Engineering Commons^™

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

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 …

Applied Machine Learning In Development Of Geospatial Information Tools For Sustainable Groundwater Management, Saul Gallegos Ramirez

Theses and Dissertations

Groundwater plays an important role in sustainable water resource management. Globally, the development of groundwater resources for irrigation provides a stable water source and enhances food security. However, developing groundwater resources is difficult, requiring the collection, synthesis, analysis, and dissemination of information about groundwater in an accessible, effective manner so decision makers have the knowledge and tools to implement sustainable management strategies. In this dissertation, I present solutions to assist with the paucity of groundwater data. The first problem I explore is extending the Palmer Drought Severity Index to near present day. This dataset is crucial to current groundwater imputation …

Classifying Open-Air Target Measurements Using Simulation-Trained Convolutional Neural Networks, Matthew M. Rofrano

Theses and Dissertations

This research focuses on the development of machine learning networks that can identify and classify airborne targets using their radar cross section response. Simulation and measurement data for five targets was collected using Altair's CadFEKO software, and the Air Force Institute's Compact Radar Range. Three machine learning models were trained using simulation data, and evaluated using the collected measurement data. Variability is introduced to the training data by applying random gaussian noise to simulation results. Gaussian noise is added to the measurement data prior to evaluation in-order to model "hostile noise jamming." Network performance is measured against a baseline performance …

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.

Suitability Of Earth-Moon Triangular Lagrange Point Orbits For Space Situational Awareness, Eric P. Burkhart

Theses and Dissertations

The second space race is looming on the horizon, and the Moon is again at center stage. All major space fairing nations have plans for future Lunar exploration, but this time with plans to stay. NASA’s Artemis program plans to put humans back on the Moon in 2025 and establish a Lunar base on the South pole in the 2030s. Meanwhile, China has landed a rover on the far side of the moon with a communications relay satellite in a halo orbit around the L2 Lagrange point, and partnered with Russia to build a Lunar research station on the Lunar …

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 …

Reinforcement Learning Framework For The Unreal Engine, Justin B. Wheeler

Master's Theses

This dissertation addresses the need for using machine learning-based methods rather than traditional rule-based methods for controlling non-playable characters (NPCs). The goal of the Reinforcement Learning Framework for the Unreal Engine is to enable game development studios to create, train, and more easily implement smarter, more compelling AI characters in major video game releases. The framework contains three distinct software libraries: an Unreal Engine reinforcement learning library whose purpose is to enable Unreal Engine levels to act as reinforcement learning environments, a python library which provides convenient abstractions and implementations to the reinforcement learning process, and a flexible connection system …

Analysis And Usage Of Natural Language Features In Success Prediction Of Legislative Testimonies, Marine Cossoul

Master's Theses

Committee meetings are a fundamental part of the legislative process in which
constituents, lobbyists, and legislators alike can speak on proposed bills at the
local and state level. Oftentimes, unspoken “rules” or standards are at play in
political processes that can influence the trajectory of a bill, leaving constituents
without a political background at an inherent disadvantage when engaging with
the legislative process. The work done in this thesis aims to explore the extent to
which the language and phraseology of a general public testimony can influence a
vote, and examine how this information can be used to promote civic …

Hybrid Modeling For Electrochemical Systems, Luis Alejandro Briceno-Mena

LSU Doctoral Dissertations

The discovery of new materials like catalysts, polymeric films, and biomolecules, is driven by industrial needs such as improving reaction or separation selectivity, enhancing therapeutic effects on medical treatments, or reducing costs of replacement. However, deployment of these advances in industrial applications is often hindered by the lack of models needed for design and optimization. Due to the novelty of materials and devices, experimental data and first principles' knowledge are scarce, making it hard to build models either via data-driven or knowledge based approaches. In this context, a way to efficiently combine domain knowledge with data could provide a pathway …

Evaluation Of Liquid Loading In Gas Wells Using Machine Learning, Abderraouf Chemmakh, Olusegun Stanley Tomomewo, Kegang Ling, Ahmed Shammari

Petroleum Engineering Student Publications

The inevitable result that gas wells witness during their life production is the liquid loading problem. The liquids that come with gas block the production tubing if the gas velocity supplied by the reservoir pressure is not enough to carry them to surface. Researchers used different theories to solve the problem naming, droplet fallback theory, liquid film reversal theory, characteristic velocity, transient simulations, and others. While there is no definitive answer on what theory is the most valid or the one that performs the best in all cases. This paper comes to involve a different approach, a combination between physics-based …

Machine Learning Applications In Malware Classification: A Metaanalysis Literature Review, Tjada Nelson, Austin O'Brien, Cherie Noteboom

Research & Publications

With a text mining and bibliometrics approach, this study reviews the literature on the evolution of malware classification using machine learning. This work takes literature from 2008 to 2022 on the subject of using machine learning for malware classification to understand the impact of this technology on malware classification. Throughout this study, we seek to answer three main research questions: RQ1: Is the application of machine learning for malware classification growing? RQ2: What is the most common machine-learning application for malware classification? RQ3: What are the outcomes of the most common machine learning applications? The analysis of 2186 articles resulting …

Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha

Faculty Publications

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). …

Stand-Up Comedy Visualized, Berna Yenidogan

Dissertations, Theses, and Capstone Projects

Stand-up comedy has become an increasingly popular form of comedy in the recent years and comedians reach audiences beyond the halls they are performing through streaming services, podcasts and social media. While comedic performances are typically judged by how 'funny' they are, which could be proxied by the frequency and intensity of laughs through the performance, comedians also explore untapped social issues and provoke conversation, especially in this age where interaction with artists goes beyond their act. It is easy to see commonalities in the topics addressed in comedians’ work such as relationships, race and politics.This project provides an interactive …

Enhancing Student'sperformance Classification Using Ensemble Modeling, Ahmed Adil Nafea, Muthanna Mishlish, Ali Muwafaq Haban Shaban, Mohammed M. Al-Ani, Khattab M Ali Alheeti, Hussam J. Mohammed

Iraqi Journal for Computer Science and Mathematics

A precise prediction of student performance is an important aspect withineducational institutions to improve results and provide personalized support ofstudents.However, the predication accuracy of student performance considers anopenissue within education field.Therefore, thispaper proposes a developedapproachto identifyperformance of students using a group modeling. This approach combinesthe strengths of multiple algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM). Afterward, thelastensemble estimatesas one of the bets logistic regressionmethodswas utilizedto create a robust and reliable predictive modelbecause it considers The experiments were evaluated usingtheOpen University Learning Analytics Dataset (OULAD)benchmark dataset.The OULADdataset considersa comprehensive dataset containingvarious characteristics related to …

Machine-Learning Based Model Predictive Control For A Three-Phase Inverter, Arturo De La Cruz

All Graduate Theses, Dissertations, and Other Capstone Projects

In recent years, the study of three-phase inverter controls has become important with the rising use of renewable energy sources (RES) in the form of distribution generation (DG). Many control types have been developed for DG inverters and others were traditional controls for the generation of the main grid power that were adapted for a system with less inertia. Among these controls is the model predictive control (MPC) which allows for a fast transient response and good reference tracking. One disadvantage of the MPC is that it does this prediction and optimization online which can limit the applications due to …

Tropospheric Correction For Insar Using Machine Learning, Ngo Hi Kenny Yue

Masters Theses

"Interferometric Synthetic Aperture Radar (InSAR) is a popular technique for studying Earth's surface deformation caused by phenomena like earthquakes and subsidence. However, its accuracy is limited by tropospheric delays caused by water vapor in the atmosphere. This limitation can be overcome by using methods that correct for tropospheric noise, such as statistical, empirical, and predictive approaches. This study explores the potential of using machine learning algorithms to predict the zenith total delay caused by tropospheric effects in InSAR measurements. The study employs two different machine learning algorithms, random forest and neural networks, to learn the relationship between numerical weather prediction …

Incorporating Novel Sensors For Reading Human Health State And Motion Intent Into Real-Time Computing Systems, Adam Sawyer

Masters Theses

"Integrating sensors that read states of the human body into everyday life is an increasing desire, especially with the rise of deep learning which requires vast stores of data to make predictions. This work explores integrating these sensors into the human experience through two methods and recording the results. The first of these methods integrates a MXene based field-effect transistor sensor for the 2019-nCov spike protein with a mobile app. This allows the user to read how saturated their breath is with Covid-19. The second method integrates 3D-printed pressure sensors, and a motion capture system, into a glove to read …

Applying Machine Learning To Biological Status (Qvalues) From Physio-Chemical Conditions Of Irish Rivers, Raúl Martín Sánchez

ICT

This thesis evaluates and optimises a variety of predictive models for assessing biological classification status, with an emphasis on water quality monitoring. Grounded in previous pertinent studies, it builds on the findings of (Arrighi and Castelli, 2023) concerning Tuscany’s river catchments, highlighting a solid correlation between river ecological status and parameters like summer climate and land use. They achieved an 80% prediction precision using the Random Forest algorithm, particularly adept at identifying "good" ecological conditions, leveraging a dataset devoid of chemical data.

On The Pursuit Of Developer Happiness: Webcam-Based Eye Tracking And Affect Recognition In The Ide, Tamsin Rogers

Honors Theses

Recent research highlights the viability of webcam-based eye tracking as a low-cost alternative to dedicated remote eye trackers. Simultaneously, research shows the importance of understanding emotions of software developers, where it was found that emotions have significant effects on productivity, code quality, and team dynamics. In this paper, we present our work towards an integrated eye-tracking and affect recognition tool for use during software development. This combined approach could enhance our understanding of software development by combining information about the code developers are looking at, along with the emotions they experience. The presented tool utilizes an unmodified webcam to capture …

Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri

Master's Projects

Disaster Detection using Twitter content is critical for emergency response, but accurately identifying relevant tweets remains challenging due to nuances, informal language, and emotional expressions. This paper presents a comparative analysis between traditional Machine Learning models, Deep Learning models and Large Language Models (LLM) for classifying disaster vs. non-disaster tweets. While existing works have applied pattern recognition and dataset-specific learning, LLMs with their deeper understanding of linguistics and semantics can potentially handle the complexities of tweets more effectively. This study leverages LLMs including Llama2, Mistral, and Falcon, Open AI GPT 3.5, hypothesizing their superior contextual comprehension will excel in tweets …

A Framework For Teaching Machine Learning For Engineers, Lauren Singelmann, Jacob Covarrubias

Practice Papers

As machine learning and artificial intelligence become increasingly prevalent in our day-to-day lives, there becomes an even greater need for literacy in machine learning for those outside of the computer science domain. This work proposes a conceptual framework for teaching machine learning to engineering students with the goal of developing the knowledge and skills needed to apply machine learning techniques to engineering problems.

Many machine learning courses in computer science, math, and statistics focus on the theoretical basis of machine learning algorithms and assessment. This framework takes a fundamentally different approach by creating a course structure for machine learning practitioners …

Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin

Conference papers

Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion …

Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient, Claudia Andrea Leiva Acevedo

ICT

Colorectal Cancer (CRC) is a globally prevalent and deadly carcinoma, necessitating advanced treatment approaches. Despite ongoing advancements, the mortality rate remains high. Various biological models, including animal studies, cell lines, and the emerging organoid model, contribute to understanding molecular mechanisms. Organoids, 3D cultures derived from tumor epithelial cells, offer advantages such as enhanced diversity, genetic modification, and extended culture capabilities. Recent applications of machine learning (ML) in predicting CRC treatment responses using organoids and tissue data indicate a promising avenue for advancing personalized therapies.

Analyzing Ground Motion Records With Cvi Fuzzy Art, Dustin Tanksley, Xinzhe Yuan, Genda Chen, Donald C. Wunsch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This paper explores using Cluster Validity Indices Fuzzy Adaptative Resonance Theory (CVI Fuzzy ART) to cluster ground motion records (GMRs). Clustering the features extracted from a supervised network trained for predicting the structure damage results in less overfitting from the trained network. Using Cluster Validity Indices (CVIs) to evaluate the clustering gives feedback to how well the data is being classified, allowing further separation of the data. By using CVI Fuzzy ART in combination with features extracted from a trained Convolutional Neural Network (CNN), we were able to form additional clusters in the data. Within the primary clusters, accuracy was …

Machine Learning For Advancing Automation And Quality Control In Robotic Welding, Joseph Kershaw

Theses and Dissertations--Mechanical and Aerospace Engineering

Welding is a vital manufacturing process across a diverse range of industries to permanently join metallic components into needed assemblies. Though widely used and studied, the process is not fully understood due to the myriad of interconnected physics that present themselves during the welding process. Electrical, thermal, structural, and fluidic mechanics are simultaneously at work influencing one another to achieve the bonding that manufacturers desire. In addition to general manufacturing issues, such as tool wear or misalignment, micro-level changes to the material structure and composition can have cascading impacts on the final weld. This complexity makes it difficult, if not …

Hard-Hearted Scrolls: A Noninvasive Method For Reading The Herculaneum Papyri, Stephen Parsons

Theses and Dissertations--Computer Science

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented …

Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Complex Systems Faculty Publications and Presentations

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Comparative Analysis Of Artificial Intelligence And Numerical Reservoir Simulation In Marcellus Shale Wells, Arya Maher Sattari

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation addresses the limitations of conventional numerical reservoir simulation techniques in the context of unconventional shale plays and proposes the use of data-driven artificial intelligence (AI) models as a promising alternative. Traditional methods, while providing valuable insights, often rely on simplifying assumptions and are constrained by time, resources, and data quality. The research leverages AI models to handle the complexities of shale behavior more effectively, facilitating accurate predictions and optimizations with less resource expenditure.

Two specific methodologies are investigated for this purpose: traditional numerical reservoir simulations using Computer Modelling Group's GEM reservoir simulation software, and an AI-based Shale Analytics …

Leveraging Data Science To Promote Sustainable Resource Recovery From Organic Waste Streams In Rural Regions, Mohammed Tamim Zaki

Graduate Theses, Dissertations, and Problem Reports (ETD)

Conventional organic waste management practices such as landfilling contribute to global warming and degrade environmental and human health. Alternatively, organic waste can be treated to recover energy, nutrients, and carbon through resource recovery technologies. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively.

A critical review of 616 peer-reviewed articles published during 2002–2022 was conducted on the use of data science methods in resource recovery from organic waste. Although applications of machine learning (ML) methods have drastically increased …