Computer Sciences Commons^™

Effective Drag Coefficient Prediction On Single-View 2d Images Of Snowflakes, Cameron Hudson

Graduate College Dissertations and Theses

The drag coefficient of snowflakes is an crucial particle descriptor that can quantify the relationships with the mass, shape, size, and fall speed of snowflake particles. Previous studies has relied on estimating and improving empirical correlations for the drag coefficient of particles, utilizing 3D images from the Multi-Angled Snowflake Camera Database (MASCDB) to estimate snowflake properties such as mass, geometry, shape classification, and rimming degree. However, predictions of the drag coefficient with single-view 2D images of snowflakes has proven to be a challenging problem, primarily due to the lack of data and time-consuming, expensive methods used to estimate snowflake shape …

Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn

Graduate College Dissertations and Theses

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the …

Developing Muscle Synergy Functions For Remote Gait Analysis, Nicole Marie Donahue

Graduate College Dissertations and Theses

Digital medicine promises to improve healthcare and enable its delivery to rural and underserved communities. A key component of digital medicine is accurate and robust remote patient monitoring. For example, remote monitoring of biomechanical measures of limb impairment during daily life could allow near real-time tracking of rehabilitation progress and personalization of rehabilitation paradigms in those recovering from orthopedic surgery. Wearable sensors have long been suggested as a means for quantifying muscle and joint loading, which can provide a direct measure of limb impairment. However, current approaches either do not provide these measures or require unwieldy wearable sensor arrays and/or …

Applications In Sentiment Analysis And Machine Learning For Identifying Public Health Variables Across Social Media, Eric Michael Clark

Graduate College Dissertations and Theses

Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion. We mined data from several public Twitter endpoints to identify content relevant to healthcare providers and public health regulatory professionals. We began by compiling content related to electronic nicotine delivery systems (or e-cigarettes) as these had become popular alternatives to tobacco products. There was an apparent need to remove high frequency tweeting entities, called bots, that would spam messages, advertisements, and fabricate testimonials. Algorithms were constructed using natural language processing and machine learning to sift human responses from automated …

Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach, Enrique Angola

Graduate College Dissertations and Theses

A novelty detection algorithm inspired by human audio pattern recognition is conceptualized and experimentally tested. This anomaly detection technique can be used to monitor the health of a machine or could also be coupled with a current state of the art system to enhance its fault detection capabilities. Time-domain data obtained from a microphone is processed by applying a short-time FFT, which returns time-frequency patterns. Such patterns are fed to a machine learning algorithm, which is designed to detect novel signals and identify windows in the frequency domain where such novelties occur. The algorithm presented in this paper uses one-dimensional …

Enabling Machine Science Through Distributed Human Computing, Mark David Wagy

Graduate College Dissertations and Theses

Distributed human computing techniques have been shown to be effective ways of accessing the problem-solving capabilities of a large group of anonymous individuals over the World Wide Web. They have been successfully applied to such diverse domains as computer security, biology and astronomy. The success of distributed human computing in various domains suggests that it can be utilized for complex collaborative problem solving. Thus it could be used for "machine science": utilizing machines to facilitate the vetting of disparate human hypotheses for solving scientific and engineering problems.

In this thesis, we show that machine science is possible through distributed human …