Mathematics Commons^™

Integrating Machine Learning Methods For Medical Diagnosis, Jazmin Quezada

Open Access Theses & Dissertations

Abstract:The rapid advancement of machine learning techniques has revolutionized the field of medical diagnosis by offering powerful tools to analyze complex data sets and make accurate predictions. In this proposed method, we present a novel approach that integrates machine learning and optimization models to enhance the accuracy of medical diagnoses. Our method focuses on fine-tuning and optimizing the parameters of machine learning algorithms commonly used in medical diagnosis, such as logistic regression, support vector machines, and neural networks. By employing optimization techniques, we systematically explore the parameter space of these algorithms to discover the most optimal configurations. Moreover, by representing …

Flexible Models For The Estimation Of Treatment Effect, Habeeb Abolaji Bashir

Open Access Theses & Dissertations

Estimation of treatment effect is an important problem which is well studied in the literature. While the regression models are one of the most commonly used techniques for the estimation of treatment effect, they are prone to model misspecification. To minimize the model misspecification bias, flexible nonparametric models are introduced for the estimation. Continuing this line of research, we propose two flexible nonparametric models that allow the treatment effect to vary across different levels of covariates. We provide estimation algorithms for both these models. Using simulations and data analysis, we illustrate the usefulness of the proposed methods.

Recursive Forms For Determinant Of K-Tridiagonal Toeplitz Matrices, Eugene Agyei-Kodie

Open Access Theses & Dissertations

Toeplitz matrices have garnered renewed interest in recent years due to their practical applications in engineering and computational sciences. Additionally, research has shown their connection to other matrices and their significance in matrix theory. For example, one study demonstrated that any matrix can be expressed as the product of Toeplitz matrices \citep{ye2016every}, while another showed that any square matrix is similar to a Toeplitz matrix \citep{mackey1999every}.

Numerous studies have examined various properties of Toeplitz matrices, including ideals of lower triangular Toeplitz matrices \citep{dogan9some}, matrix power computation with band Toeplitz structures \citep{dogan2017matrix}, and norms of Toeplitz matrices. Moreover, the use of …

Volatility Modeling Of Time Series Using Fractal And Self-Similarity Models, William Kubin

Open Access Theses & Dissertations

The study uses various methods to compare financial and geophysical time series scaling parameters and long-term memory behavior. The Cantor Detrended Fluctuation Analysis (CDFA) method is proposed to provide more accurate estimates of Hurst exponents. The CDFA method is applied to real-time series and the results are verified. The study also analyzes the memory behavior of daily Covid-19 cases before and after the announcement of effective vaccines. Low and high-frequency dataâ??s influence on the Hurst Index estimation is investigated, and a new PCDFA method is proposed. The stability of the Dow Jones Industrial Average is analyzed using a multi-scale normalized …

Classification Of Nuclear Pastas Through Alpha Shapes Model, Daniela Ramirez Chavez

Open Access Theses & Dissertations

The nuclear pasta is important because is an astromaterial with incredible strength that may be a source for gravitational waves, which observe from the rotation of neutron stars. The characterization of the pasta is vital because the nuclear phases have transport properties - compressibility, neutrino opacity, thermal conductivity, and electrical conductivity - associated with their shape for which neutron stars may be sensitive. These properties could interpret observations of supernova neutrinos, magnetic field decay, and crust cooling of accreting neutron stars. Here, we study the nuclear pasta using alpha shapes to achieve a phase characterization with the Minkowski functionals (area, …

Time Series Classification With Multistage Modeling Using Deep Learning, James Arthur

Open Access Theses & Dissertations

Time series classification (TSC) can be efficiently implemented with several techniques. Many techniques are based on analyzing 1-D signals in the time series data. In this work, we make an intrinsic analytical implementation of a new time series classification that involves a two-stage process. Firstly, by using Recurrence Plots (RP), we transform the time series into 2D images. The second stage consists in taking advantage of deep learn- ing models to perform our classification. The image illustration of time series introduces different feature types that are not available for all 1D signals, and therefore our classifi- cation problem is treated …

Mathematical Modeling Of Potassium Modulated Viral Infection, Zaira Elizabeth Mather

Open Access Theses & Dissertations

In recent years, there is a growing interest in the investigation of using potassium to treat virus infections. In the region of infection, there is a biological observation of extracel- lular potassium level being typically very low whereas the intracellular potassium levels are much higher. There are numerous biological studies showing that elevated potassium levels in the extracellular membrane tends to block virus infections. A recent effort in this direction is a collaborative research conducted by mathematicians and biologists from the University of Texas at El Paso, New Mexico State University, and the University of New Mexico, where we develop …

Numerical Study Of Cahn-Hilliard Equations, Oula Khouzam

Open Access Theses & Dissertations

In this thesis we study the well-known first-order Eyre's convex splitting numerical scheme for solving the Cahn-Hilliard equation and theoretically prove and numerically demonstrate the key properties of the scheme namely: mass conservation, unique solvability and unconditional stability. While the convex splitting scheme has been around for over two decades, explicit proofs for these important properties for the fourth order Cahn-Hillard equation are not directly available in the existing literature. This thesis aims to bridge this gap by providing the complete proofs of the aforementioned key properties of the scheme and numerically demonstrating the performance of the scheme.

Towards Reinforcement Learning Driven Mesh Adaptivity For Second Order Elliptic Problems, Augustine Twumasi

Open Access Theses & Dissertations

Adaptive mesh refinement techniques have become an indispensable tool in achieving accurate and efficiently computed solutions to problems which require impractically fine uniform meshes to obtain an accurate approximation. The adaptive algorithm involves a recursive application of SOLVE-ESTIMATE-MARK-REFINE steps where in particular the step `ESTIMATE' involves computing a posteriori error estimator based on only the numerical solution and the data of the problem. Over the years, several a posteriori error estimators have been developed and successfully applied but often times, the choice of estimator is ill suited for the problem at hand. In this research, we present two estimators namely …

Simultaneous Forecasting Of Yield Curves For Multiple Zero-Coupon Bonds Using The Heath-Jarrow-Morton Model, Ebenezer Nkum

Open Access Theses & Dissertations

Market diversification is a strategy according to which a company seeks growth by addingproducts and markets that are in a certain sense "uncorrelated" to its existing products and markets. Bonds play a major role in a well-balanced diversified portfolio because of their low correlation to other asset classes. While the correlations vary widely over time, bonds are not highly correlated with any other asset classes. Even in the simplest diversified portfolio, bonds can reduce volatility due to their low or negative correlation with stocks. Because companies can create robust diversified portfolios with bonds it is imperative that different bonds are …

Discrepancy-Based Analysis Of Measurement Sampling Points In Compressive Sensing, Felipe Batista Da Silva

Open Access Theses & Dissertations

Compressive sensing (CS) is a technique in signal processing that under certain conditions allows someone to reconstruct sparse signals from fewer linear measurements. A problem in CS is modeled in terms of an underdetermined linear system, whose associated matrix is previously designed. Then, it is of interest in CS to know what a good sampling defined by the sensing matrix is and how to measure it. In this work, we provided analytical proofs of properties of the metric discrepancy that allow us to propose a fast algorithm for discrepancy calculation. Such metric measures the quality of the sampling measurement points …

Multiplicative And Additive Arithmetic Functions And Formal Power Series, John Byron Snell

Open Access Theses & Dissertations

The theory of arithmetic functions and the theory of formal power series are classical andactive parts of mathematics. Algebraic operations on sets of arithmetic functions, called convolutions, have an important place in the theory of arithmetic functions. The theory of formal power series also has its place firmly anchored in abstract algebra. A first goal of this Thesis will be to present a parallelism of known characterizations of the concepts of multiplicative and additive for arithmetic functions (Theorems 2.1.2 and 2.2.3) on the one hand and for formal power series on the other (Theorems 3.4.3 and 3.4.4). Therefore, in Chapter …

Classification Of The Subalgebras Of The Algebra Of All 2 By 2 Matrices, Justin Luis Bernal

Open Access Theses & Dissertations

Classification of the subalgebras of the familiar algebra of all $n\times n$ real matrices over the real numbers can get quite unwieldy as all subalgebras are of dimension ranging from $1$ to $n^2$. Classification of the subalgebras of the algebra of all $2\times 2$ real matrices over the real numbers is an interesting first start.

Since $\2$ is of dimension $4$ then its possible subalgebras are of dimension $1, 2, 3,$ or $4$. The one-dimensional subalgebra and four-dimensional subalgebra need little to no attention. The two-dimensional and three-dimensional subalgebras however turn out to be of significance.

It turns out there …

Mathematical Modeling Of Microemulsification Processes, Numerical Simulations And Applications To Drug Delivery, Ogochukwu Nneka Ifeacho

Open Access Theses & Dissertations

Microemulsion systems are a great pharmaceutical tool for the delivery of formulations containing multiple hydrophilic and hydrophobic ingredients of varying physicochemical properties. These systems are gaining popularity because of its long shelf life, improved drug solubilisation capacity, easy preparation and improvement of bioavailability. Despite the advantages associated with the use of microemulsion systems in pharmaceutical industries, the major challenge impeding their use has been and continues to be the lack of understanding of these systems.

Microemulsions can be mathematically modeled by an initial boundary value problem involving a sixth order nonlinear time dependent equation. In this Thesis, we present a …

Free Semigroups And Identites For A Class Of Monoids, Enrique Salcido

Open Access Theses & Dissertations

The study of words as a mathematical object is a deep and rich field of study. Algebra, Combinatorics, Theoretical Computer Science etc., are major disciplines, which are fully using this study. Combinatorial properties (via Codes, Free Hulls, Infinite Words), and algebraic properties of words are presented in this Thesis. The free semigroup on a set (alphabet) X and finite presentation of semigroups have a central place in the algebraic study of words. The last part of the Thesis is devoted to the study of identities in the alphabet X = {x,y} for a class of monoids. The characterization of such …

Machine Learning Analysis To Characterize Phase Variations In Laser Propagation Through Deep Turbulence, Luis Fernando Rodriguez Sanchez

Open Access Theses & Dissertations

The present Dissertation is focused on the analysis of the atmospheric conditions of a turbulent environmental system and its effects on the diffraction of a laser beam that moves through it. The study is based on the optical communication of two labs placed at the summit of two mountains located in Maui, Hawaii. The emitter system is located at the Mauna Loa mountain and the receiver at the Haleakala. The distance between both mountains is 150 km. The emitter system is at a height of 3.1 km and the receiver at 3.4 km. The maritime environment at the location experiences …

Positivity-Preserving Segregate-Flux Method For Infiltration Dynamics In Tumor Growth Models, Gilbert Danso Acheampong

Open Access Theses & Dissertations

We study the positivity preserving property and an incompressibility condition in a recently proposed tumor growth model as well as its numerical simulations. In this model, the biological process is described by a free-boundary problem of hyperbolic equations that govern the in-tumor motion of cancer cells and the infiltration of immune cells. Particularly, due to an assumption that cells take constant volume (the incompressibility condition), the tumor growth/shrinkage is closely correlated to the magnitude of infiltration of immune cells into the tumor.

Despite the fact that previous simulation results largely reproduced experimental data, there remain unanswered questions that are crucial …

Using Machine Learning On An Imbalanced Cancer Dataset, James Ekow Arthur

Open Access Theses & Dissertations

With an estimated 1.4 million cancer diagnosis worldwide and the increasing death of cancer patients. It is prudent to investigate methods, approaches and smarter ways of predicting and diagnosing of cancer so that a holistic techniques can be used to curb or reduce false predictions , increase exact predictions and also meticulos prognosis information .

Can a feasible technique be developed for the general problem of prognosis and diagnosis of cancer be developed ?

We will show here that this problem of cancer prognosis and diagnosis can be efficiently tackled with the aid of machine learning techniques and the best, …

Self-Similar Models: How Close The Diffusion Entropy Analysis And The Detrended Fluctuation Analysis Are From Other Models, William Kubin

Open Access Theses & Dissertations

Financial and seismic data, like many other high frequency data are known to exhibit memory effects. In this research, we apply the concepts of L ́evy processes, Diffusion Entropy Analysis (DEA) and the Detrended Fluctuation Analysis (DFA) to examine long-range persistence (long memory) behavior in time series data. L ́evy processes describe long memory effects. In other words, L ́evy process (where the increments are independent and follow the L ́evy distribution) is self-similar. We examine the relationship between the L ́evy parameter (α) characterizing the data and the scaling exponent of DEA (δ) and that of DFA (H) characterizing …

Laplacian Spectra Of Kneser-Like Bipartite Graphs, Cesar Iram Vazquez

Open Access Theses & Dissertations

Given a,b ∈N such that a > b we define a Kneser-like bipartite graph G(a,b), whose two bipartite sets of vertices represent the a-subsets and b-subsets of S = {1,...,a + b + 1}, and whose edges are pairs of vertices X and Y such that X ∩Y = ∅. We prove that the eigenvalues of the Laplacian matrix of graphs G(a,1) are all nonnegative integers. In fact, we describe these eigenvalues, and their respective multiplicities.

Planar Motion Control Of A Cube Satellite Using Cold Gas Thrusters, Christian Lozoya

Open Access Theses & Dissertations

This Thesis presents a mathematical model developed for the computational simulation ofCubeSat movement using four thrusters that permit uniaxial translation and rotation. Arbitrary functions are fit to boundary conditions to simulate the force, acceleration, velocity, and displacement of the CubeSat along a plane. The model is used to derive a motion control algorithm assuming constant pressure and mass. A single model describes both translation and rotation. This Thesis also explores the relationship between propellant consumption and the time required to complete a displacement implied by the model.

Earthquake Magnitude Prediction Using Support Vector Machine And Convolutional Neural Network, Esther Amfo

Open Access Theses & Dissertations

A deep learning-based method Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for earthquake prediction is proposed. Large-magnitude earthquakes triggered by earthquakes can kill thousands of people and cause millions of dollars worth of economic losses. The accurate prediction of large-magnitude earthquakes is a worldwide problem.

In recent years, deep learning technology that can automatically extract features from mass data has been applied in image recognition, natural language processing, object recognition, etc., with great success. We explore to apply deep learning technology to earthquake prediction, we propose a deep learning method for continuous earthquake prediction using historical seismic events. …

Formulation And Implementation Of Iterative Method For Generating Spatially-Variant Lattices, Manuel Fernando Martinez

Open Access Theses & Dissertations

The use of a matrix-free, memory-efficient approach to generate large-scale spatially variant lattices (SVL) was explored. A matrix-free iterative SVL generation algorithm was formulated and then implemented with a tremendous memory reduction observed. The algorithm consists of solving first-order central finite-differences along the entirety of the problem space point-by-point to obtain the grating phase function Φ(𝑠⃗) to which all desired spatially variant lattice properties are applied to. The algorithm was studied to identify key areas of data and task parallelism to exploit in heterogeneous computing systems consisting of clusters of central processing units (CPU) and graphics processing units (GPU) combinations. …

Robust Statistical Inference For The Gaussian Distribution, Andrews Tawiah Anum

Open Access Theses & Dissertations

The aim of robust statistics is to develop statistical procedures which are not unduly influenced by outliers or observations that are not representative of the underlying "true" data generating process. This thesis focuses on an estimator with this characteristic. The divergence function is introduced in Chapter 2 with the sole aim of taking the function f to be the univariate normal distribution and α - [0, 1]. The estimator fails when we rely on the classic Newton's method to converge to the minimum of the density power divergence (MDPD) function. There is a tendency of such estimator never to approach …

Forecasting Crashes, Credit Card Default, And Imputation Analysis On Missing Values By The Use Of Neural Networks, Jazmin Quezada

Open Access Theses & Dissertations

A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks,- also called Artificial Neural Networks - are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI. Recent studies shows that Artificial Neural Network has the highest coefficient of determination (i.e. measure to assess how well a model explains and predicts future outcomes.) in comparison to the K-nearest neighbor classifiers, logistic regression, discriminant analysis, naive Bayesian classifier, and classification trees. In this work, the theoretical description of the neural network methodology …

Towards Analytical Techniques For Systems Engineering Applications, Griselda Valdepeñas Acosta

Open Access Theses & Dissertations

One of the main objectives of systems engineering is to design, maintain, and analyze systems that help the users. To design an appropriate system for an application domain, we need to know: what are the users' desires and preferences (so that we know in what direction we should aim to change this domain), what is the current state and what is the dynamics of this application domain, and how to use all this information to select the best alternatives for the system design and maintenance. Designing a system includes selecting numerical values for many of the parameters describing the corresponding …

Inverse Gaussian Ornstein-Uhlenbeck Applied To Modeling High Frequency Data, Emmanuel Kofi Kusi

Open Access Theses & Dissertations

With about 226050 estimated deaths worldwide in 2010, an earthquake is considered as one of the disasters that records a great number of deaths. This thesis develops a model for the estimation of magnitude of future seismic events.

We propose a stochastic differential equation arising on the Ornstein-Uhlenbeck processes driven by IG(a,b) process. IG(a,b) Ornstein-Uhlenbeck processes offers analytic flexibility and provides a class of continuous time processes capable of exhibiting long memory

behavior. The stochastic differential equation is applied to geophysics and financial stock markets by fitting the superposed IG(a,b) Ornstein-Uhlenbeck model to earthquake and financial time series.

Cantor Sets, Cantorvals, And Their Topological Structure, Ángel Adrián Agüero

Open Access Theses & Dissertations

With interesting topological properties, the Cantor set is worth studying for itself. In other areas, topological structures arise that are in fact homeomorphic to the Cantor set. In particular, we see sets that are homeomorphic to the Cantor set which result from the subsums of particular series, as well as linear combinations of algebraic sums of Cantor sets. These also result in what has been termed a Cantorval, which we also investigate.

On Numerical Stochastic Optimal Control Via Bellman's Dynamic Programming Principle, Prince Osei Aboagye

Open Access Theses & Dissertations

In this work, we present an application of Stochastic Control Theory to the Merton's portfolio optimization problem. Then, the dynamic programming methodology is applied to reduce the whole problem to solving the well-known HJB (Hamilton-Jacobi-Bellman) equation that arises from the Merton's portfolio optimization problem subject to the power utility function. Finally, a numerical method is proposed to solve the HJB equation and the optimal strategy. The numerical solutions are compared with the explicit solutions for optimal consumption and investment control policies.

Integrated Statistical And Machine Learning Algorithms For Predicting And Classifying G Protein-Coupled Receptors, Fredrick Ayivor

Open Access Theses & Dissertations

G protein-coupled receptors (GPCRs) are transmembrane proteins with important functions in signal transduction and often serve as drug targets. With increasing availability of protein sequence information, there is much interest in computationally predicting GPCRs and classifying them according to their biological roles. Such predictions are cost-efficient and can be valuable guides for designing wet lab experiments to help elucidate signaling pathways and expedite drug discovery. There are existing computational tools of GPCR prediction that involve principal component analysis (PCA), intimate sorting (IS), support vector machine, and random forest (RF) techniques using various sequence derived features. While accuracies of over 90\% …