involvement of metabolites detected in biological experiments, knowledgebases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and MetaCyc have annotated compound entries to specific pathways defined by the knowledgebase. However, these compound-pathway annotations are largely incomplete and are costly to obtain experimentally or curate from published scientific literature. This metabolite-pathway annotation incompleteness problem is amenable to machine learning (ML)-based solutions. But to date, no machine learning model has been trained on all three knowledgebases to maximize its performance and robustness. This may be due to inconsistencies in chemical …

Crab: A Novel Clustering Score Using Clustering With Rivals And Buddies For Unsupervised Learning, Allen Choi

Master's Theses

Unsupervised clustering algorithms today are used across a wide variety of fields such as biology, engineering, and industry in order to classify observations into groups where labels are not provided. This can provide important latent information regarding the observations within groups, as well as insight regarding the groups themselves. In order to judge the optimal number of clusters for an unsupervised clustering algorithm, many methods exist such as the Elbow Method and Silhouette Score; however, these methods come with drawbacks and are not necessarily flexible across many unsupervised methods. We present a novel clustering score framework relying on a resampling-based …

Inductive Biases In Field-Level Cosmological Inference From Galaxy Catalogs, James O'Connor Baldwin

Dissertations, Theses, and Capstone Projects

We perform field-level likelihood-free inference of the matter density parameter Ωm from simulated galaxy catalogs using machine learning models with differing inductive biases. Using features extracted from hydrodynamic simulations in the CAMELS suite, we investigate how both observable choice and model architecture govern the extraction of cosmological information. We consider galaxy positions and line-of-sight peculiar velocities, both separately and in combination, and compare permutation-invariant Deep Sets, implemented with either standard multilayer perceptrons (MLPs) or Kolmogorov–Arnold Networks (KANs), to graph neural networks (GNNs) implemented with MLPs, which explicitly encode spatial relations. We evaluate inference performance under both in-distribution and out-of-distribution (OOD) …

A Hybrid Approach To Phishing Email Detection: Leveraging Machine Learning And Large Language Models, Hessa Shamal Biri

Theses

Phishing attacks have reached a new level of sophistication through the deployment of large language models by attackers. The current AI-generated threats defeat existing detection systems which base their operation on past data. The thesis presents a hybrid system for phishing email detection which combines real email data with synthetic LLM-created samples to enhance traditional machine learning classifiers performance. The study created a hybrid dataset of 20,627 emails by combining 18,631 real messages from the Kaggle Email Classification Dataset with 1,996 synthetic emails. The synthetic emails were generated using four large language models LLaMA-3, Falcon, LLaVA, and Mistral to capture …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

Principles Of Privacy And Security In Artificial Intelligence And Applications, Khang Tran

Dissertations

Modern artificial intelligence (AI) systems have transformed critical domains such as healthcare, software engineering, finance, and the legal system. Despite their broad impact, concerns about trustworthiness, especially regarding privacy and security, remain major obstacles to wider adoption. Addressing these concerns requires both a systematic understanding of the privacy and security risks inherent in AI systems and the development of principled foundations for trustworthy AI that safeguard client privacy and security. This goal is particularly challenging because of the complexity of modern large-scale AI systems, the trade-offs between privacy and model utility, and the need to simultaneously ensure other important properties …

Differential-Geometric Methods For Neural Signed Distance Fields: Parameterized Surface Extraction And Curvature Regularization For Cad Models, Haotian Yin

Dissertations

Neural signed distance fields have emerged as a powerful framework for representing three-dimensional geometry through continuous and differentiable neural functions. Their flexibility, resolution independence, and compatibility with gradient-based optimization make them especially attractive for surface reconstruction and geometric learning. However, despite these advantages, two fundamental challenges remain for engineering-grade applications. First, higher-order geometric properties such as curvature are difficult to model reliably during training and often require computationally expensive second-order differentiation. Second, while neural signed distance fields provide implicit surface representations, they do not directly yield a globally consistent forward map or parameterization for downstream geometric processing.

This dissertation addresses …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Recent Progress In Aircraft Structural Health Monitoring: Sensors, Data And Intelligence, Abeer Kadhum Abd, Qasim Abbas Atiyah, Imad Abdulhussein Abdulsahib

Engineering and Technology Journal

Structural Health Monitoring (SHM) systems are essential technologies that contribute to enhancing the safety, reliability, and efficiency of aircraft structures throughout their operational lifespan. With the increasing use of lightweight materials and complex structural designs, coupled with longer maintenance intervals, traditional scheduled inspection methods have become insufficient, leading to a shift towards continuous, sensor-based monitoring systems. This research aims to provide a systematic and comprehensive review of the latest developments in aircraft structural health monitoring, focusing on sensing technologies, data collection methods, and intelligent processing techniques based on artificial intelligence. This study is based on a systematic analysis of the …

Classification Of Phishing Data Using Hybrid Mi-An Feature Selection Method, Damodar Patel, Amit Kumar Saxena, Wutiphol Sintunavarat, Abhishek Dubey

Baghdad Science Journal

Web phishing attacks have been continually evolving over the past few years, which has led customers to lose their trust in online services and e-commerce. To identify phishing data, a variety of methods and systems based on a blacklist of phishing websites are used. However, the rapid development of technology has given rise to increasingly complex techniques for creating user-attracting websites. Therefore, current blacklist-based techniques are unable to identify the recently launched phishing data, such as zero-day phishing websites. Machine learning techniques have been used in several recent studies to detect phishing data and function as an early warning system …

Study Of Compressive Strength Of Palm Kernel Shell Concrete Under Various Curing Methods And Predictive Modeling Using Machine Learning, Hyginus Obinna Ozioko, Emmanuel Ebube Eze, Edward Ingio Adah

Engineering and Technology Journal

This study evaluates the compressive strength of Palm Kernel Shell Concrete (PKSC) under different curing methods and develops machine learning models for strength prediction. Concrete mixes containing 0–100% PKS replacement of coarse aggregate were prepared at a constant water-cement ratio of 0.5 and cured by immersion, sprinkling, wet hessian, and open-air methods. Compressive strength was measured at 7, 14, 21, and 28 days. Increasing PKS content reduced slump from 82 mm to 18 mm, oven-dry density from 2390 kg/m³ to 1430 kg/m³, and initial setting time from 108 min to 76 min. Immersion curing produced the highest …

Cloud-Based Ai Framework For Ev Charging Forecasting And Infrastructure Optimization, Jerry Gao, Neeraja Abhinav Buch, Thuan Chau, Yumeng Sheng, Rong Wang, Siri Kadalbal

Faculty Research, Scholarly, and Creative Activity

The growing use of electric vehicles (EVs) has created a strong need for smart, data-driven charging management systems that can support large-scale and sustainable infrastructure. This study introduces a modular cloud-based framework that combines artificial intelligence and machine learning to provide predictive insights for energy demand and station expansion. The system mainly consists of two complementary models. The first is an AutoRegressive Integrated Moving Average (ARIMA) model that forecasts charging energy demand using transactional data from Palo Alto. The second is a Light Gradient Boosting Machine (LightGBM) model that predicts optimal charging-station locations using spatial data from the U.S. Department …

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

Data Driven Estimation Of Pore Size Using 1d Light Emissions For Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jorge Barron, Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

The quality assurance of the Laser Powder Bed Fusion Process (LPBF) has been extensively investigated over the last decade for in-situ monitoring of metal additive manufacturing. The process inherently generates voids within the bulk of the part, which can detrimentally affect the quality of the printed part. The characterization of these voids by estimating their size and identifying their geometrical features remains a challenge. This study introduces a Machine Learning (ML) based framework for estimating void sizes of varying geometries using layer-wise one-dimensional (1D) average light intensity signal obtained from the optical tomography system during the 3D printing of metallic …

Predictive Models Of Student Mental Health: The Correlation Between Academic Burnout And Depression In The Higher Education Environment, Hyejoon Song

Masters Theses

Importance: Over 80% of U.S. college students report significant emotional difficulties, and 58% endorse academic burnout as a persistent concern, yet institution-specific data at faith-integrated universities such as Liberty University remain scarce, limiting evidence-based programmatic response.

Objective: To examine the correlation between academic burnout and depressive symptomatology at Liberty University, identify the most predictive individual burnout item via Random Forest machine learning, and characterize distinct student risk profiles via KMeans clustering. Design, Setting, Participants: Cross-sectional survey (IRB-FY24-25-1491, Exempt) of 236 Liberty University students (mean age 36.9 ± 14.3 years; 84.0% female; 61.0% graduate-level), 2025–2026 academic year.

Main Outcome and Measures: …

Applicability Of Machine Learning For Shear-Wave Velocity Prediction From Conventional Well Logs: The Lsboost Approach, Rahmat Catur Wibowo, Fadsyah Muhammad Arby, Bagus Sapto Mulyatno, Ordas Dewanto, Isti Nur Kumalasari, Muh Sarkowi

Turkish Journal of Earth Sciences

Shear-wave velocity (Vs) is one of the most critical parameters for determining geomechanical properties and basin overpressure. However, assessing Vs via techniques like core analysis requires considerable effort and expense. This study predicts Vs using several approaches and compares the accuracy levels of all models. For this objective, the multiple linear regression, multiple linear stepwise regression, support vector machine, and least-squares boost (LSBoost) methodologies were selected. The six well-logging data inputs of density (RHOB), gamma-ray (GR), deep resistivity (ILD), acoustic wave velocity (Vp), shale volume (VCL), and water saturation (SW) were selected as effective variables, whereas Vs was regarded as …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Non-Destructive Prediction About Storage Quality Of Prunes In Xinjiang Using Visible And Near-Infrared Spectroscopy Technology, Wen Long, Zhang Hui, Lai Lisi

Food and Machinery

[Objective] To establish a prediction model for the soluble solid content (SSC) and firmness of prunes in Xinjiang using visible and near-infrared (Vis-NIR) spectroscopy, enabling nondestructive and accurate evaluation of the internal quality of the fruit. [Methods] Taking 280 prunes in Xinjiang as research samples, the study collects Vis-NIR reflectance spectra at different storage periods with a near-infrared system. Principal component analysis-Mahalanobis distance (PCA-MD) is applied to eliminate abnormal samples. Then, the remaining dataset is divided into a calibration set and a prediction set at a ratio of 3∶1 using the Kennard-Stone (K-S) algorithm. Partial least squares (PLS) and support …

Fault Location In Dc Microgrids Using Traveling Waves, Sajay Krishnan Paruthiyil

Electrical and Computer Engineering ETDs

In DC power systems, rapid fault location is crucial for maintaining reliable operation, particularly with the prevalence of DC-DC converters. This study investigates fault location techniques in DC systems utilizing Traveling Waves (TWs). Following data normalization, multi-resolution analysis employs discrete wavelet transform to capture high-frequency patterns of TW's wavelet coefficients. Parseval's theorem is utilized to quantify the energy of these coefficients. First, a curve-fitting technique is employed to estimate fault locations in DC microgrids. Then, two transfer learning approaches are proposed: first approach integrates Parseval energy curves into a Gaussian process estimator, while second employs feedforward neural network for fault …

Generative Ai Training And Fair Use: Can Chatgpt Turn Your Favorite Meme Into A Stupid Ghibli Animation?, Liam Donovan

The University of Cincinnati Intellectual Property and Computer Law Journal

No abstract provided.

Beyond Accuracy: Machine Learning Models For Predicting Presence Of Permanent Molar Caries In U.S. Children And Adolescents With Fairness Consideration, Pritam Deb, Lin Li, Christina R. Scherrer

Faculty Articles

Background

Although predictors of dental caries have been previously explored, a comprehensive understanding of factors influencing permanent‐molar decay in U.S. children and adolescents, especially with respect to racial and ethnic biases remains limited. This study aims to develop and evaluate machine‐learning (ML) models incorporating algorithmic fairness to predict caries in permanent molars.

Methods

Data from the National Health and Nutrition Examination Survey (NHANES) were analyzed, using the 2011–2014 cycles for training and validation and the 2015–2016 cycle for testing. The primary outcome was decayed, missing, and filled teeth (DMFT) in at least one permanent molar, dichotomized to represent the presence …

Machine Learning-Based Prediction Of Proximal Junctional Pathology After Adult Spinal Deformity Surgery: A Systematic Review And Diagnostic Test Accuracy Meta-Analysis, Shaan Patel, Shiva A. Nischal, Yi Hein Chai, Kush M. Kale, Kevin Hines, Joshua Pelta-Heller, Jack Jallo, James S. Harrop, Srinivas K. Prasad

Department of Neurosurgery Faculty Papers

BACKGROUND: Proximal junctional kyphosis (PJK) and failure (PJF) remain challenging and incompletely predictable adverse event following adult spinal deformity (ASD) surgery. Machine learning (ML) models have been proposed to improve risk stratification, but performance varies and clinical applicability remains uncertain. A systematic review and diagnostic test accuracy meta-analysis was performed to synthesize the predictive performance of ML-based models for proximal junctional pathology after ASD surgery.

METHODS: PubMed, Embase, and CENTRAL were searched from inception to February 2026. Studies developing or validating ML models predicting PJK or PJF following ASD surgery were included. Diagnostic performance was synthesized using random-effects models, pooling …

A Maintenance-Aware Machine Learning Framework For Network-Level Highway Pavement Condition Prediction, Jin Hwan Kim, Guk Gon Song, Youngguk Seo

Faculty Articles

This study develops and validates maintenance-aware machine learning models for predicting the Highway Pavement Condition Index (HPCI) on the Korean expressway network. Multiple regression and tree-based models were trained and tested using the pavement condition surveys archived in the Highway Pavement Management System (HPMS). A stacking regressor that integrates random forest, gradient boosting, and extreme gradient boosting as base learners exhibited the most robust predictions. Performance metrics indicated that the stacking ensemble achieved a mean absolute error of 0.21, a root mean square error of 0.31, and a coefficient of determination exceeding 0.73 on the testing dataset. Also, the residuals …

Prescribing Company Action Through Machine Learning And Ai, Breck T. Husong

Data Science Undergraduate Honors Theses

The purpose of this research is to implement an OpenAI Reinforced Learning prescription-giving model for improving sales on a week-by-week basis. The data used comes from a segment of High Impact Analytics’s sales data that has been anonymized for proprietary reasons. The features among the data include inventory numbers, shipments in transit, total quantity and dollars of products sold each week for the past 2 years, all aggregated at the store-item-week level. In order to build this model, Tigramite, a causal discovery model combined with prediction models XGBoost, Linear Regression, Ridge Regression, Lasso Regression, Scikit-learn’s MLP, and Keras’s Neural Model …

Extracting California's Land Grant History: A Comparative Study Of Rule-Based And Llm Methods For Historical Document Processing, Anthony Colin Herrera, Anthony Colin Herrera

Master's Theses

This thesis explores methods to extract structured data from historical documents, making them more accessible for analysis and visualization. The goal is to provide historians and Digital Humanities scholars with tools that save time and reduce the manual effort required for this kind of research. We work with census records and historical texts from the Early California Population Project and Spanish Colonial census, covering California's 18th-19th century transition from Spanish and Mexican control to U.S. statehood. The system has three components: person matching across records, family tree generation, and land grant extraction. For person matching, we use a modified Levenshtein …

A Machine Learning-Based Apogee Prediction Methodology For Experimental Student Rockets, Price Hamilton Drawdy

Senior Honors Theses

The ability to predict the maximum altitude of a rocket (apogee) in real-time is incredibly useful for collegiate-level competition rockets. This project creates a machine learning-based real-time apogee prediction methodology. Three model types were tested: linear regression, random forest, and a 3-layer multi-layer perceptron (MLP) neural network. These models were trained on a large dataset of simulated flights. All models performed well on simulated test flights, with the linear regression model showing most promise for use on edge compute. More development and real-world testing are necessary to determine how applicable this method is for real-time operation. Nevertheless, this methodology provides …