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2025

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Visual Attention And Memory Performance On Advertisements Featuring Celebrities, Kyrho Callis Jan 2025

Visual Attention And Memory Performance On Advertisements Featuring Celebrities, Kyrho Callis

College of Graduate Studies: Theses & Dissertations

VISUAL ATTENTION AND MEMORY PERFORMANCE ON ADVERTISEMENTS FEATURING CELEBRITIES

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The School Of Becoming: Speculative Essays On Existential Possibility And Humanity In Education, Timothy A. Holman Jan 2025

The School Of Becoming: Speculative Essays On Existential Possibility And Humanity In Education, Timothy A. Holman

College of Graduate Studies: Theses & Dissertations

This dissertation reimagines public education through an existential lens, advocating for a humanitarian approach that resists the prevailing dominance of standardized, data-driven systems. Drawing primarily from the philosophy of Jean-Paul Sartre (1936, 1940, 1943, 1947, 1965, 1992), it contends that education should honor freedom, nurture authenticity, embrace encounters with others, and cultivate imagination as essential to human development. Influenced by the works of William Ayers (1998, 2004, 2008, 2016, 2024), Maxine Greene (1967, 1973, 1978, 1988, 2000, 2001), Paulo Freire (1970, 2001, 2004), Christopher Emdin (2010, 2016, 2021), and Byung-Chul Han (2018, 2024), speculative essays—each focused on an existential theme—are …

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A Phenomenological Study Exploring The Lived Experiences Of First-Generation African American Students To Improve Retention Rates, Jaleesa Porter Jan 2025

A Phenomenological Study Exploring The Lived Experiences Of First-Generation African American Students To Improve Retention Rates, Jaleesa Porter

College of Graduate Studies: Theses & Dissertations

Colleges and universities play an important role in maintaining high retention rates and getting students from matriculation to graduation. There are several studies related to the reasons and barriers that result in low retention rates. This qualitative study examined how one university in southeast Georgia can improve retention rates among first-generation African American students by enhancing its practices. The findings from this study the revealed lived experiences of college students that resulted in a range of emotions, resources and barriers, and communication. Discussion and implications of the findings provide recommendations for practice and future research.

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Bringing A Missing Minority Into Focus: Foster Parents’ Voices And Their Impact On Foster Children’S Academic Experiences, Clair Rutherford Jan 2025

Bringing A Missing Minority Into Focus: Foster Parents’ Voices And Their Impact On Foster Children’S Academic Experiences, Clair Rutherford

College of Graduate Studies: Theses & Dissertations

Foster children face persistent educational inequities shaped by trauma, frequent placement changes, and systemic barrierswithin schools. This dissertation investigates how foster parents perceive and influence the educational experiences of the children in their care, emphasizing the intersection of advocacy, trauma-informed teaching, and institutional response. Grounded in trauma-informed pedagogy, critical ecology and curriculum studies, the study interrogates how schools both support and constrain foster children’s educational opportunities. An arts-based methodology was employed, combining fictionalized portraiture and digital ethnography. Data sources included foster parent social media posts, reflexive journalling, and researcher positionality as both teacher and foster parent. Composite narratives were constructed …

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Exploring The Impact Of Covid-19 And The Role Of The Church On African Americans’ Mental Well-Being In South Georgia, Adrian D. Lewis Jan 2025

Exploring The Impact Of Covid-19 And The Role Of The Church On African Americans’ Mental Well-Being In South Georgia, Adrian D. Lewis

College of Graduate Studies: Theses & Dissertations

The emergence of COVID-19 in 2020 disrupted the social dynamics of communities in the United States. Furthermore, public health mandates led to closures that changed how populations access faith. Historically, African Americans have utilized the church to cope with societal stressors. This study will explore the history of church utilization in the African American community, assess how COVID-19 public health mandates led to church closures for the first time in recent history and impacted this marginalized population, and provide a qualitative perspective on the quality of life during the closures and what changed after the closures for African Americans in …

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Assessing The Impact Of Environmental Health Risk Communication On Cancer Screening Intentions Among Private Well Owners In Rural Communities, Victoria A. Clower Jan 2025

Assessing The Impact Of Environmental Health Risk Communication On Cancer Screening Intentions Among Private Well Owners In Rural Communities, Victoria A. Clower

College of Graduate Studies: Theses & Dissertations

Background: Cancer is a leading cause of death in Georgia, with rising incidence in rural counties where many residents rely on unregulated private wells. These wells may contain contaminants such as radon, lead, nitrate, and arsenic, all linked to increased cancer risk. Limited research has explored how environmental exposure awareness influences cancer prevention behaviors such as screening. Guided by the Champion Health Belief Model, this study examined psychosocial and environmental predictors of screening intent among private well owners. Methods: Well Aware was an 11-week quasi-experimental study among 60 private well owners in southeast Georgia. Participants who attended educational listening sessions …

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Geophysics, And Capturing Ethnographic Interviews Of A Tidal Plantation Burial Ground, Kristin M. Meeuwen (Dew) Jan 2025

Geophysics, And Capturing Ethnographic Interviews Of A Tidal Plantation Burial Ground, Kristin M. Meeuwen (Dew)

College of Graduate Studies: Theses & Dissertations

Plantation development in Chatham County has a rich but incomplete history. Silk Hope, an Ogeechee District tidal plantation, is located seven miles from the city of Savannah on the Northern banks of the Little Ogeechee River. This project focuses on identifying the site boundaries of portions of Silk Hope Plantation and its historical background. Specifically, the project focused on delineating the boundaries of the associated cemetery and/or burial grounds, while estimating the number of individuals interred there using ground-penetrating radar (GPR). Finally, collaborative work with community members, including stakeholder and descendant interviews, will provide ethnographic insights into the site's history …

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A Monstrous [Un]-Becoming: Defining The Final Boy, Rhys Crumpton Jan 2025

A Monstrous [Un]-Becoming: Defining The Final Boy, Rhys Crumpton

College of Graduate Studies: Theses & Dissertations

Through an exploration of horror theory, monster theory, queer theory, and disabilities studies, this thesis defines a 21st century archetype within the serial horror narrative, known as “the Final Boy.” This character is a manifestation of a queer, often neurodivergent man in peril at the hands of a monstrous society. This harmful socionormativity is narratively represented by a monster wearing the face of a man, one whom the Final Boy must subdue and overcome if he wishes to survive. The seven tenets found in this project highlight the fluctuating morality the Final Boy is forced to engage in per the …

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Science, Students, And Self: An Autoethnography Of One Middle School Science Teacher’S Experiences And Challenges With Implementing Culturally Relevant Pedagogy, Alexandra Dalton Jan 2025

Science, Students, And Self: An Autoethnography Of One Middle School Science Teacher’S Experiences And Challenges With Implementing Culturally Relevant Pedagogy, Alexandra Dalton

College of Graduate Studies: Theses & Dissertations

This dissertation is an autoethnographic inquiry into how the implementation of culturally relevant pedagogy into the middle school classroom is filled with challenges and wins. In this dissertation I explore how implementing culturally relevant pedagogy helped me connect more with my students which in turn increased their academic success, cultural competence, and critical consciousness. I was able to accomplish this by using the research within my own classroom and reflecting on how my students reacted to the changes in my pedagogy. Using the methodology of autoethnography, I was able to incorporate my own personal experiences as research. I also include …

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Apocalyptic Technical Communication From Clockface To Briefcase: Revealing The Spurious Coin Of Nuclear-Security Rhetoric, Ryan Cheek Jan 2025

Apocalyptic Technical Communication From Clockface To Briefcase: Revealing The Spurious Coin Of Nuclear-Security Rhetoric, Ryan Cheek

English and Technical Communication Faculty Research & Creative Works

This article the Doomsday Clock and the Nuclear Football as interconnected technical communication artifacts that function as two sides of a "spurious coin" in the securitization of nuclear deterrence. While the Clock externalizes existential risk through apocalyptic rhetoric, the Football internalizes it within exclusive military command structures—together legitimizing perpetual nuclear crisis. Drawing on technical communication scholarship and critical security studies, the analysis argues that both artifacts sustain nuclearist ideology by reinforcing deterrence as common sense. The Clock's ominous countdown and the Football's ever-present launch capability are mutually validating and together normalize nuclear brinkmanship as the price of global security.

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Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan Jan 2025

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 …

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An Entropy-Bounded, General, Model-Based Framework For Lossy Compression Of Sensor Data, Steven Thompson, Maciej Zawodniok Jan 2025

An Entropy-Bounded, General, Model-Based Framework For Lossy Compression Of Sensor Data, Steven Thompson, Maciej Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In many industries, digital twinning has become an indispensable element of advanced technologies. However, digital twins are heavily reliant on extensive Internet of Things (IoT) sensor measurement data to function effectively. Consequently, data mining has become a lucrative endeavor, akin to gold rushes in the XIX century. However, the substantial volume of collected data often stresses the storage capacities for smaller to medium-sized enterprises, necessitating efficient compression techniques. Error-bound lossy compression offers substantial data reduction advantages, but introduces distortion that, when uncontrolled, can adversely affect analysis. This paper proposes an information optimization scheme that employs information entropy as a comprehensive …

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Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan Jan 2025

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

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Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan Jan 2025

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are …

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Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria Jan 2025

Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing presence of unmanned aerial vehicles (UAVs) raises serious security concerns, particularly regarding unauthorized drone operations. Recent U.S. security statistics report a sharp rise in unauthorized UAV activities, with the Federal Aviation Administration (FAA) receiving over 100 monthly reports of illegal drone operations near airports. In 2024 alone, Dedrone records 1.19 million unauthorized drone flights across major U.S. cities, highlighting the need for robust UAV detection and classification systems. In this work, a lightweight Convolutional Neural Network (CNN) model is proposed for RF-based UAV classification under noisy and multipath fading conditions. The proposed CNN consists of multiple convolutional blocks, …

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Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell Jan 2025

Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT) which is subsequently spatiotemporally imaged with an infrared (IR) camera. As all antennas have spatial variation in their radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT. This nonuniform heating causes uncertainty in defect detection and has the potential to lead to false positives and/or negatives. To this end, thermographic signal reconstruction (TSR), a well-established thermographic signal …

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Aperture-Based Fss For Dielectric Thickness Sensing, Alexander Hook, Gage Donahue, Kristen M. Donnell Jan 2025

Aperture-Based Fss For Dielectric Thickness Sensing, Alexander Hook, Gage Donahue, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Frequency selective surfaces (FSSs) are planar arrays of patch- or aperture-based elements that have a particular transmissive or reflective response. As FSS performance is affected by changes in the local (to the FSS) environment, FSSs may be used to detect changes in strain, temperature, or nearby material (substructure) thickness, amongst other parameters. To this end, an aperture-based FSS can be considered as a sensor for substructure thickness monitoring for surface mounted sensing scenarios. An aperture-based design was selected due to its ability to operate in reflection mode (and hence a one-sided measurement) without the need for a conductive backplane. In …

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Dual Parameter Fss-Based Sensing For Structural Health Monitoring Applications, Swathi Muthyala Ramesh, Doyle T. Motes, Kristen M. Donnell Jan 2025

Dual Parameter Fss-Based Sensing For Structural Health Monitoring Applications, Swathi Muthyala Ramesh, Doyle T. Motes, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

frequency selective surfaces (FSSs) are periodic arrays of conductive elements or apertures that reflect and/or transmit incident electromagnetic energy. Their response depends on parameters, such as element shape, unit cell dimensions, dielectric properties, and the local environment, making them suitable for structural health monitoring (SHM) applications. This article presents a dual-parameter FSS-based sensor design capable of measuring small-scale uni-directional longitudinal strain (0%–0.5%) and temperature (23 ◦C–223 ◦C). The sensor integrates two-unit cells: 1) a patch-based cell on a thin substrate for strain sensing, offering enhanced strain transfer and superior sensitivity (~16–18 MHz/0.1%) and 2) a loop-based cell with a temperature-sensitive …

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Analytical Model Of The Ac-Ac Dab Converter In The Egam Framework, Arnold A. Fernandes, Kartikeya Jayadurga Prasad Veeramraju, Jonathan W. Kimball Jan 2025

Analytical Model Of The Ac-Ac Dab Converter In The Egam Framework, Arnold A. Fernandes, Kartikeya Jayadurga Prasad Veeramraju, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops an analytical model of the bidirectional AC-AC Dual Active Bridge (DAB) converter. The passive components of the AC-AC DAB are subject to grid, switching, and sideband harmonics. Thus, it is impossible to model via the conventional Generalized Average Method (GAM). It has been numerically shown that Extended GAM (EGAM) can be used to model the AC-AC DAB converter. In this paper, an analytical sixteenth order EGAM-model has been developed that considers only grid harmonics at the filter components and only sideband harmonics for the transformer leakage inductor. A closed-form expression is developed for the 2D convolution product. …

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Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Kristen M. Donnell Jan 2025

Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT). The inspection surface of the SUT is imaged with an infrared (IR) camera over the inspection time. As the thermal excitation originates from a spatially varying radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT that is directly related to this power density. This nonuniform heating causes uncertainty in defect detection and has the potential to lead …

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Enhancing Fiber Optic Interferometric Sensing With Microwave Photonics-Based Dispersion Fourier Transform And Integrated Magnitude–Phase Analysis, Chen Zhu, Ruimin Jie, Chenxi Huang, Jie Huang Jan 2025

Enhancing Fiber Optic Interferometric Sensing With Microwave Photonics-Based Dispersion Fourier Transform And Integrated Magnitude–Phase Analysis, Chen Zhu, Ruimin Jie, Chenxi Huang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber optic inline interferometers are widely used for high-precision sensing due to their sensitivity, compactness, and immunity to electromagnetic interference. Traditional optical spectral analysis methods suffer from limited dynamic range due to free spectral range (FSR) constraints, while microwave photonic filtering (MPF) techniques based on dispersion Fourier transform (DFT) provide an alternative by mapping optical signals into the radio frequency (RF) domain. However, conventional passband frequency tracking in MPF systems has limited sensitivity, and the recently demonstrated phase-based methods, though highly sensitive, are constrained by phase wrapping beyond 2π. In this work, we propose and experimentally demonstrate an integrated magnitude–phase …

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Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono Jan 2025

Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono

Electrical and Computer Engineering Faculty Research & Creative Works

As smart home technologies evolve, achieving energy-efficient indoor climate management while maintaining comfort and air quality is a growing priority. This paper introduces a novel optimization framework for smart buildings that minimizes energy costs and dynamically manages indoor environmental conditions, specifically temperature, CO2 concentration, and illuminance. Unlike conventional systems, our model incorporates dynamic constraints that respond to day-night comfort requirements and leverage real-time variations in electricity prices and environmental conditions. By optimally controlling the power levels of air conditioning, air purification, and lighting systems, the framework ensures indoor comfort while significantly reducing operational costs.A nonlinear optimization approach with dynamic …

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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 Jan 2025

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, …

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A Cost-Effective Nilm Solution With Three-Point Labelling And Non-Causal Convolution Technique, Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo Jan 2025

A Cost-Effective Nilm Solution With Three-Point Labelling And Non-Causal Convolution Technique, Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Although deep learning is increasingly promising in the field of Non-Intrusive Load Monitoring (NILM) these days, the high costs of data recording and labelling represent a significant challenge for the training of supervised models. To address this, a cost-effective sequence-to-points NILM solution is proposed, integrating three-point labelling with non-causal convolution techniques. The approach introduces a semi-automatic labelling framework for obtaining NILM three-point data, which provides a low-cost data collection and labelling solution for large-scale applications. Then, a novel loss function combining coordinate loss and confidence loss is developed to address the positional misalignment and negative sample confusion in sequence-to-points scenario …

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A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao Jan 2025

A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise …

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Data-Driven Layout Design For Smart Remanufacturing: A Flexible Optimization Model And A Case Study, J. A. Afari, A. Gosavi, J. Hu, R. J. Marley Jan 2025

Data-Driven Layout Design For Smart Remanufacturing: A Flexible Optimization Model And A Case Study, J. A. Afari, A. Gosavi, J. Hu, R. J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Abstract: In remanufacturing, a vital segment of the sustainable, low-carbon circular economy, existing versions of the traditional unequal-areas facility layout problem (UA-FLP) model face significant limitations in designing layouts. To be specific, in the process of minimizing the material-handling cost (MHC), these models also alter departmental dimensions, often diverging from construction specifications. This poses a difficulty, as critical equipment required for remanufacturing, e.g., sorting and cleaning machines, have unalterable dimensions, which implies that departmental dimensions cannot be changed from specifications provided. To address this, a novel Flexible Envelope UA-FLP (FE-UA-FLP) model is proposed in this work for designing layouts wherein …

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Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley Jan 2025

Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

AI-driven healthcare decision-making is multi-faceted, requiring complex logic to adapt to evolving policies and societal demands. Effective change implementation by healthcare providers and multidisciplinary organ transplant teams depends on adaptive decision-making. The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) model introduces a novel approach to Human-AI Teaming, keeping human expertise central while dynamically adjusting to changing requirements. TSFAM employs fuzzy logic to manage imperfect data and human ambiguity, integrating the transplant surgeon perspective with the AI deep learning decision-making tool, creating a resilient solution in this critical domain. By embedding adaptive capabilities into the architecture, TSFAM exemplifies the adaptability of …

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Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim Jan 2025

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 …

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Cocreating A Programme To Prevent Injuries And Improve Performance In Australian Police Force Recruits: A Study Protocol, Myles C. Murphy, Andrea B. Mosler, Jonathan Hodgson, Sophia Nimphius, Evert Verhagen, Joanne Kemp, Alex Donaldson, Debra Langridge, Vanessa R. Sutton, Kay M. Crossley, Clare L. Ardern, Carolyn A. Emery, Mary A. Kennedy, Simone Radavelli-Bagatini, Martin Hägglund, Brady Green, G. Gregory Haff, Garth Allen, Andrea Bruder Jan 2025

Cocreating A Programme To Prevent Injuries And Improve Performance In Australian Police Force Recruits: A Study Protocol, Myles C. Murphy, Andrea B. Mosler, Jonathan Hodgson, Sophia Nimphius, Evert Verhagen, Joanne Kemp, Alex Donaldson, Debra Langridge, Vanessa R. Sutton, Kay M. Crossley, Clare L. Ardern, Carolyn A. Emery, Mary A. Kennedy, Simone Radavelli-Bagatini, Martin Hägglund, Brady Green, G. Gregory Haff, Garth Allen, Andrea Bruder

Research outputs 2022 to 2026

A healthy police force is a key component of a well-functioning society, yet 1 in 20 law enforcement recruits drop out of the recruit training programme due to injury. This drop-out rate has substantial economic and workforce ramifications. In the Western Australia Police Force, one in five recruits suffers a musculoskeletal injury during the recruit training programme, causing time-loss from work. We will now identify the critical elements of an injury prevention intervention and investigate the needs, experiences and suggested solutions to address potential implementation challenges. Our objective is to co-create an intervention with content and context experts, specifically for …

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Work Hard, Play Hard, Drink Hard: Frontier Masculinities And Female Teachers’ Experiences In A Regional Australian Mining Town, Kirsten Lambert, Christina Gray, Mary Anne Macdonald, Takei Beard, Kelly Jackson, Sarah Booth Jan 2025

Work Hard, Play Hard, Drink Hard: Frontier Masculinities And Female Teachers’ Experiences In A Regional Australian Mining Town, Kirsten Lambert, Christina Gray, Mary Anne Macdonald, Takei Beard, Kelly Jackson, Sarah Booth

Research outputs 2022 to 2026

Teacher attrition is a significant issue that has been the subject of much recent critical research and public debate. What is less often explored in the literature is the gendered nature of teaching and the unique struggles female teachers face in the workforce, particularly in remote, regional and rural (RRR) schools. As a historically female-dominated profession, women, particularly teacher-mothers face a distinctive set of barriers to career progression in these schools. We apply a critical feminist lens to qualitative data generated from interviews with 21 teachers from 6 different schools in RRR locations in Western Australia. Data reveals a toxic …

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