Digital Commons Network^™

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

The Reliability Of Yolo Object Detection On The Amd Versal In A Proton Radiation Environment, Jacob D. Brown

Theses and Dissertations

Object detection is an important operation for satellites to be able to perform, and in outer-space missions it is crucial that machine learning models perform inference on satellite images accurately and reliably. Where size, weight, power, and other constraints exist, meeting this goal for accurate and reliable object detection is challenging. Additionally, soft errors caused by radiation further disrupt and degrade the operation of object detection in satellites. This thesis studies the performance of a deep learning model on an embedded device, the AMD Versal, in the presence of soft errors. The well-known and high-performing YOLO convolutional neural network was …

Sentience, Sheetal Agrawal

Masters Theses

The increasing urgency for sustainable and adaptive systems has driven research toward embedding intelligence directly into materials rather than relying solely on external sensing and control systems. This thesis explores how smart material1 embedded systems can be designed to recognize and respond to environmental signatures, defined as measurable patterns such as temperature fluctuations and mechanical forces. Central to this investigation is the integration of shape memory alloys, particularly Nitinol, with geometry-based actuation mechanisms that amplify material behavior into functional system responses.

The central argument is that designing with smart materials is a design problem, not primarily a materials science problem …

Material Costs, Karima Weinman

Masters Theses

This thesis investigates how migration fatality and disappearance data can be reinterpreted through material craft to create a more reflective encounter with information. Working with the Missing Migrants Project's dataset, this project asks how design can communicate dimensions of human loss that conventional data visualization cannot reach.

The work situates contemporary border violence within a longer colonial history, arguing that the logics of surveillance and quantification that structured European imperial expansion persist in the databases that govern mobility in the Mediterranean today.

Terrazzo is a 15th-century Venetian flooring technique built from discarded fragments bound together into a unified surface. This …

Asymmetric Opinion Formation Of Emotional Excitable Agents, Irene Ferri, Emanuele Cozzo, Aleix Nicolás-Olivé, Albert Díaz-Guilera, Luce Prignano

Northeast Journal of Complex Systems (NEJCS)

The bounded confidence model represents a widely adopted framework for modeling opinion dynamics wherein actors have a continuous-valued opinion and interact and approach their positions in the opinion space only if their opinions are within a specified confidence threshold. Here, we propose a novel framework where the confidence bound is determined by a decreasing function of their emotional arousal, an additional independent variable distinct from the opinion value. Additionally, our framework accounts for agents' ability to broadcast messages, with interactions influencing the timing of each other's message emissions. Our findings underscore the significant role of synchronization in shaping consensus formation. …

From Frames To Strains: Analytically Modeling Inelastic Deformation Under Mapped Single-Site Impacts From High-Speed Footage To Internal State Variable Codes, Joby Milo Anthony

Doctoral Dissertations and Projects

This work adds insight to the physical phenomena of microstructural and stress strengthening of metal components by the inelastic deformation from Surface Mechanical Attrition Treatment (SMAT). Impact behaviors observed by high-speed footage of a Crank-Slider Mechanism (CSM) are examined in the context of analytically moving rigid bodies in spacetime and resolving kinematics upon impact until restitution via Finite Element Analysis (FEA). A Coupled Discrete-Finite Element Model (CDFEM) leverages Bammann plasticity, Horstemeyer damage and void nucleation, growth, and coalescence and Cho recrystallization Internal State Variable (ISV) models to show the localization of plastic strain and onset of recrystallization under any single …

A Predictive-Correlational Study Investigating Flight Students' Perceptions Of The Use Of An Artificial Intelligence Instructor In The Simulated Flight Training Environment, Zachary L. Lamothe

Doctoral Dissertations and Projects

The purpose of this quantitative predictive-correlational study is to investigate how perceived ease of use, perceived usefulness, performance expectancy, and perceived enjoyment impact attitude towards use and the behavioral intention to use an artificial intelligence flight instructor in simulated flight training among aviation students earning an aeronautical degree at a collegiate flight training school in the Mid-Atlantic region. Aviation has benefited from different technological advances, and using artificial intelligence technology for flight training could be another improvement as it becomes increasingly sophisticated. The study adds to the literature by addressing the problem of limited information on the perception of artificial …

Classification Of Hif Detection In Nev Profile Using Wavelet Transform And Convolution Neural Network, Abdul Hafiz Kassim, Mohd Abdul Talib Mat Yusoh, Aster Smith Valentinie Wilson Nottelmarc, Ahmad Farid Abidin, Sim Sy Yi, Daw Saleh Sasi Mohammed

Turkish Journal of Electrical Engineering and Computer Sciences

High impedance faults (HIFs) present a critical challenge in power systems due to their subtle signal characteristics, which often remain undetected by conventional protection methods. These faults typically do not produce significant phase disturbances, making reliable detection difficult. However, analysis of the neutral-to-earth voltage (NEV) profile under fault conditions provides a promising alternative for fault identification. Existing approaches for detecting and classifying HIFs using NEV signals remain limited and may result in inaccurate maintenance decisions. This paper proposes a fault classification framework for multiple fault types, including HIF, three-phase fault, three-phase fault to ground, double line, double line to ground, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Adapting Independent Large-Scale Pretrained Models For Human Action Recognition, Selen Pehli̇van

Turkish Journal of Electrical Engineering and Computer Sciences

Transferring knowledge from large-scale, independently pretrained image and text models to video understanding requires addressing several challenges, including maintaining generalization capabilities of models, integrating them into multimodal architectures, and fine-tuning with temporal dynamics. This study evaluates the effectiveness of parameter-efficient fine-tuning (PEFT) techniques in transferring pretrained knowledge from two independent models for video action recognition within a simple, streamlined multimodal fusion pipeline. Specifically, we adapt CLIP as the text branch and DINOv2 as the image branch, keeping both backbones frozen to preserve their pretrained robustness, while introducing lightweight, task-specific modules to adapt and fuse the branches with temporal dynamics. A …

Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer, Xin Zhang, Xu Wang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents an adaptive backstepping nonsingular fast terminal sliding mode controller integrated with a nonlinear disturbance observer to achieve precise trajectory tracking of robotic manipulators subject to model uncertainties and unknown time-varying disturbances. A dead-zone–based adaptive gain mechanism is introduced to dynamically adjust the control gain according to the deviation of the sliding surface, thereby enhancing robustness and reducing chattering. The proposed reaching law ensures fast, nonsingular, and adaptive convergence, suppressing high-frequency oscillations without compromising stability and the nonlinear disturbance observer enables real-time estimation and compensation of modeling errors, friction, and external disturbances for superior rejection. The semiglobal uniform …

Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani

Turkish Journal of Electrical Engineering and Computer Sciences

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common …

Exploring Ai-Driven Scaffolding For Critical Questioning In Argument Evaluation, Ebenezer A. Belete

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fast-paced changes caused by generative AI (GenAI) innovations call for exploring the potential benefits of GenAI in empowering 21st-century pedagogical strategies. Previous studies in the field of argumentation have shown how students can benefit from using critical questions. However, scaffolding argument evaluation through custom GenAI using critical questions has not been systematically investigated. This study involved two components: (1) designing and determining the usability of a GPT-powered conversational assistant (CQMAA Conversational Assistant) and (2) testing its impact on participants' efficacy for argument evaluation and their acceptance of GenAI as a learning tool through a pretest–posttest experiment. A convergent mixed-methods …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

Feasibility-Aware Deep Reinforcement Learning For Sustainable Timber Procurement Under Hurricane Demand Uncertainty, Jarod Wright

Theses and Dissertations

The timber supply chain connects landowners and mills to provide wood products but faces challenges from stochastic demand, seasonal variations, and disruptions such as hurricanes. Fur- thermore, sustainability concerns like transportation emissions create trade-offs in procurement. This study proposes a feasibility-aware Deep Reinforcement Learning framework for sustainable timber procurement and inventory control under joint demand–hurricane uncertainty. We develop a stochastic mathematical model capturing mill-landowner interactions, seasonal demand, hurricane- driven pricing, and carbon emissions. The problem is formulated as a constrained Markov decision process and solved using Proximal Policy Optimization with a feasibility-enforcing layer. A Mississippi-based case study with 2,100 landowners …

Design And Uncertainty Methodology In High Efficiency Filtration Media Testing, Jeremy Andrei Adriano

Theses and Dissertations

Electrospun nanofiber media are being developed as potential alternatives to conventional HEPA filtration materials due to their small fiber diameters and tunable microstructures. To support future testing of these materials, the Small-Scale Test Stand (SSTS) at the Institute for Clean Energy Technology (ICET) requires modifications to ensure accurate and repeatable aerosol filtration measurements. Electrospun filters introduce challenges including fragile media structures and uncertain filtration efficiencies that may allow particle penetration to downstream instrumentation. This work evaluates adaptations to the SSTS design to support testing of electrospun filtration media. In addition, an uncertainty framework for aerosol measurements obtained using a Scanning …

Machine Learning-Based Decision Support Models With Applications In Postsecondary Education, Marco Paolo Anglesio

Theses and Dissertations

This dissertation investigates the deployment of machine learning methodologies in an industrial engineering framework for the development of advanced decision support systems in the context of enrollment management. Drawing on techniques from educational data mining, the research addresses three key phases in the lifecycle of traditional and non-traditional students. First, it analyzes student retention using predictive classification models designed to identify individuals at elevated risk of attrition. Second, it employs temporal convolutional networks for time series forecasting, estimating aggregate enrollment levels over highly variable, finite planning horizons on the basis of partially observed data and using an asymmetric loss function. …

A Study & Comparison Of Lift Boosters For An Unmanned Aerial Vehicle, Trevor Sean Dady

Theses and Dissertations

Unmanned aerial vehicles are becoming increasingly prevalent in modern society for tasks, such as package delivery, defense, and cinematography. Due to this surge in applications, many researchers have begun to search for improvements to unmanned aerial vehicles for their lift and thrust properties. Potential solutions to magnify an unmanned aerial vehicle’s thrust capability are the implementation of Coanda surfaces, nozzles, or bowl-shaped structures on each propeller. Coanda surfaces provide additional lift force by creating a vacuum in response to the adhesion of fluid to the smooth surface. Nozzles can constrict the flow of air, while also increasing its velocity. The …

Experimental Measurements Of The Forces Acting On A Submerged Beam Using High-Speed Videography, Madelyn Burrell

Masters Theses

The interaction of structures and fluids is highly relevant in many fields but difficult to accurately and reliably characterize. This study demonstrates a novel method of simplifying such problems by analyzing the structural deformations directly, without needing to explicitly solve for the fluid aspect. Six cantilever beams of varying properties were submerged in still water and released from an initial deflection at the free end: the cases consisted of three beam shapes (one of uniform width, one with a narrower free end, and one with a wider free end), with an end mass attached or detached. High-speed videography was utilized …

Thermal Transient Analysis Of Mahogany Wood Biomass Pyrolysis: The Role Of Zeolite Catalyst Variations On Temperature Distribution, Yusri Yusri, Widya Wijayanti, Mega Nur Sasongko

Journal of Mechanical Engineering Science and Technology (JMEST)

Biomass pyrolysis is a thermochemical process that converts waste into fuel. The use of zeolite catalysts can enhance efficiency by accelerating biomass decomposition. To demonstrate its effect, thermal simulations were conducted using ANSYS Thermal Transient to analyze temperature distribution, reaction rates, and catalyst effectiveness in the pyrolysis process. This study aims to improve thermal efficiency and temperature uniformity by analyzing the effect of 10% and 20% zeolite mixtures on heat distribution and heating rates at input temperatures of 650 K, 750 K, and 850 K, using the 2D axial symmetry method. The simulation was performed under specific boundary conditions: input …

High-Surface-Area Ficus Leaf-Extracted Cuo-Zno-Nio Ternary Nanocomposites For Rapid Adsorption-Assisted Uv-A Degradation Of Direct Blue 15, Kafa’A H. Ali, Mohammed A. Atiya, Ahmed K. Hassan

Karbala International Journal of Modern Science

Direct Blue 15 (DB15) is a long-lasting synthetic dye that pollutes water and is difficult to remove during wastewater treatment. Conventional treatments fail to remove DB15 adequately due to rapid electron-hole recombination, which necessitates photocatalysts with reduced recombination rates for effective dye removal. The study aims to synthesize and evaluate a CuO-ZnO-NiO ternary system as an integrated adsorption and UV-A photocatalysis platform for DB15 removal. Ficus leaf-extracted CuO-ZnO-NiO nanocomposites with four Cu:Zn:Ni ratios (1:1:1, 1:1:2, 1:2:1, 2:1:1) were prepared by co-precipitation, followed by extensive chemical and physical characterization and dye-removal performance measurements. The CuO-ZnO-NiO nanocomposite exhibited hexagonal zinc oxide, cubic …

Simulated Intelligence, Surveillance, And Play, Atienah Cape

Theses - ALL

This MFA thesis explores modern panopticism and the feeling of being watched by creating and installing Four Real Friends, an interactive robotic artwork inspired by the 1998 Furby. The project looks at how surveillance technologies are not just outside forces, but part of daily life, often feeling familiar, playful, and even social. The installation uses hardware design, custom circuits, computer vision, and real-time data display to collect and process how visitors interact in the gallery. Its nostalgic and friendly look encourages people to take part, while still showing how data is collected and displayed as it happens. This makes surveillance …

Design And Production Of A Carbon Fiber Longboard, Julia H. Stradinger

Honors Theses

This thesis contains an overview of the design, prototyping, and manufacturing of a carbon fiber long board. The purpose of this capstone was to create a viable product for the real-world market and then determine how to manufacture it at scale with sustainable financial practices. In order to accomplish this task, engineering, accounting, and manufacturing challenges had to be overcome throughout all stages. The production process involved the carbon fiber layup, baking, water jet cutting, and assembly of the carbon fiber longboard. In order to optimize this process, continuous improvement and lean manufacturing problem solving techniques were applied. After a …

Design And Production Of A Carbon Fiber Longboard, Vince Fichtel

Honors Theses

This thesis contains an overview of the design, prototyping, and manufacturing of a carbon fiber long board. The purpose of this capstone was to create a viable product for the real-world market and then determine how to manufacture it at scale with sustainable financial practices. In order to accomplish this task, engineering, accounting, and manufacturing challenges had to be overcome throughout all stages. The production process involved the carbon fiber layup, baking, water jet cutting, and assembly of the carbon fiber longboard. In order to optimize this process, continuous improvement and lean manufacturing problem solving techniques were applied. After a …

The Design And Manufacturing Process Of The Concealed Shelf, Joshua Harvel

Honors Theses

The Haley Barbour Center for Manufacturing Excellence (CME) at the University of Mississippi challenges students to manufacture a product and put forth a balanced manufacturing line in a senior capstone project. The chosen product was a concealed shelf, or a shelf with a hidden compartment. The team aimed to design a product, formulate a project plan, analyze the market, conduct financial analysis, develop a prototype, establish a manufacturing process, and demonstrate that process in two production runs. The team used tools such as Computer Aided Design (CAD), market surveys, jig creation, and line balancing to accomplish these tasks. After two …

The Design And Manufacturing Process Of The Concealed Shelf, Mary-Del R. Jansen

Honors Theses

The Haley Barbour Center for Manufacturing Excellence (CME) at the University of Mississippi challenges students to manufacture a product and put forth a balanced manufacturing line in a senior capstone project. The chosen product was a concealed shelf, or a shelf with a hidden compartment. The team aimed to design a product, formulate a project plan, analyze the market, conduct financial analysis, develop a prototype, establish a manufacturing process, and demonstrate that process in two production runs. The team used tools such as Computer Aided Design (CAD), market surveys, jig creation, and line balancing to accomplish these tasks. After two …

Development Of A Putting Green Manufacturing Process, Tabitha R. Webster

Honors Theses

Our Capstone project investigates the end to end design, development, and production of a 6‑foot long portable putting green marketed for individuals seeking a high quality, competitively priced golf product for home or office use. The capstone project examines the full lifecycle of product creation applying manufacturing principles learned through the center of manufacturing’s coursework. From the initial concept through engineering design, market research, prototyping, manufacturing optimization, and final production the project emphasizes cross‑functional collaboration across engineering, business, and accountancy majors. Methods used to gather data included marketing surveys, CAD drawings, time studies during production runs, value stream mapping, and …