Digital Commons Network^™

Extraction Of Patient Subtypes Using Llm Generated Knowledge Graphs Integrated With A Transformer Architecture, Benjamin Holmes, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Extracting patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria) from unstructured medical notes is an ongoing challenge due to the variability of clinical language and the complex nature of patient conditions. We demonstrate a pipeline that combines named entity recognition (NER), transformer embeddings, guided dimensionality reduction, and LLM-mediated knowledge graph integration to enhance patient extraction. The approach begins with NER using the UMLS metathesaurus [1] to extract clinical terms, followed by transformation into vector embeddings using a biomedical transformer. These embeddings are augmented with structured knowledge graph representations generated through an LLM-driven …

Ontology-Based Data Organization For The Enslaved.Org Project, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

The men, women, and children forced into slavery in the Atlantic world came from diverse African societies with long histories of political, economic, and cultural development. They were taken from the trading centers of the Hausa city-states, the farming and artisanal communities of Senegambia, the Kongo and Mbundu polities of West Central Africa, and many other regions. They carried with them agricultural expertise, metallurgical skills, medical knowledge, religious traditions, and oral histories that helped sustain communities in the face of displacement and enslavement.Enslavement did not erase this intellectual and cultural inheritance, nor did it render its victims passive numbers in …

Ontology Population Using Llms, Sanaz Saki Norouzi, Adrita Barua, Antrea Christou, Nikita Gautam, Andrew Eells, Pascal Hitzler, Cogan Shimizu

Computer Science and Engineering Faculty Publications

No abstract provided.

Application Of Large Language Model Methods In Scientific And Technical Intelligence Practice, Bolin Hua, Yingze Wang

Journal of Scientific Information Research

[Purpose/significance]With the strong ability to process large-scale datasets and outstanding performance in various natural language processing tasks, large language models (LLMs) have excelled across multiple industries.Since scientific and technical intelligence primarily relies on textual data, LLMs are naturally well-suited for this field, ushering in a new wave of transformative changes. [Method /process]This article discusses the advantages of LLMs from five perspectives: low-dimensional dense vector representations of text, large-scale pre-trained models,fine-tuning and prompt learning, high-quality large-scale training data, and human alignment techniques. [Result/conclusion]LLMs have extensive applications in tasks such as intelligence identification, intelligence tracking, intelligence evaluation, and intelligence prediction, resulting in …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Digital Infrastructure Development Through Digital Infrastructuring Work: An Institutional Work Perspective, Adrian Yeow, Wee-Kiat Lim, Samer Faraj

CCX Research

Being able to understand and characterize the digital infrastructure development (DID) process has become even more pressing today due to the rapid advent and implementation of new digital infrastructure (DI) in organizations as well as since the COVID-19 crisis. While information systems (IS) research has begun to recognize the institutional nature of such digital infrastructures, there remains a gap in our understanding of how such developments unfold from an institutional perspective. Through our field study of a digital infrastructure development project involving the implementation of an enterprise-wide electronic medical record system at a large US medical facility, we show how …

Why Ai Monitoring Faces Resistance And What Healthcare Organizations Can Do About It: An Emotion-Based Perspective, Karl Werder, Lan Cao, Eun Hee Park, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Continuous monitoring of patients' health facilitated by artificial intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance to adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients' rational evaluation of the technology's costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce …

Ripples: An Automated Embedding Generation Algorithm For The Forward-Forward Algorithm, Spencer Connolly

All Graduate Theses, Dissertations, and Other Capstone Projects

The Forward-Forward algorithm (FF) is yet another novel invention by Geoffrey Hinton, the creator of the famous backpropagation algorithm (BP). Since its proposal, many papers have been published exploring its potential, and good progress has been made in increasing its viability. Though FF continually falls short of BP, its purpose is not to replace BP and preliminary research shows that there is plenty of room for growth. In this paper, we present a literature review for FF algorithms applied to Convolution Neural Networks (CNN) for image classification tasks and set the stage for applying FF to more complex datasets. The …

Future Of Bse Days 2025: Growing A Regenerative Bse, Derek M. Heeren, Santosh Pitla, Jennifer R. Keshwani, Mark Stone

Department of Agricultural and Biological Systems Engineering: Presentations and White Papers

The Future of BSE Days 2025: Growing a Regenerative BSE brought together over 150 faculty, staff, students, and partners to envision the next quarter-century of the Department of Biological Systems Engineering. The event emphasized regeneration—not only of resources and ecosystems, but also of ideas, learning models, and relationships. Across seven major sessions—three Spark Talks and four Pillar Workshops—participants explored how BSE can thrive amid technological disruption, demographic change, and societal transformation.

Key Outcomes

Redefining Impact: This session challenged participants to evolve from counting outputs to valuing relationships, collaboration, and community well-being.

Adaptive Learning Models: This discussion introduced design studios, micro-credentials, …

Examining The Disclosure Of Sensitive Information Through Mobile Applications: A Privacy Calculus And Warning Experiment On Location-Based Services, Dwayne A. Ford

CCAC Theses and Dissertations

Smartphones and mobile applications have become all but ubiquitous in society. These applications provide a plethora of functions both in standalone and network configurations. Many of these popular applications utilize Location-Based Services (LBS) to deliver value to the user. Whether for navigation, transportation or social interactions, sharing information is essential when using these applications. While LBS applications provide various benefits, the sharing of location data also creates significant privacy risks. In many cases, users are unaware of the real risks and continue to share their location data in exchange for the benefits the application provides.

The problem identified in this …

A Hybrid Deep Learning Model For Iot Network Anomaly Detection, Yonas Getachew Mulissa

CCAC Theses and Dissertations

The rapid expansion of Internet of Things (IoT) networks has heightened the need for intelligent, automated Anomaly Detection (AD) systems to identify sophisticated and evolving cyber threats. This study designed, implemented, and evaluated a broad range of deep learning models—including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs) (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (BiGRU), Bidirectional Long Short-Term Memory (BiLSTM)), Transformer-based architectures, Autoencoders, and hybrid combinations—to address the challenge of multiclass anomaly classification in IoT traffic. Using two benchmark datasets, IoT-DS-2 and CIC-IoT-2023, we conducted extensive experiments to assess classification performance, training efficiency, and …

Development Of A Phishing Risk Exposure Taxonomy On Mobile Devices In The Healthcare Industry, Christopher P. Collins

CCAC Theses and Dissertations

Phishing emails accessed on mobile devices present substantial risks to healthcare organizations when employees often operate under high cognitive load and with limited cybersecurity training. Despite widespread security awareness initiatives, healthcare workers continue to engage with phishing content on mobile platforms, posing threats to organizational data. Given the high value of healthcare data and the increasing sophistication of phishing schemes targeting healthcare professionals, there is a pressing need to enhance their ability to recognize phishing indicators on mobile devices.

This study developed and validated a Healthcare Workers Phishing Risk Exposure (HWPRE) taxonomy, designed to classify healthcare workers based on their …

An Evaluation Of Data Protection And Privacy Issues Introduced By Byod In Financial Institutions, Andy Miguel Santana

CCAC Theses and Dissertations

The idea of “bring your own device” (BYOD) allows organizational employees to conduct their tasks or processes on their own personal devices, has increased organizational efficiency significantly while allowing employees more flexibility. However, this approach also introduces major concerns about the security of organizational data as employees take their devices everywhere with them, opening more opportunities for unauthorized access to important data. Another major concern is the privacy of employee personal data. As many organizations implement BYOD, employees worry that with organizational monitoring and device management, their personal data is at risk as well. The problem this study tackles is …

Architectural Technical Debt Migrating Object Oriented Systems To Modular Architectures, Lionel Standridge

CCAC Theses and Dissertations

Architectural Technical Debt (ATD), a subset of Technical Debt (TD), arises when outdated architectural decisions present significant challenges to the maintenance and evolution of legacy object-oriented monolithic systems. These systems tend to have tightly coupled components and rigid dependencies, making it difficult to scale, adapt, and modernize. This dissertation investigated strategies for managing ATD during the transition from monolithic architectures to modular systems. By identifying the root causes of ATD in a legacy objectoriented system and evaluating various decomposition strategies, this research proposed a framework to guide practitioners in reducing ATD and improving a system’s modularity. Using quantitative metrics, the …

Enhanced Network Anomaly Detection Using Machine Learning Models, Ousmane Barry

CCAC Theses and Dissertations

This dissertation investigates enhanced network anomaly detection using Machine Learning (ML) models. The study addresses two distinct classification problems: binary classification and multiclass classification. In the binary classification task, network traffic data is categorized as either "normal" or "abnormal," where abnormal includes all non-normal traffic. Leveraging the balanced nature of the dataset, this study develops optimized models that achieve consistently high classification performance. Key metrics, including precision, recall, and F1 scores, are used to ensure robust evaluation and reliable detection across all classes.

For multiclass classification, only classes present in both training and test datasets are included to ensure meaningful …

Empirical Assessment Of Cybersecurity Competencies Through Human-Generative Artificial Intelligence (Genai) Teaming, Dariusz Witko

CCAC Theses and Dissertations

The critical shortage of skilled cybersecurity professionals, with over 750,000 unfilled positions in the United States (U.S.), combined with rising practitioner burnout and the complexity of modern cyber threats, poses significant risks to national security. As Generative Artificial Intelligence (GenAI) emerges as a potential tool to support cybersecurity operations, its role in assisting human analysts with high-demand tasks offers both opportunities and challenges. While GenAI can improve efficiency, it also introduces adversarial risks if manipulated by malicious actors. This study investigated how human-GenAI collaboration can address these challenges, focusing on the fundamental cybersecurity knowledge, skills, and task completion required for …

Empirical Analysis Of Political Districting Splitability Via Uniform Spanning Trees In Polynomial Time, Brooke C. Feinberg

Scripps Senior Theses

This work expands a recently proven conjecture that a polynomial fraction of all uniform spanning trees (USTs) are splittable into k balanced partitions on grid graphs to real-world political districting plans. We investigate whether similar structural properties hold for the planar dual graphs of U.S. counties (cnty) and tracts (t), using Wilson’s algorithm to generate uniform random spanning trees and Breadth- First Search (BFS) to check for splitability into balanced partitions. Our empirical findings suggest that real-world districting plans can be split into 2-balanced, connected partitions in a fraction of polynomial time. This result highlights the potential for scalable redistricting …

A Vision Transformer Based Assistive System For Dermatological Diagnosis In Systemic Lupus Erythematosus, Syeda Lamima Farhat

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) is a complex and often underdiagnosed autoimmune disease that affects multiple organs and presents with a wide range of symptoms-ranging from fatigue and joint pain to life-threatening organ damage. One of its most visible and diagnostically significant indicators is the Butterfly Malar Rash (BMR), a distinctive facial rash that often resembles other common dermatological conditions like rosacea, acne, eczema, and fifth disease. This overlap can lead to misdiagnosis or delayed detection, especially in busy clinical environments. To assist dermatologists in distinguishing BMR from similar facial rashes, this study explores the development of an AI-powered image classification …

Evaluating Aspect-Based Sentiment Analysis In Healthcare Drug Reviews Across Machine Learning, Deep Neural Networks, And Transformer Models, Eun Soo Park

All Graduate Theses, Dissertations, and Other Capstone Projects

Sentiment analysis has become a critical area of research in Natural Language Processing (NLP), enabling insights from unstructured text. Within this field, Aspect-Based Sentiment Analysis (ABSA) plays a practical role in domains such as healthcare, where patients drug reviews often contain diverse opinions across multiple aspects, including overall comments, perceived benefits, and side effects. However, aspect-level classification remains challenging due to class imbalance, subtle sentiment expression, and the limitations of traditional models. This research investigates the performance of three modeling paradigms: traditional machine learning (SVM, SVC, and XGBoost), deep learning (CNN-BiLSTM), and transformer-based approaches (DistilBERT sentence-pair classification). Using the UCI …

Identifying All Matches Of A Rigid Object In An Input Image Using Visible Triangles, Abdullah N. Arslan

Faculty Publications

It has been suggested that for objects identifiable by their corners, every triangle formed by these corner points can serve as a reference for detecting other corner points. This approach enables effective rigid object detection, including partial matches. However, when there are many corner points, the implementation becomes impractical due to excessive memory requirements. To overcome this, we propose a new algorithm that leverages Delaunay triangulation, considering only the triangles generated by the Delaunay triangulation to reduce the complexity of the original approach. Our algorithm is significantly faster and requires significantly less memory, offering a viable solution for large problem …

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Brain Tumor Classification Using Deep Learning: Custom Cnn Vs. Resnet50, Rayen Bentemessek

ICT

The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection …

‘Waves Of Imagination’ Unconditional Spectogram Diffusion Using Diffusion Architecture., Rahul Vanukuri

Computer Science and Engineering Theses - Archive

The swift evolution of wireless communication technologies,particularly in the field of rf signals or in CBRS bands,demands increasingly sophisticated signal processing techniques to ensure efficient transmission, reception, and spectrum management.Traditional approaches to signal generation and reconstruction, although effective in controlled environments, often struggle to cope with the challenges presented by real-world noisy conditions, hardware constraints, and limited access to large-scale datasets. In response to these limitations, this thesis explores the application of diffusion models—a class of generative models known for their ability to produce high-fidelity samples—to the domain of spectrogram generation for communication signals.

Different from conventional strategies to simulate …

Prescribed-Time Nash Equilibrium Seeking For Pursuit-Evasion Game Under Intermittent Control With Undirected/Directed Graph, Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper studies the prescribed-time Nash equilibrium (PTNE) seeking problem of the pursuit-evasion game (PEG) with second-order dynamics under the intermittent control (IC) strategy. To achieve Nash equilibrium (NE) in a user-defined prescribed-time, a time-varying high-gain function is incorporated into the design. The core challenge lies in applying IC to NE seeking, which complicates the convergence analysis and control design. To address this sticking point, we construct an auxiliary function and propose a Lyapunov function considering second-order dynamics to solve the PTNE seeking problem of PEG. Building upon the results for undirected graphs, we further extend our findings to directed …

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an …

Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without …

Anti-Jamming Attack Mixed Strategy For Formation Tracking Control Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Yongbao Wu, Jian Liu, Chaoxu Mu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, …

Applying Machine Learning And Optimization Algorithms To Perform Feature Selection, Shizhao Yu

Theses and Dissertations (Comprehensive)

The objective of feature selection in the realms of machine learning and data mining is integral, serving as an efficient mechanism to eradicate redundant or irrelevant features, and subsequently augmenting the performance of predictive models. In the contemporary landscape of big data, with the escalating dimensionality of datasets, the efficacy of traditional feature selection methodologies is compromised, due to their computational complexity and ineptitude in addressing the curse of dimensionality. This thesis posits a pioneering feature selection framework that amalgamates machine learning with advanced optimization algorithms. The methodology employs a Support Vector Machine (SVM), in conjunction with a cutting-edge metaheuristic …

Building Cyber Resilience: Educational Programs In K-12 Education, Mildred Jones, Vukica Jovanovic, Petros Katsioloudis

Engineering Technology Faculty Publications

The article discusses the challenges of teaching cybersecurity in K-12 education. Topics mentioned include the lack of access to resources and appropriate professional development, the career and technical education cybersecurity pathways, the fundamental pathways for cybersecurity and information technology and the results of program evaluation in several local community high schools from 2021 and 2022.