Physical Sciences and Mathematics Commons^™

Mapping Public Engagement With Environmental Finance In The U.S: Spatiotemporal Insights From Google Trends On Public Engagement And Policy Equity In The U.S, Hilda Afeku-Amenyo, Charles Knoble, Pankaj Lal

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

This study analyzes U.S. public engagement with environmental finance from 2015–2023 using Google Trends data for four key terms: “green bonds,” “sustainable finance,” “climate finance,” and “ESG.” Our analysis reveals a notable rise in engagement with environmental finance topics, with Environmental, Social, and Governance (ESG) investing generating the highest levels of interest. Geographically, the District of Columbia exhibited disproportionate search activity relative to other regions. While public awareness of environmental finance has grown, we highlight disparities in knowledge and accessibility of information. These findings carry significant policy implications, particularly in ensuring equitable access to sustainable financial instruments. We underscore the …

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) …

Applications Of Prior And Novel Computational Tools In Mental Health Treatment, And Their Potential To Uncover The Explanatory Gap, Ambika Vyas

University Honors Theses

The explanatory gap is a widely discussed concept in scientific and philosophical literature. In neuroscience, the solution to the explanatory gap is highly sought out, but the general consensus is that it is unsolvable. Numerous articles discuss the explanatory gap alongside computational tools and how these tools could aid neuroscientists in uncovering the mental health explanatory gap. However, significant developments in machine learning have been made since 2020, coinciding with the rise in Large Language Models (LLMs). This thesis is a literature review on computational methods, tools, and devices developed and utilized by researchers to improve how mental health disorders …

A Proactive Food Demand Forecasting-Inventory Management Approach Under Weather Disruptions, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Jun Shen

All Works

Effective demand forecasting has become crucial to strengthening system resilience, reducing food waste, and achieving sustainability in food systems. Despite recent advances in leveraging machine learning for food demand forecasting, most existing models remain static and assume stable demand patterns, posing a challenge for adapting to demand changes during disruption events. This paper develops a proactive approach that leverages demand forecasting outputs and weather disruption flags to guide inventory replenishment, ensuring adaptability to varying demand conditions across three weather disruption events while reducing waste. This paper first uses a stacking model to predict next-day demand for a food retailer, leveraging …

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 …

Parameter Density Estimation For Cardiac Electrophysiology Models Using Data Consistent Deep Learning, Michael Luo

Dissertations

Mathematical models of biological rhythms and excitable systems can provide insights into mechanisms underlying cardiac electrical dynamics. However, estimating the parameters of these models from experimental observations is often difficult due to noise, heterogeneity, and unobserved variables. For example, in an electrocardiogram (ECG) recording, information about the electrical properties of different regions of the heart is compressed into a single voltage trace. Additionally, variability within these signals may contain important information about population heterogeneity, regional differences in electrophysiology, and time-dependent modulation.

This dissertation develops, explores, and evaluates methods that perform feature-based distributional inference for complex nonlinear dynamical systems. The objective …

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 …

Predicting The Outcome Of Ischemic Hepatitis With Real-Patient Data Using Machine Learning Tools, Christiana Beard, Madison Utterback, Olcay Akman, Priya Kohli, William M. Lee, Aditi Ghosh

Spora: A Journal of Biomathematics

Ischemic hepatitis (IH) results from shock-related conditions that impair oxygenated blood flow to the liver, causing hepatocyte death. Diagnosis relies largely on clinical history due to the absence of specific diagnostic tests and limited ability to predict outcomes. This study applies machine learning methods to real-world IH patient data to improve outcome prediction. Biomedical indicators analyzed include creatinine, international normalized ratio (INR), aspartate aminotransferase (AST), alanine transaminase (ALT), and bilirubin. Data were collected from multiple U.S. centers through the Acute Liver Failure Study Group (ALFSG), a multicenter network focused on this rare condition. We implemented logistic regression, regression tree methods …

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 …

Optimizing Gated Rnns, Joshua Paul Fechete

Honors Projects

Gated recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) help fix instability present in normal recurrent neural networks. This allows them to be used for various real-world tasks, and due to their architecture, they are uniquely qualified to handle variable sized input such as text. However, even before training can begin on a machine learning model, various hyperparameters must be chosen to decide how the model will be architectured. Choosing good hyperparameters is vital for creating a model that performs well but is not larger and more computationally expensive to run than it needs …

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 …

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, …

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 …

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 …

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.

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 …

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 …

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 …

Explainable Multimodal Deep Learning Models For Variable-Length Sequences In Critically Ill Patients, Jennifer Martin, Majid Afshar, Askar Safipour Afshar, John Caskey, Dmitriy Dligach, Yanjun Gao, Jifan Gao, Guanhua Chen, Anoop Mayampurath, Matthew M. Churpek

Computer Science: Faculty Publications and Other Works

Objective
Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record (EHR) data. While incorporating unstructured notes improves accuracy, multimodal fusion and explainability remain an open challenge, particularly for variable-length temporal data. This study develops an explainable temporal modeling framework for multimodal EHR data that accommodates variable-length intensive care unit (ICU) trajectories and supports diverse outcome prediction tasks.

Methods
We introduced two multimodal recurrent neural networks (RNNs) with distinct fusion architectures (Pre-RNN and Post-RNN) that integrated structured EHR variables and unstructured clinical notes at every hourly timestep. Both architectures encoded temporal …

Artificial Intelligence In Medicine: Barriers, Solutions, And Strategies, Anil Harrison, Melissa Stradley Moreno, Caroline E. Williams, Munevver Mine Subasi, Ersoy Subasi

HCA Healthcare Journal of Medicine

The integration of artificial intelligence (AI) and machine learning (ML) into health care holds the potential to revolutionize patient care by enhancing clinical decision-making, improving diagnostic accuracy, and reducing costs. Despite this promise, adoption remains limited due to a range of technical, regulatory, educational, and cultural barriers. This paper examines these challenges and proposes strategies to support safe and effective implementation of AI in clinical practice.

Key barriers include the lack of model interpretability, often referred to as the "black box" problem, which undermines clinician trust and accountability in clinical settings, evolving regulatory frameworks and unresolved questions surrounding liability, and …

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 …

Machine Learning-Based Prediction And Experimental Validation Of The Pyrolysis Product Distribution In Tar-Rich Coals, Dong Shuai, Li Hongqiang, Wu Zhiqiang, Yu Zunyi, Lyu Zitao, Guo Wei, Yang Panxi, Fu Keming, Hao Xuanzhi, Liu Gen, Yang Bolun

Coal Geology & Exploration

Background Tar-rich coals serve as an important coal-based oil and gas resource in China, while their pyrolysis product distribution is governed by the coupling effects of coal properties and reaction conditions. Therefore, rapidly identifying the pyrolysis product distribution patterns holds great significance for the resource evaluation and experimental design of tar-rich coals.Methods Existing studies on tar-rich coals suffer from the insufficient integration of exclusive data and limited synergistic prediction capacities for multiple products. To address these issues, this study constructed a dedicated dataset involving proximate analysis, ultimate analysis, elemental molar ratios, maceral composition, and pyrolysis conditions by systematically collecting …

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

Electronic Theses and Dissertations

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 …

The Texture Of A Threat: Adversarial Training, Cnns, And Obfuscated Malware Detection, Kaelyn Haynie

Senior Honors Theses

Accurately detecting malicious programs is an expanding field of research for machine learning (ML), with a novel approach incorporating a bytecode-to-image pipeline that produces images representative of software. These images are provided to convolutional neural networks (CNNs) to be examined for malicious pattern indicators. However, CNNs struggle to generalize these patterns effectively while still being robust against adversarial data, an issue which this research addresses with adversarial training. In this paper, three unique CNN architectures (a DBFS-MC-inspired baseline, MIRACLE, and PSP-CNN) are trained for binary classification with 15,000 benign and malicious software samples encoded into images for Android, Windows, and …

Applications Of Machine Learning In Enhancing Evaporation Estimation For Small Reservoirs: A Case Study In Semi-Arid South Texas, Syed Muhammad F Abdullah, Chu-Lin Cheng, Jude A. Benavides, Jungseok Ho, Rafael M. Almeida

School of Earth, Environmental, & Marine Sciences Faculty Publications

Small reservoirs in semi-arid regions experience substantial evaporative losses but are rarely monitored at daily scales. A multi-reservoir machine learning (ML) framework was developed to estimate daily open-water evaporation. Empirical models (Penman, Penman-Monteith, Priestley-Taylor, Bowen Ratio Energy Budget) andabenchmark combination method (Daily Lake Evaporation Model-DLEM) were compared against ML models. Predictors combined gridded meteorology (gridMET) with reservoir attributes (surface area, average depth, maximum depth, and fetch). ML models (Random Forest-RF, Decision Tree-DT, K-Nearest Neighbor-KNN, and Support Vector Regression-SVR) were trained on four reservoirs using data from 2018 to 2025. Results from ML models were further validated using both DLEM and …

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, …