Engineering Commons^™

Analyzing The Impact Of Automation On Employment In Different Us Regions: A Data-Driven Approach, Thejaas Balasubramanian

Electronic Theses, Projects, and Dissertations

Automation is transforming the US workforce with the increasing prevalence of technologies like robotics, artificial intelligence, and machine learning. As a result, it is essential to understand how this shift will impact the labor market and prepare for its effects. This culminating experience project aimed to examine the influence of computerization on jobs in the United States and answer the following research questions: Q1. What factors affect how likely different jobs will be automated? Q2. What are the possible effects of automation on the US workforce across states and industries? Q3. What are the meaningful predictors of the likelihood of …

Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …

Interrogating Autism From A Multidimensional Perspective: An Integrative Framework., Mohamed T. Ali

Electronic Theses and Dissertations

Autism Spectrum Disorder (ASD) is a condition characterized by social and behavioral impairments, affecting approximately 1 in every 44 children in the United States. Common symptoms include difficulties in communication, interpersonal interactions, and behavior. While symptoms can manifest as early as infancy, obtaining an accurate diagnosis may require multiple visits to a pediatric specialist due to the subjective nature of the assessment, which may yield varying scores from different specialists. Despite growing evidence of the role of differences in brain development and/or environmental and/or genetic factors in autism development, the exact pathology of this disorder has yet to be fully …

Machine Learning For Ultraviolet Spectral Prediction, Linh Ho Manh

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Machine Learning has found wide applications in material science, including dielectric polymers, superconducting materials, and drug property prediction. The use of data analytics and machine learning methods to predict Vacuum Ultraviolet (VUV) spectra by encoding molecular structure is gaining interest because high-quality VUV spectral prediction capability would enable the study of new molecules without costly wet-lab measurements. This dissertation aims to study feature representations for molecular structures that enhance the prediction of VUV spectra via machine learning models. Both interpretable machine learning and deep learning are studied. Chapter 1 provides an overview of VUV/UV spectra retrieval, and Chapter 2 reviews …

Demand Prediction And Inventory Management Of Surgical Supplies, Rajon Paul Pantha

Graduate Theses and Dissertations

Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for …

An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …

When Less Is More: How Increasing The Complexity Of Machine Learning Strategies For Geothermal Energy Assessments May Not Lead Toward Better Estimates, Stanley P. Mordensky, John Lipor, Jacob Deangelo, Erick R. Burns, Cary R. Lindsey

Electrical and Computer Engineering Faculty Publications and Presentations

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.

Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Machine-Learning-Based Model For Hurricane Storm Surge Forecasting In The Lower Laguna Madre, Cesar E. Davila Hernandez, Jungseok Ho, Dong-Chul Kim, Abdoul Oubeidillah

Civil Engineering Faculty Publications

During every Atlantic hurricane season, storms represent a constant risk to Texan coastal communities and other communities along the Atlantic coast of the United States. A storm surge refers to the abnormal rise of sea water level due to hurricanes and storms; traditionally, hurricane storm surge predictions are generated using complex numerical models that require high amounts of computing power to be run, which grow proportionally with the extent of the area covered by the model. In this work, a machine-learning-based storm surge forecasting model for the Lower Laguna Madre is implemented. The model considers gridded forecasted weather data on …

Alzheimers Disease Stages Are Classified Based On Biomarkers Found In Cerebrospinal Fluid Using Machine Learning Classifiers, Vivek K. Tiwari

Electrical Engineering Theses

The inability of the current techniques to identify patients in the preclinical stage of Alzheimer's disease, which can persist up to ten years before clinical symptoms appear, makes early detection of the condition difficult. Several studies have shown the potential of the cerebrospinal fluid biomarkers amyloid beta 1-42, T-tau, and P-tau in the early stages of Alzheimer's disease. Based on the levels of these cerebrospinal fluid biomarkers, we employed machine learning models in this study to categorize various phases of Alzheimer's disease. The National Alzheimer's Coordination Centre database of 537 patients' electronic health records was examined, and the patients were …

Reducing Restaurant Inventory Costs Through Sales Forecasting, Tyler Mason, Chris Schoen, Trevor Gilbert, Jonathan Enriquez

Senior Design Project For Engineers

Family Restaurant is a local restaurant in the greater Atlanta area that serves a variety of dishes that include an assortment of 19 different proteins. Currently, Family Restaurant places protein orders based on business intuition, and tends to over-stock and sometimes under-stock. To minimize inventory costs by reducing over-stocking and preventing under-stocking of proteins, we applied Facebook Prophet (FB Prophet), ARIMA, and XG Boost machine learning models to predict protein demand and then fed these results into a Fixed Time Period inventory model to make an overall order suggestion based on the specified time period. We trained our models on …

Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry

Modeling, Simulation and Visualization Student Capstone Conference

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).

Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …

Overview Of The Application Of Remote Sensing In Effective Monitoring Of Water Quality Parameters, Godson Ebenezer Adjovu, Haroon Stephen, David James, Sajjad Ahmad

Civil and Environmental Engineering and Construction Faculty Research

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in …

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

Real-Time Facial Expression Recognition Using Edge Ai Accelerators, Mark Heath Smith

Theses and Dissertations

Facial expression recognition is a popular and challenging area of research in machine learning applications. Facial expressions are critical to human communication and allow us to convey complex thoughts and emotions beyond spoken language. The complexity of facial expressions creates a difficult problem for computer vision systems, especially edge computing systems. Current Deep Learning (DL) methods rely on large-scale Convolutional Neural Networks (CNN) which require millions of floating point operations (FLOPS) to accomplish similar image classification tasks. However, on edge and IoT devices, large-scale convolutional models can cause problems due to memory and power limitations. The intent of this work …

Defining Safe Training Datasets For Machine Learning Models Using Ontologies, Lynn C. Vonder Haar

Doctoral Dissertations and Master's Theses

Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes …

Characterization Of Physical And Biochemical Traits In Wheat And Corn Plants Using High Throughput Image Analysis, Kantilata Thapa

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Plant phenotyping has been recognized as a rapidly growing field of research due to the labor-intensive, destructive, and time-consuming nature of traditional phenotyping methods. These phenotyping bottlenecks can be addressed by advancements in image-based phenotyping like RGB and hyperspectral imaging for the assessment of plant traits important for breeding purposes. This study aims (1) to characterize the physical and biochemical traits of wheat and corn plants using RGB and hyperspectral imaging in the greenhouse, and (2) to estimate leaf nitrogen (N), phosphorus (P), and potassium (K) content using hyperspectral imaging and an analytical spectral device (ASD spectrometer) and compare the …

Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter

Electronic Theses and Dissertations

Glioma is one of the most aggressive forms of brain cancer. It has been shown that the microenvironments differ significantly between the core and edge regions of glioma tumors. This study obtained metabolomic profiles of glioma core and edge regions using paired glioma core and edge tissue samples from 27 human patients. Data was acquired by performing liquid-liquid metabolite extraction and 2DLC-MS/MS on the tissue samples. In addition, a boosted generalized linear machine learning model was employed to predict the metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.

A panel of 66 metabolites was found to be statistically significant …

An Artificial Intelligence Approach To Fatigue Crack Length Estimation From Acoustic Emission Signals, Shane T. Ennis

Theses and Dissertations

As in service aircraft begin to age and fatigue, a method for evaluating the operational life they are currently operating under and have remaining comes into question. Structural health monitoring is (SHM) is a popular method of structural analysis with growing interest in the aerospace industry. SHM is capable of damage assessment and structural life estimations.

The ultimate goal of the research presented in this thesis is to develop a methodology of classifying the length of a fatigue crack though the use of machine learning. The thesis has three major chapters as described below.

The first chapter deals with the …

Machine Learning Methods For Inferring The Number Of Uav Emitters Via Massive Mimo Receive Array, Yifan Li, Feng Shu, Jinsong Hu, Shihao Yan, Haiwei Song, Weiqiang Zhu, Da Tian, Yaoliang Song, Jiangzhou Wang

Research outputs 2022 to 2026

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by …

A Data-Driven Multivariate Process Monitoring Platform For Knowledge Discovery And Model Building In Industrial Applications, Estelle E. Seghers

LSU Master's Theses

In industrial chemical manufacturing processes, the amount of raw data generated can add complexity in the analysis and understanding of the process dynamics. Being able to properly interpret this data can help improve plant operation, especially regarding safety and profitability. This research has culminated in FastMan-JMP, a platform proposed for monitoring of industrial processes and optimization of the offline data-driven model-building process as part of the process monitoring workflow. FastMan-JMP is a tool developed in Python to apply various data mining and machine learning techniques quickly and easily to better understand valuable patterns and hidden trends in process data. One …

Advanced Ensemble Modeling Method For Space Object State Prediction Accounting For Uncertainty In Atmospheric Density, Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta

Mechanical and Aerospace Engineering Faculty Research & Creative Works

For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space traffic management activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainties, which are modeled using probabilistic machine learning techniques. The recently proposed "HASDM-ML," "CHAMP-ML," and "MSIS-UQ" machine learning models for density estimation (Licata and Mehta, 2022b; Licata et al., 2022b) are used in this work. The investigation is convoluted because of the spatial and temporal correlation of the atmospheric density …

Can We Explain Machine Learning-Based Prediction For Rupture Status Assessments Of Intracranial Aneurysms?, Nan Mu, M. Rezaeitaleshmahalleh, Z. Lyu, M. Wang, J. Tang, C. M. Strother, J. J. Gemmete, A. S. Pandey, J. Jiang

Michigan Tech Publications, Part 1

Although applying machine learning (ML) algorithms to rupture status assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We presented the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 112 IAs with known rupture status were selected for this study. The ML-based classification used two anatomical features, nine hemodynamic parameters, and thirteen morphologic variables. We utilized permutation feature importance, …

Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi

Theses and Dissertations

The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the …

Sustainably Providing Accurate Local River Discharge Data With Global Hydrologic Modeling And Bias Corrections, Riley Chad Hales

Theses and Dissertations

The Global Water Sustainability Initiative of the Group of Earth Observations (GEOGloWS) supported an initiative to develop a global hydrologic model. The purpose of the modeling initiative is to build a high-quality model using the best available datasets and modeling methods with the primary emphasis on accessibility of the model. The goal is to make the model a sustainable source of river discharge information to supplement the capacity of those countries without the local capacity to maintain sufficient gauge networks and local modeling capabilities and cyberinfrastructure. Past research developed a modeling approach and piloted implementations and data and visualization services …

Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha

Electronic Theses and Dissertations

The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …

Characterizing Location-Based Electromagnetic Leakage Of Computing Devices Using Convolutional Neural Networks To Increase The Effectiveness Of Side-Channel Analysis Attacks, Ian C. Heffron

Theses and Dissertations

SCA attacks aim to recover some sort of secret information, often in the form of a cipher key, from a target device. Some of these attacks focus on either power-based leakage, or EM-based leakage. Neural networks have recently gained in popularity as tools in SCA attacks. Near-field EM probes with high-spatial resolution enable attackers to isolate physical locations above a processor. This enables attackers to exploit the spatial dependencies of algorithms running on said processor. These spatial dependencies result in different physical locations above a chip emanating different signal strengths. The strengths of different locations can be mapped using the …

Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick

Theses and Dissertations

This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average …

Synchronization Of The Training Of Specialists In The Automation Of Technological Processes In Accordance With The Dynamics Of The Taxonomy Of Learning Goals, Yusuf Shodievich Avazov, Kamola Abdullaeva

Chemical Technology, Control and Management

Changes in the characteristics of modern ones, reflecting them as the "digital generation", inevitably necessitate electronic interactive, mobile and mixed, machine learning, especially in the field of automation and control of technological processes and production. As a consequence of reflecting these realities, there have been parallel changes in the taxonomy of learning objectives - from the classical to the revised rethought and digital.

In order to study the problem of synchronizing the training of future specialists in the field of automation and control of technological processes and production and funny knowledge in accordance with the dynamics of the taxonomy of …