Physical Sciences and Mathematics Commons^™

Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev

Electronic Theses and Dissertations

Reinforcement learning (RL) is a subfield of machine learning concerned with agents learning to behave optimally by interacting with an environment. One of the most important topics in RL is how the agent should explore, that is, how to choose actions in order to rate their impact on long-term reward. For example, a simple baseline strategy might be uniformly random action selection. This thesis investigates the heuristic idea that agents will learn faster if they explore by factoring the environment’s state into their decision and intentionally choose actions which are as different as possible from what they have previously observed. …

The Distribution Of The Significance Level, Paul O. Monnu

Electronic Theses and Dissertations

Reporting the p-value is customary when conducting a test of hypothesis or significance. The likelihood of getting a fictitious second sample and presuming the null hypothesis is correct is the p-value. The significance level is a statistic that interests us to investigate. Being a statistic, it has a distribution. For the F-test in a one-way ANOVA and the t-tests for population means, we define the significance level, its observed value, and the observed significance level. It is possible to derive the significance level distribution. The t-test and the F-test are not without controversy. Specifically, we demonstrate that as sample size …

Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath

Electronic Theses and Dissertations

In this thesis, the Weighted Newton-Raphson Method (WNRM), an innovative optimization technique, is introduced in statistical supervised learning for categorization and applied to a diabetes predictive model, to find maximum likelihood estimates. The iterative optimization method solves nonlinear systems of equations with singular Jacobian matrices and is a modification of the ordinary Newton-Raphson algorithm. The quadratic convergence of the WNRM, and high efficiency for optimizing nonlinear likelihood functions, whenever singularity in the Jacobians occur allow for an easy inclusion to classical categorization and generalized linear models such as the Logistic Regression model in supervised learning. The WNRM is thoroughly investigated …

A Graphical User Interface Using Spatiotemporal Interpolation To Determine Fine Particulate Matter Values In The United States, Kelly M. Entrekin

Honors College Theses

Fine particulate matter or PM2.5 can be described as a pollution particle that has a diameter of 2.5 micrometers or smaller. These pollution particle values are measured by monitoring sites installed across the United States throughout the year. While these values are helpful, a lot of areas are not accounted for as scientists are not able to measure all of the United States. Some of these unmeasured regions could be reaching high PM2.5 values over time without being aware of it. These high values can be dangerous by causing or worsening health conditions, such as cardiovascular and lung diseases. Within …

Network Intrusion Detection Using Deep Reinforcement Learning, Hamed T. Sanusi

Electronic Theses and Dissertations

This thesis delves into cybersecurity by applying Deep Reinforcement(DRL) Learning in network intrusion detection. One advantage of DRL is the ability to adapt to changing network conditions and evolving attack methods, making it a promising solution for addressing the challenges involved in intrusion detection. The thesis will also discuss the obstacles and benefits of using Classification methods for network intrusion detection and the need for high-quality training data. To train and test our proposed method, the NSL-KDD dataset was used and then adjusted by converting it from a multi-classification to a binary classification, achieved by joining all attacks into one. …

The Influence Of Urban Forms And Street Infrastructure On Pedestrian-Motorist Collisions, Taylor J. Foreman

Electronic Theses and Dissertations

Unwalkable cities are afflicted by serious issues such as increasing rates of pedestrian traffic accidents, public health concerns, and the denied right to have an accessible city. This study examines how different types of urban forms and street infrastructure contribute to the prevalence of traffic accidents in two major metropolitan cities in the United States: Atlanta, Georgia, and Boston, Massachusetts. This study utilizes geospatial analysis through the Average Nearest Neighbor and Optimized Hot Spot Analysis tools to determine the spatial distribution of traffic accidents throughout both cities. Additionally, statistical tests were conducted to explore the relationships between the number of …

Non-Inferiority Testing: Kernel Estimation And Overlap Measure, Larie C. Ward

Electronic Theses and Dissertations

In non-inferiority testing, the decision of whether a proposed treatment is non-inferior to a reference treatment depends on model assumptions and choices of acceptable tolerance limits. Here, we consider a method that employs kernels to estimate the probability density functions of both the experimental and reference populations from two independent samples. Based on these densities, we introduce a quantity called the overlap coefficient or overlap measure. A bootstrap technique is helpful in exploring the distribution and variance empirically. We derive the distribution of this measure and define a hypothesis test that can be applied to the non-inferiority setting under some …

Reinforcement Learning: Low Discrepancy Action Selection For Continuous States And Actions, Jedidiah Lindborg

Electronic Theses and Dissertations

In reinforcement learning the process of selecting an action during the exploration or exploitation stage is difficult to optimize. The purpose of this thesis is to create an action selection process for an agent by employing a low discrepancy action selection (LDAS) method. This should allow the agent to quickly determine the utility of its actions by prioritizing actions that are dissimilar to ones that it has already picked. In this way the learning process should be faster for the agent and result in more optimal policies.

Modelling Muscle Activation Using Emg Signal, Mercy U. Okonna

Electronic Theses and Dissertations

(EMG) is a method for measuring muscle activity by an electrical signal, and is useful in studying motor control, postural control, and in physical therapy. A current research topic is creating an algorithm that can use the EMG signal to reliably classify a muscle as active or inactive. This thesis presents a classification algorithm for leg muscles with a single activation spike while walking. Time is rescaled into steps, which are identified using data from cameras measuring joint angles while walking. The algorithm is based on moving averages and a convex combination of mean signal strength in active and inactive …

Bias Of Rank Correlation Under A Mixture Model, Russell Land

Electronic Theses and Dissertations

This thesis project will analyze the bias in mixture models when contaminated data is present. Specifically, we will analyze the relationship between the bias and the mixing proportion, p, for the rank correlation methods Spearman’s Rho and Kendall’s Tau. We will first look at the history of the two non-parametric rank correlation methods and the sample and population definitions will be introduced. Copulas will be introduced to show a few ways we can define these correlation methods. After that, mixture models will be defined and the main theorem will be stated and proved. As an example, we will apply this …

Public Perception Of Different Planting Techniques Using Augmented Reality, Sultana Quader Tania

Electronic Theses and Dissertations

The objective of this study was to measure public perception of the different planting techniques (block and matrix), which are used at visitor information centers (VICs) and other rights of way (ROW) areas. The main factors that affect public perception of planting techniques were identified through an extensive literature review and qualitative survey from four welcome centers in the state of Georgia. The ranking of those indicators, based on public preferences, was discovered through a quantitative survey. During the first phase of the quantitative survey, images of block and matrix were used. An iOS-based user-friendly and cost-effective augmented reality (AR) …

Multiple Imputation Using Influential Exponential Tilting In Case Of Non-Ignorable Missing Data, Kavita Gohil

Electronic Theses and Dissertations

Modern research strategies rely predominantly on three steps, data collection, data analysis, and inference. In research, if the data is not collected as designed, researchers may face challenges of having incomplete data, especially when it is non-ignorable. These situations affect the subsequent steps of evaluation and make them difficult to perform. Inference with incomplete data is a challenging task in data analysis and clinical trials when missing data related to the condition under the study. Moreover, results obtained from incomplete data are prone to biases. Parameter estimation with non-ignorable missing data is even more challenging to handle and extract useful …

Artificial Neural Network Models For Pattern Discovery From Ecg Time Series, Mehakpreet Kaur

Electronic Theses and Dissertations

Artificial Neural Network (ANN) models have recently become de facto models for deep learning with a wide range of applications spanning from scientific fields such as computer vision, physics, biology, medicine to social life (suggesting preferred movies, shopping lists, etc.). Due to advancements in computer technology and the increased practice of Artificial Intelligence (AI) in medicine and biological research, ANNs have been extensively applied not only to provide quick information about diseases, but also to make diagnostics accurate and cost-effective. We propose an ANN-based model to analyze a patient's electrocardiogram (ECG) data and produce accurate diagnostics regarding possible heart diseases …

Nonparametric Misclassification Simulation And Extrapolation Method And Its Application, Congjian Liu

Electronic Theses and Dissertations

The misclassification simulation extrapolation (MC-SIMEX) method proposed by Küchenho et al. is a general method of handling categorical data with measurement error. It consists of two steps, the simulation and extrapolation steps. In the simulation step, it simulates observations with varying degrees of measurement error. Then parameter estimators for varying degrees of measurement error are obtained based on these observations. In the extrapolation step, it uses a parametric extrapolation function to obtain the parameter estimators for data with no measurement error. However, as shown in many studies, the parameter estimators are still biased as a result of the parametric extrapolation …

Generalization Of Kullback-Leibler Divergence For Multi-Stage Diseases: Application To Diagnostic Test Accuracy And Optimal Cut-Points Selection Criterion, Chen Mo

Electronic Theses and Dissertations

The Kullback-Leibler divergence (KL), which captures the disparity between two distributions, has been considered as a measure for determining the diagnostic performance of an ordinal diagnostic test. This study applies KL and further generalizes it to comprehensively measure the diagnostic accuracy test for multi-stage (K > 2) diseases, named generalized total Kullback-Leibler divergence (GTKL). Also, GTKL is proposed as an optimal cut-points selection criterion for discriminating subjects among different disease stages. Moreover, the study investigates a variety of applications of GTKL on measuring the rule-in/out potentials in the single-stage and multi-stage levels. Intensive simulation studies are conducted to compare the performance …

Regression Tree Construction For Reinforcement Learning Problems With A General Action Space, Anthony S. Bush Jr

Electronic Theses and Dissertations

Part of the implementation of Reinforcement Learning is constructing a regression of values against states and actions and using that regression model to optimize over actions for a given state. One such common regression technique is that of a decision tree; or in the case of continuous input, a regression tree. In such a case, we fix the states and optimize over actions; however, standard regression trees do not easily optimize over a subset of the input variables\cite{Card1993}. The technique we propose in this thesis is a hybrid of regression trees and kernel regression. First, a regression tree splits over …

Data Patterns Discovery Using Unsupervised Learning, Rachel A. Lewis

Electronic Theses and Dissertations

Self-care activities classification poses significant challenges in identifying children’s unique functional abilities and needs within the exceptional children healthcare system. The accuracy of diagnosing a child's self-care problem, such as toileting or dressing, is highly influenced by an occupational therapists’ experience and time constraints. Thus, there is a need for objective means to detect and predict in advance the self-care problems of children with physical and motor disabilities. We use clustering to discover interesting information from self-care problems, perform automatic classification of binary data, and discover outliers. The advantages are twofold: the advancement of knowledge on identifying self-care problems in …

Safety Constraint Optimization Of Combination Drug Therapy In Hypertension Clinical Trials, Victor Chukwu

Electronic Theses and Dissertations

In Clinical Practice, combination drug therapy has become common in treating many disease conditions. The purpose of these combinations is often to ensure optimal efficacy and to reduce adverse effects that may arise from monotherapy. Clinical trials have also been conducted to ensure efficacy and safety of these combinations before they are introduced into the market. However, adverse effects still occur with combination therapies. The objective of this study is to (1) To determine a region of optimum doses of Drug A and Drug B in combination while focusing on efficacy alone (2) To determine a region of optimum doses …

Essays On Mixture Models, Trevor R. Camper

Electronic Theses and Dissertations

When considering statistical scenarios where one can sample from populations that are not of interest for the purposes of a study, bivariate mixture models can be used to study the effect that this missampling can have on parameter estimation. In this thesis, we will examine the behavior that bivariate mixture models have on two statistical constructs: Cronbach's alpha \cite{C51}, and Spearman's rho \cite{S04}. Chapter 1 will introduce notions of mixture models and the definition of bias under mixture models which will serve as the central concept of this thesis. Chapter 2 will investigate a particular psychometric issue known as insufficient …

Variable Selection In Accelerated Failure Time (Aft) Frailty Models: An Application Of Penalized Quasi-Likelihood, Sarbesh R. Pandeya

Electronic Theses and Dissertations

Variable selection is one of the standard ways of selecting models in large scale datasets. It has applications in many fields of research study, especially in large multi-center clinical trials. One of the prominent methods in variable selection is the penalized likelihood, which is both consistent and efficient. However, the penalized selection is significantly challenging under the influence of random (frailty) covariates. It is even more complicated when there is involvement of censoring as it may not have a closed-form solution for the marginal log-likelihood. Therefore, we applied the penalized quasi-likelihood (PQL) approach that approximates the solution for such a …

Some New And Generalized Distributions Via Exponentiation, Gamma And Marshall-Olkin Generators With Applications, Hameed Abiodun Jimoh

Electronic Theses and Dissertations

Three new generalized distributions developed via completing risk, gamma generator, Marshall-Olkin generator and exponentiation techniques are proposed and studied. Structural properties including quantile functions, hazard rate functions, moment, conditional moments, mean deviations, R\'enyi entropy, distribution of order statistics and maximum likelihood estimates are presented. Monte Carlo simulation is employed to examine the performance of the proposed distributions. Applications of the generalized distributions to real lifetime data are presented to illustrate the usefulness of the models.

Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don

Electronic Theses and Dissertations

In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes …

A Comparison Of Bridge Deterioration Models, Toktam Naderimoghaddam

Electronic Theses and Dissertations

Predicting how bridges will deteriorate is the key to budgeting financial and personnel resources. Deterioration models exist for specific components of a bridge, but no models exist for the sufficiency rating which is an overall measure of the condition and relevance of a bridge used for determining eligibility for federal funds.

We have 25 years worth of data collected by the Georgia Department of Transportation from 1992 to 2016 about all bridges in the State of Georgia. More precisely, each row in this data set includes the characteristics of each bridge along with the sufficiency rating of that bridge in …

Examining The Issue Of Compliance With Personal Protective Equipment Among Wastewater Workers Across The Southeast Region Of The United States, Tamara L. Wright

Electronic Theses and Dissertations

Wastewater workers are exposed to different occupational hazards such as chemicals, gases, viruses, and bacteria. Personal Protective Equipment (PPE) is a significant factor that can reduce or increase the probability of an accident from hazardous exposures to chemicals and microbial contaminants. The purpose of this study was to identify wastewater worker’s beliefs and practices on wearing PPE and protections offered by PPE through the integration of the Health Belief Model (HBM). Participants were workers in the wastewater industry, which included wastewater operators, laboratory analysts, maintenance workers, wastewater collection workers, equipment operators, managers, and supervisors (n=272). The instrument was a self-administered …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

Electronic Theses and Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

Modeling Volatility Of Financial Time Series Using Arc Length, Benjamin H. Hoerlein

Electronic Theses and Dissertations

This thesis explores how arc length can be modeled and used to measure the risk involved with a financial time series. Having arc length as a measure of volatility can help an investor in sorting which stocks are safer/riskier to invest in. A Gamma autoregressive model of order one(GAR(1)) is proposed to model arc length series. Kernel regression based bias correction is studied when model parameters are estimated using method of moment procedure. As an application, a model-based clustering involving thirty different stocks is presented using k-means++ and hierarchical clustering techniques.

Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon

Electronic Theses and Dissertations

Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most …

Quasi-Random Action Selection In Markov Decision Processes, Samuel D. Walker

Electronic Theses and Dissertations

In Markov decision processes an operator exploits known data regarding the environment it inhabits. The information exploited is learned from random exploration of the state-action space. This paper proposes to optimize exploration through the implementation of quasi-random sequences in both discrete and continuous state-action spaces. For the discrete case a permutation is applied to the indices of the action space to avoid repetitive behavior. In the continuous case sequences of low discrepancy, such as Halton sequences, are utilized to disperse the actions more uniformly.

A Markov Decision Process Approach To Adaptive Contact Strategies, Artur Grygorian

Electronic Theses and Dissertations

In the field of survey methodology, optimizing contact strategies helps organizations increase response rates using their allocated budget. Markov Decision Processes (MDP) are widely used to model decision-making strategies in situations where the outcomes have a random component. In this research, we use MDPs and adaptive sampling techniques to construct a strategy that, based on target audience characteristics, suggests the best contact policy. The data we use comes from the First Destination Survey conducted by the Office of Career Services at Georgia Southern University. The constructed model is quite flexible and can be used by other organizations to optimize their …

Garch(1,1) With Sifted Gamma-Distributed Errors, Alan C. Budd

Electronic Theses and Dissertations

Typical General Autoregressive Conditional Heteroskedastic (GARCH) processes involve normally-distributed errors, and they model strictly-positive error processes poorly. This thesis will present a method for estimating the parameters of a GARCH(1,1) process with shifted Gamma-distributed errors, conduct a simulation study to test the method, and apply the method to real time series data.