Digital Commons Network^™

Optimization, Machine Learning, And Networking Solutions For Cyber-Physical Systems, Xu Tao

Theses and Dissertations--Computer Science

Cyber-Physical Systems (CPS) represent a transformative paradigm that integrates sensing, computation, and actuation through networked systems to enable intelligent, context-aware applications across various domains. Despite their growing potential, CPS still face critical challenges, particularly in maintaining reliable, low-latency communication and efficient data processing in resource-constrained and dynamic environments. These challenges are further magnified in rural or remote deployments, where traditional network infrastructure is often unavailable or unreliable. This dissertation addresses these challenges through the development of optimization and inference algorithms that enhance network performance in Software-Defined Networks (SDN), using techniques such as reinforcement learning and network tomography for efficient routing. …

Information-Theoretic Methods For Efficient Training And Robust Evaluations In Self-Supervised Learning, Oscar Skean

Theses and Dissertations--Computer Science

Self-supervised learning (SSL) has become a cornerstone of modern machine learning, offering a scalable alternative to costly human annotation by constructing pretext tasks directly from raw data. While SSL has delivered strong results across vision, language, and multimodal domains, two major limitations persist: (1) SSL methods are often significantly slower to train than supervised counterparts, and (2) evaluation protocols remain narrow, with most studies relying on linear probing accuracy on the pretraining dataset. . These challenges are particularly acute for large language models (LLMs), where training costs and interpretability of intermediate representations are critical concerns.

In this work, we propose …

Cnns And Transformers For Visual Understanding: From Feature Alignment To Image Captioning, Xuehao Liu

Doctoral

Deep learning has developed rapidly since the introduction of Deep Belief Networks during the past decade. As an area of machine learning, it still has many open challenges. Among these open challenges is the issue of transparency, with deep learning models known as black boxes. Both explainability of a model for understanding the decision making process, and the transparency of the relationship between the input and output are crucial for understanding a model. The understanding of models can build trust between AI systems and humans, verify models behavior, and identify potential biases or errors.

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, Durga C. Panigrahi, Cihan H. Dagli, A. Singh

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

Nanotube Spectral Fingerprinting And Machine Learning For Optimized Bioimaging/Sensing And Disease Detection Applications In Als, Rodrigo Monroy Lopez

Open Access Master's Theses

Single-walled carbon nanotubes (SWCNTs) possess unique physicochemical and optical properties that make them ideal candidates for biomedical imaging, biosensing, and disease diagnostics. This thesis explores the potential of SWCNT-based spectral fingerprinting combined with ML (Machine Learning) algorithms to optimize bioimaging, disease detection and prediction, with a specific focus on differentiating between healthy and amyotrophic lateral sclerosis (ALS) lymphoblastic patient samples. By functionalizing SWCNTs with single-stranded DNA, we enhance their stability and target specificity, enabling their application in serum patient samples.

A comprehensive spectral analysis of DNA-SWCNTs was conducted using near-infrared fluorescence spectroscopy and other characterization techniques, including UV-Vis-absorption spectroscopy. The …

Comparison Of Dynamic Mode Decomposition With Other Data-Driven Models For Lung Cancer Incidence Rate Prediction, L. Raymond Guo, Jifu Tan, M. Courtney Hughes

Faculty Articles, Papers, and Other Scholarship

Introduction: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools but can be computationally expensive and require specialized knowledge. Dynamic mode decomposition (DMD) is an alternative that offers efficient analysis with fewer resources. This study explores applying DMD in public health using lung cancer data and compares it with other machine learning models.

Methods: We analyzed lung cancer incidence data (2000–2021) from 1,013 US counties. Machine learning models (random forest, gradient boosting machine, support vector machine) …

Neural And Computational Approach To Understanding Environmental Modulation Of Behavioral Identity In Zebrafish, John W. Hageter

Graduate Theses, Dissertations, and Problem Reports (ETD)

Organisms rely on behavior for survival. Animals engage in behaviors that allow for feeding, mating, exploring and navigating their environment among others. Necessary for these behaviors to develop are the environmental factors and underlying circuitry which make behavior possible. Specifically, how the environment guides underlying neural circuitry to develop unique facets or phenotypes of a larger behavior are key to understanding why unique behaviors exist. In this thesis, I build foundational evidence for determining these mechanisms through the use of the zebrafish local search behavior. This is a behavior that zebrafish employ following the loss of environmental illumination where they …

Ai Innovations In Rppg Systems For Driver Monitoring: Comprehensive Systematic Review And Future Prospects, Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar

All Works

Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive …

Predicting Early Hospital Readmissions For Diabetic Patients Using Machine Learning, Amanda Ferraz, Leonardo Oliveira

ICT

This project applies machine learning to predict whether diabetic patients will be readmitted to a hospital within 30 days of discharge. Early readmissions are a costly and critical issue in healthcare, often signalling gaps in post-discharge care and risk management. Diabetic patients face unfair high readmission rates compared to the general population. According to the CDC Diabetes Report Card 37.3 million people in the U.S. or 11.3% of the population had diabetes as of 2019 (CDC, 2021). Our goal here is to develop a binary classification model capable of flagging high risk patient (< 30-day readmission) based on their clinical, demographic, and administrative data. This lets healthcare institutions to take measures,

European Air Pollution And The Proposed Timelines Of Implementing The World Health Organization 2021 Air Quality Guidelines Ca3., Lukia Hartin

ICT

This research examines Ireland’s air pollution trends and evaluates whether current reductions in PM2.5, PM10, and NO₂ are sufficient to meet the WHO 2021 Air Quality Guidelines by 2040. Using four years of EPA-validated pollutant data (2020–2023), alongside Building Energy Rating (BER) and national transport datasets, the study applies the CRISP-DM methodology to guide analysis, preprocessing, modelling, and evaluation. Extensive data cleaning and alignment were required due to inconsistent station coverage, varying formats, and missing values. Forecasting models—including Random Forest, Gradient Boosting, SVR, and linear regression—were assessed using MSE, R², and trend significance to project pollutant levels across different Irish …

Customer Service Support. Utilizing Machine Learning To Classify, Prioritize And Summarize Issues., Hoai Nhan Nguyen

ICT

This capstone project investigates the application of machine learning and natural language processing (NLP) to enhance customer support operations through automated ticket classification, prioritization, and summarization. Using the multilingual Customer Support Emails dataset from Kaggle, the project follows the CRISP-DM methodology, performing extensive data cleaning, preprocessing, feature engineering, and class balancing. Five machine learning models—Decision Tree, KNN, LinearSVC, Naive Bayes, and Random Forest—were evaluated using hyperparameter tuning, cross-validation, confusion matrix analysis, and learning curves. LinearSVC demonstrated the strongest performance for both queue and priority classification, achieving accuracies of 89.8% and 81.2% respectively, with consistent generalization across folds. For summarization, extractive …

Predicting Repeat Purchases In E-Commerce Using Interpretable Machine Learning, Zahid Bhatti

ICT

This project investigates the prediction of repeat purchase behaviour in e-commerce using machine learning, with a focus on balancing predictive accuracy and interpretability. Large volumes of transactional and behavioural data are analysed to identify customer-level features that drive loyalty and repeat purchases. Various supervised learning models, including Random Forests and Logistic Regression, are evaluated for predictive performance, while SHAP (SHapley Additive Explanations) is employed to provide both global and local interpretability. The study aims to generate actionable insights for customer relationship management and marketing strategy, demonstrating how advanced predictive models can support informed business decisions without sacrificing transparency.

The Actuarial Applications Of Machine Learning And Big Data In The Life Assurance Industry: Managing Customer Retention And Customer Outcomes By The Application Of Data Science., Brian Cunningham

ICT

Lapses are an issue in the insurance industry in general. They affect a company’s profitability, cash flows and solvency. High levels of lapses can cause reputational damage that could provoke a cycle of even more lapses. It is therefore incumbent on a company to do its utmost to retain the business it has written for the term it was written for.

If a company could predict which of its policies were about to lapse, it could proactively attempt to prevent them by contacting the policyholder and engaging in a discussion to ascertain the likelihood of their choosing to leave. In …

Predicting Customer Churn And Enhancing Retention Strategies Through Machine Learning., Swan Saung Lwin

ICT

Customer Churn is a critical challenge faced by businesses across industries, especially in the digital market. Many companies struggle to predict customer churn accurately and have difficulties in carrying out effective retention strategies. Key challenges include ineffective traditional methods, lack of insights into impact of different services, generalized retention strategies, and the need to have cost-effective retention strategies. This project aims to predict customer churn using machine learning and identify the impact of key services offered by a telecommunication company.

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

Browse all Theses and Dissertations

Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

Precision Medicine Assessment Of The Radiographic Defect Angle Of The Intrabony Defect In Periodontal Lesions By Deep Learning Of Bitewing Radiographs, Patricia Angela R. Abu, Yi Cheng Mao, Yuan Jin Lin, Chien Kai Chao, Yi He Lin, Bo Siang Wang, Chiung An Chen, Shih Lun Chen, Tsung Yi Chen, Kuo Chen Li

Department of Information Systems & Computer Science Faculty Publications

In dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-intensive. Due to the high recurrence rate of periodontal disease after treatment, accurately evaluating the radiographic defect angle of the intrabony defect is vital for implementing targeted interventions, which can improve treatment outcomes and reduce recurrence. This study aims to streamline clinical practices and enhance patient care in managing periodontal disease by determining its severity based on the analysis of …

A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat

Browse all Theses and Dissertations

Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …

Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald

Browse all Theses and Dissertations

Modern machine learning (ML) models rely on large amounts of high-quality labeled data to achieve optimal performance. However, in many real-world domains, such as cyber security, acquiring sufficient labeled data is often infeasible due to cost, privacy concerns, and the rapid evolution of underlying phenomena. This challenge underscores the importance of learning under data scarcity. This thesis addresses this challenge by proposing distinct, modality-specific techniques for text and graph domains, which allow models to generalize effectively with minimal data. For text classification task, we incorporate distilled rationales from large language models and adversarial perturbations into the input space to improve …

Methods In Statistics, Machine Learning, And Deep Learning For Combining Multi-Omics Dataset, Md Mutasim Billah

Dissertations, Master's Theses and Master's Reports

Transcriptome-wide association studies (TWAS) have emerged as a powerful strategy to bridge genome-wide association studies (GWAS) with gene regulatory mechanisms by integrating genotypic data with gene expression data. While early TWAS methods typically rely on linear models and single-tissue expression references, recent advances underscore the need for flexible, multi-tissue approaches that can capture heterogeneous regulatory architectures and tissue-specific expression patterns. This dissertation introduces a three‑part research project that advances multi‑tissue transcriptome‑wide association studies (TWAS) along complementary axes of methodology, statistical power, and modelling flexibility.

In chapter One, TWAS‑CTL introduces a two‑stage cross‑tissue learner that trains any user‑chosen single‑tissue imputers (STLs) …

Transformer-Based Genomic Classification And Simulation Of Heat-Responsive Genes In Citrus Limon, Ali Fahem Neamah, Zahraa A. Abdalkareem, Mostafa A. Mohammed, Jasim Ibrahim Ahmed, Phaklen Ehkan

Mesopotamian Journal of Big Data

Iraqi lemon trees (Citrus limon), vital for regional agriculture and food security, face intensifying threats from extreme heat caused by ongoing climate change in Iraq. Native cultivars often lack thermotolerance due to low expression of protective heat-response genes. This study addresses this critical challenge by developing an AI-assisted framework that integrates real RNA-Seq data, Transformer-based deep learning, and explainable AI to classify and simulate the function of genes associated with heat stress adaptation. The primary objective is to identify key thermotolerance genes and model their biological impact, with a specific focus on indigenous citrus varieties. Using a customized transformer architecture …

Inter-Basin Groundwater Flow In West-Central Florida, Sally Yousef Elrashedy Dr

Graduate Thesis and Dissertation post-2024

The mean annual values of inter-basin groundwater flow (IGF) and its inter-annual and seasonal variability across 172 watersheds in west-central Florida were estimated using the Integrated Northern Tampa Bay (INTB) model, which is an application of the Integrated Hydrologic Model dynamically coupling HSPF and MODFLOW. The estimated IGF for the scenario without human fluxes shows spatial heterogeneity in the model domain ranging from -1291mm/year to 4808 mm/year, and the variability of IGF decreases with spatial scales. A region with gaining IGF is defined as groundwater importer and a region with losing IGF is defined as a groundwater exporter. The characteristics …

Digitally-Mediated Nature-Connectedness?: Listeners’ Emotional Responses To And Perception Of Digitized Nature Sounds, Marissa Salas

Graduate Thesis and Dissertation post-2024

• Faced with the potential closure or privatization of the U.S. National Park Service (NPS), it is important to reflect on the conservation efforts using soundscapes. This dissertation explores the gap between experts and lay listeners in listening to soundscapes through the digitally-mediated spaces (e.g. YouTube). In a pilot project conducted in 2024 that used facial emotional recognition software and a talk-aloud protocol, data on emotions was collected. Using NPS video recordings on its webpages, social media accounts and via other digital channels (YouTube, etc.) created by NPS experts, this project employed an innovative, interdisciplinary, mixed-methods experimental design to investigate …

Augmentation And Hybridization Of Conventional Forms Of Process Control With Advanced Control Methods, Daniel Paul Beahr

Graduate Theses, Dissertations, and Problem Reports (ETD)

Within the last decade in the field of process control, there has developed a distinct gap between the technological advancements of today and the well-established theory that has preceded it. This is most evident where the novel developments and structures often outpace the necessary guarantees and strong foundation required to ensure the possibility of widespread adoption. Therefore, the goals of this work are to broadly integrate those novel advanced control methods into the field of process control in such a way to increase the applicability and encourage implementation.

The first portion of this work seeks to further explore the nature …

Machine Learning Guided Insights Into Phonon Scattering Mechanisms For Tunable Thermal Transport In Materials From First-Principles, Niraj Bhatt

Open Access Dissertations

As device dimensions shrink in the current era of miniaturization, effective thermal management at the material level has become critical. In ultrasmall device lengths, traditional electronic heat transport becomes severely limited as boundary scattering effects curtail the electronic transport because the electronic mean free paths substantially exceed those of phonons. The resulting high-power- density devices generate thermal hot spots that compromise both performance and long-term reliability. This challenge has intensified the search for materials with superior phonon-mediated heat transport, a key requirement for effective thermal management in next-generation nanoelectronics. Accurately predicting thermal transport properties with near-experimental accuracy is therefore essential. …

Classification Of Variable Stars Using Convolutional Neural Network, Abhina Premachandran Bindu

Dissertations and Theses

This research focuses on developing Convolutional Neural Networks (CNNs), for the process of classifying and identifying variable stars through the analysis of unprocessed light curves from Transiting Exoplanet Survey Satellite (TESS). As astronomical data is becoming increasingly complex, and as advanced missions deploy sophisticated instruments for data collection, both the quality and quantity of the data are improving at a rapid pace. This has created an urgent need to automate the analysis process using efficient and effective methods, such as those based on machine learning. While previous research has explored machine learning approaches, there has been limited focus on implementing …

A Machine Learning Approach To Lithology Classification In Mining Using Measurement While Drilling And Exploration Data, Gbétoglo Charles Komadja, Erik Westman, Aditya Rana, Anye Vitalis

All Rsif Scholars' Publications

Accurate lithology classification is essential as variations in rock formations can significantly impact the cost and efficiency of mineral exploration and mining. Initial exploration maps provide insights into subsurface formations, though typically collected at widely spaced intervals. This study examines the use of early exploration data and Measurement While Drilling (MWD) data for lithology prediction through machine learning. The research specifically evaluates the benefit of incorporating spatial coordinates with MWD parameters to enhance classification accuracy, using support vector machine (SVM), random forest (RF), and extra gradient boosting (XGBoost) classifiers with tenfold cross-validation. The models were trained on 235,501 data points …

Ion Hydration In Bulk And Nanoconfined Water: Insights From Machine Learning Force Fields, Zachary D. Baker

Theses and Dissertations--Chemical and Materials Engineering

Understanding ionic hydration remains a central challenge in physical chemistry and materials science, as the interactions between ions and water molecules govern diverse phenomena ranging from electrolyte transport to selective ion separation. While experimental techniques have provided invaluable insights into solvation energetics and coordination numbers, they often lack atomistic resolution, particularly under nanoscale confinement where direct measurement becomes infeasible. Molecular dynamics (MD) simulations can bridge this gap; however, conventional classical force fields are limited by their simplified, fixed functional forms and empirical parameterization, whereas ab initio molecular dynamics (AIMD) achieves higher accuracy at the expense of severe computational cost and …

The Role Of Artificial Intelligence In Boosting Cybersecurity And Trusted Embedded Systems Performance: A Systematic Review On Current And Future Trends, Xiangyi Cheng, Ahmed Oun, Kaden Wince

Mechanical Engineering Faculty Works

As technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) …