Digital Commons Network^™

Ethical Sourcing In The Context Of Health Data Supply Chain Management: A Value Sensitive Design Approach, Camille Nebeker, Jean Christophe Bélisle-Pipon, Benjamin X Collins, Ashley Cordes, Kadija Ferryman, Brian J Mcinnis, Shannon K Mcweeney, Laurie L Novak, Susannah Rose, Joseph M Yracheta, Ishan C Williams, Xiaoqian Jiang, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethical, Legal And Social Implications Ai Working Group

Faculty, Staff and Student Publications

Objective: The Bridge2AI program is establishing rules of practice for creating ethically sourced health data repositories to support the effective use of ML/AI in biomedical and behavioral research. Given the initially undefined nature of ethically sourced data, this work concurrently developed definitions and guidelines alongside repository creation, grounded in a practical, operational framework.

Materials and methods: A Value Sensitive Design (VSD) approach was used to explore ethical tensions across stages of health data repository development. The conceptual investigation drew from supply chain management (SCM) processes to (1) identify actors who would interact with or be affected by the data repository …

Ethical Sourcing In The Context Of Health Data Supply Chain Management: A Value Sensitive Design Approach, Camille Nebeker, Jean Christophe Bélisle-Pipon, Benjamin X Collins, Ashley Cordes, Kadija Ferryman, Brian J Mcinnis, Shannon K Mcweeney, Laurie L Novak, Susannah Rose, Joseph M Yracheta, Ishan C Williams, Xiaoqian Jiang, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethical, Legal And Social Implications Ai Working Group

Faculty, Staff and Student Publications

Objective: The Bridge2AI program is establishing rules of practice for creating ethically sourced health data repositories to support the effective use of ML/AI in biomedical and behavioral research. Given the initially undefined nature of ethically sourced data, this work concurrently developed definitions and guidelines alongside repository creation, grounded in a practical, operational framework.

Materials and methods: A Value Sensitive Design (VSD) approach was used to explore ethical tensions across stages of health data repository development. The conceptual investigation drew from supply chain management (SCM) processes to (1) identify actors who would interact with or be affected by the data repository …

Soft Sensing Of Biological Oxygen Demand In Industrial Wastewater Using Machine Learning Models, Muhammad Hassnain, Sarada M.W. Lee, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

Traditional methods for determining biological oxygen demand (BOD) from industrial water resource recovery facilities (WRRFs) are time-consuming and often impractical for real-time process control. This study explores the application of machine learning (ML) and artificial intelligence (AI) models for the prediction of final effluent BOD (F-BOD) based on physicochemical and operational parameters by leveraging nineteen years of historical laboratory and instrumentation data from the WRRF of an essential oil manufacturing plant. The predictions from these models are then used to simulate the process dynamics, assessing the optimal operational boundary conditions for all input parameters at which the target (F-BOD) falls …

Characterisation And Prediction Of Wax Precipitation For Effective Mitigation Using Data Analytics, Alek Deng Cyer Rehan, Aliyu Adebayo Sulaimon

Platform: A Journal of Engineering

The precipitation of wax in crude oil pipelines poses a significant operational challenge for the petroleum industry. This study proposes a novel approach to characterising crude oil and predicting wax appearance temperature (WAT) using advanced data analytics techniques, including group method of data handling (GMDH), support verctor machines (SVM), extreme gradient boosting (XGBoost) and various regression models, as alternatives to conventional artificial neural networks (ANN). Crude oil was classified into four categories: heavy oil/biodegraded, paraffinic oil, waxy oil, and light oil/paraffinic condensate. A detailed characterisation framework based on the gamma distribution function (GDF) was employed to split lumped hydrocarbon fractions …

Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of …

Ensemble Learning-Based Vulnerability Detection And Forecasting, Hamdan Alshehri

University of Bisha Journal for Basic and Applied Sciences

The problem of identifying threats in cyberspace is basically a classification problem for machine learning, which is also a need of the hour. While a large number of works related to vulnerability detection have been reported recently, the majority of the works have been focused on IT systems. Various factors related to these systems are considered for the classification of risk levels in these works, and appropriate recommendations about expected risks and vulnerabilities in the future will be given to their users. This work aims to detect possible risks and vulnerabilities and notify the user in a timely manner using …

Enhanced Prediction Of Condensation Pressure Drop In Mini And Microchannels Using Physics-Assisted Machine Learning, Farshad Barghi Golezani, Jiayuan Li, Forouzan Naderi, Alexander Meylikhov, Logan M. Pirnstill, Issam Mudawar, Chirag R. Kharangate

Faculty Scholarship

Reliable prediction of frictional pressure drop during condensation in mini‑ and microchannels underpins both thermal management effectiveness and overall heat transfer performance in compact two‑phase heat exchangers, cold‑plates, and on‑chip cooling loops. Excess pressure loss burdens pumps, raises electrical consumption, and can destabilize flow, whereas under‑prediction risks temperature overshoot and premature dryout. Conventional empirical correlations and flexible machine-learning models can lose accuracy once channel size, working fluid, or operating conditions stray beyond their testing range. This study uses a physics‑assisted machine‑learning framework that overlays an XGBoost residual learner on the Kim–Mudawar separated‑flow correlation to achieve high fidelity and robustness in …

Enhanced Pmu Data Imputation Using Variational Autoencoder: A Novel Approach For Improved Accuracy, Subin Koshy, Sunitha Rajan, Elizabeth P. Cheriyan

Turkish Journal of Electrical Engineering and Computer Sciences

Data collected from various measurement devices, such as phasor measurement units (PMUs), form the backbone of power system monitoring algorithms such as state estimation algorithms. Even though modern PMUs are efficient, data inaccuracies and loss can still occur due to problems like equipment malfunctions, communication issues, and hardware or software glitches. Missing values in the datasets can also affect the overall quality, impacting the algorithms dependent on them. This article introduces a novel technique for real-time imputation of missing data using variational autoencoder (VAE) and utilizing the latent space representation acquired from VAE. The method has demonstrated enhanced accuracy in …

Toward A Clinically Actionable, Electronic Health Record-Based Machine Learning Model To Forecast 90-Day Change In Hemoglobin A1c In Youth With Type 1 Diabetes: Feasibility And Model Development Study, Erin M. Tallon, David D. Williams, Cintya Schweisberger, Colin Mullaney, Brent Lockee, Diana Ferro, Craig Vandervelden, Mitchell S. Barnes, Angelica Cristello Sarteau, Anna R. Kahkoska, Susana R. Patton, Sanjeev Mehta, Ryan Mcdonough, Marcus Lind, Leonard D'Avolio, Mark A. Clements

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Clinicians currently lack an effective means for identifying youth with type 1 diabetes (T1D) who are at risk for experiencing glycemic deterioration between diabetes clinic visits. As a result, their ability to identify youth who may optimally benefit from targeted interventions designed to address rising glycemic levels is limited. Although electronic health records (EHR)-based risk predictions have been used to forecast health outcomes in T1D, no study has investigated the potential for using EHR data to identify youth with T1D who will experience a clinically significant rise in glycated hemoglobin (HbA1c) ≥0.3% (approximately 3 mmol/mol) between diabetes clinic visits. …

Non-Invasive Detection Of Choroidal Melanoma Via Tear-Derived Protein Corona On Gold Nanoparticles: A Machine Learning Approach, Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi-Esfahani, Babak Masoomian, Fariba Ghassemi, Mojtaba Arjmand, Saeed Heidari Keshel, Fatemeh Atyabi, Rassoul Dinarvand

Wills Eye Hospital Papers

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to …

Electromagnetic Simulations Of Antennas On Gpus For Machine Learning Applications, Murat Temi̇z, Vemund Bakken

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an antenna simulation framework powered by graphics processing units (GPUs) based on an open-source electromagnetic (EM) simulation software (gprMax) for machine learning applications of antenna design and optimization. Furthermore, it compares the simulation results with those obtained through commercial EM software. The proposed software framework for machine learning and surrogate model applications will produce antenna data sets consisting of a large number of antenna simulation results using GPUs. Although machine learning methods can attain the optimum solutions for many problems, they are known to be data-hungry and require a great deal of samples for the training stage …

Novel Biological Prompting Technology: Automatic Directional Control By Eye-Tracking Sensor, Guannan Liu, Justin Wang, Hsiao Chun Wu, Gaojian Huang, Anil Kumar, Yiyan Wu, Shih Yu Chang, Yue Luo

Faculty Research, Scholarly, and Creative Activity

Biological prompting or human teleoperation have been appealing to many researchers lately. Hands-free and voice-free command or control of vehicles, aircraft, machines, robots, etc. would provide huge convenience to users and allow them for multi-tasking. In this work, we would like to dedicate to a new biological prompting function, namely automatic directional control using instantaneous eye gaze data. In this work, we would like to investigate how to apply advanced machine-learning models to design novel automatic gaze-direction identification techniques. We explore the difficulty, namely infliction with outliers and variations, in dealing with eye-tracking data and propose to transform the original …

Novel Convolution Neural Network For Enhanced Network Attack Pattern Recognition, Salahaldeen Duraibi

University of Bisha Journal for Basic and Applied Sciences

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel …

Uav-Based Transport Management For Smart Cities Using Machine Learning, Sweekruthi Balivada, Jerry Gao, Yuting Sha, Manisha Lagisetty, Damini Vichare

Faculty Research, Scholarly, and Creative Activity

Highlights: What are the main findings? A smart transport management system based on UAV data integrating advanced machine learning and deep learning techniques is proposed to enhance road anomaly detection and severity classification. The system employs a comprehensive multi-stage framework, integrating a high-precision obstacle detection model, six specialized severity classification models, and an aggregation model to deliver accurate anomaly assessment, enabling strategic, data-driven road maintenance and enhanced transportation safety. What is the implication of the main finding? A scalable and efficient solution is proposed to enhance road safety and optimize transportation management through intelligent anomaly detection and severity assessment. This …

Machine-Learning-Assisted Discovery Of Lattice Dynamics Signatures Of Sodium Superionic Conductor, Ogheneyoma Aghoghovbia, Riccardo Rurali, Mohammed Al-Fahdi, Joshua Ojih, De-En Jiang, Ming Hu

Faculty Publications

Sodium superionic conductors are key to the development of all-solid-state sodium batteries. Discovery of new superionic conductors has traditionally relied on insights from material defect chemistry and the transition/hopping theory, while the role of lattice vibrations, i.e., phonons, remains underexplored. We identify key lattice dynamics signatures that govern ionic conductivity by analyzing the phonon mean squared displacement (MSD) of Na⁺ ions. By high-throughput screening of a dataset of 3903 Na-containing structures, we establish a strong positive correlation between phonon MSD and diffusion coefficients, providing a quantitative correlation between lattice dynamics and ion transport. To accelerate this discovery, we …

Unveiling The Interplay Of Electronic And Phononic Excitations In Laser-Induced Oxygen Activation On Ru(0001), Xiangrui Wang, Jiamin Wang, Paul Spiering, Liping Liu, Jörg Meyer, Jerry L. Larue, Hongliang Xin

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding laser-induced dynamics on metal surfaces poses significant challenges due to the intricate interplay between electronic and phononic degrees of freedom, which evolve on distinct timescales. In this study, we introduce a machine learning-accelerated approach to molecular dynamics simulations that incorporates anisotropic electronic friction, providing deeper insights into these complex processes. Our framework extends the accessible time and length scales for nonadiabatic dynamics simulations, enabling a detailed investigation of the laser-induced activation of oxygen on the Ru(0001) surface. Statistical analysis reveals that strong electronic excitation dominates the first 800 fs after laser exposure. Beyond this timescale, energy deposited by electronic …

Book Reviews, Usawc Press

The US Army War College Quarterly: Parameters

No abstract provided.

Integrating Identity-Based Identification Against Adaptive Adversaries In Federated Learning, Jakub Kacper SzeląG, Ji-Jian Chin, Lauren Ansell, Sook-Chin Yip

School of Engineering, Computing and Mathematics

Federated Learning (FL) has recently emerged as a promising paradigm for privacy-preserving, distributed machine learning. However, FL systems face significant security threats, particularly from adaptive adversaries capable of modifying their attack strategies to evade detection. One such threat is the presence of Reconnecting Malicious Clients (RMCs), which exploit FL’s open connectivity by reconnecting to the system with modified attack strategies. To address this vulnerability, we propose the integration of Identity-Based Identification (IBI) as a security measure within FL environments. By leveraging IBI, we enable FL systems to authenticate clients based on cryptographic identity schemes, effectively preventing previously disconnected malicious clients …

Transparent Eeg Analysis: Leveraging Autoencoders, Bi-Lstms, And Shap For Improved Neurodegenerative Diseases Detection, Badr Mouazen, Ahmed Bendaouia, Omaima Bellakhdar, Khaoula Laghdaf, Aya Ennair, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Highlights

• Novel hybrid architecture: Combined autoencoders with bidirectional LSTM networks for enhanced EEG signal classification, achieving 98% accuracy in distinguishing AD, FTD, and healthy controls.

• Explainable AI integration: Implemented SHAP (SHapley Additive exPlanations) framework to enhance model transparency and identify entropy as the most influential feature for neurodegenerative disease detection.

• Optimal temporal segmentation: Demonstrated that 5-s EEG windows with 50% overlap provide the best balance between classification accuracy and computational efficiency.

• Comprehensive feature extraction: Utilized Power Spectral Density (PSD) analysis across standard frequency bands (Delta, Theta, Alpha, Beta, Gamma) following autoencoder-based dimensionality reduction.

• Superior performance validation: Outperformed traditional machine learning …

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Developing Workflows For Passive Acoustic Detection Of Bedload Transport, Quinn Morgan

Dissertations and Theses

Bedload transport is defined as the amount of sediment, including gravel and rocks, traveling down stream. Monitoring bedload transport is important for river safety, hydrological studies and conservation efforts. Existing methods of directly measuring bedload transport (or bedload flux) involve lowering a collection device into a river and measuring the sediment collected; which can be expensive and time consuming. Hydroacoustic sensors, such as hydrophones, have had success tracking bedload flux remotely. This works by measuring the relatively high frequency of sediment impacts to map onto total bedload transported. No perfected method for detection of sediment generated noise (SGN) currently exists. …

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

A Polar Turbulence Invariant Map With Applicability To Realisable Machine Learning Turbulence Models, James G. Wnek, Christopher Schrock, Eric M. Wolf, Mitch Wolff

Mechanical and Materials Engineering Faculty Publications

Invariant maps are a useful tool for turbulence modelling, and the rapid growth of machine learning-based turbulence modelling research has led to renewed interest in them. They allow different turbulent states to be visualised in an interpretable manner and provide a mathematical framework to analyse or enforce realisability. Current invariant maps, however, are limited in machine learning models by the need for costly coordinate transformations and eigendecomposition at each point in the flow field. This paper introduces a new polar invariant map based on an angle that parametrises the relationship of the principal anisotropic stresses, and a scalar that describes …

Hybrid Feature Selection And Ensemble Classification For Climate Change Indicators: A Machine Learning Approach, Firas Ali Hashim, Yasmin Makki Mohialden, Nadia Mahmood Hussien

Terra Joule Journal

Climate change poses a significant challenge across various domains, necessitating advanced analytical techniques for effective monitoring and decision-making. This paper introduces a hybrid machine learning pipeline that selects and classifies features of climate change indicators. A synthetic dataset, representing different climate indicators and their application fields, is generated and processed using a combination of SelectKBest and Recursive Feature Elimination with Cross-Validation (RFECV) for robust feature selection. An ensemble of random forest and gradient-boosting classifiers is employed to enhance prediction accuracy. The predicted methodology has attained the lower initial accuracy of classification of 0.4 with five chosen climate characteristics. Optimizing the …

Efficient Inference Of Performance Models In Openmp Applications, Gaurav Punjabi

Computer Science and Engineering Master's Theses

Choosing the best OpenMP parameters such as thread count, scheduling type, and chunk size is essential for optimizing parallel program performance. One of the promising approaches is to infer a (pre-trained) performance model at runtime to determine the parameters to run OpenMP parallel regions. Such a performance prediction model can require programs’ code information such as intermediate representation (IR) and other at-runtime information (e.g., input sizes) to make a performance prediction. In such a scenario, extracting or querying the IR information at runtime can create a significant runtime overhead. This thesis proposes a compiler-asssited tuning framework that shifts IR extraction …

A Mobility Data-Driven Approach To Analyzing And Modeling Park Accessibility Inequities And Use In U.S. Cities, Michaelmary Chukwu

Graduate Theses and Dissertations

Urban Park accessibility inequities have been a longstanding issue globally and the United States in particular. Despite the benefits that urban parks provide, use and access is not equitably distributed among the population, often favoring historically privileged populations over others (disadvantaged). Fortunately, improvements have been made in recent years to promote balanced park distribution in some cities; however, evidence still shows that most cities are behind. Interestingly, the rise in human mobility data has facilitated the study of human behavior and use of activity spaces. This thesis advances the existing studies on park accessibility and emerging human mobility to study …

Low-Resource Ecoacoustic Audio Classification, Enis Berk Coban

Dissertations, Theses, and Capstone Projects

Ecoacoustic monitoring via machine learning enables scalable analysis but is often constrained by labeled data scarcity, particularly in remote regions like the Arctic. This thesis confronts low-resource ecoacoustic audio classification by developing and evaluating complementary machine learning methodologies. We introduce EDANSA, the first publicly available, expert- labeled Arctic dataset of its kind, curated via novel active learning, alongside a baseline CNN. We systematically evaluate transfer learning, showing general audio embeddings effectively bootstrap classifiers for challenging Arctic sounds, significantly outperforming direct label mapping. Optimizing label utility, we investigate standard data augmentation and introduce novel audio data valuation via Shapley values, revealing …

Democratizing Community Discourse Analysis In Computational Social Science, Md Towhidul Absar Chowdhury

Theses

Community resource inequities and undetected infrastructure vulnerabilities cost municipalities billions annually, with disproportionate impacts on marginalized communities. Current computational approaches to community needs assessment suffer from two critical limitations: they rely on aggregate-level analysis that obscures granular community expressions, and they implement sophisticated computational tools that remain inaccessible to stakeholders without technical expertise. This creates what we term the Community-Computational Gap—a persistent divide where domain experts with vital contextual knowledge cannot access the analytical tools they need, while computational experts develop models without sufficient community context. This dissertation addresses these challenges through a novel methodological framework for fine-grained utterance-level classification …

Physics-Based Machine Learning Framework For Predicting Structure-Property Relationships In Ded-Fabricated Low-Alloy Steels †, Atiqur Rahman, Md Hazrat Ali, Asad Waqar Malik, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

Trends And Predictive Modeling Of Real Estate Prices In Major Saudi Arabia Cities, Meshal S. Aldahas

Theses and Dissertations

his research examines historical trends and explanatory modeling of real estate prices in major Saudi cities, with a focus on Riyadh, Jeddah, and Dammam. Using a mixed-methods approach, the study integrates quantitative data from 2010–2023, including housing and macroeconomic indicators, with qualitative insights drawn from over 320 survey responses that captured consumer sentiment on affordability, job security, and housing policies. A combination of descriptive statistics, ARIMA and Exponential Smoothing techniques was applied to detect long-term patterns, seasonal variations, and market shocks. Predictive modeling was conducted using Linear Regression, Decision Trees, and Neural Networks, with results showing that job security consistently …