Digital Commons Network^™

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 …

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Extracting California's Land Grant History: A Comparative Study Of Rule-Based And Llm Methods For Historical Document Processing, Anthony Colin Herrera, Anthony Colin Herrera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This thesis explores methods to extract structured data from historical documents, making them more accessible for analysis and visualization.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 …

Softmax Expressiveness With Linear Complexity By Investigating The Linear-Softmax Attention Accuracy Gap, Gabriel Mongaras, Eric Larson

Computer Science and Engineering Theses and Dissertations

Softmax attention Vaswani et al. [2017] Bahdanau et al. [2015] has gained traction as one of the most important parts of most modern machine learning models. Despite being the most adopted modern machine learning algorithm, exploration of softmax attention itself has been neglected. While softmax attention is a very powerful architectural component, it has a major drawback of being quadratic in sequence length. More specifically, softmax attention has quadratic complexity during training and linear complexity (when using a KV cache) during inference, which limits the sequence length it can model due to system memory constraints. Linear attention was proposed, (proposed …

Data Driven Monitoring And Control Of Laser Powder Bed Fusion Process, Jose De Jesus Galarza

Theses and Dissertations

The Laser Powder Bed Fusion Process (LPBF) has been one of the main processes of additive manufacturing, enabling the manufacturing of complex geometries, customization, and lightweight parts. Modern LPBF processes have integrated monitoring systems that capture the light emissions per layer for quality assurance. However, standard defect detection algorithms have not yet achieved the high precision required due to the inherently variable nature of the signal, insufficient data for model training, and the confounding effects of the print.

The processes still have some challenges, such as characterizing the roughness from the build parameters alone, improving the pore detection using the …

You Can’T Spell Audit Without Ai: The Current Uses Of Artificial Intelligence In Audit, Jena Perkins

Senior Honors Theses

The accounting profession continuously adapts to the innovations provided by the broader context in which it exists. Artificial intelligence (AI) is a forerunner among tools used to enhance and optimize auditing services within the accounting profession. The realm of AI offers advancements to procedures used within an audit to detect misstatements. Based on the proprietary platforms developed by Big 4 accounting firms, AI is a key component in maintaining an advanced approach towards auditing.

Influence Of Predation Risk On Elk Feeding Behavior In A Multi-Predator Environment, Rosemarie C. Pugh

All Graduate Theses and Dissertations, Fall 2023 to Present

Sometimes predators don’t just affect prey by killing them, they can also change how prey behave. For example, animals may spend more time watching for danger and less time eating, which could affect their health and survival. In Yellowstone National Park, past research has suggested that elk may reduce feeding because they are trying to avoid predators like wolves and cougars. However, most previous studies have only looked at elk behavior during the daytime and have often looked at responses from single predators rather than multiple.

In this study, I used high-resolution tracking collars equipped with motion sensors to directly …

Computer Vision And Machine Learning Approaches For Defect Detection In 3d-Printed Cementitious Materials: A Systematic Review, Muhammad Ali Musarat, Ruben Paul Borg, Jingjie Wei, Carl James Debono, Kamal Khayat

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

3D printing is evolving at a fast pace in both the manufacturing and construction sectors. These advancements can greatly benefit these industries. However, the 3D printing of concrete structures presents some challenges due to defects in the 3D concrete printed elements. Hence, this study systematically reviews Artificial Intelligence (AI)-driven techniques, such as Computer Vision and Machine Learning, to identify surface defects that can occur in 3D-printed cementitious material structures. The adopted methodology was the PRISMA statement with the aim of reporting the systematic review and meta-analysis. Two well-known databases, Web of Science and Scopus, were utilised for data extraction of …

Examining Extremes: A Comparison Between Machine Learning Based Southwestern Conus Seasonal Drought And High Fire Risk Event Synoptic Climatologies, Ethan M. Greenberg

School of Natural Resources: Dissertations, Theses, and Student Research

The Southwestern United States (SW CONUS), comprised of California, Arizona, Nevada, and Utah, is a vast region that tens of millions of people call home. Hosting ecosystems ranging from grasslands and shrublands to temperate forests and climate zones ranging from Mediterranean climates to arid deserts, the region is nearly unanimously prone to intense droughts and devastating wildfires. While fire weather conditions and drought are often studied separately or mentioned as background conditions for the other, there are relatively few studies that compare the typical meteorological conditions between the two. This study aims to more closely understand the meteorological relationship between …

Unified Deep Learning Techniques For Spatial Detection And Temporal Forecasting Across Visual Domains, John Olawale Olamofe

All Dissertations

This dissertation proposed a unified deep learning framework for spatial detection and temporal forecasting across visual domains, designed to address limited supervision, class imbalance, and scale variability. The framework was structured around four complementary principles: Domain-Aware Input Rebalancing, Representation-Centric Learning, Diversity-Driven Robustness, and Transfer Across Scale and Modality, which together enabled robust visual representation learning across heterogeneous data sources.

Domain-Aware Input Rebalancing mitigates non-uniform and sparse data distributions by actively reshaping inputs before learning via class-aware augmentation, sampling, resolution manipulation, and super-resolution. This principle underpins object detection in overhead satellite imagery (xView), dense urban aerial scenes (CADOT), temporally sparse NDVI …

Understanding Machine Learning Model Behavior Under Fairness And Privacy Constraints, David Anthony Sanchez

Open Access Theses & Dissertations

Machine learning systems deployed in high-stakes domains are increasingly expected to satisfy demands beyond predictive accuracy-including fairness across demographic groups, protection of sensitive information, and explanations that human stakeholders can inspect and trust. This thesis investigates how those demands can be met through learning frameworks that explicitly govern the relationship between data and models, arguing that trustworthiness is a design problem rather than a post hoc correction. The thesis is organized around three studies, each targeting a distinct point of data-facing control. The first develops CondFairGen, a fairness-aware conditional generator for tabular data that improves subgroup equity by dynamically reweighting …

Predicting Flight Fares With Machine Learning: Enhancing Aviation Industry Pricing Forecasting, Reem Almulla

Theses

This research investigates the increasing challenge of accurate flight fare prediction for travel agencies that are functioning and working in the post-pandemic aviation market. As the prices fluctuate while demand is unstable and competition pressure increases, therefore it influences decision-making and profitability. In most cases, traditional ticket pricing methods often can be ineffective when capturing complex and non-linear relationships within factors that influence the ticket fare dynamics. As a result, highlighting the urge of more adaptive pricing and data-driven predictive machine learning models. In response to this challenge, the research examines the effectiveness of machine learning techniques for enhancing flight …

Hybrid Machine Learning For Zero-Day Malware Detection: An Adaptive Static-Dynamic Analysis Approach, Garrett Farmer

Theses and Dissertations

In an era of swiftly evolving cyber threats, zero-day malware continues to be one of the most challenging classes of attacks to detect and mitigate. Traditional signature-based methods often fail to detect novel malicious code, leaving institutions vulnerable to unknown exploits. This thesis proposes a machine learning (ML)-based framework that is designed to detect unknown malware variants. By combining both static and dynamic techniques, such as file structure exam ination and sandbox-based runtime analysis, this approach aims to successfully capture malicious characteristics. The proposed custom pipeline addresses the computational overhead that is associ ated with deep inspections, outlining a staged …

Reward Representation Learning, Gregory M. Hyde

Dartmouth College Ph.D Dissertations

The \emph{Markov decision process} (MDP) has long served as the canonical model for sequential decision-making. However, it assumes that the reward function is Markov with respect to a given state representation---an assumption that often does not hold in practice. Instead, agents typically only perceive streams of observations and actions and must infer the latent structure according to which reward unfolds over time. From this perspective, reward prediction is initially non-Markov, reflecting a mismatch between the agent's current representation and the underlying structure of the environment.

In this thesis, we advance the view that reward is not simply a signal to …

Machine-Learning Landslide Susceptibility And Runout Modeling In The Nolichucky River Gorge After Hurricane Helene, Grace Braver

Electronic Theses and Dissertations

Extreme rainfall from Hurricane Helene (September 2024) triggered widespread landslides across the southern Appalachian region, highlighting the need for rapid landslide susceptibility assessments that capture both landslide initiation and downstream runout. Traditional susceptibility models often focus solely on initiation zones, limiting their ability to identify which slopes will generate destructive landslides or where material will travel. This study addresses that gap by (1) integrating Geographic Information System (GIS)-based machine learning susceptibility modeling using ArcGIS Pro: Maximum Entropy (MaxEnt) and Random Forest-Based and Boosted Classification and Regression (FBBC) and (2) the U.S. Geological Survey (USGS) Grfin (Growth, Flow, and Inundation) runout …

Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach

Honors Theses

Alzheimer's disease (AD) is a growing global health concern, with millions of people affected worldwide and cases expected to rise significantly in the coming decades. Early detection is critical for patient treatment and care, and recent advances in natural language processing (NLP) have shown promise in identifying linguistic markers associated with AD. However, most existing work has focused on English, leaving speakers of other languages with limited access to such tools. This study investigates how effective AD detection models trained on English data are at transferring to Greek, a low-resource language with limited dementia-related speech data available. We propose a …

Predicting Epileptic Seizures From Eeg Signals Using Machine Learning, Hamzah Luqman, Galal M Binmakhashen, Hanan Alali, Marwa Alsleebi, Hamdi A. Al-Jamimi, Motaz Alfarraj, Shahid Bashir, Brent Hedgcock, Ali Mir, Raidah Albaradie

Neurosciences

Objective: To develop a machine learning-based model for predicting epileptic seizures by analyzing electroencephalogram (EEG) signals and classifying the interictal and preictal phases.

Methods: The EEG signals were analyzed in both the time and frequency domains using advanced feature extraction techniques. Three different machine learning approaches were employed, with special focus on long short-term memory (LSTM) networks. Features were extracted and classified to differentiate between seizure and non-seizure phases. The proposed models were evaluated using the publicly available CHB-MIT EEG dataset. The study was conducted between September 2022 and September 2024.

Results: Among the tested models, the LSTM-based approach using …

Increasing The Prediction Accuracy Of Plant Oil Production Processes Through Adjusting The Parameters Of Machine Learning Models, Umidjon Ruziev, M.K. Shodiev, A.T. Rajabov

Chemical Technology, Control and Management

Vegetable oil production is characterized by high variability in output indicators due to nonlinear interactions between raw material parameters, equipment modes, and heat and mass transfer conditions. Existing approaches to applying machine learning in this field, as a rule, do not account for the impact of hyperparameter adjustments on forecasting quality across specific technological stages. The article presents a systematic methodology for adjusting model parameters (Ridge regression, SVR, GBM, LSTM) applied to three key tasks: predicting residual oil content in oil cake, color index during bleaching, and free fatty acid content during deodorization. In a set of 1000 observations, including …

Automatic Modulation Classification On Satellite Communications Across Orbital Domains, Lorraine (Raina) Freeman

Theses

In recent years, the space domain has exploded with both commercial and government activity, making situational awareness critically important. In this work, we highlight the need for signal intelligence capabilities that can transfer gracefully from terrestrial systems to space networks. This already difficult task is additionally complicated by the fact that satellites operate at multiple different orbits. Geostationary (GEO) satellites face significantly different channel conditions than their Low Earth Orbit (LEO) counterparts, meaning that any cross-orbital signal analysis tool must be able to handle widely varying conditions. Such tools may soon be critical for identifying satellites and detecting malicious cyber …

A Machine Learning Framework For Ddos Attack Detection In Sdn-Enabled Mobile Wireless Networks, Ishita Sharma, Satyam Agarwai, Shashi Shekhar Jha, Sumit Chakravarty

Faculty Articles

Attacks on network components and devices pose a significant threat to service continuity, necessitating robust detection mechanisms. This paper presents a Distributed Denial of Service (DDoS) attack detection framework tailored for heterogeneous mobile wireless networks within a Software-Defined Networking architecture. A two-tier model is proposed: localized attack detection at access points (APs) using a Multi-Layer Perceptron (MLP) classifier, and centralized detection under mobility at the controller using a Long Short-Term Memory (LSTM) model. The system incorporates novel traffic features such as flow count, speed of source IP, source and destination IP address entropy, proportion of bidirectional flows, and handover frequency, …

Robust Non-Invasive Cardiac Index Prediction Via Feature Integration And Data-Augmented Neural Networks, Chih-Hao Chang, Mei-Ling Chan, Yu-Hung Fang, Po-Lin Huang, Tsung-Yi Chen, Tsun-Kuang Chi, I Elizabeth Cha, Tzong-Rong Ger, Kuo-Chen Li, Shih-Lun Chen, Liang-Hung Wang, Jia-Ching Wang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. …

A Computer Vision Approach To Analyzing Taxane Effects On Prostate Cancer Cells, Diana Elizabeth Dancea

Electronic Theses and Dissertations 2020 - Present

Actin is a family of proteins that help create the structure of the cytoskeleton, which gives shape to the cell. In many chemotherapy treatments, researchers target actin because it controls the cell division process. Therefore, if they are able to understand the actin fibers, that may help in formulating methods to stop or slow down cancer cells from reproducing. Another important protein is PAK6, which regulates actin. In our research, a collaborative effort with Prof. Michael Lu’s lab at Florida Atlantic University, we use machine learning techniques to analyze cells which had their PAK6 protein knocked out, and compare them …

Efficient Intrusion Detection For Iomt: Integrating Machine Learning, Feature Selection, And Fuzzy Logic, Ghaida Mansour Balhareth

Electronic Theses and Dissertations 2020 - Present

The internet of medical things (IoMT) has transformed healthcare by enabling real-time patient monitoring, remote diagnoses, and effective data exchange among connected medical devices and clinical systems. The increasing reliance on interconnected medical equipment has also intensified cybersecurity risks, as resource-constrained devices and wireless communication channels are vulnerable to attacks such as man-in-the-middle, spoofing, data injection, and ransomware. Intrusion Detection Systems (IDSs) play a critical role in mitigating these threats; however, traditional IDS approaches often struggle with high-dimensional IoMT data, class imbalance, and uncertainty in traffic patterns, which can increase false alarms and reduce reliability in safety-critical environments. This dissertation …

Real-Time Data Collection System For Low-Cost Diesel Emissions Monitoring, Zachary Driskill

Theses and Dissertations

Diesel trucks are a major source of pollutants, including particulate matter (PM) and NO$_x$. Roadside emissions monitoring can identify high-emitting diesel vehicles, but conventional sensing systems rely on expensive, complex sensing instruments that limit widespread deployment. Low-cost sensors offer increased scalability due to their small size, low cost, and low power consumption, but they have lower accuracy and slower response time. This thesis presents the design and evaluation of a low-cost roadside emissions monitoring system, establishing the foundation for a scalable and widely deployable alternative. We apply machine learning calibration to roadside emission monitoring to improve the usability of low-cost …

Advanced Machine Learning Approaches For Bankruptcy Prediction In The Shipping Industry: A Comparative Analysis Across Time Horizons, Minchul Sohn, Saeyeon Roh, Minsu Kwon

Plymouth Business School

This study evaluates machine learning methods for predicting bankruptcies in the Korean shipping industry over 1-, 3-, and 5-year horizons. Using an imbalanced dataset, preprocessing techniques such as outlier capping, skewness correction, and SMOTE-based oversampling are applied. Two feature sets—financial variables and those enriched with macroeconomic and shipping indicators—are analyzed. Results demonstrate that ensemble methods like LightGBM, CatBoost, XGBoost outperform linear models and deep neural networks across all horizons. Short-term predictions are driven by liquidity factors, while long-term forecasts benefit from shipping-specific indices like freight rates. SHAP‐based analysis reveals that internal cash‐flow as critical for one‐year predictions, whereas prolonged freight …

Ai Method For Classification Of Diagnosis Of Near-Infrared Breast Lesion Images, Kaiquan Chen, Fangyang Shen, Honggang Wang, Zhengchao Dong, Jizhong Xiao, Ming Ma, Afroza Aktar, Christopher Chow, Wenxiong Zhang

Publications and Research

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, …

Federated Learning With Deep Learning: A Comprehensive Survey, Sarah H. Mnkash, Faiz A. Alawy, Israa T. Ali

Iraqi Journal of Computers, Communications, Control and Systems Engineering

Federated Learning (FL) introduces a decentralized machine learning paradigm where model training occurs directly on user devices, ensuring data privacy by keeping sensitive information local. This approach ensures data privacy and security by keeping all sensitive or personally identifiable information local to the device, thereby eliminating the need to transfer or centralize raw data. In this survey, we examine federated deep learning — its challenges, recent developments, application domains, privacy preservation strategies, and future trends. We will give some primitive reference papers of the heterogeneity of statistics and systems, communication cost, fairness, and trust. Applications in health, Internet of Things …

Roadmap On Artificial Intelligence-Augmented Additive Manufacturing, Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei-Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C.L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano-Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan

Faculty Publications

Artificial intelligence-augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state-of-the-art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models …

Efficacy Analysis In Clinical Trials: A Comprehensive Review Of Statistical And Machine Learning Approaches, Dhrubajyoti Ghosh, Samhita Pal

Faculty Articles

Efficacy testing is a cornerstone of clinical trials, ensuring that medical interventions achieve their intended therapeutic effects. Over the decades, a wide range of statistical methodologies have been developed to address the complexities of clinical trial data, including parametric, nonparametric, Bayesian, and machine learning approaches. Parametric methods, such as t-tests, ANOVA, and LMMs, have traditionally been the foundation of efficacy testing due to their efficiency under well-defined assumptions. Nonparametric techniques, including the Friedman test, Brunner-Munzel test, and modern extensions like nparLD, have emerged as robust alternatives, particularly for skewed, ordinal, or non-normal data. Bayesian methodologies have enabled the incorporation of …

Rethinking News Classification Through A Multi-Dimensional Framework, Luana De Jesus Ferreira

Honors Theses

This thesis proposes a multi-dimensional framework for news classification that evaluates articles across three independent dimensions: headline accuracy, language neutrality, and content reliability. These dimensions produce both a continuous reliability score and a five-tier interpretive scale, while additionally classifying articles by genre and topic. To operationalize this framework, a structured annotation protocol was developed and applied to a dataset of 373 news articles drawn from 79 outlets spanning a wide range of contemporary media ecosystem. A binary Logistic Regression classifier trained on the ISOT Fake News Dataset was then evaluated against this dataset to examine how a model trained on …