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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Artificial Neural Network Models For Pattern Discovery From Ecg Time Series, Mehakpreet Kaur
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 …
Analysis On Sharp And Smooth Interface, Elizabeth V. Hawkins
Analysis On Sharp And Smooth Interface, Elizabeth V. Hawkins
Electronic Theses and Dissertations
In biology, minimizing a free energy functional gives an equilibrium shape that is the most stable in nature. The formulation of these functionals can vary in many ways, in particular they can have either a smooth or sharp interface. Minimizing a functional can be done through variational calculus or can be proved to exist using various analysis techniques. The functionals investigated here have a smooth and sharp interface and are analyzed using analysis and variational calculus respectively. From the former we find the condition for extremum and its second variation. The second variation is commonly used to analyze stability of …
Reduced Dataset Neural Network Model For Manuscript Character Recognition, Mohammad Anwarul Islam
Reduced Dataset Neural Network Model For Manuscript Character Recognition, Mohammad Anwarul Islam
Electronic Theses and Dissertations
The automatic character recognition task has been of practical interest for a long time. Nowadays, there are well-established technologies and software to perform character recognition accurately from scanned documents. Although handwritten character recognition from the manuscript image is challenging, the advancement of modern machine learning techniques makes it astonishingly manageable. The problem of accurately recognizing handwritten character remains of high practical interest since a large number of manuscripts are currently not digitized, and hence inaccessible to the public. We create our repository of the datasets by cropping each letter image manually from the manuscript images. The availability of datasets is …
Explicit Pseudo-Kähler Metrics On Flag Manifolds, Thomas A. Mason Iii
Explicit Pseudo-Kähler Metrics On Flag Manifolds, Thomas A. Mason Iii
Electronic Theses and Dissertations
The coadjoint orbits of compact Lie groups each carry a canonical (positive definite) Kähler structure, famously used to realize the group's irreducible representations in holomorphic sections of certain line bundles (Borel-Weil theorem). Less well-known are the (indefinite) invariant pseudo-Kähler structures they also admit, which can be used to realize the same representations in higher cohomology of the sections (Bott), and whose analogues in a non-compact setting lead to new representations (Kostant-Langlands). The purpose of this thesis is to give an explicit description of these metrics in the case of the unitary group G=Un.
Association Rules Patterns Discovery From Mixed Data, Welendawa Acharige Charith A. Elson
Association Rules Patterns Discovery From Mixed Data, Welendawa Acharige Charith A. Elson
Electronic Theses and Dissertations
Finding Association Rules has been a popular unsupervised learning technique for dis covering interesting patterns in commercial data for well over two decades. The method seeks groups of data attributes and their values where their probability density of these attributesattherespectivevaluesismaximized. Therearecurrentlywell-establishedmeth ods for tackling this problem for data with categorical (discrete) attributes. However, for the cases of data with continuous variables, the techniques are largely focusing on cate gorizing continuous variables into intervals of interest and then relying on the categorical data methods to address the problem. We address the problem of finding association rules patterns in mixed data by …
Experiments On The Neural Network Approach To The Handwritten Digit Classification Problem, William Meissner
Experiments On The Neural Network Approach To The Handwritten Digit Classification Problem, William Meissner
Electronic Theses and Dissertations
When the MNIST dataset was introduced in 1998, training a network was a multiple week problem in order to receive results far less accurate than an average CPU can produce within a couple of hours today. While this indicates that training a network on such a dataset is not the complicated problem it may have been twenty years ago, the MNIST dataset makes a good tool for study and testing with beginner and medium complexity neural networks. This paper follows along with the work presented in the online textbook “Neural Networks and Deep Learning” by Michael Nielson and an updated …
Multiple Imputation Using Influential Exponential Tilting In Case Of Non-Ignorable Missing Data, Kavita Gohil
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 …
Predicting Student Sustainability Knowledge, Attitudes, And Behaviors: Effects Of Demography, Environmental Science Education, And Sustainability Intervention Programs At Georgia Southern University, Bailey Chandler
Electronic Theses and Dissertations
This mixed-methods research approach assesses trends in sustainability knowledge, attitudes, and behaviors in the student population at Georgia Southern University (GS). Students completed a baseline survey in order to determine their knowledge and attitudes about sustainability and what sustainable behaviors they perform. A total of 1,328 useable responses to the baseline survey were gathered. Effects of race, academic class, gender, and prior completion of an environmental science course were tested, as well as the effect of campus (Statesboro and Armstrong). Previously taking an environmental science course and race/ethnicity were the two strongest indicators of knowledge score. Males scored higher than …
Public Perception Of Different Planting Techniques Using Augmented Reality, Sultana Quader Tania
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) …
Nonparametric Misclassification Simulation And Extrapolation Method And Its Application, Congjian Liu
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 …