Social and Behavioral Sciences Commons^™

Planetary Exploration Via Fully Automatic Topological Structure Extraction Using Adaptive Resonance, Jonathan Kissi

Electronic Thesis and Dissertation Repository

Renewed interest in Solar System exploration, along with ongoing improvements in computing, robotics and instrumentation technologies, have reinforced the case for remote science acquisition systems development in space exploration. Testing systems and procedures that allow for autonomously collected science has been the focus of analogue field deployments and mission planning for some time, with such systems becoming more relevant as missions increase in complexity and ambition. The introduction of lidar and laser scanning-type instruments into the geological and planetary sciences has proven popular, and, just as with the established image and photogrammetric methods, has found widespread use in several research …

Essays In Aging, Marital Stability, And Mental Health, Avigyan Sengupta

Theses and Dissertations

This dissertation presents three chapters about understudied characteristics of the older population. As the United States and other developed countries' populations age, more dedicated research is needed to understand and implement policies to improve the welfare of this demographic group. Though there is a vast literature on various life-cycle outcomes of the elderly, gaps remain. Two such aspects have been examined here: marital stability and mental health.

Chapter 1 investigates how changes in household wealth affect the likelihood of divorce among older adults aged 50 and above in the United States. Using panel data from the Health and Retirement Study …

Artificial Intelligence's Ability To Detect Online Predators, Olatilewa Osifeso

Electronic Theses, Projects, and Dissertations

Online child predators pose a danger to children who use the Internet. Children fall victim to online predators at an alarming rate, based on the data from the National Center of Missing and Exploited Children. When making online profiles and joining websites, you only need a name, an email and a password without identity verification. Studies have shown that online predators use a variety of methods and tools to manipulate and exploit children, such as blackmail, coercion, flattery, and deception. These issues have created an opportunity for skilled online predators to have fewer obstacles when it comes to contacting and …

Using Natural Language Processing To Understand The Lived Experiences Of People Identifying With Adhd: What Themes Emerge In Social Media Posts?, Gabby C. Scalzo

Theses and Dissertations

Compared to the amount of research conducted on how to identify and understand children with Attention-Deficit Hyperactivity Disorder (ADHD), there has been relatively little work done to understand the lived experiences of adults with ADHD. Increased understanding of how adults with ADHD conceptualize themselves in the context of their diagnosis would help clinical experts tailor research and treatments to better serve these communities. However, there are several barriers towards conducting high-quality qualitative research, including time- and labor-intensity. This study, informed by qualitative research traditions, used innovative data sources (i.e., social media) and analytic techniques (i.e., machine learning) to reduce these …

Pmt, Cbt, And Hybrid Models: Is Machine Learning The Future Of Poverty Targeting?, Luca Orion Heidelberg

Senior Projects Spring 2024

Addressing poverty in developing countries without relying on income data is becoming increasingly important, particularly since the Covid-19 pandemic has exacerbated the issue farther than anyone expected. This paper reviews literature on various poverty targeting models including Proxy Means Test, Community-Based Targeting, hybrid models, and machine learning-based models in hopes of finding the best method. The findings highlight the importance of model parameters, particularly in PMT, also revealing that the number of potential beneficiaries analyzed and number of indicators utilized can influence the targeting accuracy. CBT incorporates community involvement in the poverty targeting process at a lower cost than PMT, …

Unraveling Water Quality Issues In The Colorado River Basin: Utilizing Remote Sensing Satellite Images, Statistical, And Machine Learning For Improved Monitoring, Godson Ebenezer Adjovu

UNLV Theses, Dissertations, Professional Papers, and Capstones

This research was aimed at exploring innovative and cost-effective tools in understanding the spatiotemporal variability of water quality parameters in the Colorado River Basin (CRB), which includes the Colorado River and major reservoirs and lakes in the USA including Lake Mead. The river which arises in the state of Colorado and empties into the Republic of Mexico at the Gulf of California, is a source of water to seven US states and the Republic of Mexico and provides water to about 40 million people and million acres of farmlands in seven states in the western US and the Republic of …

Statistical And Biological Analyses Of Acoustic Signals In Estrildid Finches, Moises Rivera

Dissertations, Theses, and Capstone Projects

Acoustic communication is a process that involves auditory perception and signal processing. Discrimination and recognition further require cognitive processes and supporting mechanisms in order to successfully identify and appropriately respond to signal senders. Although acoustic communication is common across birds, classical research has largely disregarded the perceptual abilities of perinatal altricial taxa. Chapter 1 reviews the literature of perinatal acoustic stimulation in birds, highlighting the disproportionate focus on precocial birds (e.g., chickens, ducks, quails). The long-held belief that altricial birds were incapable of acoustic perception in ovo was only recently overturned, as researchers began to find behavioral and physiological evidence …

Investigating English-Language Dialect-Adjusted Models, Samiha Datta

Computer Science Senior Theses

This thesis describes several approaches to better understand how large language models interpret different dialects of the English language. Our goal is to consider multiple contexts of textual data and to analyze how English-language dialects are realized in them, as well as how a variety of machine learning techniques handle these differences. We focus on two genres of text data: news and social media. In the news context, we establish a dataset covering news articles from five countries and four US states and consider language modeling analysis, topic and sentiment distributions, and manual analysis before performing nine experiments and evaluating …

Analyzing The Impact Of Automation On Employment In Different Us Regions: A Data-Driven Approach, Thejaas Balasubramanian

Electronic Theses, Projects, and Dissertations

Automation is transforming the US workforce with the increasing prevalence of technologies like robotics, artificial intelligence, and machine learning. As a result, it is essential to understand how this shift will impact the labor market and prepare for its effects. This culminating experience project aimed to examine the influence of computerization on jobs in the United States and answer the following research questions: Q1. What factors affect how likely different jobs will be automated? Q2. What are the possible effects of automation on the US workforce across states and industries? Q3. What are the meaningful predictors of the likelihood of …

Examining Factors Related To Tobacco Treatment Engagement Among Tobacco Dependent Black/African American; Hispanic/Latino Cancer Patients: An Analysis Of Memorial Sloan Kettering Cancer Center’S Tobacco Treatment Program, Gleneara E. Bates-Pappas

Dissertations, Theses, and Capstone Projects

Among patients diagnosed with cancer, persistent tobacco use is associated with adverse clinical outcomes such as worse treatment side effects, decreased effectiveness of cancer treatment (chemotherapy, radiotherapy, and surgery), all increasing risk of recurrence, second primary cancers, and poor survival. Despite the clinical importance of tobacco cessation in the context of high quality cancer care, engaging Black/African American and Hispanic/Latino cancer patients in tobacco treatment programs can be challenging. Prior studies with the general adult population demonstrate that Black/African American and Hispanic/Latino smokers are referred to and accept tobacco cessation treatment at lower rates compared to non-Hispanic White smokers. This …

A Machine Learning Approach To Deepfake Detection, Delaney Conrad

All Undergraduate Theses and Capstone Projects

The ability to manipulate videos has been around for decades but a process that once would take time, money, and professionals, can now be created by anyone due to the rapid advancement of deepfake technology. Deepfakes use deep learning artificial intelligence to make fake digital content, typically in the form of swapping a person’s face in a video or image. This technology could easily threaten and manipulate individuals, corporations, and political organizations, so it is essential to find methods for detecting deepfakes. As the technology for creating deepfakes continues to improve, these manipulated videos are becoming increasingly undetectable. It is …

The Basil Technique: Bias Adaptive Statistical Inference Learning Agents For Learning From Human Feedback, Jonathan Indigo Watson

Theses and Dissertations--Computer Science

We introduce a novel approach for learning behaviors using human-provided feedback that is subject to systematic bias. Our method, known as BASIL, models the feedback signal as a combination of a heuristic evaluation of an action's utility and a probabilistically-drawn bias value, characterized by unknown parameters. We present both the general framework for our technique and specific algorithms for biases drawn from a normal distribution. We evaluate our approach across various environments and tasks, comparing it to interactive and non-interactive machine learning methods, including deep learning techniques, using human trainers and a synthetic oracle with feedback distorted to varying degrees. …

Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat

MSU Graduate Theses

Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …

Optimal Portfolio Construction For Oil-Based Sovereign Wealth Funds, Mohammed Saeed Alshowaikhat

CGU Theses & Dissertations

Chapter 1 of this dissertation delves into the economic challenges faced by oil-exporting countries that rely heavily on a single income source, with a particular focus on Saudi Arabia as a case study. The primary objective is to examine the efforts of Saudi Arabia's sovereign wealth fund in diversifying revenue streams and mitigating risks associated with an excessive dependence on oil. To achieve this, the study proposes an adaptation of the subset-optimization algorithm within the mean-variance model, aiming to enhance portfolio construction in sovereign wealth funds. Chapter 2 of the dissertation conducts a comparative analysis between portfolios constructed using the …

Applying Data Science And Machine Learning To Understand Health Care Transition For Adolescents And Emerging Adults With Special Health Care Needs, Lisamarie Turk

Nursing ETDs

A problem of classification places adolescents and emerging adults with special health care needs among the most at risk for poor or life-threatening health outcomes. This preliminary proof-of-concept study was conducted to determine if phenotypes of health care transition (HCT) for this vulnerable population could be established. Such phenotypes could support development of future studies that require data classifications as input. Mining of electronic health record data and cluster analysis were implemented to identify phenotypes. Subsequently, a machine learning concept model was developed for predicting acute care and medical condition severity. Three clusters were identified and described (Cluster 1, n …

Precision Weed Management Based On Uas Image Streams, Machine Learning, And Pwm Sprayers, Jason Allen Davis

Graduate Theses and Dissertations

Weed populations in agricultural production fields are often scattered and unevenly distributed; however, herbicides are broadcast across fields evenly. Although effective, in the case of post-emergent herbicides, exceedingly more pesticides are used than necessary. A novel weed detection and control workflow was evaluated targeting Palmer amaranth in soybean (Glycine max) fields. High spatial resolution (0.4 cm) unmanned aircraft system (UAS) image streams were collected, annotated, and used to train 16 object detection convolutional neural networks (CNNs; RetinaNet, Faster R-CNN, Single Shot Detector, and YOLO v3) each trained on imagery with 0.4, 0.6, 0.8, and 1.2 cm spatial resolutions. Models were …

A New Comprehensive And Practical Taxonomy Of Demands Healthcare Professionals Experience: The Development Process And Testing Using Machine Learning, Phoebe Xoxakos

All Dissertations

Given the complex (Ratnapalan & Lang, 2020) and high stress environment of healthcare organizations (Freshwater & Cahill, 2010), a better understanding of the conditions in which healthcare professionals work is important. Although previous research has resulted in somewhat limited categories of the demands on healthcare professionals (Borteyrou et al., 2014; Shanafelt et al., 2020), a comprehensive taxonomy that covers the breadth and depth of demands is lacking. Using longitudinal data collected over 28 measurement waves spanning two years during the COVID-19 pandemic, the present studies outline the development of a taxonomy based on an in-depth literature review of related workplace …

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

Transportation Mode Choice Behavior In The Era Of Autonomous Vehicles: The Application Of Discrete Choice Modeling And Machine Learning, Sangwan Lee

Dissertations and Theses

New mobility technologies, such as shared mobility services (e.g., car-sharing) and, more importantly, autonomous vehicles (AVs), continue to evolve. The supply-side advancement will likely disrupt and transform transportation mode choice behaviors, and create a new paradigm since they are emerging and becoming increasingly feasible alternatives to the existing modes of transportation. Accordingly, this dissertation employs discrete choice modeling (DCM) and machine learning (ML) using a U.S. nationwide stated choice experiment to understand how travelers adopt new transportation modes or continue to use conventional modes of transportation.

This dissertation consists of three papers. The first examines future market shares of each …

Exploring The Effectiveness Of Multiple-Exemplar Training For Visual Analysis Of Ab-Design Graphs, Verena S. Bethke

Dissertations, Theses, and Capstone Projects

In behavior analysis, data are usually analyzed using visual analysis of the graphed data. There are a wide range of methods used to visually analyze data, from a basic ‘textbook’ style approach to the use of visual aids, decision-rubrics, and computer-based approaches. In the literature, there have been some comparisons of the efficacy of different approaches. Visual analysis as a behavior can be taught using a variety of methods, independent of how the skill itself is to be performed. Teaching methods include lecture, online instruction, and equivalence-based instruction. There is not much research on the teaching of visual analysis specifically, …

A Machine Learning Approach To Text-Based Sarcasm Detection, Lara I. Novic

Dissertations, Theses, and Capstone Projects

Sarcasm and indirect language are commonplace for humans to produce and recognize but difficult for machines to detect. While artificial intelligence can accurately analyze sentiment and emotion in speech and text, it may struggle with insincere and sardonic content, although it is possible to train a machine to identify uttered and written sarcasm. This paper aims to detect sarcasm using logistic regression and a support vector machine (SVM) and compare their results to a baseline.

The models are trained on headlines from a Kaggle dataset containing headlines from the satirical news website The Onion and serious news website Huffpost (formerly …

Modelling And Forecasting Methods In Financial Economics, Shuo Gao

Dissertations, Theses, and Capstone Projects

This dissertation consists of three chapters.

Chapter 1: Behavioral heterogeneity among investors has been shown to explain the volatile nature of stock markets. In this chapter, I investigate the different behaviors of investors by proposing a heterogeneous agent model based on Chiarella et al. (2012) which involves fundamentalists, chartists, and noise traders with two-state hidden-Markov regime switching expectations. By applying the S&P 500 and CPI data from January 1990 to December 2020, the model shows strong evidence of behavioral heterogeneity among different groups of traders. After an in-sample backtesting and out-of-sample forecasting which further evaluate the capability of the model, …

Predicting Gross Metropolitan Product Worldwide Using Statistical Learning Models, Socio-Economic, And Satellite Imagery Data, Simin Joshaghani

Boise State University Theses and Dissertations

Gross metropolitan product (GMP) is one the most critical indicators for determining a metropolitan area’s economic performance. While GMP data currently exists for major cities in the US and OECD countries, the rest of the world is a blind spot. This study aims at estimating the GMP of 1289 cities in non-US and OECD countries, where no official city-level statistics are produced. We perform this estimation through multiple machine learning models, using night-time lights satellite imagery, and other publicly available data. We analyze eight spatial databases and four cross-sectional datasets and derive a feature vector of covariates through various techniques, …

Modeling And Analysis Of Subcellular Protein Localization In Hyper-Dimensional Fluorescent Microscopy Images Using Deep Learning Methods, Yang Jiao

UNLV Theses, Dissertations, Professional Papers, and Capstones

Hyper-dimensional images are informative and become increasingly common in biomedical research. However, the machine learning methods of studying and processing the hyper-dimensional images are underdeveloped. Most of the methods only model the mapping functions between input and output by focusing on the spatial relationship, whereas neglect the temporal and causal relationships. In many cases, the spatial, temporal, and causal relationships are correlated and become a relationship complex. Therefore, only modeling the spatial relationship may result in inaccurate mapping function modeling and lead to undesired output. Despite the importance, there are multiple challenges on modeling the relationship complex, including the model …

Implicit Cost Of Retaliatory Tariffs By Mexico On U.S. Cheese Export, Pengyan Sun

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Mexico imposed retaliatory tariffs on U.S. cheeses ranging from 20 to 25 percent in July 2018. In order to provide valuable information for the government and farmers, my research estimated the implicit cost of retaliatory tariffs by Mexico on U.S. cheese exports. In particular, I estimate the difference between the forecasted value of cheese exported to Mexico and the actual value of cheese exported to Mexico using four different models. The total impact to the U.S. economy from the losses due to retaliatory tariffs was assessed by IMPLAN, an input/output model. The results showed that Mexican tariffs decreased U.S. industry …

Toward Global Localization Of Unmanned Aircraft Systems Using Overhead Image Registration With Deep Learning Convolutional Neural Networks, Rachel Linck

Graduate Theses and Dissertations

Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery …

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

A Remote Sensing And Machine Learning-Based Approach To Forecast The Onset Of Harmful Algal Bloom (Red Tides), Moein Izadi

Dissertations

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena all around the world especially in Florida’s coastal areas due to local environmental factors and global warming in a larger scale. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, I developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models, the K. brevis abundance is used as the target, and 10 …

An Exploratory Analysis Of Time Series Econometric Data For Retention Forecasting Using Deep Learning, John C. O'Donnell

Theses and Dissertations

Officer retention in the Air Force has been researched many times in an attempt to better predict the personnel needs of the Air Force for the future. There has been previous work done in regards to specific AFSCs and how their retention compares to specific yet similar private sector jobs. This study considers different econometric time series statistics as a feature space and an average Air Force officer separation rate as the response variable for the multivariate time series analysis deep learning techniques. The econometric indicators used in this study are New Business Formations, New Durable Good Orders, and the …

Detecting User Emotions From Audio Conversations With The Smart Assistants, Sunanda Guha

MSU Graduate Theses

With the proliferation of smart home devices like Google Home or Amazon Alexa, significant research endeavors are being carried out to improve the user experience while interacting with these smart assistants. One such dimension in this endeavor is ongoing research on successful emotion detection from short voice commands used in smart home environment. Besides facial expression and body language, etc., speech plays a pivotal role in the classification of emotions when it comes to smart home application. Upon successful implementation of accurate emotion recognition, the smart devices will be able to intelligently and empathetically suggest appropriate actions based on the …