Digital Commons Network^™

Ucf Libraries 2026 Faculty Research Showcase, Thomas R. Caswell, Patrick D. Green, Rae Mair, Yuqing Li, Athena Hoeppner, Sai Deng, Jeanne Piascik, Nokimul Hasan Arif, Peter Spyers-Duran, Tina Buck

Faculty Scholarship and Creative Works

The University of Central Florida (UCF) Libraries’ Faculty Research Showcase is an annual tradition that celebrates the scholarly achievements, research, and creative work of library faculty.

The 2026 Showcase took place on August 4, 2026, and features six engaging presentations covering a diverse range of topics:

• Thomas Caswell – UCF Libraries Faculty Scholarship: Growth & Impact

• Patrick Green – Making Moves: A Cross-Institutional Presentation on Library Skill Transference

• Rae Mair & Yuqing Li – Generative AI as Peer Tutor: Designing & Evaluating a Prompt to Support Source Identification & Evaluation

• Athena Hoeppner – Have you ever …

A Data-Driven Nutrient Density Scoring Framework For Beef Using Principal Component Analysis, Teja Vuppala

All Graduate Theses and Dissertations, Fall 2023 to Present

Beef is one of the most nutrient-rich foods in the human diet, providing high-quality protein, iron, omega-3 fatty acids, B vitamins, and a wide range of other compounds important to health. However, current nutrition scoring systems used on food labels were designed to compare different foods to one another — for example, beef versus broccoli — and do not work well for judging the nutritional quality of different beef samples relative to each other. A grass-fed steak and a conventionally-finished steak can carry nearly identical Nutrition Facts panels while differing substantially in their content of omega-3 fatty acids, vitamins, and …

Effective Traffic Routing Under Disastrous Events Through Machine Learning, Eric Itemuagbor

All Theses

Disastrous events such as hurricanes, wildfires, and floods routinely disrupt transportation networks, preventing emergency response, evacuation, and the delivery of critical supplies. Conventional optimization-based routing methods offer strong theoretical guarantees but cannot scale and are often too slow to support time-sensitive decision-making during a disaster, while prior machine learning approaches to disaster routing have largely prioritized prediction accuracy over inference speed and data efficiency. This thesis investigated whether lightweight machine learning models can deliver both competitive routing accuracy and superior inference speed when trained on limited data, addressing a practical gap between existing research and real-world deployment needs.

The road …

Essays On Reputation And Feedback System In Digital Markets, Huiyu Yang

All Dissertations

Online review systems play an increasingly important role in reducing information frictions in digital markets. This dissertation studies three aspects of these systems, including sellers' incentives to manipulate reviews, the design of rating systems, and how horizontally differentiated negative reviews affect consumer demand. The first chapter investigates key drivers influencing online sellers' decisions to invest in fake reviews. I first construct a model in which sellers strategically invest in fake reviews to influence consumers, who form beliefs about product quality based on observed reviews. I then empirically test the model's predictions on the main determinants of fake review prevalence using …

Ai-Driven Segmentation And Volumetric Response Modeling Of Liver Regions To Radiotherapy, Aashish Chandra Gupta

Dissertations and Theses (Open Access)

In liver-directed radiotherapy (RT), liver regions receiving higher doses typically undergo atrophy while contralateral/adjacent lower-dose regions may exhibit compensatory hypertrophy through regeneration of healthy tissue. Optimizing the RT plan to promote regional hypertrophy while minimizing the risk of developing atrophy has the potential to enhance post-RT liver function and long-term survivorship. However, current clinical practice largely relies on global liver dose-volume metrics during RT-planning, which may obscure favorable dose-response correlation and limit actionable guidance for clinicians. Therefore, we hypothesized that post-RT regional liver response is governed by a combination of region-specific dose-volume and patient clinical features, and that these responses …

From Geometry To Recognition: A Quantitative Framework For Tcr-Pmhc Structural Analysis In The Age Of Predicted Structures, Kun Hee Kim

Dissertations and Theses (Open Access)

Recognition of antigen by a T cell begins with a single physical event, the engagement of the T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC), and this interaction is encoded in the geometry with which the receptor docks onto the composite pMHC surface. Whether that docking geometry is biologically organized, and whether the structures now produced at scale by prediction preserve recognition or only reproduce that organization, are the two questions this thesis addresses. Two gaps stand in the way. The first is a gap of measurement, because docking geometry has never been made quantitatively comparable across many …

Advancements In Modern Seismic Monitoring: Integrating Novel And Traditional Methods For Earthquake Detection, Characterization, And Structural Imaging, Marc Adrian Garcia

Open Access Theses & Dissertations

Modern seismic monitoring has been transformed by machine-learning methods that detect and locate earthquakes at scales manual analysis cannot reach. This dissertation develops, validates, and applies such workflows across three settings that span the range of modern monitoring problems: a major subduction-zone aftershock sequence, the tectonic questions that sequence can answer, and an urban region without any local monitoring at all. First, I construct a high-resolution catalog for the aftershock sequence of the September 8, 2017, Mw 8.2 Tehuantepec, Mexico earthquake, by integrating deep-learning phase detection with established location and relocation methods. The resulting catalog of 11,374 relocated earthquakes is …

A Hybrid Machine Learning-Based Feasibility Prediction Of 3d Mechanical Designs Using Scalar And Geometric Features, Md Mohsin Uddin Fahim

Open Access Theses & Dissertations

The computational bottleneck of structural feasibility screening frequently hinders the transition from a digital 3D model to a physically manufactured component. Traditionally, engineers have relied on either overly rigid heuristic constraint checks or computationally exhaustive finite element simulations. To address this inefficiency, this thesis proposes and validates a hybrid machine-learning framework to predict the structural manufacturability of 3D mechanical designs. Moving beyond the conventional reliance on isolated scalar parameters, the proposed methodology extracts and integrates both scalar manufacturing constraints (e.g., tolerance, minimum feature thickness) and spatial geometric descriptors (e.g., bounding volume, aspect ratio) directly from STL mesh data. Utilizing a …

Machine Learning In Flow Cytometry For Acute Myeloid Leukemia Diagnosis: A Scoping Review, Katrina Jezzela M. Dela Pena Md, Mls (Ascp)

Other Specialties

Manuscript version:

Introduction: Flow cytometry is essential for the diagnosis of acute myeloid leukemia (AML), but conventional analysis is labor-intensive, operator-dependent, and increasingly complex. Machine learning (ML) may support automated analysis; however, its readiness for clinical implementation remains unclear. This scoping review mapped ML approaches applied to flow cytometry for AML-related diagnosis.

Methods: PubMed was searched for English-language primary studies applying artificial intelligence, ML, or deep learning to human flow cytometry data for AML diagnosis or classification. Eligible studies reported diagnostic performance metrics. Study characteristics, preprocessing, data representation, model architecture, validation strategy, performance metrics, interpretability, and implementation considerations were descriptively …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

Integrating Ai-Based Electricity Demand Forecasting With Solar Grid Planning To Enhance Sustainability And Reliability, Anas Thamer Mustafa, Omar Sharaf Al-Deen Al-Yozbaky

AUIQ Technical Engineering Science

Proper electricity-demand forecasting is essential for reliable power-system planning, particularly in urban networks facing rapid demand growth and transformer overloading. However, many previous studies have treated load forecasting and renewable-energy integration as separate tasks, which limits their usefulness for practical planning. This study develops an integrated forecasting–planning framework that links AI-based electricity-demand forecasting with photovoltaic (PV) system design and transformer-loading assessment. The framework is applied to real daily data from the Al-Intisar 132/33 kV substation in Mosul, Iraq, covering electrical load, temperature, population, and date-related variables for the period 2022–2024. Fourteen forecasting models from four methodological categories were evaluated: machine-learning …

Evaluation Of Gridded Precipitation Products And Machine-Learning Models For Rainfall Prediction In Arid Regions - Case Study In Qassim Region, Ksa, Ragab Rabeiy

Yanbu Journal of Engineering and Science

Receiving rainfall predictions in dry regions such as Saudi Arabia is challenging due to limited observational stations and the temporal and spatial variability of rainfall events. This study explores the performance of nine remote sensing rainfall datasets over the Qassim region of central Saudi Arabia. High-resolution rainfall data from ground-based sources were used to evaluate the accuracy of CHIRPS, TRMM, GPM, PERSIANN, CMORPH, CFSR, TerraClimate, TerraClimate_Monthly, and ERA5 datasets. Performance metrics such as R2, RMSE, MAE, and bias were calculated to quantify the accuracy of each dataset. Analysis revealed that ERA5 generally demonstrated better accuracy than the other datasets with …

Application Of Machine Learning For Correcting Defect-Induced Neuromorphic Circuit Inference Errors, Vedant V. Sawal, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

This paper presents a machine learning-based approach to correct inference errors caused by stuck-at faults in fully analog ReRAM-based neuromorphic circuits. Using a Design-Technology Co-Optimization (DTCO) simulation framework, the study models and analyzes six spatial defect types - circular, circular-complement, ring, row, column, and checkerboard - across multiple layers of a multi-array neuromorphic architecture for handwritten digit recognition tasks. It is demonstrated that the proposed correction method, which employs a lightweight neural network trained on the circuit's 10 output voltages, can improve inference accuracy from 55% to 90% (+ 35 percentage points) in highly degraded defective scenarios. Results show that …

Modeling Comparative Judgments With Machine Learning, Monoshiz Mahbub Khan

Theses

Many tasks can involve analyzing data points and assigning unbound numerical scores or labels. However, without a well-defined standard or point of comparison, such data annotation can require significant cognitive effort from human judges. To mitigate this issue, I propose the use of comparative judgments. Comparative judgments involve pairs of data points and a comparative label instead of a direct one. This process would only ask the human judge to compare the pair of data points and decide which one should have a higher label, but would not ask for the exact value. Moreover, ranking or sorting based tasks with …

Stylometric And Formal Patterns In The Scholarly Impact Of Scientific Literature, Joshua Ange, Eric Godat, Rajani Sudan

SMU Journal of Undergraduate Research

Scientific communication is typically tied to promoting public engagement and interest in science, increasing scientific literacy, and playing an essential role in policymaking. The success of public communication of scientific findings is largely associated with secondary characteristics of research (e.g. the style of writing and presentation), rather than the primary content or research quality. But it is unclear to what extent the success of scientific literature intended for working scientists is influenced by those same secondary characteristics. Does the writing style of scientific articles impact their success in academic spheres? In this study, we explore the stylometric and formal characteristics …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations 2020 - Present

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

To Study The Reward And Recognition Practices On Employee Motivation And Performance With Referenceto It Industry In Pune Region, Harsha Sammangi, Aditya Jagatha, Ruthwik Gullipalli

Research & Publications

Machine learning has substantially improved consumer credit-risk prediction, yet its deployment in lending decisionsraises persistent concerns regarding demographic fairness, financial exclusion, explainability, and regulatory defensibility. Thisstudy develops and empirically evaluates a Fairness-Aware Credit Intelligence (FACI) framework — a four-layer architectureintegrating predictive modeling, in- and post-processing fairness intervention, explainability and human policy override, andportfolio-level simulation and governance — using loan-level data from 412,683 consumer lending applications spanning 2021–2026. The study compares a traditional credit scorecard, gradient boosting and deep neural network models, two single-constraint fairness-aware models (demographic parity and equal opportunity), and the integrated FACI framework acrosspredictive accuracy (AUC-ROC), approval rates, …

Coupled-Pendulum Modeling In An Ode Class: An Assignment On Fourier-Initialized Gradient Descent In Machine Learning, Huy Truong, Andrew Bennett

CODEE Journal

As data-driven methods are increasingly used in science and engineering, students benefit from learning to integrate machine learning techniques with traditional mathematical modeling. We present a hands-on extra-credit assignment for an undergraduate ordinary differential equations (ODE) course that enables students to compare classical analytical methods with data-driven approaches on the same physical system. Using a coupled-pendulum system---two pendulums connected by a spring---with real experimental data acquired via video tracking of a real physical setup, students work through three models in a guided Jupyter notebook with all code provided. First, they fit a neural network with Fourier features as a purely …

Ai-Driven Detection Of Neurodevelopmental Disorder From Emotional Speech Using A Hybrid Cnn–Bilstm–Attention Framework, Nayarah Shabir, Parveen Lehana, Sheema Khan

School of Medicine Publications

Neurodevelopmental disorders (NDDs) are associated with impairments in communication, behavior, and social interaction, making accurate diagnosis clinically challenging. Autism Spectrum Disorder (ASD), a major NDD, often exhibits atypical speech patterns characterized by altered prosody and reduced emotional expressiveness. The study proposes a hybrid dual-path framework for ASD detection from emotional speech using two strategies: PCA–GMM-based acoustic modeling and a CNN–BiLSTM–Attention architecture for spectral–temporal feature learning. The proposed framework captures probabilistic, spectral, and temporal speech characteristics for robust ASD classification. Acoustic analysis demonstrated clear separability between ASD and non-ASD speech, while the deep learning framework achieved stable and reliable performance across …

Machine Learning-Based Forecasting Of Energy Consumption In A Nigerian Tertiary Institution, O. A. Adebimpe, S. S. Showunmi, F. T. Adedeji

Engineering and Technology Journal

An accurate electricity consumption forecast is key to effective energy planning and management in Nigerian tertiary institutions. While applications of machine learning (ML) in forecasting have increased, their deployment in predicting energy consumption in Nigerian tertiary institutions has been limited. Also, the significance of weather-related variables, academic and non-academic activities, and the staff and students' population in predicting energy consumption at a tertiary institution has been underexplored. This study addresses these by developing and evaluating six forecasting models, including Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), Support Vector Regression (SVR), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), …

Smart-Twin: Systematic Modeling And Real-Time Transportation With Digital Twins, Hovannes Kulhandjian

Mineta Transportation Institute

Urban transportation networks are increasingly affected by everyday congestion, non-recurring disruptions, and limited real-time visibility into what is happening on the road. Transportation agencies require advanced tools that support faster, proactive management and data-driven infrastructure decisions rather than passive monitoring. This report presents SMART-TWIN, an Artificial Intelligence (AI)–driven Digital Twin framework designed for real-time traffic operations, disruption detection, and performance-based decision-making support. The SMART-TWIN architecture integrates microscopic traffic simulation (detailed traffic modeling), long-horizon synthetic demand generation simulating real travel patterns, supervised machine learning, and performance analysis within a unified and reproducible workflow. A corridor-scale Digital Twin was developed for the …

Data-Driven Characterization Of Counties In The Prison Industrial Complex Using Clustering Analysis, Riley N. Tuccio

Capstone Projects

This project investigates the complex relationship between counties that house prisons in the United States and the rurality associated with them. The central research question explores how both county characteristics, such as variables corresponding to cost of living and demographics of a county, and prison characteristics, such as programming available to inmates and staffing levels, differ across the census-designated rural-urban distinctions. Furthermore, the study examines whether modern data science methods can more accurately define and distinguish these characteristics, providing a nuanced understanding of the Prison Industrial Complex (PIC) and its manifestation across various American communities. The motivation for this research …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas

Computer Science Faculty Research & Creative Works

The herpes simplex virus 1 (HSV-1) US3 is a multifunctional serine/threonine kinase that promotes HSV-1 replication and spread. But its role and the mechanisms by which US3 regulates actin cytoskeletal remodeling remain poorly defined. We combined flow cytometry, confocal microscopy, immunoprecipitation-mass spectrometry (IP-MS), protein complex mapping, and machine learning to characterize US3-mediated F-actin dynamics. Flow cytometry and confocal microscopy showed that wild-type HSV-1 induces significant F-actin remodeling, while the ΔUS3 mutant displays F-actin levels comparable to uninfected cells, identifying US3 as a key regulator. IP-MS identified 47 high-confidence US3 interactors enriched in conserved actin regulatory complexes, including Arp2/3 nucleation machinery, …

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

Ai-Powered Synthetic Biology: Current Situation, Challenges, And Future Perspectives, Izem Olcay Sahin, Nuriye Gokce, Duygu T. Yildirim, A. Baki Yildirim, Hilal Akalın, Donald Martin, Tommaso Beccari, Oscar Vicente, Iza Radecka, Fideline Tchuenbou-Magaia, Robert S. Marks, Ratnesh Lal, Satya Prakash, Adam Mechler, Mario Petrov Milkov, Svetlana Fotkova Georgieva, Ilia Iliev, Kosi Gramatikoff, Ed Judge, Milica Markovic, Radka Kaneva, Galina Aleksieva Yaneva, Nadya Vasileva Agova, Nikoleta Dobromirova Ivanova, Mariya Kiryakova Tsvetkova, Ivelin Rosenov Iliev, Michel Salzet, Kisung Ko, Michele Maffia, Chiara Coppola, Matteo Bertelli

Research Outputs: 2025-Present

Synthetic biology has evolved from a set of engineering aspirations to an operationally sophisticated discipline, and artificial intelligence (AI) is its fastest-growing accelerant. This review traces that convergence across six interlocking domains: systems-level biological modeling, de novo protein engineering, metabolic and microbial programming, multi-omics data integration, regulatory element design, and clinical translation. For each domain, we survey established results, integrate findings from 2010–2026 literature, and articulate the trajectories that will define the next decade. Emerging themes include physics-informed neural networks for mechanistically constrained biological modeling, drug design, and federated learning architectures that allow global omics collaboration without centralizing sensitive data, …

Geospatial Assessment Of Urban Expansion And Green Area Decline In Karbala Using Machine Learning: Supporting The Achievement Of Sdgs, Doaa Taha Yaseen, Oday Zakariya Jasim

Iraqi Journal of Architecture and Planning

Urban sprawl is a major challenge to sustainable development. This research examines geospatial patterns of urban growth and vegetation cover reduction in the Karbala District between 2017 and 2024. Sentinel-2 satellite imagery classification using a Support Vector Machine (SVM) model was evaluated, and accuracy was assessed using confusion matrices and a comparative evaluation against the Esri Sentinel Land Cover dataset. The SVM approach provided better classification precision. The change-detection analysis shows that the conversion of green and cultivated lands into built-up areas is significant. Although there has been an expansion of pivot-irrigated areas in other districts of the Karbala Governorate, …

Statistical Machine Learning For Calibrating Climate Model Wind Fields., Augustine Ouru

Graduate Theses and Dissertations

This thesis investigates statistical calibration methods for wind data from Global Climate Models (GCMs), with emphasis on wind speed and wind direction. The analysis uses 30 years of daily observed and GCM wind records from 9 spatial locations in the South East USA. Wind is represented in polar coordinates, but also often modeled in Cartesian coordinates. Four calibration models are compared: a marginal wind speed model, an independent Cartesian model. The calibration models are based on semi-parametric quantile regression, a flexible conditional density estimator fitted using neural networks. The neural network objective functions are optimized using maximum likelihood estimation (MLE) …

The Pivotal Role Of Artificial Intelligence In Optimizing Pharmaceutical Formulation And Delivery, Ali Khidher Abbas, Mustafa M. Noori, Ibtihal Abdulkadhim Dakhil, Mohammed Hussain Al-Mayahy

Al-Esraa University College Journal for Medical Sciences

Artificial intelligence (AI) has emerged as a transformative technological paradigm within the pharmaceutical sciences, offering innovative and forward-looking solutions to surmount the inherent limitations associated with conventional drug development, formulation design, and therapeutic delivery systems. Traditional pharmaceutical methodologies frequently entail protracted development timelines, extensive experimental procedures, and substantial financial expenditures, particularly within the domains of formulation optimization and drug delivery design. The integration of AI-driven technologies – encompassing machine learning (ML), deep learning (DL), artificial neural networks (ANNs), and predictive analytics – has considerably accelerated pharmaceutical research by refining decision-making processes, alleviating experimental burden, and enhancing formulation efficiency. The present …