Other Engineering Commons^™

Comprehensive Study On Explainable Artificial Intelligence For Enhanced Decision-Making, Eman Taher, Wessam H. El-Behaidy, Doaa S. Elzanfaly

Computer Science

Artificial Intelligence (AI) models are often criticized for their black-box nature, particularly in high-stakes domains such as finance, healthcare, and business decision-making, where transparency, accountability, and trust are essential. As machine learning advances with more complex architectures, including deep neural networks and ensemble models, the need for explainability becomes increasingly critical. This study focuses on Explainable Artificial Intelligence (XAI) as a key approach to enhance interpretability in classification, regression, and clustering tasks that serve as the foundation of data-driven analytical systems. XAI methods such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Integrated Gradients provide a means …

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 …

Ai-Driven Prediction And Reconstruction Of Missing Cased-Hole Logs For Improved Well System Understanding, Samuel Avilez Martinez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Well logging is a fundamental technique in formation evaluation providing continuous, real-time measurements of geological and petrophysical properties within a well. Through the systematic analysis of well logs, engineers and geoscientists can accurately determine critical formation characteristics, including porosity, permeability, lithology, and fluid composition. Well logging is fundamental for making informed decisions and reducing uncertainties in the exploration and development of oil and gas reservoirs.

Despite its significance, the acquisition of reliable well log data in oil and gas wells is often compromised by various operational, mechanical, and formation-related challenges, as well as pressure, fluid, and equipment constraints. In high-risk …

Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer

Data Science Undergraduate Honors Theses

Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These …

Computer Vision In A Robotic Arm, Jack Maxwell

College of Engineering Summer Undergraduate Research Program

We used a machine learning-based object detection algorithm to give a robotic arm the ability to "see" with its camera.

Design And Development Of A Clinical Decision Support System For The Diagnosis Of Parkinson’S Disease Using Artificial Intelligence, Saravanan S

Theses and Dissertations

Parkinson’s disease (PD) is a degenerative neurological condition marked by motor symptoms like tremors, bradykinesia, and stiffness. It is observed that early and precise diagnosis of this disease is crucial, as that will have a significant impact on effective disease management and intervention. This thesis explores the technical feasibility of applying AI techniques to recognize patterns from spiral and wave drawings, which are usually a unique signature type for PD patients.

The focus of the work is to diagnose the disease through novel deep transfer learning techniques, to diagnose the severity of the disease, and also to develop effective model …

Generative Data Augmentation: Using Dcgan To Expand Training Datasets For Chest X-Ray Pneumonia Detection, Ryan D. Maier

Master's Theses

Recent advancements in computer vision have demonstrated remarkable success in image classification tasks, particularly when provided with an ample supply of accurately labeled images for training. These techniques have also exhibited significant potential in revolutionizing computer-aided medical diagnosis by enabling the segmentation and classification of medical images, leveraging Convolutional Neural Networks (CNNs) and similar models. However, the integration of such technologies into clinical practice faces notable challenges. Chief among these is the obstacle of acquiring high-quality medical imaging data for training purposes. Patient privacy concerns often hinder researchers from accessing large datasets, while less common medical conditions pose additional hurdles …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Enhancing Reservoir Modeling And Simulation Through Artificial Intelligence And Machine Learning: A Smart Proxy Modeling Approach, Andrew Timothy Jenkins

Graduate Theses, Dissertations, and Problem Reports (ETD)

The application of numerical reservoir simulation (NRS) has been a common approach within the oil and gas industry for decades, providing a means to model and forecast dynamic subsurface interactions, as a basis for reservoir management and development decisions. These techniques have expanded to application within carbon capture utilization and storage (CCUS) projects as domestic and global policy shift towards reducing carbon emissions while maintaining the energy needs of our modern society. NRS techniques have become a core process for permitting approval in Class VI (large-scale geological sequestration) wells due to the fundamental similarity of these types of subsurface processes. …

Shape Inverse Prediction Of Magnetic Field-Actuated Soft Robots By Neural Network Machine Learning, Lineth J. Perez Monsalve

Theses and Dissertations

Soft robotics has drawn tremendous interest in recent years because the compliance and motion of soft robotics enable biocompatibility and versatility for many applications, such as human-machine interaction, wearable and assistive devices, and health monitoring. This study introduces a novel predictive modeling approach using neural networks for shape control of magnetic soft robots. The robots are made of silicone materials embedded with hard magnetic particles, which respond to the external magnetic field provided by a ring-type of permanent magnet. These robots, free from physical connections to external devices, i.e., non-tethered actuation, hold significant potential for applications in healthcare, such as …

Characterizing Students’ Engineering Design Strategies Using Energy3d, Jasmine Singh, Viranga Perera, Alejandra Magana, Brittany Newell

Discovery Undergraduate Interdisciplinary Research Internship

The goals of this study are to characterize design actions that students performed when solving a design challenge, and to create a machine learning model to help future students make better engineering design choices. We analyze data from an introductory engineering course where students used Energy3D, an open source computer-aided design software, to design a zero-energy home (i.e. a home that consumes no net energy over a period of a year). Student design actions within the software were recorded into text files. Using a sample of over 300 students, we first identify patterns in the data to assess how students …

Top-Down Model Development Using Data Generated From A Complex Numerical Reservoir Simulation With Water Injection, Yvon Andrea Martinez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Numerical simulation and data-driven modeling are two current approaches in engineering reservoir modeling. Numerical reservoir simulation attempts to match past production history by modifying reservoir properties of the model. After multiple computationally intensive trial and error efforts, accurate history matches are identified. These history matches are used by project management for production forecasting purposes. Data-driven reservoir modeling utilizes measured data and is, therefore, free of assumptions that are often included in numerical reservoir simulations. Artificial intelligence and machine learning algorithms are technologies implemented in the development of a data-driven reservoir model with efforts to learn fluid flow through porous media …

Utilization Of A Numerical Reservoir Simulation With Water And Gas Injection For Verification Of Top Down Modeling, Ashley Konya

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary purpose of this thesis was to confirm the capabilities of artificial intelligence and machine learning through Top Down Modeling in history matching and predicting the oil, gas, and water production rates, reservoir pressure, and water saturation, of one limb of an anticline with water and gas injection. Several other characteristics were also applied to make the model more realistic to industry standards. The second purpose of this thesis was to determine the minimum amount of training and calibration data required in order to obtain good results for this particular dataset by increasing the blind validation in one year …

Machine Learning With Multi-Class Regression And Neural Networks: Analysis And Visualization Of Crime Data In Seattle, Erkin David George

Honors Projects

This article examines the implications of machine learning algorithms and models, and the significance of their construction when investigating criminal data. It uses machine learning models and tools to store, clean and analyze data that is fed into a machine learning model. This model is then compared to another model to test for accuracy, biases and patterns that are detected in between the experiments. The data was collected from data.seattle.gov and was published by the City of Seattle Data Portal and was accessed on September 17, 2018. This research will be looking into how machine learning models can be used …

Baseline Data From Servo Motors In A Robotic Arm For Autonomous Machine Fault Diagnosis, Jacob Brown

Mechanical Engineering Undergraduate Honors Theses

Fault diagnosis can prolong the life of machines if potential sources of failure are discovered and corrected before they occur. Supervised machine learning, or the use of training data to enable machines to discover these faults on their own, makes failure prevention much easier. The focus of this thesis is to investigate the feasibility of creating datasets of various faults at both the component and system level for a servomotor and a compatible robotic arm, such that this data can be used in machine learning algorithms for fault diagnosis. The faults induced at the component level in different servomotors include: …

Enhanced Grain Partitioning Of X-Ray Microtomography Segmented Images, Nicholas C. Skrivanos Ii

LSU Master's Theses

In the field of petroleum engineering, rock samples are often taken from wells during the drilling process. Grain partitioning of digital three-dimensional microtomography segmented images obtained from these samples provides valuable in-situ properties and statistics that allow for accurate particle and structure characterization. This information can be used directly in detailed production and reservoir analysis, and can also be used to generate realistic packing models for advanced simulation. Additionally, the partitioned image can be used as a building block for realistic hydraulic fracture modeling. This technology has applications in other fields as well, such as core analysis in soil sciences …

Application Of Machine Learning On Fracture Interference, Dennis Wayne Chamberlain Jr.

Graduate Theses, Dissertations, and Problem Reports (ETD)

A method has been developed that locates and determines well-to-well hydraulic fracture interference (frac-hit) in shale plays using hard data. This method uses Artificial Neural Networks (ANN) with designated parameters and target outputs in conjunction with graphs of gas flowrate, tubing pressure, and cumulative gas prediction. The method was created to address the significant increase in frac-hit occurrences due to the infill wells being completed in shale plays. The production data of the well is first cleaned to eliminate outliers in the initial timeframe of the well and periods of no production so that the ANN model can be accurately …

Digital Forensic Tools & Cloud-Based Machine Learning For Analyzing Crime Data, Majeed Kayode Raji

College of Graduate Studies: Theses & Dissertations

Digital forensics is a branch of forensic science in which we can recreate past events using forensic tools for legal measure. Also, the increase in the availability of mobile devices has led to their use in criminal activities. Moreover, the rate at which data is being generated has been on the increase which has led to big data problems. With cloud computing, data can now be stored, processed and analyzed as they are generated. This thesis documents consists of three studies related to data analysis. The first study involves analyzing data from an android smartphone while making a comparison between …

Fault Classification And Location Identification On Electrical Transmission Network Based On Machine Learning Methods, Vidya Venkatesh

Theses and Dissertations

Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases with as the configurations get more complex. Therefore, prediction of faults (type …

A Review Of Approaches To Solving The Problem Of Bim Search: Towards Intelligence-Assisted Design, Hamed Khademi, Avril Behan

Conference papers

Due to the growing adoption of BIM and the rising popularity of cloud computing, BIM models are increasingly stored in central cloud repositories or Common Data Environments. Effective management and exploitation of these models creates the requirement for BIM retrieval systems. Thus far, the BIM industry has utilized general-purpose, text-based search techniques that operate on BIM metadata. This paper highlights the need for a domain-specific BIM search engine and reviews various approaches to address the problem of BIM search. Three main approaches were identified as context-, geometry-, and content-based BIM retrieval. For a comprehensive BIM retrieval system, all three approaches …