Digital Commons Network^™

Visible And Invisible Empires: The Revival Of The Ku Klux Klan 100 Years Ago, Christian Molina

Theses

Hatred and violence breed division, and division breeds more hatred and more violence. In an America where our politics has become name-calling, finger-pointing, scapegoating, and whataboutisms, and violence, unfortunately, it is safe to say our nation has entered this self-perpetuating cycle. A place for malicious groups concerned with growth, power, and profit to capture the angry, the stressed, and the less informed in their webs of elaborate lies and misleading, misinterpreted numbers the way they have in decades past.

Understanding The Relationship Between Mental Health And Criminal Justice Involvement: Pathways, Disparities, And Improving System Responses, Haley Bly

Theses

This capstone project examines the complex relationship between mental health and involvement in the criminal justice system, emphasizing how pathways, external conditions, structural inequities, and system responses interact to shape long-term outcomes for persons with mental illness (PwMI). While mental illness alone is not a predictor of criminal behavior, the intersection between psychological disorders, social strain, stigma, and systemic failures significantly heightens the risk of criminal justice involvement. Combining four working papers, this capstone examines these relationships across individual, environmental, institutional, and community levels, ultimately arguing that treatment and rehabilitative-centered reforms are essential for improving the experiences of individuals with …

Understanding Crime Through Harm: A Multi-Stage Study Of Rochester, Ny, Hayder Alhafedhi

Theses

This capstone project develops, critiques, and applies a Crime Harm Index (CHI) for the City of Rochester, New York, as an alternative to traditional crime measures that rely solely on raw counts. Rather than treating all offenses as equal “one crime = one unit,” the project weights crimes by the severity of harm they cause, using New York State sentencing guidelines as the core metric. Across four working papers, the project moves from conceptual foundations and limitations of harm-based metrics, to the construction of a Rochester-specific Crime Harm Index, and finally to a set of spatial regression models that link …

The Impact Of Screen Time Patterns On Digital Behaviour And Productivity: A Data Analytics Study Across Age Groups, Maytha Alshamsi

Theses

The increasing incidence of screen time of all ages has generated a greater interest in the effect of screens on the human psyche. This paper looks at behavioural data of 2, 000 individuals aged 13-64 to investigate the predictability of a composite mental- health score by various modalities of screen-time, lifestyle and psychological charac- teristics. We determine, based on exploratory statistics, multivariate regression and su- pervised machine-learned models, that in as much as the influence of total screen time and social-media consumption has significant relationships with worse mental health, the effects are small when juxtaposed to those of lifestyle and …

The Impact Of Artificial Intelligence On Employee Attrition And Organizational Profitability, Saeed Almehairi

Theses

Artificial Intelligence (AI) has become a transformative force in the modern workplace, reshap- ing decision-making processes, enhancing operational efficiency, and influencing the dynamics of human resource management. One of the critical areas where AI demonstrates substantial potential is in predicting, managing, and mitigating employee attrition. Employee attrition remains a pressing organizational challenge, incurring substantial costs in recruitment, training, and lost productivity. This research investigates the dual impact of AI on employee attrition and organizational profitability, exploring both the opportunities and the challenges that arise from its integration into human resource and business operations. The study examines how AI-driven predictive analytics …

Multilingual Identity Document Information Extraction Via Dynamic Templates And Hybrid Ocr, Khalifa Rashed

Theses

The thesis introduces a unified, automated document intelligence system that can in-house resolve significant issues in identity authentication and data OCR by two complementary technological components: advanced facial biometrics and powerful multilingual optical character recognition. The system directly addresses the inefficiencies of the slow speed of document processing by humans, human error and high cost of operation by offering a single pipeline for verifying the identity of the user via facial comparison and digitizing textual data contained in documents. The former module adopts an advanced face verification pipeline. It starts with an image pre-processing step, which improves the quality of …

Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi

Theses

The paper explores whether there is a connection between operational performance, customer sentiment, and digital administrative complexity among major regional Middle Eastern airlines, such as Flag Carriers (e.g., Emirates) and Low-Cost carriers (LCCs) (e.g., Air Arabia). With the use of a highly detailed dataset of customer review and operations data, the study proves that a large service paradox is present in which, despite the high Net Promoter Scores (NPS: 48.004) of the carriers, which are positively reinforced by delivering world-class soft products, the loyalty is constantly disrupted by low-frequency but high-severe operational delays (Delay_Minutes). Since it has been analyzed that …

Assessing The Impact Of Codec-Induced Audio Degradation On Voice Biometric Systems, Suhil Ali Almuhaisni

Theses

This study examines the robustness of voice biometrics when speech signals undergo audio codec transformations and sampling rate variations, conditions common in telecommunication networks. Speaker verification systems such as ECAPA-TDNN perform well on clean datasets, but their accuracy declines when low-bitrate codecs compress speech or when signals are resampled at reduced frequencies. In real-world deployments, systems adapt audio to bandwidth and storage limitations, often removing subtle acoustic details that support consistent speaker recognition. The research will analyse how codec settings and sampling rates, particularly those optimized for efficiency in bandwidth-limited systems, influence the stability of speaker embeddings. Instead of ranking …

Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti

Theses

Urban infrastructure delivery is a complex process influenced by project type, spatial distribution, stakeholder coordination, and data quality. This thesis examines the application of Microsoft Power BI, a no-code visual analytics tool, as a means to analyze performance patterns in urban projects and inform decision-making in smart city governance. The study uses a structured secondary dataset simulating over 600 infrastructure projects across various departments and neighborhoods. By transforming raw data into an interactive dashboard, the research explores relationships between delay rates, customer satisfaction, budget compliance, and technology adoption. The analysis revealed that project categories such as metal furniture and police …

Machine Learning Approaches For Predicting And Preventing Vehicle Theft, Hamad Almutawa

Theses

This study develops a data-driven framework for predicting vehicle theft using machine-learning techniques applied to a comprehensive crime dataset containing demographic, spatial ,and operational policing variables. The dataset, sourced from publicly available crime records, included 9,712 cases and 30 variables, with minimal missing data and balanced class distribution. After performing extensive preprocessing—including outlier analysis, missing-value imputation, categorical encoding, feature selection, and correlation testing—four supervised classification models were implemented: Logistic Regression, Random Forest, Support Vector Machine, and a Neural Network. Bivariate statistical tests, including Chi-square and Mann–Whitney U, revealed significant relationships between theft occurrence and factors such as crime area, luxury …

Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry

Theses

Occupational stress and deteriorating mental health constitute an increasing issue in the global community, and the problem of how job demands, personal resources, and cognitive processes interrelate to determine employee well-being is hardly known. This dissertation will study these dynamics by incorporating theory-driven Structural Equation Modeling (SEM) with data-driven Machine Learning (ML) methods that offer explanatory and predictive information. The study examines the influences of job demands, personal resources, effort–reward imbalance (ERI), emotional labor, cognitive appraisal, and social support on the stress and mental health outcomes of a sample of 5,000 employees working in various industries and regions of the …

Predicting Mental Health Risk In Remote Workers: A Machine Learning Approach, Majed Al Mheiri

Theses

This dissertation explores the application of machine learning in mental health risk prediction of remote workers, which is increasingly becoming a significant issue, with the practise of flexible working reshaping organisational practises. It is based on the theoretical framework that has been used previously such as Job Demands Resources model and Stress-Strain model which emphasise the role of workload, support systems and personal resources in determining the well-being of employees. The context of the study indicates the growing rate of remote and hybrid employment, and the associated increase in the number of issues associated with stress and isolation, as well …

Defaking Deepfakes: Designing And Evaluating Ai-Powered Digital Media, Saniat Javid Sohrawardi

Theses

The rapid spread of generative AI has revolutionized media production, creating new challenges for information integrity as convincing deepfakes proliferate. Journalists play a critical role in upholding credible public information, yet existing deepfake detection technologies often overlook their specific workflows and requirements. This dissertation addresses these needs by identifying what journalists require from detection tools and evaluating usability in realistic scenarios through the following works: Journalists' Needs and Tool Design: Through qualitative user studies, we uncover journalists' preferences for tools that provide transparent, explainable evidence and context-aware analysis integrated into news verification routines. These insights drive the design of DeFake, …

Transgender Adolescents' Healthcare Rights: The Gap Between Gender-Affirming-Care (Gac) Policymakers’ And Healthcare Providers’ Goals, Jayden Galli

Theses

Transgender people have marginalized healthcare experiences that result in decreased health outcomes. For transgender adolescents, gender-affirming-care (GAC) is even more difficult to access and being restricted with increasing frequency. This mixed methods study was conducted to examine transgender healthcare through the gaps between GAC policymakers’ and healthcare providers’ goals. General research has focused on the relationship between GAC and transgender health, GAC policy development, and the discrepancies between GAC policy outcomes and medical professionals’ advice. This leads to the research question about how policymakers and healthcare providers can collaborate to bridge the gap between GAC policies and transgender minors' healthcare …

Persuasive Pathways In Digital Apologies: The Role Of Persuasion Routes In Engagement With Youtuber Apology Videos, Darrin Majocha

Theses

This study explores how viewers process YouTube apology videos and how those processing methods relate to their judgments of the creator and their willingness to continue engaging with the channel. Guided by the Elaboration Likelihood Model, a single-session online survey experiment randomly assigned 413 adult participants to watch one of five publicly available apology videos from well-known YouTubers. After viewing, participants reported the extent to which they engaged in central versus peripheral processing, rated the creator's credibility and the apology's sincerity, and indicated their intentions to engage. Central processing was positively associated with engagement intentions, whereas peripheral processing was weakly …

Accessible Preservation: Developing A Low-Cost, Iiif-Compliant Workflow For Small Art Collections, Robyn Palescandolo

Theses

This project explores the development of an affordable, standards-based digital collections management system designed for small galleries, independent artists, and emerging institutions. Using the artist’s own studio collection as a case study, the research integrates accessible hardware and open-source technologies, including the International Image Interoperability Framework (IIIF), Artwork Archive, GitHub Pages, and the Internet Archive, to establish a transparent, replicable workflow for cataloguing and publication. The project demonstrates how interoperability, metadata consistency, and open access can be achieved without reliance on costly proprietary systems, such as TMS Collections or PastPerfect.

The workflow includes high-resolution image capture, 3D scanning, data normalization, …

Predicting Inmate Overcrowding To Improve Facility Management, Mayed Ali Alameri

Theses

Overcrowding is a major challenge to correction systems because the conventional forecasting techniques are inaccurate and inadequate in most cases. This paper will solve this by constructing and testing a machine learning-based model to predict facility-level overcrowding. With the use of the XGBoost Regressor model on a dataset comprising of U.S. correctional facilities, the research identified key structural drivers but showed that the static facility attributes alone have limited predictive power (r-square approx 0.18) The discussion shows that overcrowding is non-linear, and a complicated problem not confined to the facility characteristics but to the larger, non-measurable regional influences, of which …

Microgrid Approach With Data-Driven Monitoring To Enhance The Resilience Of Water Distribution Systems, Binod Ale Magar

Theses

Existing centralized water supply systems are critically threatened by the joint effects of climate and socioeconomic changes, extreme weather events, and physical degradation due to aging, and their changeability. The uncertain and changing drivers pose challenges for water supply systems in providing sustainable water services during disruptions. The energy field dealing related issues has endorsed the microgrid approach with a decentralized energy supply with dispersed local energy sources. The application of energy microgrids has demonstrated their resiliency for sustainable energy supply. For improved resiliency of a water distribution system, this study investigated the application of a microgrid approach and the …

Echo’S Repertoire: Performing Science Fiction And Trans Care Models, Juniper Blue

Theses

Echo’s Repertoire is a mixture of a passion piece and a call for more work with performing science fiction. It focuses on interpersonal communication within trans studies and the experiences of transgender individuals and myself. Echo’s Repertoire is also an example of the Lyrical Model proposed by Awkward-Rich as a new model for trans affirming speech. The show is laid out non-linearly and follows along with Echo as he starts to build new relationships with the advice of his spaceship ai, Eep, who is a previous version of himself prior to transformation (transition).

Debating Empire: The Yucatan Crisis And The Political Aftermath Of The Mexican-American War, Colton Matthew Warnes

Theses

This study examines the impact of the Caste War of Yucatán had on the geopolitical relationship between the United States and Mexico in the aftermath of the Mexican-American War. The briefly independent Republic of Yucatán requested foreign intervention to end the Caste War. The United States debated intervention in Yucatán, while Mexico, still reeling from the loss of the Mexican-American War and dealing with internal political conflict, made steps to reincorporate Yucatán back into Mexico and to diplomatically resist American expansion. Examining the political landscape of the United States and Mexico in the spring of 1848 offers an alternate perspective …

Beyond The Mozart Effect: How Musical Experience And Emotional Response Shape Individual Differences In Spatial Reasoning, Connor Watkinson

Theses

Research on the Mozart effect (the short-term enhancement of spatial-temporal reasoning after music listening) has yielded inconsistent results, often due to overlooking individual differences and using limited stimuli. This study examined multiple musical conditions (popular lyrical, classical, and white noise control) while considering the moderating roles of musical experience and the mediating influence of emotional responses. Undergraduate participants (N = 162) completed spatial reasoning assessments before and after an eight-minute listening session, alongside measures of musical background, listening habits, mood, and music-evoked emotions.Analyses revealed no overall difference in spatial reasoning improvement between music and white noise conditions. However, substantial individual …

Flood Susceptibility Assessment Through Gis Integrated Analytical Hierarchy Process And Machine Learning Models, Sujan Shrestha

Theses

Flooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for two regions: Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP), and the Briar Creek watershed of Mecklenburg County, North Carolina using an integrated GIS and machine …

Modeling The Effects Of Shed Target Receptors On The Efficacy Of Cancer Immunotherapy Agents, Bridget M. Torsey

Theses

Cancer cells often shed receptors targeted by immunotherapies. Shed receptors can reduce drug efficacy by binding to free drug, preventing its binding to membrane-bound receptors. The goal of this dissertation is to investigate the effects of shed targets on the efficacy of cancer immunotherapies. First, we study liquid tumors by extending a PK/PD model to include receptor shedding, drug-induced enhancement of shedding, drug binding to shed receptors, and drug-induced tumor lysis. We use our model to elucidate the effect of shed target receptors on the efficacy of immunotherapies through uncertainty and sensitivity analyses. Our findings support the claim that the …

Comparative Analysis Of Machine Learning Models For Spam Email Detection, Rashed Almarri

Theses

The current research examines one of the most effective approaches to spam email detection based on machine learning and natural language processing (NLP). The study is placed in the context of the rising cyber threats and the influx of emails, where the spam/ham data is to be classified correctfully using the combination of the Logistic Regression, NLP (including tokenization, lemmatization, and TF-IDF vectorization). The questions of the research were devoted to the efficiency of such an approach and the interpretation of its results. The data used are obtained by a publicly available Kaggle data set that contains 5,572 labeled email …

The Role Of Ai And Predictive Policing In Crime Prevention, Saif Salem Mohammad Hassan Abdulla

Theses

The current thesis examines the application of Artificial Intelligence (AI) in the arena of predictive policing and crime forecasting using an integrated case study based on the empirical approach supported by a narrative review of literature. Due to the growing use of digital data by the law-enforcement agencies, AI techniques, including machine learning and spatio-temporal modelling, are implemented to detect patterns of crimes, predict high-risk areas, and assist law-enforcement decision-making. Though these technologies have the potential to make the processes of accuracy and resource allocation better, they also bring up the issue of the fairness, transparency, and disproportionate effects on …

Predicting Violent Crime Hotspots, Mohammad Alhammadi

Theses

This thesis presents a systematic literature review and an empirical demonstration focused on predicting violent-crime hotspots. Drawing on 50 studies published between 2010 and 2025, the review synthesises methodological developments across hotspot mapping, spatio-temporal modelling, risk terrain analysis, and machine-learning approaches. The review highlights a clear evolution from retrospective density maps to more dynamic, data-driven techniques, while also identifying persistent challenges related to data bias, temporal granularity, environmental context, fairness, and operational implementation. To complement the review, the thesis applies kernel density estimation (KDE) and three ensemble machine-learning models—Random Forest, Gradient Boosting, and XGBoost—to 769,680 geocoded violent-crime incidents recorded in …

Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar

Theses

The paper explores how statistical analysis and machine learning can be used to identify the fraud patterns in the police reports. The study aims at establishing the most important predictive factors and indicators distinguishing fraudulent and valid cases with the use of structured data of police databases. The work was done in the background of the increase in financial fraud instances and the rising necessity of the introduction of automated detection systems in police departments. Police reports of the pastwere mined down to data and analyzed on SPSS 1, to carry out statistical operations. The sample was structured data which …

Optimizing Human Resource Decisions: Predicting Promotions Using Data Analytics, Mohammad Khalid A Mohammad Abdulrahim

Theses

Proper and objective selection of high potential employees to promote them is a major dilemma in the Human Resources (HR) department, more so in sensitive and hierarchal environments in the public sector where subjectivity is likely to take place. This paper is based on this ubiquitous issue, and it seeks to develop, experiment, and examine a clear and equitable machine learning model that can forecast the possibility of an employee to get a promotion according to organized past HR records. The technique was solid preprocessing, alleviation of extreme class imbalances on the basis of the Synthetic Minority Over-sampling Technique (SMOTE), …

Security Vulnerabilities In Cloud Storage: A Comparative Study Of Google Drive, Dropbox, And Onedrive, Sultan Majid Alshamsi

Theses

In recent years, use of internet has increased manifold. People have started creating more data, and instead of saving it locally, people have started preferring storing it “online”. Companies are also following the same path to maintain accessibility and availability of their data. Thus, data has become a central part of our lives. People, companies, and institutions rely heavily on cloud storage solutions these days for managing their data. Some of the main cloud storage solution are Google Drive, Dropbox, and OneDrive. These are sophisticated solutions developed by tech giants, and general expectation of the public is that these solutions …

Modeling Economic Effects On Climate Change, Ahmed Abdulqader

Theses

Climate change represents one of the most critical challenges facing the global economy today. While environmental impacts receive considerable attention, the economic implications are equally significant and require rigorous quantitative analysis. This thesis investigates the relationship between climate change and major economic indicators including GDP growth, inflation, trade balances, and employment across 50 countries from 1990 to 2023. The research employs a comprehensive multi-methodological approach combining panel data econometrics, time-series analysis, and machine learning techniques. Fixed-effects and random-effects panel regression models reveal statistically significant relationships between climate-related disaster frequency and economic performance. Specifically, disaster count demonstrates a positive coefficient of …