Data Science Commons^™

Automated Machine Learning Framework For Demand Forecasting In Wholesale Beverage Alcohol Distribution, Jenna Ford, Christian Nava, Jonathan Tan, Bivin Sadler

SMU Data Science Review

This paper covers the development, testing, and implementation of an automatic framework for analyzing and forecasting demand for an alcoholic beverage distributor’s products at varying levels of granularity. Rather than look at macroscale geographic demand for a product from a distribution center, this framework will look at the localized customer level demand for that product before aggregating total demand. The approach will better capture individual behavior variations for each customer and allow for a more accurate estimation of the total monthly demand for that product. To best account for each product’s influencing factors, each product is analyzed separately per customer …

Multi-Modal Classification Using Images And Text, Stuart J. Miller, Justin Howard, Paul Adams, Mel Schwan, Robert Slater

SMU Data Science Review

This paper proposes a method for the integration of natural language understanding in image classification to improve classification accuracy by making use of associated metadata. Traditionally, only image features have been used in the classification process; however, metadata accompanies images from many sources. This study implemented a multi-modal image classification model that combines convolutional methods with natural language understanding of descriptions, titles, and tags to improve image classification. The novelty of this approach was to learn from additional external features associated with the images using natural language understanding with transfer learning. It was found that the combination of ResNet-50 image …

Analysis Of The Commercial Real Estate Market In A Post Covid-19 World, Brandon Croom, Sean Kennedy, Sandesh Ojha, Justin Sparks

SMU Data Science Review

The volatility in the commercial real estate market has been greatly influenced by the new societal practices brought about by the COVID-19 pandemic. The COVID-19 pandemic has added additional factors to already complex modeling to value and predict commercial real estate prices. Although multiple methodologies have been applied to commercial real estate valuation, these methods have not yet taken the COVID-19 pandemic factor into account. The main contribution of this article lies in developing an application for commercial real estate valuation which includes the COVID-19 pandemic factor. Thought this article a Hedonic model was developed to compare the impacts of …

Sars-Cov-2 Pandemic Analytical Overview With Machine Learning Predictability, Anthony Tanaydin, Jingchen Liang, Daniel W. Engels

SMU Data Science Review

Understanding diagnostic tests and examining important features of novel coronavirus (COVID-19) infection are essential steps for controlling the current pandemic of 2020. In this paper, we study the relationship between clinical diagnosis and analytical features of patient blood panels from the US, Mexico, and Brazil. Our analysis confirms that among adults, the risk of severe illness from COVID-19 increases with pre-existing conditions such as diabetes and immunosuppression. Although more than eight months into pandemic, more data have become available to indicate that more young adults were getting infected. In addition, we expand on the definition of COVID-19 test and discuss …

Computing For Numeracy: How Safe Is Your Covid-19 Social Bubble?, Charles Connor

Numeracy

The COVID-19 pandemic has led many people to form social bubbles. These social bubbles are small groups of people who interact with one another but restrict interactions with the outside world. The assumption in forming social bubbles is that risk of infection and severe outcomes, like hospitalization, are reduced. How effective are social bubbles? A Bayesian event tree is developed to calculate the probabilities of specific outcomes, like hospitalization, using example rates of infection in the greater community and example prior functions describing the effectiveness of isolation by members of the social bubble. The probabilities are solved for two contrasting …

Creating A Machine Learning Model For The Prediction Of Refugee Flows, Esther Mead

Theses and Dissertations

The world-wide refugee problem has a long history, but continues to this day, and will unfortunately continue into the foreseeable future. Efforts to adequately anticipate, mitigate and prepare for refugee flows, however, are still lacking. There are many potential causes for refugee flows, but the published research has primarily focused on identifying ways to integrate already existing refugees into the various communities wherein they ultimately reside after having fled their home countries, rather than on the idea of figuring out ways to prevent the situations that initially caused them to flee. The model proposed in this dissertation uses a set …

Challenges When Identifying Migration From Geo-Located Twitter Data, Caitrin Armstrong, Ate Poorthuis, Matthew Zook, Derek Ruths, Thomas Soehl

Geography Faculty Publications

Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to infer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code …

Adverse Health Effects Of Kratom: An Analysis Of Social Media Data, Abdullah Wahbeh, Tareq Nasralah, Omar El-Gayar, Mohammad A. Al-Ramahi, Ahmed El Noshokaty

Computer Information Systems Faculty Publications (Archived)

This study investigates the adverse healthcare effects associated with the use of kratom. Using machine learning techniques, we analyzed a total of 36,516 users’ posts related to kratom. The results and analysis showed that social media could help identify important insights related to the use of kratom. The sentiment and emotion analyses showed that the kratom experience was negative and largely associated with anger, fear, disgust, and sadness. The results from. topic modeling showed that kratom is associated with a number of healthcare issues such as rashes and itching, urination, constipation, loss of appetite/weight, dry mouth, seizures, nausea, heartburn, dehydration, …

The State Of #Digitalentrepreneurship: A Big Data Leximancer Analysis Of Social Media Activity, Violetta Wilk, Helen Cripps, Alexandru Capatina, Adrian Micu, Angela-Eliza Micu

Research outputs 2014 to 2021

This paper examined online sentiment, key themes and patterns evident in social media activity about digital entrepreneurship. It provides a snapshot-in-time, visual-first perspective on social media user-generated-content (UGC) to better understand the topic of digital entrepreneurship. Global data consisting of 31,017 publicly available UGC which used the #digitalentrepreneurship (hashtag) and the keywords ‘digital entrepreneurship’ were collected. A computer assisted qualitative data analysis software (CAQDAS), Leximancer, was used for an automated text-mining analysis. There is positive online sentiment surrounding digital entrepreneurship technology, ecosystem and industry, and one which promotes women transformation of digital entrepreneurship globally. Negative sentiment pointed out that future …

Forecasting The Prices Of Cryptocurrencies Using A Novel Parameter Optimization Of Varima Models, Alexander Barrett

Computational and Data Sciences (PhD) Dissertations

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper.

These models are trained on 3 years of historical data, …

Aps March Meeting 2021 (Online) Updates On Scientific Research During Pandemic Times, Vianney Gimenez-Pinto

Title III Professional Development Reports

While the ongoing global pandemic continues to affect our everyday lives, researchers in Science, Technology, Engineering and Math found a way to come together at the American Physical Society (APS) March Meeting 2021. The conference was online-only and had more than 11,000 registered attendants who actively participated in the program during March 14- 19, 2021.

Developing A New Analytical Model For Combatting Crises: A Comprehensive Review, Seth Spire

Research Awards

As the world emerges from the COVID-19 pandemic, it is critical to reflect on the lessons learned and prepare for potential future health crises. While data analytics and artificial intelligence (AI) played a pivotal role in managing the pandemic, there is much room for improvement and further use. This study examines the current state of data science tools employed during COVID-19, evaluating their advantages, limitations, and challenges in their broader implementation. We also review literature on future directions for AI in healthcare. Our findings highlight significant challenges, including difficulties in accessing usable data and common distrust of AI models for …

A Hybrid Gene Selection Strategy Based On Fisher And Ant Colony Optimization Algorithm For Breast Cancer Classification, Mohammed Hamim, Ismail El Moudden, Mohan D. Pant, Hicham Moutachaouik, Mustapha Hain

EVMS School of Health Professions Faculty Publications

Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction …

Reading Datasets: Strategies For Interpreting The Politics Of Data Signification, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading (extrapolating the literal meaning of values in a dataset), a connotative reading (tracing the socio-political provenance of data semantics), and a deconstructive reading …

Review Of Forecasting Univariate Time-Series Data With Application To Water-Energy Nexus Studies & Proposal Of Parallel Hybrid Sarima-Ann Model, Cory Sumner Yarrington

Graduate Theses, Dissertations, and Problem Reports (ETD)

The necessary materials for most human activities are water and energy. Integrated analysis to accurately forecast water and energy consumption enables the implementation of efficient short and long-term resource management planning as well as expanding policy and research possibilities for the supportive infrastructure. However, the integral relationship between water and energy (water-energy nexus) poses a difficult problem for modeling. The accessibility and physical overlay of data sets related to water-energy nexus is another main issue for a reliable water-energy consumption forecast. The framework of urban metabolism (UM) uses several types of data to build a global view and highlight issues …

Is Technological Progress A Random Walk? Examining Data From Space Travel, Michael Howell, Daniel Berleant, Hyacinthe Aboudja, Richard Segall, Peng-Hung Tsai

Journal of the Arkansas Academy of Science

Improvement in a variety of technologies can often be successful modeled using a general version of Moore’s law (i.e. exponential improvements over time). Another successful approach is Wright’s law, which models increases in technological capability as a function of an effort variable such as production. While these methods are useful, they do not provide prediction distributions, which would enable a better understanding of forecast quality

Farmer and Lafond (2016) developed a forecasting method which produces forecast distributions and is applicable to many kinds of technology. A fundamental assumption of their method is that technological progress can be modeled as a …

Identifying Sleep-Related Factors Associated With Cognitive Function In A Hispanics/Latinos Cohort: A Dual Random Forest Approach, Li Xiaojin, Cui Licong, Wang Fei, Paul E Schulz, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Disordered sleep is associated with poor cognitive function and cognitive decline. However, little is known regarding the association of sleep-related factors with cognitive function in underrepresented cohorts such as the Hispanic/Latino population. Leveraging the National Sleep Research Resource, one of the most comprehensive collections of sleep studies, we identified a Hispanic/Latino cohort of 1,031 lower cognitive function cases and 2,062 normal controls. We developed a novel dual random forest (DRF) approach to discriminate cases against controls for estimating the potential impact of sleep-related variables related to the decline of cognitive function. Several important sleep-related factors were identified which may be …

Representer Theorems In Banach Spaces: Minimum Norm Interpolation, Regularized Learning And Semi-Discrete Inverse Problems, Rui Wang, Yusheng Xu

Mathematics & Statistics Faculty Publications

Learning a function from a finite number of sampled data points (measurements) is a fundamental problem in science and engineering. This is often formulated as a minimum norm interpolation (MNI) problem, a regularized learning problem or, in general, a semi discrete inverse problem (SDIP), in either Hilbert spaces or Banach spaces. The goal of this paper is to systematically study solutions of these problems in Banach spaces. We aim at obtaining explicit representer theorems for their solutions, on which convenient solution methods can then be developed. For the MNI problem, the explicit representer theorems enable us to express the infimum …

Applications Of Machine Learning To Facilitate Software Engineering And Scientific Computing, Natalie Best

Computational and Data Sciences (PhD) Dissertations

The use of machine learning has risen in recent years, though many areas remain unexplored due to lack of data or lack of computational tools. This dissertation explores machine learning approaches in case studies involving image classification and natural language processing. In addition, a software library in the form of two-way bridge connecting deep learning models in Keras with ones available in the Fortran programming language is also presented.

In Chapter 2, we explore the applicability of transfer learning utilizing models pre-trained on non-software engineering data applied to the problem of classifying software unified modeling language diagrams where data is …

Machine Learning And Bioinformatic Insights Into Key Enzymes For A Bio-Based Circular Economy, Japheth E. Gado

Theses and Dissertations--Chemical and Materials Engineering

The world is presently faced with a sustainability crisis; it is becoming increasingly difficult to meet the energy and material needs of a growing global population without depleting and polluting our planet. Greenhouse gases released from the continuous combustion of fossil fuels engender accelerated climate change, and plastic waste accumulates in the environment. There is need for a circular economy, where energy and materials are renewably derived from waste items, rather than by consuming limited resources. Deconstruction of the recalcitrant linkages in natural and synthetic polymers is crucial for a circular economy, as deconstructed monomers can be used to manufacture …