Data Science Commons^™

A Data Exploration Of Jeopardy! From 1984 To The Present, Brian S. Hamilton

Dissertations, Theses, and Capstone Projects

The gameshow Jeopardy! has been around in its current iteration—hosted by Alex Trebek—since 1984. During this time, it has accumulated data on clues, contestants, and possible strategies on how to win. Using a crowd-sourced archive called J! Archive, this project seeks to find trends in the topics that the game covers and take a deeper look into the performance of its contestants. It employs topic modeling, a text-analysis method, to organize the hundreds of thousands of archived clues and statistical analysis to rate the performance of contestants by gender. Using web-based visualization tools, the data is shown in an …

Sensory Stressors Impact Species Responses Across Local And Continental Scales, Ashley A. Wilson

Master's Theses

Pervasive growth in industrialization and advances in technology now exposes much of the world to anthropogenic night light and noise (ANLN), which pose a global environmental challenge in terrestrial environments. An estimated one-tenth of the planet’s land area experiences artificial light at night — and that rises to 23% if skyglow is included. Moreover, anthropogenic noise is associated with urban development and transportation networks, as the ecological impact of roads alone is estimated to affect one-fifth of the total land cover of the United States and is increasing in space and intensity. Existing research involving impacts of light or noise …

Team Formation Using Recommendation Systems, Shreyas Patil

Theses

The importance of team formation has been realized since ages, but finding the most effective team out of the available human resources is a problem that persists to the date. Having members with complementary skills, along with a few must-have behavioral traits, such as trust and collaborativeness among the team members are the key ingredients behind team synergy and performance. This thesis designs and implements two different algorithms for the team formation problem using ideas adapted from the recommender systems literature. One of the proposed solutions uses the Glicko-2 rating system to rate the employees’ skills which can easily separate …

The Transcript Profile Changes With Developmental Maturation Of Fetal Lung Type 2 Cells: An Analysis Of Rnaseq Data, Heber C. Nielsen, Volodymyr Orlov, Rebecca Holsapple, Monnie Mcgee

SMU Data Science Review

In this paper, we utilize next-generation sequencing (NGS) data from the LungMap project to identify and characterize the developmental RNA transcriptome in alveolar epithelial type II cells of embryonic mouse lungs of gestational ages embryonic days 16 (E16) and 18 (E18). Late gestation lung cellular maturation is necessary for survival at birth. Using R and the BioConductor packages for RNAseq analysis, we analyze changes in the mouse lung RNA transcriptome as this maturation process takes place. We particularly identify the cluster of genes whose expression changes markedly between immature (E16) and mature (E18) lungs which can be used to define …

Forecasting Power Consumption In Pennsylvania During The Covid-19 Pandemic: A Sarimax Model With External Covid-19 And Unemployment Variables, Jackson Au, Javier Saldaña Jr., Ben Spanswick, John Santerre

SMU Data Science Review

In this paper, we present how electrical consumption can reveal insight into the novel COVID-19 pandemic spread. We analyze electrical power consumption provided by PPL Electric Utilities, Department of Labor’s unemployment claims, and the COVID-19 cases/deaths for the State of Pennsylvania to study the impact of the pandemic on the infrastructure. Using a SARIMA model as our benchmark and we analyzed the use of a SARIMAX model to forecast the power consumption in Pennsylvania 14 days ahead. Our work quantifies and illuminates the effect that the strict legislation passed to minimize the spread of COVID19 had a on power consumption. …

Compressed Dna Representation For Efficient Amr Classification, John Partee, Robert Hazell, Anjli Solsi, John Santerre

SMU Data Science Review

In this paper, we explore a representation methodology for the compression of DNA isolates. Using lossless string compression via tokenization of frequently repeated segments of DNA, we reduce the length of the isolates to be counted as k-mers for classification. With this new representation, we apply a previously established feature sampling method to dramatically reduce the feature space. In understanding the genetic diversity, we also look at conserving biological function across these spaces. Using a random forest model we were able to predict the resistance or susceptibility of bacteria with 85-90\% accuracy, with a 30-50\% reduction in overall isolate length, …

Spoken Language Recognition On Open-Source Datasets, Brady Arendale, Samira Zarandioon, Ryan Goodwin, Douglas Reynolds

SMU Data Science Review

The field of speaker and language recognition is constantly being researched and developed, but much of this research is done on private or expensive datasets, making the field more inaccessible than many other areas of machine learning. In addition, many papers make performance claims without comparing their models to other recent research. With the recent development of public multilingual speech corpora such as Mozilla's Common Voice as well as several single-language corpora, we now have the resources to attempt to address both of these problems. We construct an eight-language dataset from Common Voice and a Google Bengali corpus as well …

Predicting Attrition - A Driver For Creating Value, Realizing Strategy, And Refining Key Hr Processes, Kevin Mendonsa, Maureen Stolberg, Vivek Viswanathan, Scott Crum

SMU Data Science Review

Talent is the most important asset for every organization's success. While attrition (or churn) and turnover can refer to both employees and customers, this paper will focus on employee attrition only. Many organizations accept attrition as an inevitable cost of doing business and do nothing to adopt or implement mitigating strategies to combat it. World class companies on the other hand take deliberate measures to understand, control and mitigate attrition (turnover) at every stage. Unmitigated attrition can have a devastating effect on an organization's bottom line and market value. In addition, the “invisible" costs of low employee morale, reduced employee …

An Effective Method For Attribute Subset Selection, Considering The Resource In Pattern Recognition, Bakhtiyorjon Bakirovich Akbaraliev

Chemical Technology, Control and Management

An analytical method for determining informative sets of features (INP) is developed, taking into account the resource for criteria based on the use of a measure of dispersion of classified objects. The areas of existence of the solution are defined. The statements and properties for the Fischer-type information criterion are proved, using which the proposed analytical method for determining the INP guarantees optimal results in the sense of maximizing the selected functional. The appropriateness of choosing this type of informative criterion is justified. A method for transforming attributes is proposed. The universality of the method in relation to the type …

The Most-Cited Articles In Data In Brief Journal: A Bibliometric Analysis Using Scopus Data, Lusiana Wulansari, Ansari Saleh Ahmar, Agus Rochmat, Nurmawati, Akbar Iskandar

Library Philosophy and Practice (e-journal)

Bibliometric analysis is one of the research approaches that utilizes quantitative and mathematical data to address problems posed in the context of visualization to see patterns in the field of science. In fact, bibliometric analysis may also include a wider overview of the names of the most influential writers in the area of science. This data analysis would discuss the most-cited articles in Data in Brief Journal including the countries, authors. The data was collected on 31st May 2020 of Scopus database. The literature review was conducted using the keyword: ISSN (2352-3409). The bibliometric analysis is visualized utilizing the VosViewer …

Blockchain Technology And Freight Forwarder Exploration Of Implications Focused On Practitioners In Shanghai, Johannes Van Bohemen

World Maritime University Dissertations

No abstract provided.

How Port Logistics Competitiveness Evolves Among Major Ports In China And Europe (1998-2018), Jiawei Wang

World Maritime University Dissertations

No abstract provided.

Data Visualization And Infographics Design Art404g/Dsp Xxx, Harrison Dekker

Library Impact Statements

No abstract provided.

Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding, Mohammed Hasanuzzaman, Sayantan Mitra, Sriparna Saha

Articles

In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when …

Multi‑View Clustering For Multi‑Omics Data Using Unifed Embedding, Mohammed Hasanuzzaman, Sayantan Mitra, Sriparna Saha

Department of Computer Science Publications

In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when …

Big Data Analytics Applied To Healthcare, Xuejuan Zhang, Boris Vishnevsky

School of Continuing and Professional Studies Student Papers

In this paper, we review the recent literature related to Big Data Analytics (BDA). We also discuss ways of applying BDA in Healthcare. In Section 1, we discuss the definition of Big Data Analytics and its characteristics. In Section 2, we discuss the healthcare ecosystem's main stakeholders and the data of each main stakeholder. Section 3 discusses the challenges and opportunities of leveraging Big Data Analytics by healthcare stakeholders.

Cell Assembly Detection In Low Firing-Rate Spike Train Data, Phan Minh Duc Truong

Mathematics Theses and Dissertations

Cell assemblies, defined as groups of neurons forming temporal spike coordination, are thought to be fundamental units supporting major cognitive functions. However, detecting cell assemblies is challenging since they can occur at a range of time scales and with a range of precisions, from synchronous spikes to co-variations in firing rate. In this dissertation, we use a recently published cell assembly detection (CAD) algorithm that is capable of detecting assemblies at a range of time scales and precisions. We first showed that the CAD method can be applied to sparser spike train data than what have previously been reported. This …

Iba Newsletter [August 2020], Communications Department, Office Of The Registrar

IBA News

No abstract provided.

A Study Of Information Bots And Knowledge Bots, Amartya Hatua

Dissertations

In this dissertation, a study of different aspects of information bots and knowledge bots is done. The research contributes to a better understanding of the various characteristics of information bots as well as the different patterns and factors responsible for the information diffusion in a social network. This research also shows how these factors can be used to predict information diffusion for a particular topic in a social network. The second part of the research is focused on strategies for improving the knowledge base of knowledge bots, where two different approaches are studied. In the first approach, knowledge is transferred …

Variable Compact Multi-Point Upscaling Schemes For Anisotropic Diffusion Problems In Three-Dimensions, James Quinlan

Dissertations

Simulation is a useful tool to mitigate risk and uncertainty in subsurface flow models that contain geometrically complex features and in which the permeability field is highly heterogeneous. However, due to the level of detail in the underlying geocellular description, an upscaling procedure is needed to generate a coarsened model that is computationally feasible to perform simulations. These procedures require additional attention when coefficients in the system exhibit full-tensor anisotropy due to heterogeneity or not aligned with the computational grid. In this thesis, we generalize a multi-point finite volume scheme in several ways and benchmark it against the industry-standard routines. …

Empirical Studies Of Deep Learning On Information Diffusion On Social Networks And Collective Task Learning For Swarm Robotics, Trung T. Nguyen

Dissertations

Researchers in multiple disciplines have recently adopted deep learning because of its ability of high accuracy representation learning from big and complex data. My research goal in this thesis is developing deep learning models for information diffusion analysis on social networks and collective tasks learning in swarm robotics. Firstly, the information diffusion on social networks is modeled as a multivariate time series in three dimensions with ten features. Then, we applied time-series clustering algorithms with Dynamic Time Warping to discover different patterns of our models. Then, we build a prediction model based on LSTM, which outperforms traditional time-series prediction methods. …

Machine Learning Approaches For Improving Prediction Performance Of Structure-Activity Relationship Models, Gabriel Idakwo

Dissertations

In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to assess the activity and properties of small molecules. In silico methods such as Quantitative Structure-Activity/Property Relationship (QSAR) are used to correlate the structure of a molecule to its biological property in drug design and toxicological studies. In this body of work, I started with two in-depth reviews into the application of machine learning based approaches and feature reduction methods to QSAR, and then investigated solutions to three common challenges faced in machine learning based QSAR studies.

First, to improve the prediction accuracy of learning …

A Novel Correction For The Adjusted Box-Pierce Test — New Risk Factors For Emergency Department Return Visits Within 72 Hours For Children With Respiratory Conditions — General Pediatric Model For Understanding And Predicting Prolonged Length Of Stay, Sidy Danioko

Computational and Data Sciences (PhD) Dissertations

This thesis represents the results of three research projects that underline the breadth and depth of my interests.

Firstly, I devoted some efforts to the well-known Box-Pierce goodness-of-fit tests for time series models which has been an important research topic over the last few decades. All previously proposed tests are focused on changes of the test statistics. Instead, I adopted a different approach that takes the best performing test and modifying the rejection region. Thus, I developed a semiparametric correction of the Adjusted Box-Pierce test that attains the best I error rates for all sample sizes and lags and outperforms …

Shotgun Metagenomic Sequencing Data Of Sunflower Rhizosphere Microbial Community In South Africa, Olubukola Oluranti Babalola, Temitayo Tosin Alawiye, Carlos M. Rodriguez Lopez, Ayansina Segun Ayangbenro

Horticulture Faculty Publications

This dataset presents shotgun metagenomic sequencing of sunflower rhizosphere microbiome in Bloemhof, South Africa. Data were collected to decipher the structure and function in the sunflower microbial community. Illumina HiSeq platform using next generation sequencing of the DNA was carried out. The metagenome comprised 8,991,566 sequences totaling 1,607,022,279 bp size and 66% GC content. The metagenome was deposited into the NCBI database and can be accessed with the SRA accession number SRR10418054. An online metagenome server (MG RAST) using the subsystem database revealed bacteria had the highest taxonomical representation with 98.47%, eukaryote at 1.23%, and archaea at 0.20%. The most …

Human Trafficking In Nepal: Can Big Data Help?, Shushant Khanal

Undergraduate Research Journal

This paper provides an overview of human trafficking in Nepal, identifies strategies implemented by the government of the country to handle the problem and possibilities of using big data as a solution to the problem of human trafficking in Nepal. Big data, may be defined as the collection of a large volume of data from the past that is processed using machine learning and artificial intelligence to find a common pattern. The use of big data in tackling the problem of human trafficking is not new in developed countries like the United States but it is still a foreign idea …

Applications Of Artificial Intelligence And Graphy Theory To Cyberbullying, Jesse D. Simpson

Graduate Theses/Dissertations

Cyberbullying is an ongoing and devastating issue in today's online social media. Abusive users engage in cyber-harassment by utilizing social media to send posts, private messages, tweets, or pictures to innocent social media users. Detecting and preventing cases of cyberbullying is crucial. In this work, I analyze multiple machine learning, deep learning, and graph analysis algorithms and explore their applicability and performance in pursuit of a robust system for detecting cyberbullying. First, I evaluate the performance of the machine learning algorithms Support Vector Machine, Naïve Bayes, Random Forest, Decision Tree, and Logistic Regression. This yielded positive results and obtained upwards …

Colleague To Banner Migration: Data Conversion Guide For Institutional Research, Laura Osborn

Masters Theses & Doctoral Dissertations

When the SDBOR decided to migrate their current student information system into a shared system with HR and Finance, adjustments needed to be made to accommodate for current Banner settings and work around tables that were already populated with HRFIS data. The change in data type of the student identifier from that of a 7-digit numeric field to a 9- digit alpha-numeric field poses problems for running aggregate data calculations. Additional complications include having some information such as first-generation status that was not migrated between the systems, and cases such as college coding where tables that were designed for student …

Understanding Spatial Language In Radiology: Representation Framework, Annotation, And Spatial Relation Extraction From Chest X-Ray Reports Using Deep Learning, Surabhi Datta, Yuqi Si, Laritza Rodriguez, Sonya E Shooshan, Dina Demner-Fushman, Kirk Roberts

Faculty, Staff and Student Publications

Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as …

Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam

Student Works (2020-2029)

One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. Implementation of DM techniques in different areas of civil engineering has recently given very good results. However, application of DM in SHM is not used as much as expected, thus, many challenges are still ahead. Therefore, it is necessary to develop the applicability of DM in SHM. …

Statistical Methods For Resolving Intratumor Heterogeneity With Single-Cell Dna Sequencing, Alexander Davis

Dissertations and Theses (Open Access)

Tumor cells have heterogeneous genotypes, which drives progression and treatment resistance. Such genetic intratumor heterogeneity plays a role in the process of clonal evolution that underlies tumor progression and treatment resistance. Single-cell DNA sequencing is a promising experimental method for studying intratumor heterogeneity, but brings unique statistical challenges in interpreting the resulting data. Researchers lack methods to determine whether sufficiently many cells have been sampled from a tumor. In addition, there are no proven computational methods for determining the ploidy of a cell, a necessary step in the determination of copy number. In this work, software for calculating probabilities from …