Data Science Commons^™

Benefits Of The Snakemake Workflow Management Software In Comparision To Traditional Programming (Presentation), Josh Loecker

Honors Capstone Projects

No abstract provided.

Application Of Machine Learning Techniques To Forecast Harmful Algal Blooms In Gulf Of Mexico, Bala Tripura Sundari Yerrapothu

Master's Theses

The Harmful Algal Blooms (HABs) forecast is crucial for the mitigation of health hazards and to inform actions for the protection of ecosystems and fisheries in the Gulf of Mexico (GoM). For the sake of simplicity of our application we assume ocean color satellite imagery from the National Oceanic and Atmospheric Administration as a proxy for HABs.

In this study we use a deep neural network trained on the 2-Dimensional time series proxy data to provide a forecast of the HABs’ manifestations in the GoM.Our approach analyzes between both spatial and temporal features simultaneously. In addition, the network also helps …

A Fully-Automated, Deep Learning-Based Framework For Ct-Based Localization, Segmentation, Verification And Planning Of Metastatic Vertebrae, Tucker Netherton, Tucker James Netherton

Dissertations and Theses (Open Access)

Palliative radiotherapy is an effective treatment for the palliation of symptoms caused by vertebral metastases. Visible evidence of disease is localized on medical images as part of the treatment planning process. However, complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with wrong level treatments of the spine. In addition, erroneous manual contouring of anatomic structures is a major failure mode in radiotherapy treatment planning.

The purpose of this study is to mitigate the challenges associated with treatment planning of the spine by automating the treatment planning process for three-dimensional conformal …

Improving Treatment Of Local Liver Ablation Therapy With Deep Learning And Biomechanical Modeling, Brian Anderson, Kristy Brock, Laurence Court, Carlos Eduardo Cardenas, Erik Cressman, Ankit Patel

Dissertations and Theses (Open Access)

In the United States, colorectal cancer is the third most diagnosed cancer, and 60-70% of patients will develop liver metastasis. While surgical liver resection of metastasis is the standard of care for treatment with curative intent, it is only avai lable to about 20% of patients. For patients who are not surgical candidates, local percutaneous ablation therapy (PTA) has been shown to have a similar 5-year overall survival rate. However, PTA can be a challenging procedure, largely due to spatial uncertainties in the localization of the ablation probe, and in measuring the delivered ablation margin.

For this work, we hypothesized …

Using Deep Learning To Automate The Diagnosis Of Skin Melanoma, Akhil Reddy Alasandagutti

Honors Theses

Machine learning and image processing techniques have been widely implemented in the field of medicine to help accurately diagnose a multitude of medical conditions. The automated diagnosis of skin melanoma is one such instance. However, a majority of the successful machine learning models that have been implemented in the past have used deep learning approaches where only raw image data has been utilized to train machine learning models, such as neural networks. While they have been quite effective at predicting the condition of these lesions, they lack key information about the images, such as clinical data, and features that medical …

Statistical Analysis Of 2017-18 Premier League Match Statistics Using A Regression Analysis In R, Bergen Campbell

Undergraduate Theses and Capstone Projects

This thesis analyzes the correlation between a team’s statistics and the success of their performances, and develops a predictive model that can be used to forecast final season results for that team. Data from the 2017-2018 Premier League season is to be gathered and broken down within R to highlight what factors and variables are largely contributing to the success or downfall of a team. A multiple linear regression model and stepwise selection process is then used to include any factors that are significant in predicting in match results.

The predictions about the 17-18 season results based on the model …

Thruster Communication For Subsurface Environments; Turning Waste Noise Into Useful Data, Stephen Cronin

Doctoral Dissertations and Master's Theses

Acoustic communication serves as one of the primary means of wirelessly communicating underwater. Whereas much of the developments in the field of wireless communication have focused on radio frequency technology, water highly absorbs radio waves rendering the link not feasible for most all subsurface operations. While acoustic links have enabled new capabilities for systems operating in this challenging environment, it has yet to reach the commodity availability of radio systems, meaning that an entire class of small, low-cost systems have been unable to make use of these links. The systems in question are primarily autonomous underwater vehicles (AUVs), as they …

A Smarter Way To Manage Mass Transit In A Smart City: Rail Network Management At Singapore’S Land Transport Authority, Steven M. Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

There is no widely agreed upon definition of a supposed “Smart City.” Yet, when you see city employees — in this case city-state employees — working in what are obviously smarter ways, “you know it when you see it.” One such example of a smarter way to work in a smart city setting is the way that employees of the Land Transport Authority (LTA) in Singapore are using a new generation of data driven, AI-enabled support systems to manage the city’s urban rail network. We spoke to LTA officers Kong Wai, Ho (Director of Integrated Operations and Planning) and Chris …

Discovering Kepler’S Third Law From Planetary Data, Boyan Kostadinov, Satyanand Singh

Publications and Research

In this data-inspired project, we illustrate how Kepler’s Third Law of Planetary Motion can be discovered from fitting a power model to real planetary data obtained from NASA, using regression modeling. The power model can be linearized, thus we can use linear regression to fit the model parameters to the data, but we also show how a non-linear regression can be implemented, using the R programming language. Our work also illustrates how the linear least squares used for fitting the power model can be implemented in Desmos, which could serve as the computational foundation for this project at a lower …

Unsupervised And Supervised Learning For Rna-Protein Interactions And Annotations, Kateland Sipe

Honors Projects

This project analyzed the base and amino acid interactions and annotations through the use of unsupervised and supervised learning techniques. For unsupervised learning, clustering found the data was not able to be distinguished into clear groups which matched the original annotations through kmeans clustering and hierarchical clustering. For supervised learning, the use of random forest, glmnet, and deep learning neural networks were successful in creating accurate predictions. However, machine learning likely will not be able to replace the original complex program, but could be used for possible simplification.

382— Wiyn Open Cluster Study: Ubvri Photometry Of Ngc 2204, Kylie Snyder, Dante Scarazzini

GREAT Day Posters

The purpose of this project was to study the open star cluster NGC2204 using images taken at Kitt Peak National Observatory using the WIYN 0.9m telescope. These images were analyzed photometrically with the intention of determining the reddening, metallicity, age, and distance modulus of the star cluster. Each image was analyzed using software that determined the point spread function and applied that function to determine the magnitude of each star in that image. These magnitudes were taken for each filter, UBVRI, and then combined and averaged to create a single catalog. Standard stars, taken on the same night, were used …

An Exploratory Analysis Of The Bgsu Learning Commons Student Usage Data, Emily Eskuri

Honors Projects

The purpose of this study was to explore past student usage data in individualized tutoring sessions from the Learning Commons from two academic years. The Bowling Green State University (BGSU) Learning Commons is a learning assistance center that offers various services, such as individualized tutoring, math assistance, writing assistance, study hours, and academic coaching. There have been limited research studies into how big data and analytics can have an impact in higher education, especially research utilizing predictive analytics.

This project applied analytics to individualized tutoring data in the Learning Commons to create a better understanding of why those trends happen …

Integrating Common Data Analytics Tools Into Non-Technical Undergraduate Curricula, Kurt Kirstein

All Faculty Scholarship for the College of Education and Professional Studies

Aside from statistics courses, accessible data analytics skills are often excluded from traditional non-technical university programs. These are topics that are typically the domain of programs that focus on math, statistics and computer science. Yet the need for these skills in non-technical disciplines is changing. A rapid expansion of data-related processes in organizations of many types requires individuals who have at least a working knowledge of common analytic tools. This article briefly describes three categories of data analytics tools that can be useful for graduates in any discipline. The first category covers descriptive tools that allow students to learn what …

Exploring Ai And Multiplayer In Java, Ronni Kurtzhals

Student Academic Conference

I conducted research into three topics: artificial intelligence, package deployment, and multiplayer servers in Java. This research came together to form my project presentation on the implementation of these topics, which I felt accurately demonstrated the various things I have learned from my courses at Moorhead State University. Several resources were consulted throughout the project, including the work of W3Schools and StackOverflow as well as relevant assignments and textbooks from previous classes. I found this project relevant to computer science and information systems for several reasons, such as the AI component and use of SQL data tables; but it was …

Oit Web App, Stanley Ritsema

Honors Theses

The goal of this project was to create a web app to assist WMU’s help desk in handling various user issues relating to Office365 and WebEx. The three issues of unblocking email, enabling live-streaming, and changing the URL of a personal meeting room all require administrative access but the OIT department wanted to empower front desk staff to handle such requests. The project was designed using a web server that takes user input from a help desk employee and executes java functions that make API calls. In the end, we were able to successfully create this proof-of-concept prototype for two …

Automl For Anomaly Detection Of Time Series And Sequences Of Short Text, Cynthia Freeman

Computer Science ETDs

Automated approaches for parameter and algorithm selection greatly democratize fields such as machine learning, saving time and money as hiring experts can be prohibitively expensive. Unfortunately, anomaly detection is difficult to automate due to subjectivity and class imbalance. An anomaly detection system is presented that incorporates human-in-the-loop techniques and is dynamic, scalable, and able to work with non-annotated data. By focusing on meta-features of the input data, the system can intelligently choose the most promising anomaly detection methods. The system is agnostic to the medium of data; it only expects the data to be sequential in nature.

Implementing A Registry Federation For Materials Science Data Discovery, Raymond L. Plante, Chandler A. Becker, Andrea Medina-Smith, Kevin Brady, Alden Dima, Benjamin Long, Laura M. Bartolo, James A. Warren, Robert J. Hanisch

Copyright, Fair Use, Scholarly Communication, etc.

As a result of a number of national initiatives, we are seeing rapid growth in the data important to materials science that are available over the web. Consequently, it is becoming increasingly difficult for researchers to learn what data are available and how to access them. To address this problem, the Research Data Alliance (RDA) Working Group for International Materials Science Registries (IMRR) was established to bring together materials science and information technology experts to develop an international federation of registries that can be used for global discovery of data resources for materials science. A resource registry collects high-level metadata …

B31: Identifying New G Protein Coupled Receptor Kinase 2 And 3 Substrates Among Proteins Closely Linked To Breast Cancer With Positive Prognosis, Theresa Tran

Annual Research Symposium

No abstract provided.

Simulated Contact Tracing Of Covid-19 Propagation At Kutztown University For Fall 2020, Dale E. Parson

Computer Science and Information Technology Faculty

From mid-May through August 2020 the author designed, built, revised, and analyzed resulting data from two simulation programs for virtual contact tracing of COVID-19 infection propagation at Kutztown University in the fall 2020 semester. The first was command-line driven and non-graphical, with results distributed to faculty and administrators on May 28. The second was a three-dimensional interactive graphical simulation, distributed to faculty, administrators, and the public as a narrated video via YouTube on July 16. The algorithm is an adaptation of spreading activation as used in theoretical psychology and artificial intelligence research since the 1970s. It propagates discrete, probable infections …

The Agnostic Structure Of Data Science Methods, Domenico Napoletani, Marco Panza, Daniele Struppa

MPP Published Research

In this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by `forcing’, i.e. on the basis of their ability to reorganize the data for further analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within …