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Triboinformatic Approaches For Surface Characterization: Tribological And Wetting Properties, Md Syam Hasan

Theses and Dissertations

Tribology is the study of surface roughness, adhesion, friction, wear, and lubrication of interacting solid surfaces in relative motion. In addition, wetting properties are very important for surface characterization. The combination of Tribology with Machine Learning (ML) and other data-centric methods is often called Triboinformatics. In this dissertation, triboinformatic methods are applied to the study of Aluminum (Al) composites, antimicrobial, and water-repellent metallic surfaces, and organic coatings.Al and its alloys are often preferred materials for aerospace and automotive applications due to their lightweight, high strength, corrosion resistance, and other desired material properties. However, Al exhibits high friction and wear rates …

Full-Body Biomechanical Characterization Of Children With Hypermobile Ehlers-Danlos Syndrome During Gait And Activities Of Daily Living, Anahita Alahmoradiqashqai

Theses and Dissertations

Hypermobile Ehlers-Danlos syndrome (hEDS) is an inherited connective tissue disorder, often under-diagnosed, and presenting with frequent chronic pain and severe musculoskeletal symptoms that can drastically reduce the quality of life during one’s life span. While there are limited quantitative approaches in the literature on adult movements, the biomechanics of movements during activities of daily living (ADLs) in children have not been investigated comprehensively. Therefore, the primary purpose of this dissertation was to characterize the biomechanics of the musculoskeletal system and investigate the biomechanics of hEDS by quantifying joint dynamics and muscle activations during ADLs and gait in the pediatric population. …

Essays On Fake Review Detection, Managerial Response, And Consumer Perceptions, Long Chen

Theses and Dissertations

This dissertation investigates how online reviews and managerial responses jointly affect consumer perceptions. I first examine and compare the outcomes of multiple fake review classifiers using various algorithms, including traditional machine learning methods and recently developed deep learning methods (essay I). Then, based on the findings of the first essay, I examine the interrelationship between fake review detection, managerial response, and hotel ratings and ratings’ growths (essay II).The first essay is a comparative study on the methodology of identifying fake reviews. Although online reviews have attracted much attention from academia and industry for over fifteen years, how to identify fake …

The Search For Life: Exoplanet Detection With Deep Learning, Natasha Scannell

Theses and Dissertations

The discovery of new exoplanets, planets outside of our solar system, is essential for increasing our understanding of the universe. Exoplanets capable of harboring life are particularly of interest. Over 600 GB of data was collected by the Kepler Space Telescope, and about 30 GB is being collected each day by the Transiting Exoplanet Survey Satellite since its launch in 2018. Traditional methods of experts examining this data manually are no longer tractable; automation is necessary to accomplish the task of vetting all of this data to identify planet candidates from astrophysical false positives.

Previous state-of-the-art models, Astronet and Exonet, …

Detection Of Stealthy False Data Injection Attacks Against State Estimation In Electric Power Grids Using Deep Learning Techniques, Qingyu Ge

Theses and Dissertations

Since communication technologies are being integrated into smart grid, its vulnerability to false data injection is increasing. State estimation is a critical component which is used for monitoring the operation of power grid. However, a tailored attack could circumvent bad data detection of the state estimation, thus disturb the stability of the grid. Such attacks are called stealthy false data injection attacks (FDIAs). This thesis proposed a prediction-based detector using deep learning techniques to detect injected measurements. The proposed detector adopts both Convolutional Neural Networks and Recurrent Neural Networks, making full use of the spatial-temporal correlations in the measurement data. …

Information Retrieval Of Opioid Dependence Medications Reviews From Health-Related Social Media, Seyedeh Samaneh Omranian

Theses and Dissertations

Social media provides a convenient platform for patients to share their drug usage experience with others; consequently, health researchers can leverage this potential data to gain valuable information about users’ drug satisfaction. Since the 1990s, opioid drug abuse has become a national crisis. In order to reduce the dependency of opioids, several drugs have been presented to the market, but little is known about patient satisfaction with these treatments. Sentiment analysis is a method to measure and interpret patients’ satisfaction. In the first phase of this study, we aimed to utilize social media posts to predict patients’ sentiment towards opioid …

A Reinforcement Learning Approach To Sequential Acceptance Sampling As A Critical Success Factor For Lean Six Sigma, Hani A. Khalil

Theses and Dissertations

In the 21st century, globalization coupled with technological advancement and free trade has created competition among various businesses enterprises. This competition has led many businesses to adopt various management techniques such as acceptance sampling aimed at transforming their processes in order to remain competitive in the global market and adapt to new market demands. The successful implementation of acceptance sampling is highly dependent on what the academic literature refers to as acceptance sampling optimization. A literature review on the optimization of acceptance sampling has not shown any work that studied whether acceptance sampling and machine learning (ML) plans can be …

Old Dogs, New Tricks: Authoritarian Regime Persistence Through Learning, Nicholas Ryan Davis

Theses and Dissertations

How does diffusion lead to authoritarian regime persistence? Political decisions, regardless of what the actors involved might believe or espouse, do not happen in isolation. Policy changes, institutional alterations, regime transitions-- these political phenomena are all in some part a product of diffusion processes as much as they are derived from internal determinants. As such, political regimes do not exist in a vacuum, nor do they ignore the outside world. When making decisions about policy and practice, we should expect competent political actors to take a look at the wider external world. This dissertation project presents a theory of regime …

Automated Digit Recognition On Sound Pressure Level Meters Based On Deep Learning, Che-Wei Tung

Theses and Dissertations

Sound pressure level (SPL) meter is one of the useful devices used for measuring the sound level pressure. The measurement device displays the SPL value in decibels (dB) on a standard LCD screen (no backlight). We could base on the digit number shown on the LCD screen to do some adjustments or evaluations. Thus, SPL has been widely used in several fields to quantify different noise, such as industrial, environmental, and aircraft noise. However, in my basic knowledge, there is no previous study used machine learning to auto-recognize the digit on the SPL meter. This thesis presents a novel system …

Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh

Theses and Dissertations

In this thesis, we investigate the performance of a series of classification methods for the

Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting

LOS for an inpatient in an hospital is a challenging task but is essential for the operational

success of a hospital. Since hospitals are faced with severely limited resources including

beds to hold admitted patients, prediction of LoS will assist the hospital staff for better

planning and management of hospital resources. The goal of this project is to create a

machine learning model that predicts the length-of stay for each patient …

Predicting Hospital Length Of Stay In Intensive Care Unit, Namita Singh

Theses and Dissertations

In this thesis, we investigate the performance of a series of classification methods for the

Prediction of the hospital Length of Stay (LoS) in Intensive Care Unit (ICU). Predicting

LOS for an inpatient in an hospital is a challenging task but is essential for the operational

success of a hospital. Since hospitals are faced with severely limited resources including

beds to hold admitted patients, prediction of LoS will assist the hospital staff for better

planning and management of hospital resources. The goal of this project is to create a

machine learning model that predicts the length-of stay for each patient …

A Hierarchical, Fuzzy Inference Approach To Data Filtration And Feature Prioritization In The Connected Manufacturing Enterprise, Phillip Matthew Lacasse

Theses and Dissertations

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency …

Using Advanced Post-Processing Methods With The Hrrr-Tle To Improve The Prediction Of Cold Season Precipitation Type, Timothy Thielke

Theses and Dissertations

In this study we explore advanced statistical methods with the operational High-Resolution Rapid Refresh Model (HRRR) Time-Lagged Ensemble (TLE) to improve the prediction of cold season precipitation type. TLEs are a computationally efficient method to provide a slightly improved probabilistic forecast as the differences between model runs are an approximation of initial condition uncertainty. We apply evolutionary programming, weight-decay bias correction, and Bayesian Model Combination with fifteen HRRR forecast variables that potentially relate to precipitation type for station locations in the contiguous United States that are along and to the east of 100 W longitude to obtain probabilistic precipitation type …