Data Science Commons^™

Climate Change Impact On Bridge Scour Risk In Ny State: A Gis-Based Risk Analysis Model, Muhammad Hassan Butt

Publications and Research

Bridge scour, the primary cause of bridge failure in the United States, escalates post-severe storms, necessitating effective mitigation. This study employs a GIS-based risk analysis model to assess climate change's impact on bridge scour and associated risks in New York State. Data from the National Bridge Inventory, climate hazard maps, and geospatial data are integrated.

Unmasking Shadows: Unraveling Crime Patterns In Nyc's Boroughs, Jack Hachicho, Muhammad Hassan Butt

Publications and Research

New York City's crime dynamics have been on the rise for decades. Brooklyn and The Bronx have been disproportionately affected. This research aims to understand the crime landscape in these boroughs to formulate effective policies. Using crime data from official sources, statistical analyses, and data visualizations, the study identifies patterns and trends. The data encompasses over 400,000 reported incidents collected over the past 10 years, meticulously categorized by borough, crime type, and demographic information. Brooklyn has the highest overall crime rate, followed by The Bronx. Most shooting victims are Black. This highlights the need for holistic community programs to address …

Topic Modeling On Reddit Data For Analysing Mental Health Issues Among College Students, Qing Chen, Dezjeron Dorsey, Mia Mcguire, Patricia Medina, Mark Hughes

Publications and Research

Mental health issues among college students have become a prevalent concern, and understanding the main struggles and factors that correlate with poor mental health is essential for institutions to provide better support. This project aims to analyze mental health issues among college students using data from the Reddit platform. By leveraging the Reddit API and employing advanced natural language processing techniques, including BERTopics, we seek to gain insights into the emotional well-being and challenges faced by college students. Incorporating techniques such as embedding, Clustering, and Topic Creation enhances the depth and sophistication of our analysis of mental health issues among …

Les Expositions Turnus, Une Page D’Histoire Transnationale Des Beaux-Arts En Suisse À La Fin Du Xixe Siècle. Et Comment Découvrir Les Humanités Numériques, Béatrice Joyeux-Prunel

Artl@s Bulletin

Cet article présente le travail de la classe d’introduction aux humanités numériques de l’Université de Genève sur les expositions Turnus en Suisse à partir des années 1840. Près de 50 catalogues ont été retranscrits, décrits et structurés à l’aide de scripts Python, puis géolocalisés. Les données ont été ajoutées à BasArt, le répertoire mondial de catalogues d’expositions d’Artl@s (https://artlas.huma-num.fr/map). Elles permettent de mieux comprendre les premières années de ces expositions et leurs dynamiques locales, fédérales et internationales. Le Turnus fut une plaque tournante pour les artistes suisses, voire un tremplin vers le marché européen de l’art.

Development Of An App For The Kalamazoo Nature Center, Ernest Au

Honors Theses

Kalamazoo Nature Center (KNC), which has been recognized by its peers as one of the top nature centers in the country, is home to over 14 miles of hiking trails winding through woods, wetlands, and prairies. There are numerous places/plots in KNC that have an interesting and impressive history besides being home to a variety of animals and hundreds of wildflowers and other plant life. To improve the visitor’s experience at KNC, we will design a software app via the senior capstone project at the department of Computer Science at WMU. As the first step towards establishing a reference model …

Association Between Viral Infections And Glioma Risk: A Two-Sample Bidirectional Mendelian Randomization Analysis, Lin Pan, Jiaxin Ren, Fei Ren, Yifan Li, Haoqun Xie, Hongyu Chen, Davy Deng, Jie Lu, Hui Li, Bo Wu, Youqi Chen, Fei Peng, Vinay K Puduvalli, Ke Sai, Yunqian Li, Ye Cheng, Yonggao Mou

Faculty, Staff and Student Publications

Background: Glioma is one of the leading types of brain tumor, but few etiologic factors of primary glioma have been identified. Previous observational research has shown an association between viral infection and glioma risk. In this study, we used Mendelian randomization (MR) analysis to explore the direction and magnitude of the causal relationship between viral infection and glioma.

Methods: We conducted a two-sample bidirectional MR analysis using genome-wide association study (GWAS) data. Summary statistics data of glioma were collected from the largest meta-analysis GWAS, involving 12,488 cases and 18,169 controls. Single-nucleotide polymorphisms (SNPs) associated with exposures were used as instrumental …

Teaching Reproducibility To First Year College Students: Reflections From An Introductory Data Science Course, Brennan L. Bean

Journal on Empowering Teaching Excellence

Access the online Pressbooks version of this article here.

Modern technology threatens traditional modes of classroom assessment by providing students with automated ways to write essays and take exams. At the same time, modern technology continues to expand the accessibility of computational tools that promise to increase the potential scope and quality of class projects. This paper presents a case study where students are asked to complete a “reproducible” final project in an introductory data science course using the R programming language. A reproducible project is one where an instructor can easily regenerate the results and conclusions from the submitted …

Applications Of Causal Inference Methods For The Estimation Of Effects Of Bone Marrow Transplant And Prescription Drugs On Survival Of Aplastic Anemia Patients, Yesha M. Patel

Computational and Data Sciences (PhD) Dissertations

This dissertation provides an in-depth exploration into the treatment effectiveness for aplastic anemia using causal inference methods, structured around three pivotal research papers. Each paper contributes to a nuanced understanding of treatment impacts, specifically focusing on bone marrow transplantation (BMT) and prescription drugs, and the identification of optimal treatment strategies.

The first paper, "Causal Inference Analysis for Assessing the Effect of Bone Marrow Transplantation on the One-Year Survival of Adult and Pediatric Aplastic Anemia Patients," sets the foundation. It examines the short-term effectiveness of BMT in both adult and pediatric patients, providing crucial insights into how this treatment affects survival …

Random Variable Spaces: Mathematical Properties And An Extension To Programming Computable Functions, Mohammed Kurd-Misto

Computational and Data Sciences (PhD) Dissertations

This dissertation aims to extend the boundaries of Programming Computable Functions (PCF) by introducing a novel collection of categories referred to as Random Variable Spaces. Originating as a generalization of Quasi-Borel Spaces, Random Variable Spaces are rigorously defined as categories where objects are sets paired with a collection of random variables from an underlying measurable space. These spaces offer a theoretical foundation for extending PCF to natively handle stochastic elements.

The dissertation is structured into seven chapters that provide a multi-disciplinary background, from PCF and Measure Theory to Category Theory with special attention to Monads and the Giry Monad. The …

Wavelet Compression As An Observational Operator In Data Assimilation Systems For Sea Surface Temperature, Bradley J. Sciacca

LSU New Orleans Theses and Dissertations

The ocean remains severely under-observed, in part due to its sheer size. Containing nearly billion of water with most of the subsurface being invisible because water is extremely difficult to penetrate using electromagnetic radiation, as is typically used by satellite measuring instruments. For this reason, most observations of the ocean have very low spatial-temporal coverage to get a broad capture of the ocean’s features. However, recent “dense but patchy” data have increased the availability of high-resolution – low spatial coverage observations. These novel data sets have motivated research into multi-scale data assimilation methods. Here, we demonstrate a new assimilation approach …

Motion Simulation And Risk Assessment Of Dropped Objects In Offshore Operations, Hanqi Yu

LSU New Orleans Theses and Dissertations

Subsea pipelines are a cost-effective and reliable way to transport hydrocarbons in offshore oil and gas development. However, dropped objects can pose hazards and cause damage to pipelines. This dissertation briefly introduces the hazards and hidden dangers caused by dropped containers, discusses motion simulation methods for different object shapes, and reviews risk assessment procedures for handling dropped objects in offshore operations. Ongoing research at the University of New Orleans' towing tank on dropped container models is also discussed. Using ANSYS Fluent, we simulate trajectories of container models and calculate relevant hydrodynamic coefficients for different dropped angles. We then apply risk …

Making Data Meaningful: Stakeholder Perceptions On Data Visualization And Data Management Practices Within A Multi-Tiered System Of Supports (Mtss), Domenick Saia

Dissertations

Data-driven decision-making and collaboration are core pillars of a multi-tiered system of supports (MTSS); however, timely and accessible data use, as well as data literacy and visualization literacy skills, are challenges school leaders and educators face related to implementing such frameworks. I hypothesized efficient data management systems and data visualization tools enable school teams to predict student learning outcomes, readily communicate, and better understand student data. The purpose of this study design was to highlight a need for more efficient data structures that allow school stakeholders to balance their roles within an MTSS framework more effectively. The context of this …

Review Classification Using Natural Language Processing And Deep Learning, Brian Nazareth

Electronic Theses, Projects, and Dissertations

Sentiment Analysis is an ongoing research in the field of Natural Language Processing (NLP). In this project, I will evaluate my testing against an Amazon Reviews Dataset, which contains more than 100 thousand reviews from customers. This project classifies the reviews using three methods – using a sentiment score by comparing the words of the reviews based on every positive and negative word that appears in the text with the Opinion Lexicon dataset, by considering the text’s variating sentiment polarity scores with a Python library called TextBlob, and with the help of neural network training. I have created a neural …

General Population Projection Model With Census Population Data, Takenori Tsuruga

Electronic Theses, Projects, and Dissertations

The US Census Bureau offers a wide range of data, and within this array, the American Community Survey 5-Year Estimate (ACS5) serves as a valuable resource for understanding the US population. This project embarks on an exploration of Machine Learning and the Software Development process with the goal of generating effective population projections from ACS5 data. The project aims to provide methods to make predictions for every city and town in the US, encompassing their total population and population divided into 5-year age groups. It's worth noting that while the generation of these projections is grounded in the generalized statistical …

The Effect Of The Number Of Hidden Layers On The Performance Of Deep Q-Network For Traveling Salesman Problem, Benzfica Hanif, Aisyah Larasati, Rudi Nurdiansyah, Trung Le

Knowledge Engineering and Data Science

The Traveling Salesman Problem (TSP) effectively represents the complex distribution issues encountered by couriers, who must carefully plan a route that includes all customer addresses while minimizing the distance traveled. As the magnitude of deliveries and the range of destinations expand, the courier's responsibility becomes progressively challenging. In this particular context, the objective of our research is to expand the existing knowledge and explore the complete capabilities of Deep Q-Network (DQN) models in order to achieve the most efficient route determination. This endeavor can potentially bring about significant changes in the courier and delivery service sector. The foundation of our …

Stacked Lstm-Gru Long-Term Forecasting Model For Indonesian Islamic Banks, Yayat Sujatna, Adhitio Satyo Bayangkari Karno, Widi Hastomo, Nia Yuningsih, Dody Arif, Sri Setya Handayani, Aqwam Rosadi Kardian, Ire Puspa Wardhani, L.M Rasdi Rere

Knowledge Engineering and Data Science

The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of …

Exploration And Statistical Modeling Of Profit, Caleb Gibson

Undergraduate Honors Theses

For any company involved in sales, maximization of profit is the driving force that guides all decision-making. Many factors can influence how profitable a company can be, including external factors like changes in inflation or consumer demand or internal factors like pricing and product cost. Understanding specific trends in one's own internal data, a company can readily identify problem areas or potential growth opportunities to help increase profitability.

In this discussion, we use an extensive data set to examine how a company might analyze their own data to identify potential changes the company might investigate to drive better performance. Based …

Predictive Model For Cfpb Consumer Complaints, Vyshnavi Nalluri

Electronic Theses, Projects, and Dissertations

Within the dynamic and highly competitive financial industry, the timely and efficient resolution of customer complaints stands as a central challenge, particularly in the intricate domain of mortgage services. The traditional processes for handling these complaints have long been recognized as laborious and resource-intensive, a situation that financial institutions, including the esteemed Wells Fargo, are keen to improve.

Currently, the industry largely relies on basic data analytics for identifying trends in customer complaints. However, this approach has its limitations, especially when dealing with complaints within the mortgage services domain. In response to this challenge, this research advocates the adoption of …

Addressing The Analytical And Computational Challenges Using Machine Learning In Biomedical Research, Yizhuo Wang

Dissertations and Theses (Open Access)

In the contemporary healthcare field, professionals are confronted with an ever-growing volume of clinical data stored in electronic health records, alongside the genomic data stemming from laboratory experiments. As a response to this deluge of data, the application of machine learning (ML) techniques is gaining popularity since ML techniques have demonstrated an exceptional proficiency in processing big data and deciphering complex nonlinear patterns that are intrinsic to biomedical research.

My research leverages ML's capabilities to address the computational challenges spanning diverse areas, including adaptive clinical trial designs, survival analysis, and high-dimensional genetic data analysis. Specifically, Chapter 2 focused on the …

Selecting Patient-Reported Outcome Measures For A Patient-Facing Technology

Faculty, Staff and Student Publications

OBJECTIVE: This article provides insight into our process and considerations for selecting patient-reported outcome measures (PROMs) designed for self-reporting symptoms and quality-of-life among breast cancer (BCA) patients undergoing oral anticancer agent treatment via a patient-facing technology (PFT) platform.

METHODS: Following established guidelines, we conducted a thorough assessment of a specific set of PROMs, comparing their content to identify the most suitable options for studying BCA patients.

RESULTS: We recommend utilizing the combination of EORTC QLQ-C30 + EORTC QLQ-BR45 as the preferred instrument, especially when developing a dedicated "breast cancer-only" application.

DISCUSSION: When developing and maintaining a dashboard for a PFT …