Physical Sciences and Mathematics Commons^™

Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan

LSU Master's Theses

The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 …

Learning About Large Scale Image Search: Lessons From Global Scale Hotel Recognition To Fight Sex Trafficking, Abby Stylianou

McKelvey School of Engineering Theses & Dissertations

Hotel recognition is a sub-domain of scene recognition that involves determining what hotel is seen in a photograph taken in a hotel. The hotel recognition task is a challenging computer vision task due to the properties of hotel rooms, including low visual similarity between rooms in the same hotel and high visual similarity between rooms in different hotels, particularly those from the same chain. Building accurate approaches for hotel recognition is important to investigations of human trafficking. Images of human trafficking victims are often shared by traffickers among criminal networks and posted in online advertisements. These images are often taken …

Enabling Auditing And Intrusion Detection Of Proprietary Controller Area Networks, Brent C. Stone

Theses and Dissertations

The goal of this dissertation is to provide automated methods for security researchers to overcome ‘security through obscurity’ used by manufacturers of proprietary Industrial Control Systems (ICS). `White hat' security analysts waste significant time reverse engineering these systems' opaque network configurations instead of performing meaningful security auditing tasks. Automating the process of documenting proprietary protocol configurations is intended to improve independent security auditing of ICS networks. The major contributions of this dissertation are a novel approach for unsupervised lexical analysis of binary network data flows and analysis of the time series data extracted as a result. We demonstrate the utility …

Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss

Computer Science and Computer Engineering Undergraduate Honors Theses

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing …

Efficacy Of Deep Learning In Support Of Smart Services, Basheer Mohammed Basheer Qolomany

Dissertations

The massive amount of streaming data generated and captured by smart service appliances, sensors and devices needs to be analyzed by algorithms, transformed into information, and minted to extract knowledge to facilitate timely actions and better decision making. This can lead to new products and services that can dramatically transform our lives. Machine learning and data analytics will undoubtedly play a critical role in enabling the delivery of smart services. Within the machine-learning domain, Deep Learning (DL) is emerging as a superior new approach that is much more effective than any rule or formula used by traditional machine learning. Furthermore, …

Towards Scalable Characterization Of Noisy, Intermediate-Scale Quantum Information Processors, Travis Luke Scholten

Physics & Astronomy ETDs

In recent years, quantum information processors (QIPs) have grown from one or two qubits to tens of qubits. As a result, characterizing QIPs – measuring how well they work, and how they fail – has become much more challenging. The obstacles to characterizing today’s QIPs will grow even more difficult as QIPs grow from tens of qubits to hundreds, and enter what has been called the “noisy, intermediate-scale quantum” (NISQ) era. This thesis develops methods based on advanced statistics and machine learning algorithms to address the difficulties of “quantum character- ization, validation, and verification” (QCVV) of NISQ processors. In the …

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 …

Flow Adaptive Video Object Segmentation, Fanqing Lin

Theses and Dissertations

We tackle the task of semi-supervised video object segmentation, i.e, pixel-level object classification of the images in video sequences using very limited ground truth training data of its corresponding video. Recently introduced online adaptation of convolutional neural networks for video object segmentation (OnAVOS) has achieved good results by pretraining the network, fine-tuning on the first frame and training the network at test time using its approximate prediction as newly obtained ground truth. We propose Flow Adaptive Video Object Segmentation (FAVOS) that refines the generated adaptive ground truth for online updates and utilizes temporal consistency between video frames with the help …

Making A Good Thing Better: Enhancing Password/Pin-Based User Authentication With Smartwatch, Bing Chang, Yingjiu Li, Qiongxiao Wang, Wen-Tao Zhu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Wearing smartwatches becomes increasingly popular in people’s lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this …

Toward Real-Time Flip Fluid Simulation Through Machine Learning Approximations, Javid Kennon Pack

Theses and Dissertations

Fluids in computer generated imagery can add an impressive amount of realism to a scene, but are particularly time-consuming to simulate. In an attempt to run fluid simulations in real-time, recent efforts have attempted to simulate fluids by using machine learning techniques to approximate the movement of fluids. We explore utilizing machine learning to simulate fluids while also integrating the Fluid-Implicit-Particle (FLIP) simulation method into machine learning fluid simulation approaches.

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

A Model-Based Ai-Driven Test Generation System, Dionny Santiago

FIU Electronic Theses and Dissertations

Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. …

Machine Learning Methods For Activity Detection In Wearable Sensor Data Streams, Roy Adams

Doctoral Dissertations

Wearable wireless sensors have the potential for transformative impact on the fields of health and behavioral science. Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals in natural environments; however, extracting reliable high-level inferences from these raw data streams remains a key data analysis challenge. In this dissertation, we address three challenges that arise when trying to perform activity detection from wearable sensor streams. First, we address the challenge of learning from small amounts of noisy data by proposing a class of conditional random field models for …

Beam-Target Helicity Asymmetry E In K0Λ And K0Σ0 Photoproduction On The Neutron, D. H. Ho, R. A. Schumacher, A. D’Angelo, A. Deur, J. Fleming, C. Hanretty, T. Kageya, F. J. Klein, E. Klempt, M. M. Lowry, H. Lu, V. A. Nikonov, P. Peng, A. M. Sandorfi, A. V. Sarantsev, I. I. Strakovsky, N. K. Walford, X. Wei, R. L. Workman, K. P. Adhikari, S. Adhikari, D. Adikaram, Z. Akbar, J. Ball, L. Barion, M. Bashkanov, C. D. Bass, M. Battaglieri, I. Bedlinskiy, A. S. Biselli, Wesley P. Gohn

Physics and Astronomy Faculty Publications

We report the first measurements of the E beam-target helicity asymmetry for the γ^→ n^→ → K⁰Λ and K⁰Σ⁰ channels in the energy range 1.70 ≤ W ≤ 2.34 GeV. The CLAS system at Jefferson Lab uses a circularly polarized photon beam and a target consisting of longitudinally polarized solid molecular hydrogen deuteride with low background contamination for the measurements. The multivariate analysis method boosted decision trees is used to isolate the reactions of interest. Comparisons with predictions from the KaonMAID, SAID, and Bonn-Gatchina models are presented. These results will help separate the …

Using Machine Learning To Accurately Predict Ambient Soundscapes From Limited Data Sets, Katrina Lynn Pedersen

Theses and Dissertations

The ability to accurately characterize the soundscape, or combination of sounds, of diverse geographic areas has many practical implications. Interested parties include the United States military and the National Park Service, but applications also exist in areas such as public health, ecology, community and social justice noise analyses, and real estate. I use an ensemble of machine learning models to predict ambient sound levels throughout the contiguous United States. Our data set consists of 607 training sites, where various acoustic metrics, such as overall daytime L50 levels and one-third octave frequency band levels, have been obtained. I have data for …

Machine Learning For Ecosystem Services, Simon Willcock, Javier Martínez-López, Danny A.P. Hooftman, Kenneth J. Bagstad, Stefano Balbi, Alessia Marzo, Carlo Prato, Saverio Sciandrello, Giovanni Signorello

Rubenstein School of Environment and Natural Resources Faculty Publications

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where …

Using Chronicling America’S Images To Explore Digitized Historic Newspapers & Imagine Alternative Futures, Elizabeth Lorang, Leen-Kiat Soh

University of Nebraska-Lincoln Libraries: Conference Presentations and Speeches

This presentation situates the work of the Aida team broadly as well as hinges this work on some very specific challenges for digital libraries. In doing so demonstrate the many types of questions and domains to be explored in digitized newspapers.

Using Aviris And Machine Learning To Map And Discriminate Bull Kelp And Giant Kelp Along The Pacific Coast Of The United States, Tanner Thompson, Dr. Ryan Jensen

Journal of Undergraduate Research

Kelp forests provide food and shelter for many organisms, and they are an important part of coastal ecosystems throughout the world. Along the Pacific coast of the United States, kelp forests are made up of two species of kelp: bull kelp (Nereocystis Leutkana) and giant kelp (Macrocystis Pyrifera). While similar, these two species are physiologically and structurally different.

Toward Audio Beehive Monitoring: Deep Learning Vs. Standard Machine Learning In Classifying Beehive Audio Samples, Vladmir Kulyukin, Sarbajit Mukherjee, Prakhar Amlathe

Computer Science Faculty and Staff Publications

Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing …

Predicting National Basketball Association Success: A Machine Learning Approach, Adarsh Kannan, Brian Kolovich, Brandon Lawrence, Sohail Rafiqi

SMU Data Science Review

In this paper, we present a machine learning based approach to projecting the success of National Basketball Association (NBA) draft prospects. With the proliferation of data, analytics have increasingly be- come a critical component in the assessment of professional and collegiate basketball players. We leverage player biometric data, college statistics, draft selection order, and positional breakdown as modelling features in our prediction algorithms. We found that a player's draft pick and their college statistics are the best predictors of their longevity in the National Basketball Association.

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 …

Man, Machine, Scientific Models And Creation Science, Steven M. Gollmer

Proceedings of the International Conference on Creationism

Historically, physics was the most quantitative of the sciences. Geologists and biologists built their models based on observation, categorization and generalization. This distinction between qualitative and quantitative sciences prompted the quote attributed to Ernest Rutherford that “All science is either physics or stamp collecting.” In the intervening 80 years all sciences have exploded in the use of quantitative measures to find patterns and trends in data. A review of a half-century of creationist literature shows that this transition has not been lost to the creationist community.

As this trend continues to accelerate, two areas of caution need to be taken …

Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi

SMU Data Science Review

In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …

Transfer Learning With Mixtures Of Manifolds, Thomas Boucher

Doctoral Dissertations

Advances in scientific instrumentation technology have increased the speed of data acquisition and the precision of sampling, creating an abundance of high-dimensional data sets. The ability to combine these disparate data sets and to transfer information between them is critical to accurate scientific analysis. Many modern-day instruments can record data at many thousands of channels, far greater than the actual degrees of freedom in the sample data. This makes manifold learning, a class of methods that exploit the observation that high-dimensional data tend to lie on lower-dimensional manifolds, especially well-suited to this transfer learning task. Existing manifold-based transfer learning methods …

The Silencing Power Of Algorithms: How The Facebook News Feed Algorithm Manipulates Users' Perceptions Of Opinion Climates, Callie Jessica Morgan

University Honors Theses

This extended literature review investigates how the architecture and features of the Facebook Newsfeed algorithm, EdgeRank, can inhibit and facilitate the expression of political opinions. This paper will investigate how Elisabeth Noelle-Neumann's theory on public opinion, Spiral of Silence, can be used to assess the Facebook news feed as a political opinion source that actively shapes users' perceptions of minority and majority opinion climates. The feedback loops created by the algorithm's criteria influences users' decisions to self-censor or express their political opinions with interpersonal connections and unfamiliar connections on the site.

Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our IoT system …

Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn

Electrical & Computer Engineering Theses & Dissertations

The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify …

Baseline Assisted Classification Of Heart Rate Variability, Elham Harirpoush

Electronic Thesis and Dissertation Repository

Recently, among various analysis methods of physiological signals, automatic analysis of Electrocardiogram (ECG) signals, especially heart rate variability (HRV) has received significant attention in the field of machine learning. Heart rate variability is an important indicator of health prediction and it is applicable to various fields of scientific research. Heart rate variability is based on measuring the differences in time between consecutive heartbeats (also known as RR interval), and the most common measuring techniques are divided into the time domain and frequency domain. In this research study, a classifier based on analysis of HRV signal is developed to classify different …

Evaluating Beach Water Quality And Dengue Fever Risk Factors By Satellite Remote Sensing And Artificial Neural Networks, Abdiel Elias Laureano-Rosario

USF Tampa Graduate Theses and Dissertations

Climatic variations, together with large-scale environmental forces and human development affect the quality of coastal recreational waters, creating potential risks to human health. These environmental forces, including increased temperature and precipitation, often promote specific vector-borne diseases in the Caribbean and Gulf of Mexico. Human activities affect water quality through discharges from urban areas, including nutrient and other pollutants derived from wastewater systems. Both water quality of recreational beaches and vector-borne diseases can be better managed by understanding their relationship with local environmental forces.

I evaluated how changes in vector-borne diseases and poor recreational water quality were related to specific environmental …