Digital Commons Network^™

Parameter Estimation And Inference Of Spatial Autoregressive Model By Stochastic Gradient Descent, Gan Luan

Dissertations

Stochastic gradient descent (SGD) is a popular iterative method for model parameter estimation in large-scale data and online learning settings since it goes through the data in only one pass. While SGD has been well studied for independent data, its application to spatially-correlated data largely remains unexplored. This dissertation develops SGD-based parameter estimation and statistical inference algorithms for the spatial autoregressive (SAR) model, a common model for spatial lattice data.

This research contains three parts. (I) The first part concerns SGD estimation and inference for the SAR mean regression model. A new SGD algorithm based on maximum likelihood estimator (MLE) …

Machine Learning And Computer Vision In Solar Physics, Haodi Jiang

Dissertations

In the recent decades, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the life of human beings, including communication, transportation, the power grid, national defense, space travel, and more. This dissertation explores new machine learning and computer vision techniques to tackle this difficult task. Specifically, the dissertation addresses four interrelated problems in solar physics: magnetic flux tracking, fibril tracing, Stokes inversion and vector magnetogram generation.

First, the dissertation presents a new deep learning method, named SolarUnet, to identify and track solar magnetic flux elements in …

Long Term Predictive Modeling On Big Spatio-Temporal Data, Yong Zhuang

Graduate Doctoral Dissertations

In the era of massive data, one of the most promising research fields involves the analysis of large-scale Spatio-temporal databases to discover exciting and previously unknown but potentially useful patterns from data collected over time and space. A modeling process in this domain must take temporal and spatial correlations into account, but with the dimensionality of the time and space measurements increasing, the number of elements potentially contributing to a target sharply grows, making the target's long-term behavior highly complex, chaotic, highly dynamic, and hard to predict. Therefore, two different considerations are taken into account in this work: one is …

Computational Methods For Comparative Analyses Of Discourses, Zachary Stine

Theses and Dissertations

An underexplored aspect of the abundant linguistic data available for computational analysis is the potential for conducting large-scale comparative analyses between different discourses as a more rigorous complement to existing methods. A significant obstacle in comparative studies is the requirement that a researcher distinguish between discursive distinctions that are superficial and those that reflect deeper, structural relationships between the discourses being compared. In this dissertation, I describe two methodologies for comparing discourses in terms of their underlying structures, drawing on distributional semantic models and information theory. These methodologies are explored within three case studies in which the discourses of several …

Energy Consumption Forecasting Using Machine Learning, Mahdi Mohammadigohari

Theses

Forecasting electricity demand and consumption accurately is critical to the optimal and costeffective operation system, providing a competitive advantage to companies. In working with seasonal data and external variables, the traditional time-series forecasting methods cannot be applied to electricity consumption data. In energy planning for a generating company, accurate power forecasting for the electrical consumption prediction, as a technique, to understand and predict the market electricity demand is of paramount importance. Their power production can be adjusted accordingly in a deregulated market. As data type is seasonal, Persistence Models (Naïve Models), Seasonal AutoRegressive Integrated Moving Averages with eXogenous regressors (SARIMAX), …

Exploiting Building Demand Flexibility Through Machine Learning For Building-To-Grid Integration, Hannah Charlene Fontenot

Dissertations - ALL

Demand flexibility – the ability to adjust a building's load profile across different timescales – is a key aspect of the ongoing effort to increase interconnectivity between buildings and the power grid. By harnessing their demand flexibility, buildings can provide significant benefits to the grid and bolster grid resilience and reliability. To facilitate the transition toward the "smart grid", new and intelligent control approaches are required that can seamlessly integrate building, occupant, and grid data and effectively control multiple building assets to provide grid services while maintaining occupants' required thermal comfort levels and reducing the building's overall energy consumption and …

Nondestructive Detection Of Codling Moth Infestation In Apples Using Pixel-Based Nir Hyperspectral Imaging With Machine Learning And Feature Selection, Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva, Akinbode A. Adedeji

Electrical and Computer Engineering Faculty Publications

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing …

A Hybrid Machine Learning Framework For Predicting Students’ Performance In Virtual Learning Environment, Edmund Evangelista

All Works

Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature …

Correlating Sentiment In Reddit’S Wallstreetbets With The Stock Market Using Machine Learning Techniques, Sultan Ali Alzaabi

Theses

The issue that this study addresses is to observe whether there exists a statistical relation between the stock market and Reddit’s wallstreetbets. Previous research mainly focused on the relation between the stock market and Twitter. To gather data for the study, comments were scrapped from the subreddit wallstreetbets for a period of four months, Jan 1, 2021, till April 30, 2021. Different sentiment classifiers were, then, applied on a sample of the data to observe the most accurate classifier for the study. The study concluded that the most accurate sentiment classifier was an SVM classifier trained on 80% of Reddit …

Deep Learning For Automatic Microscopy Image Analysis, Shenghua He

McKelvey School of Engineering Graduate Student Theses & Dissertations

Microscopy imaging techniques allow for the creation of detailed images of cells (or nuclei) and have been widely employed for cell studies in biological research and disease diagnosis in clinic practices.Microscopy image analysis (MIA), with tasks of cell detection, cell classification, and cell counting, etc., can assist with the quantitative analysis of cells and provide useful information for a cellular-level understanding of biological activities and pathology. Manual MIA is tedious, time-consuming, prone to subject errors, and are not feasible for the high-throughput cell analysis process. Thus, automatic MIA methods can facilitate all kinds of biological studies and clinical tasks. Conventional …

Codes Of Ethics: Extending Classification Techniques With Natural Language Processing, Zachary Glass, E. Susanna Cahn

The Journal of Values-Based Leadership

Language is an indicator of how stakeholders view an ethics code’s intent, and key to distinguishing code properties, such as promoting ethical-valued decision-making or code-based compliance. This article quantifies ethics codes’ language using Natural Language Processing (NLP), then uses machine learning to classify ethics codes. NLP overcomes some inherent difficulties of “measuring” verbal documents. Ethics codes selected from lists of “best” companies were compared with codes from a sample of Fortune 500 companies. Results show that some of these ethics codes are different enough from the norm to be distinguished by an algorithm; indicating as well that lists of “best” …

Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun

SMU Data Science Review

This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …

Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho

SMU Data Science Review

Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …

Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol

Theses and Dissertations

Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics …

Machine Learning And Radiomic Features To Predict Overall Survival Time For Glioblastoma Patients, Lina Chato, Shahram Latifi

Electrical & Computer Engineering Faculty Research

Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A …

Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …

Quantum State Estimation And Tracking For Superconducting Processors Using Machine Learning, Shiva Lotfallahzadeh Barzili

Computational and Data Sciences (PhD) Dissertations

Quantum technology has been rapidly growing; in particular, the experiments that have been performed with superconducting qubits and circuit QED have allowed us to explore the light-matter interaction at its most fundamental level. The study of coherent dynamics between two-level systems and resonator modes can provide insight into fundamental aspects of quantum physics, such as how the state of a system evolves while being continuously observed. To study such an evolving quantum system, experimenters need to verify the accuracy of state preparation and control since quantum systems are very fragile and sensitive to environmental disturbance. In this thesis, I look …

The Potential Of Remotely Sensed Vegetation Indices For Monitoring Pasture Condition, Pouria Ramzi, Karen Holmes

Resource management technical reports

The Department of Primary Industries and Regional Development (DPIRD) is developing an integrated monitoring system using remote sensing and on-ground measurements to track pasture condition across Western Australia’s pastoral region. We extended and adapted the methods developed in the Pastoral Lease Assessment Using Geospatial Analysis (PLAGA) project (Robinson et al. 2012), which combined remotely sensed vegetation indices (VIs) with on-ground pasture condition observations to assess the potential of using different vegetation indices in a statewide condition monitoring system.

There were 6 regions in WA’s pastoral rangelands with DPIRD on-ground condition traverse points: Kimberley and Broome, Pilbara, Yalgoo and Sandstone, Goldfields, …

Adaptive Human-Robot Motion Transfer For Complete Body Imitation, Francisco Villa

Computer Science and Engineering Theses - Archive

Programming robot systems to perform certain tasks is a big challenge especially if such programming is to be performed by persons who are not experts in robotics. For example, when programming a robot to serve as an exercise trainer, the person defining the motions might more naturally be a person in the exercise domain rather than a robotics expert. To address this, this thesis investigates programming by demonstration or teleoperation using full direct body motion. The goal is to reproduce gaits, gestures, and postures on a humanoid robot from observed human demonstrations. Fine motor movements such as movement of fingers …

Translation Of Array-Based Loop Programs To Optimized Sql-Based Distributed Programs, Md Hasanuzzaman Noor

Computer Science and Engineering Dissertations - Archive

Most programs written to operate on data are usually expressed in terms of array operations in sequential loops. However, these programs do not scale to large amount of data generated by scientific experiments and industrial and commercial markets. Given the success of machine learning algorithms on large amount of data and the recent shift of industries to data-driven decision making, the data scientists who are not familiar with Big Data frameworks have to rewrite the sequential programs to distributed data-parallel programs by hand. We present a novel framework, called SQLgen, that automatically translates sequential loops to distributed data-parallel programs. SQLgen …

Advanced Risk Models To Predict Surgical Site Infections Using Machine Learning Approaches And Risk-Adjusted Control Charts, Rabia Emhamed Al Mamlook

Dissertations

Even though most infections are treatable with antibiotics, Surgical Site Infections (SSIs) remain a significant healthcare problem as they increase hospital stays, require additional treatments, reduce a patient’s quality of life, and increase morbidity and mortality. To reduce the risks and to encourage the highest quality healthcare, it is important to measure, predict, and monitor the risk of SSIs. Preoperative prediction of SSIs is essential to facilitate the implementation of preventative strategies for high-risk patients. The overarching goal of this dissertation is to investigate new opportunities of using machine-learning (ML) to improve the accuracy and use of pre-operative prediction models …

A Comparative Study Of Machine Learning-Based Approach For Network Traffic Classification, Kien Trang, An Hoang Nguyen

Knowledge Engineering and Data Science

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to …

Determining States Of Movement In Humans Using Minimally Processed Eeg Signals And Various Classification Methods, Maurice Barnett

All Theses

Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without …

Comparing Machine Learning Techniques With State-Of-The-Art Parametric Prediction Models For Predicting Soybean Traits, Susweta Ray

Department of Statistics: Dissertations, Theses, and Student Research

Soybean is a significant source of protein and oil, and also widely used as animal feed. Thus, developing lines that are superior in terms of yield, protein and oil content is important to feed the ever-growing population. As opposed to the high-cost phenotyping, genotyping is both cost and time efficient for breeders while evaluating new lines in different environments (location-year combinations) can be costly. Several Genomic prediction (GP) methods have been developed to use the marker and environment data effectively to predict the yield or other relevant phenotypic traits of crops. Our study compares a conventional GP method (GBLUP), a …

Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy

All Dissertations

Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these …

Three Essays On Us Phd Education And Applications Of Machine Learning Methods, Qing Zang

Dissertations

This study mainly talks about U.S. PhD education and applications of machine learning methods. On one hand, PhD education, as a way to cultivate future researchers, attracts a lot of attention. On the other hand, machine learning methods are utilized more and more often in academic research. This study, as an attempt, would like to contribute to these two fields. The first essay discusses whether education can reduce and eliminate discrimination. By using the data of PhD recipients’ earning in the U.S., we investigate if PhD education can shrink the earning gaps in three groups: Female vs male, disabled vs …

Exploration Of Dark Chemical Genomics Space Via Portal Learning: Applied To Targeting The Undruggable Genome And Covid-19 Anti-Infective Polypharmacology, Tian Cai, Li Xie, Muge Chen, Yang Liu, Di He, Shuo Zhang, Cameron Mura, Philip Boume, Lei Xie

Publications and Research

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning , to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) …

Estimation And Interpretation Of Machine Learning Models With Customized Surrogate Model, Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae Eung Sung, Ki Il Kim

All Works

Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are …

Factors Influencing Intent To Take A Covid-19 Test In The United States, Sheila Rutto

Theses and Dissertations

In 2020, COVID-19 became the first pandemic in the world’s history that brought the entire world to an abrupt and unexpected halt. Since the first reported case of the disease to date, the novel coronavirus has been able to wreak havoc in literary every corner of the globe and left an ever-growing number of unprecedented fatalities. The normal way of life has been disrupted, and the level of uncertainty about the end of this pandemic continues to manifest to many. Due to the urgency to bring this pandemic under control, medical officers have been able to recommend actions that people …

Phenology Dictates The Impact Of Climate Change On Geographic Distributions Of Six Co-Occurring North American Grasshoppers, Nathan P. Lemoine

Biological Sciences Faculty Research and Publications

Throughout the last century, climate change has altered the geographic distributions of many species. Insects, in particular, vary in their ability to track changing climates, and it is likely that phenology is an important determinant of how well insects can either expand or shift their geographic distributions in response to climate change. Grasshoppers are an ideal group to test the hypothesis that phenology correlates with range expansion, given that co-occurring confamilial, and even congeneric, species can differ in phenology. Here, I tested the hypothesis that early- and late-season species should possess different range expansion potentials, as estimated by habitat suitability …