Data Science Commons^™

Integration Of Internet Of Things And Health Recommender Systems, Moonkyung Yang

Electronic Theses, Projects, and Dissertations

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch …

Data Analytics In Hotel And Integrated Resort Brands: An Evaluation Of Past Literature And Proposed Research For The Future, Luke Andrew Walocko

UNLV Theses, Dissertations, Professional Papers, and Capstones

Data analytics in hotel and integrated resort brands is a growing strategy implemented to support business decisions designed to generate revenue or save costs. This study utilizes a literature review of data analytics related publications to provide recommendations on future research topics to improve the quality of literature related to data analytics in hotel and integrated resort brands. The study is not limited to hospitality specific research and uses research from all industries to identify gaps in publications for hospitality scholars to explore. Three proposed research questions for future exploration were composed based on the comparison of literature written for …

Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (Uas) Multispectral Models, Benjamin T. Fraser, Russell G. Congalton

Faculty Publications

Forest disturbances—driven by pests, pathogens, and discrete events—have led to billions of dollars in lost ecosystem services and management costs. To understand the patterns and severity of these stressors across complex landscapes, there must be an increase in reliable data at scales compatible with management actions. Unmanned aerial systems (UAS or UAV) offer a capable platform for collecting local scale (e.g., individual tree) forestry data. In this study, we evaluate the capability of UAS multispectral imagery and freely available National Agricultural Imagery Program (NAIP) imagery for differentiating coniferous healthy, coniferous stressed, deciduous healthy, deciduous stressed, and degraded individual trees throughout …

Characterizing Long Covid: Deep Phenotype Of A Complex Condition, Rachel R. Deer, Madeline A. Rock, Nicole Vasilevsky, Leigh Carmody, Halie Rando, Alfred J. Anzalone, Marc D. Basson, Tellen D. Bennett, Timothy Bergquist, Eilis A. Boudreau, Carolyn T. Bramante, James Brian Byrd, Tiffany J. Callahan, Lauren E. Chan, Haitao Chu, Christopher G. Chute, Ben D. Coleman, Hannah E. Davis, Joel Gagnier, Casey S. Greene, Ramakanth Kavuluru

Institute for Biomedical Informatics Faculty Publications

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies.

METHODS: The Human Phenotype Ontology …

Data-Driven Damage Initiation Criteria For Carbon Fiber Reinforced Polymer Composites, Alexander Richard Post

College of Computing and Digital Media Dissertations

Computational progressive failure analysis (PFA) is vital for the design, verification, and validation of carbon fiber reinforced polymer (CFRP) composites. However, the computational cost of PFA is usually high due to the complexity of the model. The damage initiation criterion is one of the essential components of a PFA code to determine the transition of a material’s state from pristine or microscopically damaged to macroscopically damaged. In this thesis, data-driven models are developed to determine the matrix damage initiation based on the Mohr-Coulomb model and Hashin model. For 2D plane stress states, the computational cost for determining damage initiation can …

Constructing Ethical Order Of Digital World, Guoyu Wang, Hong Mei

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the digital transformation and the construction of digital China, a digital world parallel to the real world is being born. Historically, numbers have shaped our perception of the world and human beings. Digital transformation will further expand human cognitive space and means, release productivity, and change the way people think and behave. However, the virtuality and "outof domain" characteristics of people and things in the digital world have caused ethical problems in the digital world. Therefore, it is urgent to construct and improve the ethical order of the digital world. The article summarizes the existing three paths of scientific …

Atp Synthase: Investigating Its In Vivo Rotation And Testing The Inhibitory Effects Of Diarylquinolines, Sarah Hassan Radwan

Archived Theses and Dissertations

No abstract provided.

Provenance: An Intermediary-Free Solution For Digital Content Verification, Bilal Yousuf, M. Atif Qureshi, Brendan Spillane, Gary Munnelly, Oisin Carroll, Matthew Runswick, Kirsty Park, Eileen Culloty, Owen Conlan, Jane Suiter

Articles

The threat posed by misinformation and disinformation is one of the defining challenges of the 21st century. Provenance is designed to help combat this threat by warning users when the content they are looking at may be misinformation or disinformation. It is also designed to improve media literacy among its users and ultimately reduce susceptibility to the threat among vulnerable groups within society. The Provenance browser plugin checks the content that users see on the Internet and social media and provides warnings in their browser or social media feed. Unlike similar plugins, which require human experts to provide evaluations and …

Topology And Ecology: Deducing States Of The Upper Mississippi River System, Killian Davis

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Interpolating Missing Data And Comparing Performance Of Common Interpolation Techniques From A 30-Year Water Quality Dataset, Wako Bungula, Danelle M. Larson Dr., Killian Davis, Richard Erickson Dr., Amber Lee, Casey Mckean, Frederick Miller, Alaina Stockdill, Enrika Hlavacek

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Mathematical Modelling And Simulation Using An Efficient Pinns Algorithm To Understand Spread Of Infection In Enclosed Spaces, Long Nguyen, Arkaprovo Ghosal, Rudra Nagalia, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Sentiment Analysis Of Radicalization On Social Media, Anna Singley

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

R Shiny's Self-Organizing Map, Zury Betzab Marroquin, Joshua Walsh, Trenton Wesley

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Mathematical Modeling And Analysis Of Covid-19 Epidemic With Vaccination, Caitlin Seibel, Tina Huang, Jackson Reisman, Erika Johanna Martinez Salinas, Viswanathan Arunachalam, Moatlhodi Kgosimore, Anuj Mubayi, Padmanabhan Seshaiyer, Allen Bone Sehunelo

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen

Mathematics, Physics, and Computer Science Faculty Articles and Research

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky …

Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments, Martinson Ofori

Masters Theses & Doctoral Dissertations

Traditional means of on-farm weed control mostly rely on manual labor. This process is time-consuming, costly, and contributes to major yield losses. Further, the conventional application of chemical weed control can be economically and environmentally inefficient. Site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. To solve this using computer vision, precision agriculture researchers have used remote sensing weed maps, but this has been largely ineffective for early season weed control due to problems such as solar reflectance and cloud cover in satellite imagery. With the current advances in artificial …

The Forestecology R Package For Fitting And Assessing Neighborhood Models Of The Effect Of Interspecific Competition On The Growth Of Trees, Albert Y. Kim, David N. Allen, Simon P. Couch

Statistical and Data Sciences: Faculty Publications

Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research …

Research Information Management In The United States: Part One, Findings And Recommendations, Rebecca Bryant, Jan Fransen, Pablo De Castro, Brenna Helmstutler, David Scherer

Copyright, Fair Use, Scholarly Communication, etc.

Research information management (RIM) is a rapidly growing area of investment in US research universities. RIM systems that support the collection and use of research outputs metadata have been in place for many years. Globally, the RIM ecosystem is quite mature in locales where national research assessment exercises like the United Kingdom’s Research Excellence Framework (REF) and the Excellence in Research for Australia (ERA) require institutions to collect and report on the outputs of institutional research. A pan-European community of practice is led by euroCRIS.

This report describes six discrete RIM use cases detailed in the companion report:

• Faculty …

Representation Learning On Multi-Layered Heterogeneous Network, Delvin Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Network data can often be represented in a multi-layered structure with rich semantics. One example is e-commerce data, containing user-user social network layer and item-item context layer, with cross-layer user-item interactions. Given the dual characters of homogeneity within each layer and heterogeneity across layers, we seek to learn node representations from such a multi-layered heterogeneous network while jointly preserving structural information and network semantics. In contrast, previous works on network embedding mainly focus on single-layered or homogeneous networks with one type of nodes and links. In this paper we propose intra- and cross-layer proximity concepts. Intra-layer proximity simulates propagation along …

Analytical Models For Traffic Congestion And Accident Analysis, Hongrui Liu, Rahul Ramachandra Shetty

Mineta Transportation Institute

In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records …

Topic Modeling For Multi-Aspect Listwise Comparison, Delvin Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

As a well-established probabilistic method, topic models seek to uncover latent semantics from plain text. In addition to having textual content, we observe that documents are usually compared in listwise rankings based on their content. For instance, world-wide countries are compared in an international ranking in terms of electricity production based on their national reports. Such document comparisons constitute additional information that reveal documents' relative similarities. Incorporating them into topic modeling could yield comparative topics that help to differentiate and rank documents. Furthermore, based on different comparison criteria, the observed document comparisons usually cover multiple aspects, each expressing a distinct …

The Negative Effects Of Cyberbullying Among Secondary School Adolescents In Tanzania, Hezron Zacharia Onditi, Jennifer Shapka

Journal of Humanities and Social Sciences

Cyberbullying and its associated consequences on children and adolescents has recently become a problem of global concern. Using phenomenological research design, this study explores the negative effects of cyberbullying on Tanzanian secondary school female and male adolescents. A total of 20 adolescents (50% female) in secondary schools (Form I to Form IV) who identified themselves as victims of cyberbullying were involved in the interview to share their lived experiences on the negative effects of cyberbullying. From thematic analysis, results indicated that, similar to their counterpart adolescents in the developed countries, Tanzanian female and male adolescents have experienced emotional, social, cognitive, …

Automated Data Processing: Making Community Indicators Possible For Lafayette, Indiana, Jace T. Newell, Eli W. Coltin, Eric D. Flaningam

The Journal of Purdue Undergraduate Research

No abstract provided.

Facilitating Team-Based Data Science: Lessons Learned From The Dsc-Wav Project, Chelsey Legacy, Andrew Zieffler, Benjamin S. Baumer, Valerie Barr, Nicholas J. Horton

Statistical and Data Sciences: Faculty Publications

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their …

Concept Drift Adaptation With Incremental–Decremental Svm, Honorius Gâlmeanu, Răzvan Andonie

Computer Science Faculty Scholarship

Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a …

Addressing The Learning Loss During The Covid-19 Pandemic Through The Adaptation Of Virtual Platforms, Nazrul I. Khandaker, Anika Nawar Mayeesha, Violeta Escandon Correa, Toralv Munro, Andrew Singh, Matthew Khargie, Ality Aghedo, Jasmin Budhan, Krishna Mahabir, Belal A. Sayeed

Publications and Research

The York College-hosted NASA MAA (MUREP AEROSPACE ACADEMY) has always played a pivotal role in minimizing the learning loss during the summer months, which was heightened during the pandemic. Support from AT&T, Con Edison and NASA enabled the MAA program at York College to offer a virtual STEM education with an earth science concentration to 1000 plus underserved K1-12 students from the community last summer, including 160 high school students. Two factors made this endeavor fruitful: allowing additional time to engage in STEM lessons and increasing self-motivation to successfully accomplish assigned tasks. Students built partnerships and resolved technical issues with …

Leveraging The Popularity Of Virtual Conferencing Due To The Covid-19 Pandemic To Create New Opportunities For Stem Education, Andrew Singh, Nazrul I. Khandaker, Violeta Escandon Correa, Omadevi Singh, Ariel Skobelsky, Farhan Tanvir, Brian Sukhnandan, Matthew Khargie, Elton Selby, Masud Ahmed

Publications and Research

Due to the COVID-19 pandemic, virtual learning has become a necessity for K9-16 education. Virtual classwork has been administered through platforms such as Google Classroom, Clever, and iReady. During the summer of 2021, the City University of New York (C.U.N.Y) York College campus hosted its NASA MAA MUREP (Minority University Research and Education Project Aerospace Academy) program virtually using a combination of Zoom, Google Docs, and even Canva, which some students requested as a more intuitive alternative to Microsoft PowerPoint. Students were mentored to use the scientific method to explore their interests in the STEM field, with a geoscience or …

Feature Engineering Vs Feature Selection Vs Hyperparameter Optimization In The Spotify Song Popularity Dataset, Alan Cueva Mora, Brendan Tierney

Conference Papers

Research in Featuring Engineering has been part of the data pre-processing phase of machine learning projects for many years. It can be challenging for new people working with machine learning to understand its importance along with various approaches to find an optimized model. This work uses the Spotify Song Popularity dataset to compare and evaluate Feature Engineering, Feature Selection and Hyperparameter Optimization. The result of this work will demonstrate Feature Engineering has a greater effect on model efficiency when compared to the alternative approaches.

Crest Or Trough? How Research Libraries Used Emerging Technologies To Survive The Pandemic, So Far, Scout Calvert

University of Nebraska-Lincoln Libraries: Faculty Publications

Introduction

In the first months of the COVID-19 pandemic, it was impossible to tell if we were at the crest of a wave of new transmissions, or a trough of a much larger wave, still yet to peak. As of this writing, as colleges and universities prepare for mostly in-person fall 2021 semesters, case counts in the United States are increasing again after a decline that coincided with easier access to the COVID vaccine. Plans for a return to campus made with confidence this spring may be in doubt, as we climb the curve of what is already the second …

Identification Of Factors Associated With Fume Events Using Text Mining And Data Mining Methods, Mary B. O'Connor

Doctoral Dissertations and Master's Theses

Pilots, flight attendants, and passengers can be exposed to toxic compounds when the bleed air that supplies the cabin and flight deck is contaminated with pyrolyzed hydraulic fluid or oil from turbine jet engines. These fume events occur sporadically and can result in acute or chronic exposure in air crews and can have catastrophic consequences if flight crew members become impaired or incapacitated. The purpose of this research was to explore unstructured textual data and identify important factors associated with these events. Models using machine learning algorithms were developed and tested using variables gleaned from the text mining process and …