Operations Research, Systems Engineering and Industrial Engineering Commons^™

Harnessing Ml And Iiot For Traceability In Continuous Production Systems: A Conceptual Framework, Kholoud M. Abdelaal

Theses and Dissertations

In the era of rapid technological advancement, the manufacturing sector faces increasing pressure to leverage emerging technologies to enhance operational efficiency and minimize waste. In this context, traceability plays a pivotal role, as it provides complete visibility of processes and products throughout manufacturing systems, enabling them to identify areas for improvement and take corrective actions accordingly. Additionally, traceability ensures compliance, supports product recalls, provides a clear understanding of the system’s performance, and enables fact-driven decision-making in multiple aspects of the manufacturing system. Although the broad spectrum of traceability applications in batch production-based plants, traceability remains challenging to achieve in continuous …

Ai-Based Porosity Detection In Babbitt Bore Turning, Kaylee Leggett, Hannah Adams Gemmell, Kaisa Shingleton

Williams Honors College, Honors Research Projects

For this project, an external company reached out to the University of Akron requesting assistance with defect detection during their vertical turning operations. As babbitt is removed in a vertical turning process, it occasionally reveals defects, mainly porosity, which can lead to costly downstream failures of the part. Current inspection techniques involve use of dye penetrant, which is time consuming, labor intensive, unergonomic, and a source of human error. The goal of the project is to create an alternative inspection method using an AI-based machine-learning model. After the turning operation, a camera is deployed to perform an in-place inspection, taking …

Security-Oriented Voice Authentication Using Machine Learning, Ifeoluwa Stella Elegbe

College of Graduate Studies: Theses & Dissertations

This study develops and evaluates a machine learning and deep learning-based voice authentication system for secure identity verification. As traditional authentication methods such as passwords, PINs, and security tokens continue to face challenges, including identity theft, forgetting, and unauthorized access, voice biometrics offers a more secure, convenient, and user-friendly alternative, especially for remote, hands-free, and accessibility-focused applications. The study adopts a closed-set speaker identification framework, where the system determines the most likely speaker from a predefined group of enrolled users. A structured methodology is implemented, beginning with audio preprocessing and feature extraction. Key acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), …

Skeleton-Based Human Action Recognition Using Spatio-Temporal Latent Features With A Gcn Model, Avazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin

Chemical Technology, Control and Management

Owing to its resilience to visual noise and viewpoint variations, skeleton-based analysis has become a cornerstone of human action recognition research. Despite its practical significance, existing methodologies often suffer from a reliance on single-stream skeletal representations, which fail to encompass the full complexity of action features. This study introduces Latent Features for Human Action Recognition (LFHAR), a novel architecture designed to overcome these limitations by utilizing diverse spatio-temporal latent representations for improved feature extraction. The approach applies graph-based transformations to individual skeletal frames in temporal sequences, then arranges the derived graph features into spatio-temporal matrices. Evaluation of standard datasets demonstrates …

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Algorithms For Assessing Soil Salinity Levels Based On Remote Sensing Imagery, Bobomurod Mamitjonovich Tojiboev

Chemical Technology, Control and Management

This article investigates methods for assessing soil salinity levels based on satellite (remote sensing) imagery and their calculation algorithms. Determining the degree of salinity plays a crucial role in the rational use of land resources and increasing agricultural efficiency. The study analyzes indices for determining soil salt content using remote sensing technologies, particularly multispectral images obtained from satellite systems such as Landsat and Sentinel (for example, SI - Salinity Index, NDVI - Normalized Difference Vegetation Index, and others). Furthermore, algorithms are developed based on these indices that enable automatic determination of salinity assessments. Artificial intelligence, machine learning, and geographic information …

Scalable And Adaptive Agile Framework For Semiconductor Foundry: Advanced Packaging And Heterogeneous Integration Perspective, Pravin Thorat

Harrisburg University Dissertations and Theses

This research addressed the critical requirement for a scalable and adaptive agile framework specifically designed for the unique demands of semiconductor foundries specializing in advanced packaging and heterogeneous integration (HI). The semiconductor industry was encountering growing pressure to innovate and respond quickly to rapidly evolving demands, yet traditional manufacturing processes often struggled to adapt. Existing agile frameworks, mainly developed for the software industry, lacked the necessary adaptations to address the complexities of semiconductor manufacturing, including extended lead times, high capital investment, rigorous quality requirements, and the integration of various technologies. This research gap hindered the ability of semiconductor foundries to …

Fff Process Parameter Identification With Machine Learning Models, Owen Davis Smith

Honors Theses

Additive manufacturing (AM) has seen increasing popularity in recent times, owing to its efficiency and high speeds, particularly with processes such as Fused Filament Fabrication (FFF). Input process parameters have large impacts on the final part. Incomplete process parameters, which can occur for a variety of reasons, make tasks such as replicating AM studies difficult. A machine learning model can be trained on in-situ layer-wise images collected during a print to combat this issue, predicting process parameters with sufficient data. In this study, two parameters were tested: infill pattern orientation and extrusion width. Twelve parts were produced per parameter and …

Detection Of Http Flood Attacks Based On Machine Learning Algorithms, Norbek Karimov, Furkat Rakhmatov, Oybek Xolmuminov

Chemical Technology, Control and Management

This paper analyzes the effectiveness of Random Forest and SVM models for detecting HTTP Flood attacks. Experimental results demonstrate that both models achieve high accuracy. Evaluation was conducted using Precision, Recall, and F1 Score metrics. Additionally, key features of network traffic were extracted through correlation analysis to enable real-time application of the models in attack detection. The findings provide important insights into detecting DDoS attacks using machine learning and improving model performance.

Utilizing Ai For Improved Credit Risk Assessment, Emel Baglarbasi

Harrisburg University Dissertations and Theses

As the finance sector continues to evolve, traditional risk assessment methods struggle to calculate default risk and identify nonlinear relationships accurately. This research examines an alternative risk assessment model designed to estimate credit risk more accurately and efficiently in the credit processes of individual customers, which are one of the primary sources of income for the banking sector. It presents the theoretical design of an AI-based model. The use of this AI model can reduce human error in processes, improve risk assessment accuracy, and expedite procedures. The study adopts a postpositivist worldview and employs a quantitative research design. Algorithms including …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Synthetic Data–Driven Early Prediction Framework For Acute Kidney Injury In Patients Receiving Vancomycin And Ceftazidime/Avibactam, Maryam Ramazani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Background: The nephrotoxic risks of combining ceftazidime/avibactam (AVI) with vancomycin (VAN) remain underexplored, despite both agents independently being linked to acute kidney injury (AKI). This study assessed the risk of AKI associated with concurrent VAN and ceftazidime/avibactam (VAN-AVI) therapy and developed synthetic data models to enable early prediction of AKI.

Methods: We conducted a retrospective analysis using electronic health record data from hospitalized adults between 2015 and 2022. The incidence of AKI was compared among patients receiving VAN-AVI or VAN in combination with piperacillin/tazobactam (VAN-TPZ) versus VAN monotherapy. AKI was defined as a composite of de novo and recurrent AKI …

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Learning To Accelerate Globally Optimal Solutions: Applications In The Ac Optimal Power Flow Problem, Muhammet Fatih Cengil

Graduate Theses and Dissertations

The Alternating Current Optimal Power Flow (AC-OPF) problem is a fundamental optimization challenge critical to ensuring the economical and reliable operation of power grids. While fast heuristic methods provide upper-bound solutions, assessing their quality requires lower bounds obtained from relaxations of the AC-OPF problem. This dissertation focuses on finding globally optimal solutions to the AC-OPF problem by enhancing the effectiveness and efficiency of Quadratic Convex (QC) relaxations. Leveraging machine learning techniques, we aim to achieve tighter relaxations faster and improve computational performance, enabling practical scalability for real-time applications.

In Chapter 2, we propose a machine learning-based method to accelerate the …

Integrating Machine Learning And Simulation For Resource Planning Of Hospital Systems Based On Predicted Length Of Stay, S M Atikur Rahman

Open Access Theses & Dissertations

Recently Hospital Systems faced a high invasion of patients generated by several events such as health crisis related epidemic (COVID, FLU) or seasonal flows. Hence, managing hospital bed availability and efficiency with proper care is obligatory for addressing the challenges associated with the overburden of patients. However, the Length of stay (LOS) is often increased due to the high patient influx and overcrowding problem occurs within the Hospital. It resolves these issues, it is essential for hospital authority to predict the Patients LOS which is the crucial indicator for the use of medical resources (allocation, utilization of providers and resource) …

Using Convolutional Neural Networks For Autonomous Drone Navigation, Joshua Jowers

Industrial Engineering Undergraduate Honors Theses

Unmanned Aerial Vehicles (UAVs), more commonly known as drones, serve various purposes, notably in military applications. Consequently, there arises a need for navigation methods impervious to intercepted signals [1]. Previous research has explored numerous solutions, including machine learning. This paper delves into a specific machine learning approach employing a Convolutional Neural Network (CNN) to discern image locations [2]. It elucidates the conversion of a CNN model between two machine learning libraries and presents results from multiple experiments examining parameters and factors influencing the approach's efficacy. These experiments encompass testing different data sources, image quantities, and processing pipelines to gauge their …

Generalization Through Diversity: Improving Unsupervised Environment Design, Wenjun Li, Pradeep Varakantham, Dexun Li

Research Collection School Of Computing and Information Systems

Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e.g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board). Due to this dependence, small changes in the environment (e.g., positions of obstacles in the maze, size of the board) can severely affect the effectiveness of the policy learned by the agent. To that end, existing work has proposed training RL agents on an adaptive curriculum of environments (generated automatically) to improve performance on out-of-distribution (OOD) test scenarios. Specifically, existing research has employed the potential for the …

Detecting Pathobiomes Using Machine Learning, Valerie Jackson, Valerie Jackson

Industrial Engineering Undergraduate Honors Theses

Machine learning is a field with high growth potential due to the overall continuous progressions, developments, advancements, and improvements caused by the way it is used to help interpret and use large amounts of data [1]. One type of data that can be collected and analyzed by these machine learning models is data that is associated with DNA and information that the DNA gives. The research will be focusing specifically on using machine learning technology to detect pathobiomes indicative of salmonella pork. The pathobiome associated with salmonella is very similar to others, and this causes a problem for classification/detection with …

The Impact Of Case Management Intervention For Insured Asthma Patients In Louisiana, An Empirical Study, Mohamed Mohamed Ohaiba

LSU Doctoral Dissertations

Asthma is a chronic condition whose symptoms are managed/prevented using medication and interventions. The overarching objective of this study was to evaluate the impact of patients' demographics on case management enrollment and healthcare utilization, as well as to develop machine learning models to predict high-cost patients.

To accomplish these goals, the Man-Whiteness test, the chi-squares test, logistic regression and odds ratios, and machine learning models were implemented. The average cost of the non-enrolled CM group was significantly higher than the enrolled group (p-value .0001). In addition, the non-enrolled groups had considerably more visits to the emergency department than the other …

Automated Registration Of Titanium Metal Imaging Of Aircraft Components Using Deep Learning Techniques, Nathan A. Johnston

Theses and Dissertations

Studies have shown a connection between early catastrophic engine failures with microtexture regions (MTRs) of a specific size and orientation on the titanium metal engine components. The MTRs can be identified through the use of Electron Backscatter Diffraction (EBSD) however doing so is costly and requires destruction of the metal component being tested. A new methodology of characterizing MTRs is needed to properly evaluate the reliability of engine components on live aircraft. The Air Force Research Lab Materials Directorate (AFRL/RX) proposed a solution of supplementing EBSD with two non-destructive modalities, Eddy Current Testing (ECT) and Scanning Acoustic Microscopy (SAM). Doing …

Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill

Theses and Dissertations

Federated learning (FL) is a budding machine learning (ML) technique that seeks to keep sensitive data private, while overcoming the difficulties of Big Data. Specifically, FL trains machine learning models over a distributed network of devices, while keeping the data local to each device. We apply FL to a Parkinson’s Disease (PD) telemonitoring dataset where physiological data is gathered from various modalities to determine the PD severity level in patients. We seek to optimally combine the information across multiple modalities to assess the accuracy of our FL approach, and compare to traditional ”centralized” statistical and deep learning models.

A Machine Learning Approach For Early Diagnosis Of Transthyretin Amyloid Cardiomyopathy Among Heart Failure Patients, Tanjim Ahmed

Graduate Theses, Dissertations, and Problem Reports (ETD)

Transthyretin Amyloid Cardiomyopathy (ATTR-CM) is a rare, progressive, and fatal disease. Prevalence of ATTR-CM ranges from 4 to 17 per 100000 cases where the mean survival time is less than 4 years. It has a history of being underdiagnosed and misdiagnosed. The diagnosis delay has a weighted mean of 6.1 years for wild-type ATTR-CM. Low awareness, the necessity of invasive procedures, and lack of treatment are the key reasons for delayed diagnosis. But, with the introduction of non-invasive tests like nuclear scintigraphy with ^99mTC-PYP and the disease modifying drug Tafamidis, the diagnosis delay signifies a missed opportunity to increase …

Integrated Machine Learning And Optimization Approaches, Dogacan Yilmaz

Dissertations

This dissertation focuses on the integration of machine learning and optimization. Specifically, novel machine learning-based frameworks are proposed to help solve a broad range of well-known operations research problems to reduce the solution times. The first study presents a bidirectional Long Short-Term Memory framework to learn optimal solutions to sequential decision-making problems. Computational results show that the framework significantly reduces the solution time of benchmark capacitated lot-sizing problems without much loss in feasibility and optimality. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, …

Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James

McKelvey School of Engineering Graduate Student Theses & Dissertations

Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …

Human Gait Movement Analysis Using Wearable Solutions And Artificial Intelligence, Samaneh Davarzani

Theses and Dissertations

Gait recognition systems have gained tremendous attention due to its potential applications in healthcare, criminal investigation, sports biomechanics, and so forth. A new solution to gait recognition tasks can be provided by wearable sensors integrated in wearable objects or mobile devices. In this research a sock prototype designed with embedded soft robotic sensors (SRS) is implemented to measure foot ankle kinematic and kinetic data during three experiments designed to track participants’ feet ankle movement. Deep learning and statistical methods have been employed to model SRS data against Motion capture system (MoCap) to determine their ability to provide accurate kinematic and …

Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann

Doctoral Dissertations and Master's Theses

The focus of this research is to develop an approach that enhances the elicitation and specification of reusable cybersecurity requirements. Cybersecurity has become a global concern as cyber-attacks are projected to cost damages totaling more than $10.5 trillion dollars by 2025. Cybersecurity requirements are more challenging to elicit than other requirements because they are nonfunctional requirements that requires cybersecurity expertise and knowledge of the proposed system. The goal of this research is to generate cybersecurity requirements based on knowledge acquired from requirements elicitation and analysis activities, to provide cybersecurity specifications without requiring the specialized knowledge of a cybersecurity expert, and …

Development Of Flood Prediction Models Using Machine Learning Techniques, Bhanu Kanwar

Doctoral Dissertations

"Flooding and flash flooding events damage infrastructure elements and pose a significant threat to the safety of the people residing in susceptible regions. There are some methods that government authorities rely on to assist in predicting these events in advance to provide warning, but such methodologies have not kept pace with modern machine learning. To leverage these algorithms, new models must be developed to efficiently capture the relationships among the variables that influence these events in a given region. These models can be used by emergency management personnel to develop more robust flood management plans for susceptible areas. The research …

Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel

Theses and Dissertations

Infrastructure is a key component in the well-being of our society that leads to its growth, development, and productive operations. A well-built infrastructure allows the community to be more competitive and promotes economic advancement. In 2021, the ASCE (American Society of Civil Engineers) ranked the American infrastructure as substandard, with an overall grade of C-. The overall ranking suffers when key infrastructure categories are not maintained according to the needs of the population. Therefore, there is a need to consider alternative methods to improve our infrastructure and make it more sustainable to enhance the overall grade. One of the challenges …

Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn

Graduate Theses and Dissertations

Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …