Physical Sciences and Mathematics Commons^™

Using Generative Adversarial Networks To Classify Structural Damage Caused By Earthquakes, Gian P. Delacruz

Master's Theses

The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and in particular Deep Learning (DL) has become one of the most …

Predicting Drug Misuse Status Using Machine Learning On Electronic Health Records, Robert Arnold Kania

Master's Theses

Substance misuse is a major problem in the world. in 2014, as many as 52,404 deaths in the US were caused by drug overdoses. in 2001, the monetary cost of drug misuse has been estimated to be 414 billion dollars. in this work, we explore the use of different machine learning algorithms in the prediction of cocaine misuse using structured and unstructured data found in electronic health records. These records contain various attributes that can help with this prediction, including but not limited to chart text data, previous diagnoses of certain diseases and information about the area the patient lives …

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

Evaluating Projections And Developing Projection Models For Daily Fantasy Basketball, Eric C. Evangelista

Master's Theses

Daily fantasy sports (DFS) has grown in popularity with millions of participants throughout the world. However, studies have shown that most profits from DFS contests are won by only a small percentage of players. This thesis addresses the challenges faced by DFS participants by evaluating sources that provide player projections for NBA DFS contests and by developing machine learning models that produce competitive player projections.

External sources are evaluated by constructing daily lineups based on the projections offered and evaluating those lineups in the context of all potential lineups, as well as those submitted by participants in competitive FanDuel DFS …

Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu

Master's Theses

Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …

Improving Ultra-Wideband Localization By Detecting Radio Misclassification, Cory A. Mayer

Master's Theses

The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.

Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of …

A Study Into The Feasibility Of Using Natural Language Processing And Machine Learning For The Identification Of Alcohol Misuse In Trauma Patients, Andrew Phillips

Master's Theses

Alcohol misuse is a leading cause of premature death in the United States, with nearly a third of trauma patients found to have elevated blood alcohol levels upon admission. However, timely intervention has been shown to reduce this. It is thus important to be able to quickly screen patients to identify alcohol misuse. Many medical centers use standardized questionnaires to identify alcohol misuse, but since these instruments are not usually a part of routine care, there are many cases where it is not done.

In this study, large quantities of notes were processed with natural language processing and machine learning …

Categorizing Blog Spam, Brandon Bevans

Master's Theses

The internet has matured into the focal point of our era. Its ecosystem is vast, complex, and in many regards unaccounted for. One of the most prevalent aspects of the internet is spam. Similar to the rest of the internet, spam has evolved from simply meaning ‘unwanted emails’ to a blanket term that encompasses any unsolicited or illegitimate content that appears in the wide range of media that exists on the internet.

Many forms of spam permeate the internet, and spam architects continue to develop tools and methods to avoid detection. On the other side, cyber security engineers continue to …

Predicting Changes To Source Code, Justin James Roll

Master's Theses

Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement …

Towards An Automated Weight Lifting Coach: Introducing Lift, Michael Andrew Lady

Master's Theses

The fitness device market is young and rapidly growing. More people than ever before take count of how many steps they walk, how many calories they burn, their heart rate over time, and even their quality of sleep. New, and as of yet, unreleased fitness devices have promised the next evolution of functionality with exercise technique analysis. These next generation of fitness devices have wrist and armband style form factors, which may not be optimal for barbell exercises such as back squat, bench press, and overhead press where a sensor on one arm may not provide the most relevant data …

Can Clustering Improve Requirements Traceability? A Tracelab-Enabled Study, Brett Taylor Armstrong

Master's Theses

Software permeates every aspect of our modern lives. In many applications, such in the software for airplane flight controls, or nuclear power control systems software failures can have catastrophic consequences. As we place so much trust in software, how can we know if it is trustworthy? Through software assurance, we can attempt to quantify just that.

Building complex, high assurance software is no simple task. The difficult information landscape of a software engineering project can make verification and validation, the process by which the assurance of a software is assessed, very difficult. In order to manage the inevitable information overload …

Computer Sketch Recognition, Richard Steigerwald

Master's Theses

Tens of thousands of years ago, humans drew sketches that we can see and identify even today. Sketches are the oldest recorded form of human communication and are still widely used. The universality of sketches supersedes that of culture and language. Despite the universal accessibility of sketches by humans, computers are unable to interpret or even correctly identify the contents of sketches drawn by humans with a practical level of accuracy.

In my thesis, I demonstrate that the accuracy of existing sketch recognition techniques can be improved by optimizing the classification criteria. Current techniques classify a 20,000 sketch crowd-sourced dataset …

A Neural Network Approach To Border Gateway Protocol Peer Failure Detection And Prediction, Cory B. White

Master's Theses

The size and speed of computer networks continue to expand at a rapid pace, as do the corresponding errors, failures, and faults inherent within such extensive networks. This thesis introduces a novel approach to interface Border Gateway Protocol (BGP) computer networks with neural networks to learn the precursor connectivity patterns that emerge prior to a node failure. Details of the design and construction of a framework that utilizes neural networks to learn and monitor BGP connection states as a means of detecting and predicting BGP peer node failure are presented. Moreover, this framework is used to monitor a BGP network …