Southeastern University - Lakeland
Bryant University
Southern Methodist University
James Madison University
Minnesota State University Moorhead
Georgia Southern University
Eastern Illinois University
Minnesota State University, Mankato
General Population Projection Model With Census Population Data,
2023
California State University, San Bernardino
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 …
Predictive Model For Cfpb Consumer Complaints,
2023
California State University, San Bernardino
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 …
Review Classification Using Natural Language Processing And Deep Learning,
2023
California State University – San Bernardino
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 …
The Psychological Science Accelerator's Covid-19 Rapid-Response Dataset,
2023
Harrisburg University of Science and Technology
The Psychological Science Accelerator's Covid-19 Rapid-Response Dataset, Erin M. Buchanan, Andree Hartanto
Research Collection School of Social Sciences
In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with …
Understanding Collective Performance: Human Factors And Team Science,
2023
Embry-Riddle Aeronautical University
Understanding Collective Performance: Human Factors And Team Science, Joseph Keebler
Math Department Colloquium Series
This talk will focus on modern issues with team science. Joe will discuss a variety of projects he's been involved with aimed at improving teamwork in complex sociotechnical systems including military, aviation, and healthcare. He will discuss major theoretical facets of teamwork and provide evidence-based best practices that were utilized to improve teams in applied settings.
Data Quality Checks: Implementation With Popular Data Collection Crowdsourcing Platforms,
2023
Kennesaw State University
Data Quality Checks: Implementation With Popular Data Collection Crowdsourcing Platforms, James Down, Gregory Balkcom, Kristine Duncan, Ngan (An) Truong, Andrew Lewis
Symposium of Student Scholars
The utilization of online crowdsourcing platforms for data collection has increased over the past two decades in the field of public health due to the ease of use, the cost-saving benefits, the speed of the data collection process, and the accessibility of a potentially true representative population. Although these platforms offer many advantages to researchers, significant drawbacks exist, such as poor data quality, that threaten the reliability and validity of the study. Previous studies have examined data quality concerns, but differences in results arise due to variations in study designs, disciplinary contexts, and the platforms being investigated. Therefore, this study …
Machine Learning In Minecraft: Proof Of Concept For Object Detection Oriented Autonomous Bots In Minecraft,
2023
Kennesaw State University
Machine Learning In Minecraft: Proof Of Concept For Object Detection Oriented Autonomous Bots In Minecraft, John Merkin
Symposium of Student Scholars
Machine learning provides new methods of problem solving through applied pattern recognition. An interesting challenge is to utilize machine learning in the automation of tasks and behaviors in virtual environments. Minecraft is an open-world, sandbox style game giving players nearly limitless freedom to alter a procedurally generated world. In the survival game mode, the player must collect resources to craft tools and build structures. The collection of resources can be tedious, so this project seeks to automate the standard initial task of collecting wood. By combining a convolutional neural network with API, a bot can collect resources while remaining scalable …
Repeated Games In The Presence Of Incomplete Information,
2023
Iran Banking Institute
Repeated Games In The Presence Of Incomplete Information, Reza Habibi
The Journal of Economics and Politics
There are many strategic situations at which a game theoretical framework should be used to analyze the equilibrium decisions at which the incomplete information annoy the process of deriving the certain rules for making decisions. In these cases, players use signals of each other's to get proper decisions. For example, in economic environment, some macro-economic latent variables induce incomplete information. Morris and Shin (2000) referred this type of game as global game and studied one-shot type of it. However, in practical situations, it is a type of repeated game. In the current paper, following notations of Morris and Shin (2000), …
The Impacts Of The Covid-19 Pandemic On Mental Health Across Different Genders And Sexualities,
2023
Miss Porter's School
The Impacts Of The Covid-19 Pandemic On Mental Health Across Different Genders And Sexualities, Jiale Zhu, Jonas Katona
Undergraduate Research Journal for the Human Sciences
Current studies report an increase in psychological distress as a result of the COVID-19 pandemic. This study is interested in examining mental health disparities and how the COVID-19 pandemic has disproportionately impacted marginalized groups—and more specifically, those identified by sex, gender, and sexuality—compared with the general population. This study also considers the effects and ramifications of different policy measures taken during the course of the pandemic. We perform exploratory data modeling and analysis on several important and publicly available datasets taken during the pandemic on mental health and COVID-19 infection data across various identity groups to look for significant disparities, …
Towards Robust Long-Form Text Generation Systems,
2023
University of Massachusetts Amherst
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Doctoral Dissertations
Text generation is an important emerging AI technology that has seen significant research advances in recent years. Due to its closeness to how humans communicate, mastering text generation technology can unlock several important applications such as intelligent chat-bots, creative writing assistance, or newer applications like task-agnostic few-shot learning. Most recently, the rapid scaling of large language models (LLMs) has resulted in systems like ChatGPT, capable of generating fluent, coherent and human-like text. However, despite their remarkable capabilities, LLMs still suffer from several limitations, particularly when generating long-form text. In particular, (1) long-form generated text is filled with factual inconsistencies to …
Foundations Of Node Representation Learning,
2023
University of Massachusetts Amherst
Foundations Of Node Representation Learning, Sudhanshu Chanpuriya
Doctoral Dissertations
Low-dimensional node representations, also called node embeddings, are a cornerstone in the modeling and analysis of complex networks. In recent years, advances in deep learning have spurred development of novel neural network-inspired methods for learning node representations which have largely surpassed classical 'spectral' embeddings in performance. Yet little work asks the central questions of this thesis: Why do these novel deep methods outperform their classical predecessors, and what are their limitations?
We pursue several paths to answering these questions. To further our understanding of deep embedding methods, we explore their relationship with spectral methods, which are better understood, and show …
Bayesian Structural Causal Inference With Probabilistic Programming,
2023
University of Massachusetts Amherst
Bayesian Structural Causal Inference With Probabilistic Programming, Sam A. Witty
Doctoral Dissertations
Reasoning about causal relationships is central to the human experience. This evokes a natural question in our pursuit of human-like artificial intelligence: how might we imbue intelligent systems with similar causal reasoning capabilities? Better yet, how might we imbue intelligent systems with the ability to learn cause and effect relationships from observation and experimentation? Unfortunately, reasoning about cause and effect requires more than just data: it also requires partial knowledge about data generating mechanisms. Given this need, our task then as computational scientists is to design data structures for representing partial causal knowledge, and algorithms for updating that knowledge in …
Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes,
2023
University of Massachusetts Amherst
Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe
Masters Theses
Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.
Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …
Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level,
2023
Louisiana State University and Agricultural and Mechanical College
Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken
LSU Master's Theses
Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …
Utilizing Non-Negative Least Squares For Data-Driven Discovery Of Dynamics,
2023
George Mason University
Utilizing Non-Negative Least Squares For Data-Driven Discovery Of Dynamics, Tracey G. Oellerich
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Incorporating Adaptive Human Behavior Into Epidemiological Models Using Equation Learning,
2023
Georgia Institute of Technology - Main Campus
Incorporating Adaptive Human Behavior Into Epidemiological Models Using Equation Learning, Austin Barton, Jordan Klein, Jonathan Greer, Kevin Flores, Patrick Haughey
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Parameter Estimation In Epidemiological And Climate Models Using Ensemble Smoothing With Multiple Data Assimilation,
2023
George Mason University
Parameter Estimation In Epidemiological And Climate Models Using Ensemble Smoothing With Multiple Data Assimilation, Emmanuel Fleurantin
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Critical Transitions In Mental Health: Van Gogh Case Study,
2023
University of Portland
Critical Transitions In Mental Health: Van Gogh Case Study, Anna Singley
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Bayesian Adaptive Smoothing For Activation Detection In Fmri,
2023
Illinois State University
Bayesian Adaptive Smoothing For Activation Detection In Fmri, Juan Florez
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
The Double Edged Sword Of The Pandemic: Exploring Associations Between Covid-19 And Social Isolation In The Usa,
2023
University of Kansas
The Double Edged Sword Of The Pandemic: Exploring Associations Between Covid-19 And Social Isolation In The Usa, Alexander Fulk
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
