Data Science Commons^™

Big Data: Ethics, Resources, And Potential Collaboration, Matthew Zook

Geography Presentations

This presentation goes over 10 simple rules for responsible big data research.

Branched Water Resources Management Models, Toshtemir Khojakulov, Rashid Oteniyazov, Fazliddin Kholmuminov

Bulletin of TUIT: Management and Communication Technologies

This paper describes the distribution of water resources in Uzbekistan, the elimination of water problems. Aral Sea water resources and water resources distribution models have been introduced in the country.

Unsupervised Data Mining Technique For Clustering Library In Indonesia, Robbi Rahim, Joseph Teguh Santoso, Sri Jumini, Gita Widi Bhawika, Daniel Susilo, Danny Wibowo

Library Philosophy and Practice (e-journal)

Organizing school libraries not only keeps library materials, but helps students and teachers in completing tasks in the teaching process so that national development goals are in order to improve community welfare by producing quality and competitive human resources. The purpose of this study is to analyze the Unsupervised Learning technique in conducting cluster mapping of the number of libraries at education levels in Indonesia. The data source was obtained from the Ministry of Education and Culture which was processed by the Central Statistics Agency (abbreviated as BPS) with url: bps.go.id/. The data consisted of 34 records where the attribute …

Development Of Algorithm Of Priority Estimation Of Parameters For Building Models., Toshtemir Khojakulov

Bulletin of TUIT: Management and Communication Technologies

The article reviewed the development of the priority assessment algorithm, the operation of the control object, the quality and reliability of the measuring path, the quality and reliability of computer equipment and communication channels, problem solving, etc. With a solution to the problem offers an algorithm. Considering the above estimates as some generalized coordinates, we obtain an effective mathematical description of complex production processes

A New Feature Selection Method Based On Class Association Rule, Sami A. Al-Dhaheri

Dissertations, Theses, and Capstone Projects

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may …

Exploring Media Portrayals Of People With Mental Disorders Using Nlp, Swapna Gottipati, Mark Chong, Andrew Wei Kiat Lim, Benny Haryanto Kawidiredjo

Research Collection School Of Computing and Information Systems

Media plays an important role in creating an impact in society. Several studies show that news media and entertainment channels, at times may create overwhelming images of the mental illness that emphasize criminality and dangerousness. The consequences of such negative impact may impact the audience with stigma and on the other hand, they impair the self-esteem and help-seeking behavior of the people with mental disorders. This is the first study to examine the Singapore media’s portrayal of persons with mental disorders (MDs) using text analytics and natural language processing. To date, most studies on media portrayal of people with MDs …

Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed

Theses and Dissertations

We contribute in saving the lives of cancer patients through early detection and diagnosis, since one of the major challenges in cancer treatment is that patients are diagnosed at very late stages when appropriate medical interventions become less effective and full curative treatment is no longer achievable. Cancer classification using gene expressions is extremely challenging given the complexity and high dimensionality of the data. Current classification methods typically rely on samples collected from a single tissue type and perform a prerequisite of gene feature selection to avoid processing the full set of genes. These methods fall short in taking advantage …

Delineating Knowledge Domains In Scientific Domains In Scientific Literature Using Machine Learning (Ml), Abhay Maurya, Smarajit Paul Choudhury Mr., Kshitij Jaiswal Mr.

Library Philosophy and Practice (e-journal)

The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like …

Exchanges In A Virtual Environment For Diabetes Self-Management Education And Support: Social Network Analysis, Carlos A Pérez-Aldana, Allison A Lewinski, Constance M Johnson, Allison A Vorderstrasse, Sahiti Myneni

Faculty, Staff and Student Publications

BACKGROUND: Diabetes remains a major health problem in the United States, affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and clinical outcomes. Widespread internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, and clinical and behavioral outcomes. In particular, diabetes interventions based on virtual environments have the potential to improve diabetes self-efficacy and support, while being highly feasible and usable. However, little is known about the patterns of social interactions and support taking place within type 2 diabetes-specific virtual communities.

OBJECTIVE: The objective of this study was to …

Knowledge Network Embedding Of Transcriptomic Data From Spaceflown Mice Uncovers Signs And Symptoms Associated With Terrestrial Diseases, Amber M. Paul, Charlotte A. Nelson, Ana Uriarte Acuna, Ryan T. Scott, Atul J. Butte, Egle Cekanaviciute, Sergio E. Baranzini

Publications

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab …

Flow-Based And Packet-Based Intrusion Detection Using Blstm, Brook Andreas, Jayaweera Dilruksha, Eric Mccandless

SMU Data Science Review

Abstract. Networks are always under the threat of malicious intrusions. Deep learning models are used to help identify and mitigate intrusions before damage can occur. Various types of deep learning models have been researched, built, and tested with the goal of improving intrusion detection and efficiencies. In this paper, a two-phase deep learning approach called a Hybrid Intrusion Detection System (HIDS) is proposed that uses Bi-Directional Long Short-Term Memory Neural Network (BLSTM) to assess both flow-based network data and packet-based data. This approach is unique because BLSTM is employed rather than a traditional Deep Neural Network (DNN) and two models …

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, …

Red Drum And Spotted Seatrout Live-Release Tournament Mortality And Dispersal, T. Reid Nelson, Crystal Hightower, Sean P. Powers

University Faculty and Staff Publications

Although catch-and-release fishing tournaments undoubtedly reduce mortality of target species, postrelease mortality and fish stockpiling at release sites remain common concerns related to these tournaments. The impacts of liverelease tournaments on freshwater species have been widely studied. However, research on estuarine sport fishes is lacking even though catch-and-release tournaments targeting these species are prevalent and popular recreational fisheries exist. Therefore, we estimated the post-weigh-in mortality and dispersal of Red Drum Sciaenops ocellatus and Spotted Seatrout Cynoscion nebulosus released from the 2016–2018 Alabama Deep Sea Fishing Rodeo live-weigh-in categories using acoustic telemetry. To concurrently estimate overall post-weigh-in mortality and dispersal, we …

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 …

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 …

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 …

Statistical Approaches For Estimation And Comparison Of Brain Functional Connectivity, Jifang Zhao

Theses and Dissertations

Drug addiction can lead to many health-related problems and social concerns. Functional connectivity obtained from functional magnetic resonance imaging (fMRI) data promotes a variety of fundamental understandings in such association. Due to its complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computationally efficient algorithm to estimate the parameters. Our method is used to identify functional connectivity and …

Proposed Data Governance Framework For Small And Medium Scale Enterprises (Smes), Rejoice Okoro

All Graduate Theses, Dissertations, and Other Capstone Projects

Data governance is not a one size fits all, instead, it should be an evolutionary process that can be started small and measurable along the way. This research aims at proposing a data governance framework by ensuring data management processes, data security and control are compliant with laws and policies. This article also presents the first results of a comparative analysis between three data privacy laws and outlines five components which together form a data governance framework for SMEs. The data governance model documents data quality roles and their type of interaction with data quality management activities exploring how data …

Modelling Acoustics In Ancient Maya Cities: Moving Towards A Synesthetic Experience Using Gis & 3d Simulation, Graham Goodwin, Heather Richards-Rissetto

Department of Anthropology: Faculty Publications

Archaeological analyses have successfully employed 2D and 3D tools to measure vision and movement within cityscapes; however, built environments are often designed to invoke synesthetic experiences. GIS and Virtual Reality (VR) now enable archaeologists to also measure the acoustics of ancient spaces. To move toward an understanding of synesthetic experience in ancient Maya cities, we employ GIS and 3D modelling to measure sound propagation and reverberation using the main civic-ceremonial complex in ancient Copán as a case study. For the ancient Maya, sight and sound worked in concert to create ritually-charged atmospheres and architecture served to shape these experiences. Together …