Using Network Modeling To Understand The Relationship Between Sars-Cov-1 And Sars-Cov-2, 2020 Florida State University
Using Network Modeling To Understand The Relationship Between Sars-Cov-1 And Sars-Cov-2, Elizabeth Brooke Haywood, Nicole A. Bruce
Biology and Medicine Through Mathematics Conference
No abstract provided.
Decision Tree For Predicting The Party Of Legislators, 2020 CUNY New York City College of Technology
Decision Tree For Predicting The Party Of Legislators, Afsana Mimi
Publications and Research
The motivation of the project is to identify the legislators who voted frequently against their party in terms of their roll call votes using Office of Clerk U.S. House of Representatives Data Sets collected in 2018 and 2019. We construct a model to predict the parties of legislators based on their votes. The method we used is Decision Tree from Data Mining. Python was used to collect raw data from internet, SAS was used to clean data, and all other calculations and graphical presentations are performed using the R software.
Gait Characterization Using Computer Vision Video Analysis, 2020 College of William and Mary
Gait Characterization Using Computer Vision Video Analysis, Martha T. Gizaw
Undergraduate Honors Theses
The World Health Organization reports that falls are the second-leading cause of accidental death among senior adults around the world. Currently, a research team at William & Mary’s Department of Kinesiology & Health Sciences attempts to recognize and correct aging-related factors that can result in falling. To meet this goal, the members of that team videotape walking tests to examine individual gait parameters of older subjects. However, they undergo a slow, laborious process of analyzing video frame by video frame to obtain such parameters. This project uses computer vision software to reconstruct walking models from residents of an independent living retirement ...
Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), 2020 East Tennessee State University
Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt
Electronic Theses and Dissertations
Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its ...
Early Warning Solar Storm Prediction, 2020 University of Tennessee, Knoxville
Early Warning Solar Storm Prediction, Ian D. Lumsden, Marvin Joshi, Matthew Smalley, Aiden Rutter, Ben Klein
Chancellor’s Honors Program Projects
No abstract provided.
Physical Dispersions Of Meteor Showers Through High Precision Optical Observations, 2020 The University of Western Ontario
Physical Dispersions Of Meteor Showers Through High Precision Optical Observations, Denis Vida
Electronic Thesis and Dissertation Repository
Meteoroids ejected from comets form meteoroid streams which disperse over time due to gravitational perturbations and non-gravitational forces. When stream meteoroids collide with the Earth's atmosphere, they are visible as meteors emanating from a common point-like area (radiant) in the sky. Measuring the size of meteor shower radiant areas can provide insight into stream formation and age. The tight radiant dispersion of young streams are difficult to determine due to measurement error, but if successfully measured, the dispersion could be used to constrain meteoroid ejection velocities from their parent comets. The estimated ejection velocity is an uncertain, model-dependent value ...
Visualizing Metabolic Network Dynamics Through Time-Series Metabolomic Data., 2020 Institute for Systems Biology, 401 Terry Ave. N., Seattle, 98109, WA, United States
Visualizing Metabolic Network Dynamics Through Time-Series Metabolomic Data., Lea F Buchweitz, James T Yurkovich, Christoph Blessing, Veronika Kohler, Fabian Schwarzkopf, Zachary A King, Laurence Yang, Freyr Jóhannsson, Ólafur E Sigurjónsson, Óttar Rolfsson, Julian Heinrich, Andreas Dräger
Articles, Abstracts, and Reports
BACKGROUND: New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine.
RESULTS: The results comprise two animated videos that allow for new insights ...
Text Analytics, Nlp, And Accounting Research, 2020 Singapore Management University
Text Analytics, Nlp, And Accounting Research, Richard M. Crowley
Research Collection School Of Accountancy
The presentation covered: What is text analytics and NLP?; How text analytics has evolved in the accounting literature since the 1980s; What current (as of 2020) methods are used in the literature; What methods are on the horizon.
Finding Music In Chaos: Designing And Composing With Virtual Instruments Inspired By Chaotic Equations, 2020 Louisiana State University
Finding Music In Chaos: Designing And Composing With Virtual Instruments Inspired By Chaotic Equations, Landon P. Viator
LSU Doctoral Dissertations
Using chaos theory to design novel audio synthesis engines has been explored little in computer music. This could be because of the difficulty of obtaining harmonic tones or the likelihood of chaos-based synthesis engines to explode, which then requires re-instantiating of the engine to proceed with sound production. This process is not desirable when composing because of the time wasted fixing the synthesis engine instead of the composer being able to focus completely on the creative aspects of composition. One way to remedy these issues is to connect chaotic equations to individual parts of the synthesis engine instead of relying ...
A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, 2020 The University of Western Ontario
A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki
Electronic Thesis and Dissertation Repository
Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data ...
Multi-Class Twitter Data Categorization And Geocoding With A Novel Computing Framework, 2020 Clemson University
Multi-Class Twitter Data Categorization And Geocoding With A Novel Computing Framework, Sakib Mahmud Khan, Mashrur Chowdhury, Linh B. Ngo, Amy Apon
This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labeled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets ...
Methodological Issues Of Spatial Agent-Based Models, 2020 University of Minnesota - Twin Cities
Methodological Issues Of Spatial Agent-Based Models, Steven Manson, Li An, Keith C. Clarke, Alison Heppenstall, Jennifer Koch, Brittany Krzyzanowski, Fraser Morgan, David O'Sullivan, Bryan C. Runck, Eric Shook, Leigh Tesfatsion
Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe ...
Model-Based Machine Learning To Identify Clinical Relevance In A High-Resolution Simulation Of Sepsis And Trauma, 2020 UVM Larner College of Medicine
Model-Based Machine Learning To Identify Clinical Relevance In A High-Resolution Simulation Of Sepsis And Trauma, Zachary H. Silberman Md, Robert Chase Cockrell Phd, Gary An Md
Larner College of Medicine Fourth Year Advanced Integration Teaching/Scholarly Projects
Introduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques ...
Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central Wv, 2020 West Virginia University
Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central Wv, Oluwasegun O. Abatan
Graduate Theses, Dissertations, and Problem Reports
Fluvial deposits represent some of the best hydrocarbon reservoirs, but the quality of fluvial reservoirs varies depending on the reservoir architecture, which is controlled by allogenic and autogenic processes. Allogenic controls, including paleoclimate, tectonics, and glacio-eustasy, have long been debated as dominant controls in the deposition of fluvial strata. However, recent research has questioned the validity of this cyclicity and may indicate major influence from autogenic controls. To further investigate allogenic controls on stratal order, I analyzed the facies architecture, geomorphology, paleohydrology, and the stratigraphic framework of the Middle Pennsylvanian Allegheny Formation (MPAF), a fluvial depositional system in the Appalachian ...
Elucidating The Properties And Mechanism For Cellulose Dissolution In Tetrabutylphosphonium-Based Ionic Liquids Using High Concentrations Of Water, Brad Crawford
Graduate Theses, Dissertations, and Problem Reports
The structural, transport, and thermodynamic properties related to cellulose dissolution by tetrabutylphosphonium chloride (TBPCl) and tetrabutylphosphonium hydroxide (TBPH)-water mixtures have been calculated via molecular dynamics simulations. For both ionic liquid (IL)-water solutions, water veins begin to form between the TBPs interlocking arms at 80 mol % water, opening a pathway for the diffusion of the anions, cations, and water. The water veins allow for a diffusion regime shift in the concentration region from 80 to 92.5 mol % water, providing a higher probability of solvent interaction with the dissolving cellulose strand. The hydrogen bonding was compared between small and ...
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, 2020 University of Kentucky
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, Kyle Eric Helfrich
Theses and Dissertations--Mathematics
Despite the recent success of various machine learning techniques, there are still numerous obstacles that must be overcome. One obstacle is known as the vanishing/exploding gradient problem. This problem refers to gradients that either become zero or unbounded. This is a well known problem that commonly occurs in Recurrent Neural Networks (RNNs). In this work we describe how this problem can be mitigated, establish three different architectures that are designed to avoid this issue, and derive update schemes for each architecture. Another portion of this work focuses on the often used technique of batch normalization. Although found to be ...
Modeling Gene Expression With Differential Equations, 2020 Arcadia University
Modeling Gene Expression With Differential Equations, Madison Kuduk
Gene expression is the process by which the information stored in DNA is convertedinto a functional gene product, such as protein. The two main functions that makeup the process of gene expression are transcription and translation. Transcriptionand translation are controlled by the number of mRNA and protein in the cell. Geneexpression can be represented as a system of first order differential equations for the rateof change of mRNA and proteins. These equations involve transcription, translation,degradation and feedback loops. In this paper, I investigate a system of first orderdifferential equations to model gene expression proposed by Hunt, Laplace, Miller andPham ...
The Effects Of Automated Grading On Computer Science Courses At The University Of New Orleans, 2019 University of New Orleans, New Orleans
The Effects Of Automated Grading On Computer Science Courses At The University Of New Orleans, Jerod F A Dunbar
University of New Orleans Theses and Dissertations
This is a study of the impacts of the incorporation, into certain points of the Computer Science degree program at the University of New Orleans, of Course Management software with an Autograding component. The software in question, developed at Carnegie Mellon University, is called “Autolab.” We begin by dissecting Autolab in order to gain an understanding of its inner workings. We can then take out understanding of its functionality and apply that to an examination of fundamental changes to courses in the time since they incorporated the software. With that, we then compare Drop, Failure, Withdrawal rate data from before ...
Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, 2019 Southern Methodist University
Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas
SMU Data Science Review
In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle ...
Ordinal Hyperplane Loss, 2019 Kennesaw State University
Ordinal Hyperplane Loss, Bob Vanderheyden
Analytics and Data Science Dissertations
This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize ...