Social and Behavioral Sciences Commons^™

Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth

Electronic Theses, Projects, and Dissertations

The longstanding prevalence of hypertension, often undiagnosed, poses significant risks of severe chronic and cardiovascular complications if left untreated. This study investigated the causes and underlying risks of hypertension in females aged between 18-39 years. The research questions were: (Q1.) What factors affect the occurrence of hypertension in females aged 18-39 years? (Q2.) What machine learning algorithms are suited for effectively predicting hypertension? (Q3.) How can SHAP values be leveraged to analyze the factors from model outputs? The findings are: (Q1.) Performing Feature selection using binary classification Logistic regression algorithm reveals an array of 30 most influential factors at an …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury

Graduate Theses and Dissertations

The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …

Ai Approaches To Understand Human Deceptions, Perceptions, And Perspectives In Social Media, Chih-Yuan Li

Dissertations

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens' perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups.

This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine …

Tempers Rising: The Effect Of Heat On Spite, Jake C. Cosgrove

Master's Theses

The relationship between heat and harmful outcomes is well documented, with research connecting various adverse economic outcomes to the climate. In the presence of increasing global warming and climate change, understanding why the climate leads to negative economic outcomes is essential for forming peaceful institutions of the future. We study how behavioral economic outcomes change in the presence of heat through a lab experiment involving 1,110 observations conducted in five different countries. This paper specifically focuses on the social preference outcome of spite. We find that increased time exposure to the treatment effect of heat is required to elicit an …

Application Of Sentiment Analysis And Machine Learning Techniques To Predict Daily Cryptocurrency Price Returns, Edward Wu

CMC Senior Theses

This paper examines the effects of social media sentiment relating to Bitcoin on the daily price returns of Bitcoin and other popular cryptocurrencies by utilizing sentiment analysis and machine learning techniques to predict daily price returns. Many investors think that social media sentiment affects cryptocurrency prices. However, the results of this paper find that social media sentiment relating to Bitcoin does not add significant predictive value to forecasting daily price returns for each of the six cryptocurrencies used for analysis and that machine learning models that do not assume linearity between the current day price return and previous daily price …

Glacier Segmentation From Remote Sensing Imagery Using Deep Learning, Bibek Aryal

Open Access Theses & Dissertations

Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. In recent years, remote sensing imagery has been preferred over riskier and resource-intensive field visits for tracking landscape level changes like glaciers. However, periodic manual labeling of glaciers over a large area is not feasible due to the considerable amount of time it requires while automatic segmentation of glaciers has its own set of challenges. Our work aims to study the challenges associated with segmentation of glaciers from remote sensing imagery …

Legislative Language For Success, Sanjana Gundala

Master's Theses

Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is "For" and the bill passes or alignment is "Against" and the bill fails). The process of finding what …

Analyzing Behavioral Adaptation To Covid-19 And Return To Pre-Pandemic Baselines In A Cohort Of College Seniors, Vlado Vojdanovski

Computer Science Senior Theses

As the critical phase of the COVID-19 pandemic seems to be winding down, it is important to analyze the adjustment to COVID-19 and return to normalcy of various populations. In this study we focus on the behavioral adjustments exhibited by a cohort of N=114 college seniors. To infer COVID-19 adjustment we compare the 2021 year (second year of COVID-19) to the 2020 year (first year of COVID-19) and 2019 (prepandemic baseline year). We begin with a broad analysis between the second and first covid year, finding that the second year of COVID-19 shows significant returns to pre-pandemic baselines on multiple …

Data-Driven Studies On Social Networks: Privacy And Simulation, Yasanka Sameera Horawalavithana

USF Tampa Graduate Theses and Dissertations

Social media datasets are fundamental to understanding a variety of phenomena, such as epidemics, adoption of behavior, crowd management, and political uprisings. At the same time, many such datasets capturing computer-mediated social interactions are recorded nowadays by individual researchers or by organizations. However, while the need for real social graphs and the supply of such datasets are well established, the flow of data from data owners to researchers is significantly hampered by privacy risks: even when humans’ identities are removed, or data is anonymized to some extent, studies have proven repeatedly that re-identifying anonymized user identities (i.e., de-anonymization) is doable …

Feature Investigation For Stock Returns Prediction Using Xgboost And Deep Learning Sentiment Classification, Seungho (Samuel) Lee

CMC Senior Theses

This paper attempts to quantify predictive power of social media sentiment and financial data in stock prediction by utilizing a comprehensive set of stock-related fundamental and technical variables and social media sentiments. For conducting sentiment analysis, this study employs a pretrained finBERT model that provides three different sentiment classifications and respective softmax scores. Hence, the significance of these variables is evaluated with XGBoost regression and Shapley Additive exPlanations (SHAP) frameworks. Through investigating feature importance, this study finds that statistical properties of sentiment variables provide a stronger predictive power than a weighted sentiment score and that it is possible to quantify …

Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani

LSU Doctoral Dissertations

The safety of highway-railroad grade crossings (HRGC) is still an issue in the United States of America (USA). The grade crossing is where a railroad crosses a road at the same level without any over or underpass. To improve the safety of crossings, the crossings’ condition should be explored from several aspects such as engineering design (speed limit, warning signs, etc.), road condition (number of lanes, surface markings, etc.), rail design (the type of track, ballast, etc.), temporal variables (weather, visibility, time of day, lightning, etc.), social variables (population, race, etc.), and last but not least, spatial variables (the type …

Glacier Segmentation In Satellite Images For Hindu Kush Himalaya Region, Bibek Aryal

Open Access Theses & Dissertations

Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. Monitoring glaciers in the Himalayan Hindu Kush (HKH) region is of high importance especially when we consider the impact of recent climate change on them. Our work aims to provide an automated method to outline glaciers using machine learning techniques and publicly available remote sensing imagery.In this work, we present ways to delineate glaciers from Landsat-7 imagery using various machine learning and computer vision techniques. The multi-step methodology that we present in this work is generalizable across different types of satellite and overhead imagery, …

Three Essays On Health Economics And Policy Evaluation, Shishir Shakya

Graduate Theses, Dissertations, and Problem Reports

This dissertation consists of three essays on the U.S. Health care policy. Each paragraph below refers to the three abstracts for the three chapters in this dissertation, respectively. I provide quantitative evidence on how much Prescription Drug Monitoring Programs (PDMPs) affects the retail opioid prescribing behaviors. Using the American Community Survey (ACS), I retrieve county-level high dimensional panel data set from 2010 to 2017. I employ three separate identification strategies: difference-in-difference, double selection post-LASSO, and spatial difference-in-difference. I compare how the retail opioid prescribing behaviors of counties, that are mandatory for prescribers to check the PDMP before prescribing controlled substances …

Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings, Abigail Atchison

Computational and Data Sciences (MS) Theses

The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the …

Commonsense Knowledge In Sentiment Analysis Of Ordinance Reactions For Smart Governance, Manish Puri

Theses, Dissertations and Culminating Projects

Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping. …

Dish: Democracy In State Houses, Nicholas A. Russo

Master's Theses

In our current political climate, state level legislators have become increasingly impor- tant. Due to cuts in funding and growing focus at the national level, public oversight for these legislators has drastically decreased. This makes it difficult for citizens and activists to understand the relationships and commonalities between legislators. This thesis provides three contributions to address this issue. First, we created a data set containing over 1200 features focused on a legislator’s activity on bills. Second, we created embeddings that represented a legislator’s level of activity and engagement for a given bill using a custom model called Democracy2Vec. Third, we …

Data Driven Approach To Characterize And Forecast The Impact Of Freeway Work Zones On Mobility Using Probe Vehicle Data, Mohsen Kamyab

Wayne State University Dissertations

The presence of work zones on freeways causes traffic congestion and creates hazardous conditions for commuters and construction workers. Traffic congestion resulting from work zones causes negative impacts on traffic mobility (delay), the environment (vehicle emissions), and safety when stopped or slowed vehicles become vulnerable to rear-end collisions. Addressing these concerns, a data-driven approach was utilized to develop methodologies to measure, predict, and characterize the impact work zones have on Michigan interstates. This study used probe vehicle data, collected from GPS devices in vehicles, as the primary source for mobility data. This data was used to fulfill three objectives: develop …

A Tacticians Guide To Conflict, Vol. 1: Advancing Explanations & Predictions Of Intrastate Conflict, Khaled Eid

CGU Theses & Dissertations

Intrastate conflict is an ever-evolving problem – causes, explanation, and predictions are increasingly murky as traditional methods of analysis focus on structural issues as precursors of conflict. Often times these theories do not consider the underlying meso and micro dynamics that can provide vital insights into the phenomena. Tactical decision-makers are left using models that rely on highly aggregated, country level data to create proper courses of actions (COAs) to address or predict conflict. The shortcoming is that conflicts morph quite rapidly and structural variables can struggle capture such dynamic changes. To address this some tacticians are using big data …

Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar

Legacy Theses & Dissertations (2009 - 2024)

Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …

Applications In Sentiment Analysis And Machine Learning For Identifying Public Health Variables Across Social Media, Eric Michael Clark

Graduate College Dissertations and Theses

Twitter, a popular social media outlet, has evolved into a vast source of linguistic data, rich with opinion, sentiment, and discussion. We mined data from several public Twitter endpoints to identify content relevant to healthcare providers and public health regulatory professionals. We began by compiling content related to electronic nicotine delivery systems (or e-cigarettes) as these had become popular alternatives to tobacco products. There was an apparent need to remove high frequency tweeting entities, called bots, that would spam messages, advertisements, and fabricate testimonials. Algorithms were constructed using natural language processing and machine learning to sift human responses from automated …

Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data, Christopher A. Ramezan

Graduate Theses, Dissertations, and Problem Reports

High spatial resolution (HR) (1m – 5m) remotely sensed data in conjunction with supervised machine learning classification are commonly used to construct land-cover classifications. Despite the increasing availability of HR data, most studies investigating HR remotely sensed data and associated classification methods employ relatively small study areas. This work therefore drew on a 2,609 km2, regional-scale study in northeastern West Virginia, USA, to investigates a number of core aspects of HR land-cover supervised classification using machine learning. Issues explored include training sample selection, cross-validation parameter tuning, the choice of machine learning algorithm, training sample set size, and feature selection. A …

Multimodal Depression Detection: An Investigation Of Features And Fusion Techniques For Automated Systems, Michelle Renee Morales

Dissertations, Theses, and Capstone Projects

Depression is a serious illness that affects a large portion of the world’s population. Given the large effect it has on society, it is evident that depression is a serious health issue. This thesis evaluates, at length, how technology may aid in assessing depression. We present an in-depth investigation of features and fusion techniques for depression detection systems. We also present OpenMM: a novel tool for multimodal feature extraction. Lastly, we present novel techniques for multimodal fusion. The contributions of this work add considerably to our knowledge of depression detection systems and have the potential to improve future systems by …

Improving Swarm Performance By Applying Machine Learning To A New Dynamic Survey, John Taylor Jackson

Master's Theses

A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is …

Using Autoencoder To Reduce The Length Of The Autism Diagnostic Observation Schedule (Ados), Sara Hussain Daghustani

Electronic Theses, Projects, and Dissertations

This thesis uses autoencoders to explore the possibility of reducing the length of the Autism Diagnostic Observation Schedule (ADOS), which is a series of tests and observations used to diagnose autism spectrum disorders in children, adolescents, and adults of different developmental levels. The length of the ADOS, directly and indirectly, causes barriers to its access for many individuals, which means that individuals who need testing are unable to get it. Reducing the length of the ADOS without significantly sacrificing its accuracy would increase its accessibility. The autoencoders used in this thesis have specific connections between layers that mimic the sectional …

Radical Recognition In Off-Line Handwritten Chinese Characters Using Non-Negative Matrix Factorization, Xiangying Shuai

Senior Projects Spring 2016

In the past decade, handwritten Chinese character recognition has received renewed interest with the emergence of touch screen devices. Other popular applications include on-line Chinese character dictionary look-up and visual translation in mobile phone applications. Due to the complex structure of Chinese characters, this classification task is not exactly an easy one, as it involves knowledge from mathematics, computer science, and linguistics.

Given a large image database of handwritten character data, the goal of my senior project is to use Non-Negative Matrix Factorization (NMF), a recent method for finding a suitable representation (parts-based representation) of image data, to detect specific …

Lexical Mechanics: Partitions, Mixtures, And Context, Jake Ryland Williams

Graduate College Dissertations and Theses

Highly structured for efficient communication, natural languages are complex systems. Unlike in their computational cousins, functions and meanings in natural languages are relative, frequently prescribed to symbols through unexpected social processes. Despite grammar and definition, the presence of metaphor can leave unwitting language users "in the dark," so to speak. This is not problematic, but rather an important operational feature of languages, since the lifting of meaning onto higher-order structures allows individuals to compress descriptions of regularly-conveyed information. This compressed terminology, often only appropriate when taken locally (in context), is beneficial in an enormous world of novel experience. However, what …

Identification Of Informativeness In Text Using Natural Language Stylometry, Rushdi Shams

Electronic Thesis and Dissertation Repository

In this age of information overload, one experiences a rapidly growing over-abundance of written text. To assist with handling this bounty, this plethora of texts is now widely used to develop and optimize statistical natural language processing (NLP) systems. Surprisingly, the use of more fragments of text to train these statistical NLP systems may not necessarily lead to improved performance. We hypothesize that those fragments that help the most with training are those that contain the desired information. Therefore, determining informativeness in text has become a central issue in our view of NLP. Recent developments in this field have spawned …

Complex Network Analysis For Scientific Collaboration Prediction And Biological Hypothesis Generation, Qing Zhang

Theses and Dissertations

With the rapid development of digitalized literature, more and more knowledge has been discovered by computational approaches. This thesis addresses the problem of link prediction in co-authorship networks and protein--protein interaction networks derived from the literature. These networks (and most other types of networks) are growing over time and we assume that a machine can learn from past link creations by examining the network status at the time of their creation. Our goal is to create a computationally efficient approach to recommend new links for a node in a network (e.g., new collaborations in co-authorship networks and new interactions in …

Predicting Music Genre Preferences Based On Online Comments, Andrew J. Sinclair

Master's Theses

Communication Accommodation Theory (CAT) states that individuals adapt to each other’s communicative behaviors. This adaptation is called “convergence.” In this work we explore the convergence of writing styles of users of the online music distribution plat- form SoundCloud.com. In order to evaluate our system we created a corpus of over 38,000 comments retrieved from SoundCloud in April 2014. The corpus represents comments from 8 distinct musical genres: Classical, Electronic, Hip Hop, Jazz, Country, Metal, Folk, and World. Our corpus contains: short comments, frequent misspellings, little sentence struc- ture, hashtags, emoticons, and URLs. We adapt techniques used by researchers analyzing other …