Data Science Commons^™

Convolutional Neural Networks For Deflate Data Encoding Classification Of High Entropy File Fragments, Nehal Ameen

LSU New Orleans Theses and Dissertations

Data reconstruction is significantly improved in terms of speed and accuracy by reliable data encoding fragment classification. To date, work on this problem has been successful with file structures of low entropy that contain sparse data, such as large tables or logs. Classifying compressed, encrypted, and random data that exhibit high entropy is an inherently difficult problem that requires more advanced classification approaches. We explore the ability of convolutional neural networks and word embeddings to classify deflate data encoding of high entropy file fragments after establishing ground truth using controlled datasets. Our model is designed to either successfully classify file …

Machine Learning Based Restaurant Sales Forecasting, Austin B. Schmidt

LSU New Orleans Theses and Dissertations

To encourage proper employee scheduling for managing crew load, restaurants have a need for accurate sales forecasting. We predict partitions of sales days, so each day is broken up into three sales periods: 10:00 AM-1:59 PM, 2:00 PM-5:59 PM, and 6:00 PM-10:00 PM. This study focuses on the middle timeslot, where sales forecasts should extend for one week. We gather three years of sales between 2016-2019 from a local restaurant, to generate a new dataset for researching sales forecasting methods.

Outlined are methodologies used when going from raw data to a workable dataset. We test many machine learning models on …

Machine Learning For Terminal Procedure Chart Change Detection, Anthony M. Marchiafava

LSU New Orleans Theses and Dissertations

Terminal Procedure Charts are a constantly updated and necessary tool for aircraft personnel to approach and take off from airport runways safely. Detecting changes within these charts is a time-consuming and laborious process. Here machine learning techniques were used to predict regions of change in charts based on detecting the charts image regions and comparing features extracted from those regions. Outlined are methodologies to detect differences between two separate charts to produce images with changed regions clearly indicated. Both more conventional computer vision and machine learning techniques were applied. For images with minor shifts, the proposed model is able to …

Stationary Probability Distributions Of Stochastic Gradient Descent And The Success And Failure Of The Diffusion Approximation, William Joseph Mccann

Theses

In this thesis, Stochastic Gradient Descent (SGD), an optimization method originally popular due to its computational efficiency, is analyzed using Markov chain methods. We compute both numerically, and in some cases analytically, the stationary probability distributions (invariant measures) for the SGD Markov operator over all step sizes or learning rates. The stationary probability distributions provide insight into how the long-time behavior of SGD samples the objective function minimum.

A key focus of this thesis is to provide a systematic study in one dimension comparing the exact SGD stationary distributions to the Fokker-Planck diffusion approximation equations —which are commonly used in …

Time Series Forecasting With Applications To Finance, Viswapriya Misra

Theses

In finance, many phenomena are modeled as time series. This thesis investigates time series forecasting problems in finance, precisely the stock price prediction problem. We employ and compare traditional statistical algorithms like MA, ARIMA, and ARMA-GARCH with newly developed deep learning-based algorithms such RNNs, LSTMs, GRUs, TCNs, and bidirectional LSTMs and GRUs for predicting stock prices. We perform a comprehensive study and present all the experimental results on different datasets. We find that ARIMA and GRU perform better for single-step stock price prediction than other deep learning architectures. Adding market and economic indicators do not improve the performance of the …

Short Term Temperature Forecasting Using Lstms, And Cnn, Darshan Shah

Theses

Weather forecasting is a vital application in present times. We can use the predictions to minimize the weather related loss. Use of machine learning and deep learning algorithms for forecasting, can eliminate or reduce the necessity of big data and high computation dependent process of parameterization. Long Short-Term Memory (LSTM) is a widely used deep learning architecture for time series forecasting. In this paper, we aim to predict one day ahead average temperature using a 2-layer neural network consisting of one layer of LSTM and one layer of 1D convolution. The input is pre-processed using a smoothing technique and output …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Digital Technology Needs In Maternal Mental Health: A Qualitative Inquiry, Alexandra Zingg, Laura Carter, Deevakar Rogith, Amy Franklin, Sudhakar Selvaraj, Jerrie Refuerzo, Sahiti Myneni

Faculty, Staff and Student Publications

Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. Women who are more prone to experiencing peripartum depression (PPD), such as those of low-socioeconomic background or in high-risk pregnancies, can benefit the most from such technologies. However, current digital interventions directed towards this population provide suboptimal support, and their responsiveness to end user needs is quite limited. Our objective is to understand the digital terrain of information needs for low-socioeconomic status women with high-risk pregnancies, specifically within …

Spaceflight And The Differential Gene Expression Of Human Stem Cell-Derived Cardiomyocytes, Eugenie Zhu

Master's Projects

The National Aeronautics and Space Administration (NASA) has performed many experiments on the International Space Station (ISS) to further understand how conditions in space can affect life on Earth. This project analyzed GLDS-258, a gene set from NASA’s GeneLab repository which examines the impact of microgravity on human induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs). While many datasets have been run through NASA’s RNA-Seq Consensus Pipeline (RCP) to study differential gene expression in space, a Homo sapiens dataset has yet to be analyzed using the RCP. The aim of this project was to run the first Homo sapiens dataset, GLDS-258, through the …

Prediction Of Financial Capacity Using Diffusion Compartment Imaging, Lok Yi Tai

Master's Projects

Financial Capacity (FC) is the ability to manage one’s financial affairs, which is essential for autonomy and independence particularly for aging adults. Since dementia develops gradually, it is often difficult to detect the early signs that this cognitive dysfunction is developing This project aims to use Neurite orientation dispersion and density imaging (NODDI) to identify the white matter tracts that are associated with FC. Diffusion Tensor Images (DTI) and T1 Magnetic Resonance Images (MRI) of 18 Alzheimer’s Disease (AD) subjects, 47 Mild Cognitive Impaired (MCI) subjects, and 193 healthy control (CN) are compared to neuropsychological tests. Orientation Dispersion Index (ODI) …

Wildfire Risk Prediction For A Smart City, Rekha Rani

Master's Projects

Wildfires are uncontrolled fires that may lead to the destruction of biodiversity, soil fertility, and human resources. There is a need for timely detection and prediction of wildfires to minimize their disastrous effects. In this research, we propose a wildfire prediction model that relies on multi-criteria decision making (MCDM) to explicitly evaluates multiple conflicting criteria in decision making and weave the wildfire risks into the city’s resiliency plan. We incorporate fuzzy set theory to handle imprecision and uncertainties. In the process, we create a new data set that includes California cities’ weather, vegetation, topography, and population density records. The model …

An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …

Visualizing Decision Trees And Forests Using Radial Trees, Angela Kozma

Computer Science and Information Technology Faculty

Data visualization has become a big representation of many company’s data and schedules. Now people are not using just simple bar graphs and pie charts in business meetings but utilizing other fields of study and even more complex graphs. By using multiple visualizations to display their results and projects, it is letting more outside people understand what they are working on and can lead to more viewpoints on the topic being displayed. Also, schedules for projects are now being displayed visually so the workers can see how much time each part of their project is going to take. With this …

Federated Learning In Gaze Recognition (Fligr), Arun Gopal Govindaswamy

College of Computing and Digital Media Dissertations

The efficiency and generalizability of a deep learning model is based on the amount and diversity of training data. Although huge amounts of data are being collected, these data are not stored in centralized servers for further data processing. It is often infeasible to collect and share data in centralized servers due to various medical data regulations. This need for diversely distributed data and infeasible storage solutions calls for Federated Learning (FL). FL is a clever way of utilizing privately stored data in model building without the need for data sharing. The idea is to train several different models locally …

Improving Additional Adversarial Robustness For Classification, Michael Guo

McKelvey School of Engineering Graduate Student Theses & Dissertations

Although neural networks have achieved remarkable success on classification, adversarial robustness is still a significant concern. There are now a series of approaches for designing adversarial examples and methods to defending against them. This paper consists of two projects. In our first work, we propose an approach by leveraging cognitive salience to enhance additional robustness on top of these methods. Specifically, for image classification, we split an image into the foreground (salient region) and background (the rest) and allow significantly larger adversarial perturbations in the background to produce stronger attacks. Furthermore, we show that adversarial training with dual-perturbation attacks yield …

Reporting Of Eating Disorder Deaths, Katherine Mobley, Amy Hord

Symposium of Student Scholars

Those affected by eating disorders experience disturbances in eating behaviors which are often related to underlying psychiatric disorders such as anxiety, depression, or obsessive-compulsive disorder (Parekh, 2017, Drieberg et al., 1998 p.53). The duplicitous nature of the disorder makes it difficult to diagnose, and the tole it takes on an individual’s physical health makes its mortality rate the second highest among psychiatric disorders (Guinhut et al., 2021 p.130). Even if the correct education and resources are accessible to certain individuals, negative stigmatization about the disorder can make sufferers unlikely to seek help (Becker et al., 2010). Findings from analysis of …

Med-Bert: Pretrained Contextualized Embeddings On Large-Scale Structured Electronic Health Records For Disease Prediction, Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, Degui Zhi

Faculty, Staff and Student Publications

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pretraining of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. Inspired by BERT, we propose Med-BERT, which adapts the BERT framework originally developed …

Characterizing The Northern Hemisphere Circumpolar Vortex Through Space And Time, Nazla Bushra

LSU Doctoral Dissertations

This hemispheric-scale, steering atmospheric circulation represented by the circumpolar vortices (CPVs) are the middle- and upper-tropospheric wind belts circumnavigating the poles. Variability in the CPV area, shape, and position are important topics in geoenvironmental sciences because of the many links to environmental features. However, a means of characterizing the CPV has remained elusive. The goal of this research is to (i) identify the Northern Hemisphere CPV (NHCPV) and its morphometric characteristics, (ii) understand the daily characteristics of NHCPV area and circularity over time, (iii) identify and analyze spatiotemporal variability in the NHCPV’s centroid, and (iv) analyze how CPV features relate …

Machine Learning Methods For Depression Detection Using Smri And Rs-Fmri Images, Marzieh Sadat Mousavian

LSU Doctoral Dissertations

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data.

In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either …

Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark

SMU Data Science Review

In 1992, the Louisiana black bear (Ursus americanus luteolus) was placed on the U.S. Endangered Species List. This was due to bear populations in Louisiana being small and isolated enough where their populations couldn’t intersect with other populations to grow. Interchange of individuals between subpopulations of bears in Louisiana is critical to maintain genetic diversity and avoid inbreeding effects. Utilizing GPS (Global Positioning System) data gathered from 31 radio-collared bears from 2010 through 2012, this research will investigate how bears traverse the landscape, which has implications for gene exchange. This paper will leverage machine learning tools to improve upon existing …