Digital Commons Network^™

Surveying The Role Of Visual Analytics In Human-Machine Teaming, Naga Datha Saikiran Battula

Theses

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Advancing Prediction Of Stimulant Medication Misuse Through Graph Representation Learning, Hamid Razavi

Theses

The misuse of stimulant prescription medications poses a significant and escalating public health concern in the United States, particularly among young adults. Addressing this issue requires sophisticated methodologies capable of uncovering complex patterns and relationships in data. Geometric Deep Learning, a paradigm designed to analyze data with non-Euclidean structures, has achieved remarkable success across various domains, offering a powerful framework for tackling complex graph structure data challenges.

This study leverages Graph Convolutional Networks (GCNs) to predict the likelihood of stimulant medication misuse using data from the National Survey on Drug Use and Health (NSDUH). Individuals are represented as nodes in …

Utilitarian And Self-Representational: Player-To-Player Character Relationships In Final Fantasy Xiv, Zu Er Guan

Theses

This thesis studies the relationships between video game players and their player characters, the characters that they control, through the lens of player purpose and intent by conducting research using an online survey developed and analyzed using an interdisciplinary methodology based on existing studies in the field regarding avatar identification, player psychology, and queer and feminist theory. This thesis posits that are two types of player relationships with their player characters based on player intent—player characters as the personal avatar and representation of the self, and player characters as a tool for the execution of player control and gameplay experience. …

Optimizing Wind Wing Wall Ventilation In High-Rise Buildings Within Dense Urban Hot And Humid Climate., Arushi Bhatia

Theses

This study investigates how wing walls can increase natural ventilation in high-rise structures, particularly in hot, humid locations such as Mumbai, India. Using wind flow simulations and computational fluid dynamics (CFD) modeling, the study investigates how wind direction and the installation of wing walls affect airflow throughout the building. The study examines four scenarios, each analyzing how airflow varies when a building is located near surrounding structures of differing heights. It uses comprehensive CFD simulations to assess how alternative wing wall depth design can affect ventilation, as well as how wall-to-window ratio variation can contribute to airflows. Following these simulations, …

From Factories To Homes: A Comparative Analysis Of Adaptive Reuse In New England Industrial Buildings For Multi-Family Residential Conversion, Jeremy Weeden

Theses

The adaptive reuse of industrial buildings offers a promising path toward sustainable urban development, particularly in New England. This study investigates the feasibility and energy efficiency of converting an older, unused industrial building into a multi-family residential structure by evaluating three distinct wall assemblies that preserves the exterior brick facade: existing structural brick, a typical adapted wall assembly with interior 2x4 wood construction, and a high-efficiency adapted wall assembly with an additional interior 2x6 wood construction. Through a detailed case study analysis focusing on the building’s core and shell, this research aims to identify the most effective wall assembly for …

Comparative Analysis Of Earth-Bermed And Conventional; Single-Family, Residential Homes In The Hudson Valley Region, Beacon, New York, Michael G. Patchen

Theses

This thesis is a comparative analysis of the environmental sustainability between two similar square-foot, single-family residential homes constructed as a bermed shelter and the conventional wood frame in the Hudson Valley Region, Beacon, New York. The research will focus on the two designs' embodied carbon, cost, and energy use. To quantify the environmental impact of these two construction methods, an in-depth understanding of the diverse host climate is necessary. Climate data and the results from the experiments are collected by performing life cycle, cost, and energy analysis, providing insights into the environmental implications of residential construction methods. The methodology will …

Pixel Personalities Ai, Patrick Casey

Theses

The Pixel Personality AI project explores how AI characters can be embedded in augmented reality (AR) environments to foster user interaction and storytelling. By situating AI personas within real-world contexts, the project examines how design principles, storytelling techniques, and user-centric technology can create engaging and intuitive experiences. Through iterative prototyping and design refinement, the project addressed challenges in aesthetic design, interface usability, and AR integration. The resulting low-poly aesthetic and customized visual filters enhance spatial storytelling while maintaining a balance between user focus and environmental immersion. This research highlights the potential of AI-driven narratives to reshape AR experiences, emphasizing user …

Rental Property Demand And Supply Analysis Using Machine Learning, Ahmed Amiri

Theses

The real estate market in Dubai is famous for its activity and stimulating potential due to the geographical position of the emirate, well-developed transport and legal framework. Timely determination and prediction of rental price is crucial for investors, property owners and managers, tenants and authorities in their decision making to maximize returns and ensure market stability. Traditional approaches are insufficient for the analysis of the temporal and spatial relations between property characteristics and market processes, which require the use of sophisticated machine learning algorithms. This paper aims to predict the rental prices of properties in Dubai using machine learning models …

Amorfe: An Ai-Integrated Interactive Brainstorming Interface, Ipshita Pal

Theses

Amorfe is an interactive multi-touch digital interface that reimagines brainstorming and collaboration using artificial intelligence (AI). Designed to make ideation more dynamic, the platform allows participants to explore, merge, and refine ideas in real time. AI is a creative catalyst, offering unpredictable outcomes and streamlining the brainstorming process. This makes Amorfe engaging, intuitive, and inclusive, encouraging active participation. The platform’s potential was demonstrated through a hypothetical scenario: “How do we create more green spaces on the RIT campus?” Four participants explored various brainstorming phases through touch interaction, leading to the creation of a prototype simulation video and a promotional video. …

Dissonance, Shreya Talegaonkar

Theses

Cognitive dissonance represents a profound psychological phenomenon where conflicting beliefs create mental tension. My thesis, Dissonance, transforms this abstract concept into an immersive multimedia installation that visually explores the human mind's intricate journey of reconciling internal contradictions. Using Cinema 4D, Adobe AfterEffects, and Resolume Arena, the project creates dynamic 3D animations projection-mapped across an 8x8x6 cubic feet space. Carefully designed blackout curtains and strategic seating immerse participants in a transformative narrative of psychological exploration. Audiences navigate through four distinct phases: Consistency, Chaos, Turmoil, and Adaptation. Each phase invites viewers to experience the unsettling yet profound journey of internal conflict resolution, …

The Relationship Between Death Depression And Death Anxiety Among Cancer Patients In Saudi Arabia, Doaa A. Almostadi

Theses

This study explored the relationship between death anxiety and death depression among cancer patients in Saudi Arabia. The study sample consisted of 100 Saudi cancer patients, 50 male and 50 female, ranging in age from 18 to 85 with a mean age of 45.5. All participants completed a survey questionnaire that included three parts: the first part contained a demographic data form; the second part consisted of 20 statements to measure death anxiety using the Arabic Death Anxiety Scale; the third part was a 21- item, questionnaire designed to measure depression about impending death using the Death Depression scale-Revised Arabic …

Undeserving And Underserved: Violence Victims And Society, Venita Monet C. D'Angelo

Theses

This capstone includes three papers that explore how structural disadvantage, place-based dynamics, and institutional responses shape the experiences of individuals and communities. The first paper focuses on the relationship between concentrated disadvantage, micro-places, and individual outcomes. It shows how crime is not randomly distributed but clusters in specific locations within disadvantaged neighborhoods. Drawing on theories of social disorganization and cumulative disadvantage, the paper explains how neighborhood and micro-place conditions intersect to shape long-term risks and outcomes for residents. The second paper analyzes a community-based intervention designed to reduce recidivism and revictimization. The evaluation considers the program’s design, goals, and limitations, …

Assessment Of Food Allergy Management And Interactions With Campus Dining Halls Among College Students At Rochester Institute Of Technology, Meghan Taylor

Theses

Objective: This study explored college student dining experiences to identify areas for improving Dining Service allergen management. Method: A cross-sectional survey assessed specific allergies, frequency of allergic reactions, confidence and satisfaction with Dining Services, epinephrine auto-injector use, challenges, suggestions and use of dietitian services. Analyses included quantitative and qualitative methods. Results: Of 74 survey respondents, 28 were eligible, with 61% reporting two or more allergies (mean 2.4 ± 2.0). Tree nuts, peanuts, and milk were most common. Students with multiple allergies were more likely to carry epinephrine auto-injectors (p=0.015), though overall adherence was low. Concerns included cross-contact, inconsistent allergen labeling, …

Improving Obsolescence Management By Enhancing Supplier-Customer Collaboration, Mora Issa

Theses

There are currently many legacy facilities operating in the Middle East & Africa region that have plans in place to extend the plant life. Oil prices have been steadily going up, leading to increases in production, and driving economic growth. In order to extend the life of these older facilities, the sites are budgeting to replace critical parts accordingly. Obsolescence is an inevitable part of every product lifecycle and, with the new wave of technology and AI in the market, facilities face the challenge of increasing profitability in the fast-paced economy. The number of more efficient upgrades available presents an …

Data Privacy Protection For Zero-Permission Sensors In Iot Systems, Xinyi Liu

Theses

The Internet of Things (IoT) has revolutionized various domains through real-time decision-making and automation, with sensors serving as foundational components. Zero-permission sensors, such as accelerometers and gyroscopes, are widely used in most IoT systems, enabling unrestricted data access without permission. Despite being traditionally perceived as low-sensitivity, recent studies have shown that these sensors can be exploited to infer sensitive personal information, raising serious privacy concerns. Local Differential Privacy (LDP) is a rigorous technique for numerical data privacy protection even when the data collector, such as an IoT service provider, is untrusted. LDP ensures privacy by adding noise to sensor readings …

Predicting Students At Risk Of Dropping Out In The College Of Medicine At Mohammed Bin Rashid University Of Medicine And Health Sciences. A Machine Learning Approach To Understand Attrition Factors Over The Past Seven Years, Moza Sulaiman Al-Zaabi

Theses

This master’s thesis investigates student attrition within the College of Medicine at Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) by applying the CRISP-DM methodology to analyse a dataset of 19 variables and around 2,000 records from Student Admission and Academic Performance sources. Although the dataset was relatively small, several machine learning models were developed to predict students at risk of dropping out, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Of these models, the ANN model, particularly when combined with Principal Component Analysis (PCA), achieved the highest performance with an accuracy …

Cognitive And Emotional Theory Of Mind In Eating Disorders: The Mediating Role Of Alexithymia, Morgan R. Johnson

Theses

A number of studies have documented deficits in theory of mind (ToM), or the ability to infer the mental states of others, among individuals with eating disorders (EDs). Much of this research has focused on emotional ToM, as opposed to cognitive ToM, where mental inferences do not contain affective content. Alexithymia, a difficulty in the ability to correctly identify and describe one’s emotions, has also been reported among individuals with EDs. Alexithymia was hypothesized to mediate the relationship between ED symptoms and emotional ToM, as it has been shown in past research to significantly mediate emotion processing deficits in those …

Ai-Driven Anomaly Detection In Cybersecurity, Mohamed Almansoori

Theses

India has witnessed a huge level of digitization in the last two decades which has greatly impacted its economy and society. But the increasing use of technology has also intensified the number and level of threats to cyberspace in the country. Signature-based detection systems that have dominated the cybersecurity field for years have not been up to the challenge of modern attackers who use more complex and diverse attack methods. These challenges are well understood and this research focuses on improving the cybersecurity anomaly detection in India during the period between 2003 and 2022 using advanced AI and machine learning. …

Chest Disease Classification Using Transfer Learning, Adarsh Neema

Theses

In the domain of medical diagnostics, the precise classification of chest diseases has become very important, particularly during the occurrence of the COVID-19 pandemic. This project proposes an innovative approach to automate the classification process through the usage of deep learning technique, and more specifically making use of convolutional neural networks (CNNs) and transfer learning, with a prime focus on the RESNET50 architecture. The primary objective is to develop a model which is intelligent enough to detect various chest conditions, including COVID- 19, viral pneumonia, bacterial pneumonia, and normal cases. Utilizing the rigorously pre-trained knowledge encoded within RESNET50, trained on …

Greenmind: A Climate-Positive News Portal, Surabhi Singh

Theses

Greenmind is a website that promotes positive climate narratives for environmentally conscious individuals seeking hope and actionable solutions. It redefines how we approach climate change narratives by focusing on hope and progress. Unlike traditional media that often rely on fear to grab attention, Greenmind delivers stories showcasing actionable solutions and positive impacts already happening across the globe. Its core mission is to empower individuals to see themselves as agents of change, fostering collective action rather than promoting fear as a tactic for change. Greenmind’s approach to climate narratives is supported by research emphasizing the power of positive storytelling. Studies from …

Input Of Anthropogenic Debris Across A Rural To Urban Gradient In The Lake Ontario Watershed, Paige Arieno

Theses

Anthropogenic debris (AD) is now ubiquitous across terrestrial, marine, and freshwater environments. While plastic is typically the dominant material in AD, non-plastic materials, including metal, glass, processed wood, and concrete, are a large part of the diverse debris entering and moving through the environment and may have similar environmental impacts. Current estimates of plastic entering the Great Lakes are coarse and none exist for other AD. This gap precludes development of source-based mitigation plans. This study evaluated debris quantity and composition in tributaries and storm sewers entering the Rochester Embayment of Lake Ontario. Using LittaTraps™ installed in storm drains, we …

Evaluation Of A Token Reinforcement System For Assignment Completion In Students With Educational Disabilities, Brandon O'Dell

Theses

Token reinforcement systems are an effective tool in applied behavior analysis. Studies as early as the 1960s and 70s have demonstrated the effectiveness of token economy from addressing the behaviors of psychiatric patients (e.g., Allyon & Azrin, 1965) to supporting the academic achievement of students in school settings (e.g., McLaughlin & Malaby, 1972) Most recently, Espinoza and Hackenberg (2024) discussed the history and effectiveness of token reinforcement, while providing practical recommendations for its application. The current study aimed to implement a token reinforcement system to increase assignment completion and accuracy in three eighth-grade students with various medical and educational diagnoses …

Wildflowers A Novel, Kelli Roberts

Theses

Abstract for Wildflowers

Wildflowers is a feminist literary novel that examines the intersection of sex, power, and free will, exploring how these forces shape and define human agency. The story follows a group of women drawn to an enigmatic leader who promises enlightenment and empowerment through embracing their desires. As they navigate the fine line between self-actualization and manipulation, the novel delves into themes of liberation, coercion, and the choices we make to create the lives we long for. Provocative and thought-provoking, Wildflowers invites readers to consider the complexities of desire, the cost of empowerment, and the courage required to …

Comparative Analysis Of Artificial Intelligence And Statistical Models For Li-Ion Battery Cells State Estimation In Electric Vehicles, Rasha H.A. Tabasha

Theses

This graduate paper presents a comparative study of statistical modeling and Artificial Intelligence (AI) approaches for estimating essential parameters of lithium-ion (Li-ion) batteries in electric vehicles (EVs). The key parameters estimated in this study are voltage, current, and temperature of Li-ion battery cells in EVs. A precise state estimation is required to ensure safety operation, to optimize the Battery Management System (BMS), and to enhance the battery life. Variations in EVs batteries parameters may occur due to different reasons such as sensor faults or attacks. As a result, obtaining an accurate estimation is essential to maintain EV battery efficiency. Using …

How Leadership Styles Impact Lgbtq+ Civil Rights, Milo Thompson

Theses

Leaders rely on different leadership styles to legitimize their rule and obtain political power. However, these leadership styles have positive or negative impacts for protecting human rights in the LGBTQ+ community. This thesis examines how political leadership styles impact protecting or repressing the civil rights of the LGBTQ+ community. This article argues that constitutionalist leaders who abide by the “rule of law” will better protect LGBTQ+ civil rights than personalist, ideological, or performative leadership styles. Drawing on both qualitative and quantitative research methods, this paper examines how leadership styles impact LGBTQ+ discrimination.

Integrative Hyperspectral Approaches For Advanced Soil Property Analysis And Environmental Monitoring, Nayma Binte Nur

Theses

Hyperspectral imaging has emerged as a powerful tool for enhancing the analysis of diverse soil properties and environmental monitoring. This study refines the precision of soil biogeophysical analyses by improving the estimation of soil moisture content (SMC), soil organic matter (SOM), total carbon (C), and nitrogen (N) through hyperspectral remote sensing techniques. The research examines the capabilities of two prominent moisture retrieval models: the multilayer radiative transfer model (MARMIT) and the modified soil water parametric (SWAP)-Hapke model. These models are evaluated using hyperspectral imagery derived from unmanned aerial systems (UAS) and goniometric data collected across various environmental settings. The findings …

Understanding The Relationship Between Humble Leadership, Reflecting Dialogue, Psychological Safety And Job Satisfaction: A Study Of Japanese Correctional Officers, Yayoi Kimura

Theses

This study investigates the relationships between humble leadership, reflecting dialogue, psychological safety, and job satisfaction among correctional officers in Japanese correctional institutions. Correctional officers face high levels of stress due to their demanding roles in rehabilitating and managing inmates, yet the impact of organizational factors on their job satisfaction remains underexplored. As Japan undergoes significant changes in its penal system, understanding the factors that contribute to a supportive work environment is crucial. First, this study examines the relationship between psychological safety and job satisfaction among correctional officers. Second, it explores whether facilities that practice reflecting dialogue and humble leadership experience …

Balancing The Scales In The Fashion Industry, Theresa J. Fennell

Theses

The standards of beauty, particularly in the fashion industry, have long been a source of societal debate and controversy. Although beauty can be seen as “subjective,” over the years, certain standards have become institutionalized, creating exclusionary ideals that have dominated the fashion industry. Historically, these standards have favored slim body types, marginalizing larger, plus-size bodies. This bias is deeply entrenched in the industry, influencing everything from design to representation in advertising. However, the body positivity movement has emerged as a transformative force, advocating for the acceptance of diverse body types and pushing for greater inclusivity in fashion. Despite these advances, …

Predicting Newborn Low Birth Weight: A Machine Learning Approach Using Maternal Health And Demographic Data, Rayan Elsayed

Theses

Low birth weight (LBW), defined by the World Health Organization as a birth weight under 2500 grams, remains a major global health challenge due to its association with increased risks for adverse neonatal and long-term health outcomes. This study aims to identify maternal health factors that contribute to LBW and to develop predictive models to support early identification of at-risk pregnancies. Two machine learning models, Logistic Regression (LR) and Random Forest (RF), were developed to analyze the relationship between maternal factors and LBW outcomes. The RF model achieved an accuracy of 96.36%, demonstrating robust predictive performance across metrics, making it …