Digital Commons Network^™

Inflection Points In Academic Career Trajectories: Statistical Modeling And Interactive Visualization, Aicha Malouche

Theses

This thesis examines the temporal structure of academic career trajectories, with a particular focus on identifying non-linear patterns of research productivity and moments of maximum acceleration in scientific impact. Situated within the broader context of bibliometric evaluation, the study responds to long-standing limitations of aggregate and linear career models that obscure heterogeneity across disciplines and national research systems. Drawing on theories of cumulative advantage, life-cycle productivity, and structural stratification, the research seeks to clarify how and when elite researchers experience peak growth in impact over the course of their careers. The study is guided by four research questions: (1) when …

Optimizing Motor Insurance Premiums In The Uae Using Predictive Analytics, Samar Abdelsalam

Theses

The car insurance industry in the UAE and GCC faces growing challenges including premium inflation, fraudulent claims, inconsistent pricing models, and lagging innovation in underwriting. This study investigates how predictive analytics and machine learning can enhance motor insurance premium optimization, with a focus on incorporating socio-demographic, behavioral, environmental, and vehicle-specific risk factors. Using a publicly available dataset of over 125,000 insurance policy records, this research applies a CRISP-DM framework to develop and evaluate supervised learning models including Linear Regression, Decision Trees, and Random Forests. Results highlight Linear Regression as the most effective model (RMSE = 193.62, R² = 0.9016), enabling …

Multimedia-Based Interventions To Improve Science Learning Outcomes For Deaf Students At Wangsel Institute For The Deaf., Cheten Tshering Mr., Tshering Wangchuk

Journal of Science Education for Students with Disabilities

Efforts have been made to ensure inclusive learning opportunities for all students, including those from diverse backgrounds. One key strategy to enhance children’s learning and academic performance involves the thoughtful selection and application of instructional tools tailored to meet learners' specific needs. This study examines the impact of multimedia-based instruction on science achievement among deaf students at Wangsel Institute for the Deaf in Bhutan. Using a quasi-experimental design, the research compared pretest and post-test results between a control group and an experimental group. Data analysis included descriptive statistics (t-test) and ANCOVA. The findings indicated a statistically significant differences (p = …

Evaluating The Effectiveness Of Smart City & Iot Solutions In Improving Urban Sustainability, Mobility, And Quality Of Life In The Uae, Aisha Alnuaimi

Theses

The United Arab Emirates has an aim to develop smart cities incorporating advanced technologies such as the Internet of Things (IoT), to enhance urban sustainability, mobility, and improve citizens quality of life. However, based on the significant funds invested by the government, there exists an important gap within the government systems in measuring the effectiveness of such activities given the lack of data quantifying the benefits gained from such smart initiatives. To address this gap, this study delivers the benefits gained from smart initiatives by using a mixed-methods approach combining both quantitative and qualitative data. The research is based on …

Multipath Exploitation For Radar Sensing In An Urban Environment Using The Music Algorithm For Jammer Mitigation, Daniel De Zutter

Theses

With the increased push for spectrum sharing techniques, the technological world surrounding radar has been constantly pushing to find new methodologies. From being able to integrate with a communication system to mitigating the effects of other users on a system, the research community is pushing forward. We look to tackle the problem of mitigating a jammer using a multipath MUltiple SIgnal Classification (MUSIC) based approach. We take multiple steps to implement this and generate a framework for future work. We first take a look at understanding the current state of the art research surrounding DFRC, multipath exploitation, MUSIC algorithms, RIS …

Customer Lifetime Value Prediction In E-Commerce, Shaimaa Alantali

Theses

The e-commerce market across the world currently deals with an urgent problem which has emerged because businesses now use automated pricing systems everywhere. Online merchants who automate their decision processes to handle changing customer needs and limited stock situations must face external business forces which push their teams to start destructive pricing battles that harm all businesses in the sector. The research investigates how a unified computational framework which combines demand modeling with advanced data analytics methods can enhance pricing strategies through market instability detection and prevention systems. The study creates a market simulator through data-driven methods which uses retail …

Leveraging Ai, Iot, And Predictive Analytics For Crisis Management And Urban Resilience In Dubai, Suhail Bin Kalli

Theses

The thesis will discuss how smart technologies could be incorporated in the crisis management structures of Dubai to make the city more resilient. As Dubai grows more urbanized, it experiences increased problems with managing its crisis especially in the high population density places, traffic congestion, utility disruption, and extreme weather. This paper discusses how artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics can be used to ameliorate the response to emergencies, manage the allocation of resources, and enhance the coordination between different agencies. The crisis management systems at Dubai are still ineffective despite the level of technologies …

Optimizing Marketing Campaigns To Maximize The Response Rate For A Supermarket Using Machine Learning Techniques, Alia Almansoori

Theses

Supermarkets must improve their marketing tactics at a time of changing consumer behavior and increasing competition in the retail industry. Engaging the wide and sophisticated consumer base of today's supermarkets is difficult using traditional methods. This proposal presents a data-driven approach using cutting-edge machine learning methods to enhance supermarket marketing strategies. The main goal is to increase the response rate of marketing initiatives, which will improve consumer engagement and ultimately increase revenue. Additional primary objectives are customer segmentation and targeting, predictive modeling, personalization, ongoing performance monitoring, and ROI evaluation. The current problem centers around personalization and accuracy. Due to the …

Cochlear Micropump Drug Delivery System: A Noninvasive Technique For Murine Intracochlear Drug Delivery, Janette Collins

Theses

In a 2018 World Health Organization (WHO) study, over 400 million people worldwide were affected by hearing loss (World Health Organization, 2023). Cochlear implants have provided a conventional route to aid inner ear issues but are ineffective either delivering to unintended targets or delivering unknown concentrations of a particular drug and create toxicity to the user. With medical advances and using 3-D printing technology, new opportunities to use programmable drug delivery systems to treat auditory disorders have been created. Borkholder Labs in Rochester, NY have created and improved a miniaturized implantable and scalable peristaltic micropump that provides programmable and accurate …

Predicting Student Academic Performance Using Behavioural And Parental Engagement Data From Learning Management Systems, Saeed Alfalasi

Theses

This paper explores how behavioral, academic, and parental engagement data provided within the xAPI-Edu-Data dataset can be used to predict the academic performance of students when training on machine learning models with supervised learning. Due to the developing demands of the data-driven initial selection of the learners under risk, the study will create a valid and explainable predictive model that can consider the most significant factors of student success in Learning Management System (LMS). The research is based on behavioral engagement and self-regulation learning theories; the observations included in the analysis of student interaction, i.e. resource usage, classroom engagement and …

Statistical Optimization And Anfis Modeling Of Process And Heat Treatment Effects In Gmaw-Waam 316l Stainless Steel, Joel Sam John

Theses

Additive manufacturing has evolved from the concept of merely rapid prototyping tools to that of a viable technology capable of manufacturing industrial grade parts and components. As a result, the use of metal additive manufacturing techniques has expanded in recent years, fueled by the rapid growth of manufacturing sectors amidst the 4.0 industrial revolution. Within the metal-AM domain, the concept of gas metal arc welding – wire arc additive manufacturing (GMAW-WAAM) has gained increasing traction due to its low operational costs, high deposition rates, and its ability for large scale manufacturing.  The demand of stainless-steel parts has increased owing to …

Leveraging Open-Source Llms For Infrastructure Drift Detection And Correction, Kemoy S. Campbell

Theses

Infrastructure as Code (IaC) enables organizations to provision and manage infrastructure at scale using version-controlled, declarative specifications. Despite these advantages, infrastructure drift where deployed resources diverge from their intended configuration remains a persistent operational challenge and is often addressed manually and reactively. Recent advances in Large Language Models (LLMs) have accelerated their adoption within software engineering and DevOps workflows; however, the sensitive nature of IaC artifacts, particularly Terraform state files, has limited organizational willingness to rely on public LLM services for drift detection and remediation. This thesis investigates the feasibility of using open-source, self-hosted LLMs to assist with IaC drift …

Enhancing Json Schema Inference Via Semantic And Structural Modeling Of Heterogeneous Data, Justin R. Namba

Theses

Schema discovery is finding the structure of data. It helps users understand the meaning of data and write queries to manipulate it. This is typically easy for relational databases, but complex for non-relational (NoSQL) databases with documents. For relational databases, the schema is predefined because the data they contain is structured, but for NoSQL databases, data is usually unstructured or semi-structured. Here, we focus on a type of semi-structured data called JSON, which is a collection of documents that consists of nested key-value pairs. A JSON key and value are similar to a column name and its associated data instance …

Predicting Undergraduate Fallout And Success, Ammar Ahmed Alzarooni

Theses

Universities need to uncover students who are likely to drop out early and do something about it. It should be easy to find and use this information. This thesis evaluated interpretable machine-learning models (MLMs) for predicting three final student outcomes including Dropout, Enrolled and Graduate, using solely demographic, administrative/financial and first-semester academic variables. Study employed publicly accessible Portuguese UCI student retention dataset (4,424 records) and incorporated end-of-Semester-1 feature window to prevent look-ahead leakage and align with actual advising cycles. We trained and compared models using stratified validation, explicit class-imbalance handling and decision-threshold optimization to make dropout detection most critical factor. …

Impact And Requirements Of Future Robots In The Urban Realm: Dubai Silicon Oasis Case Study, Abduljalil Abbasi

Theses

Robots are moving from factories and warehouses into the streets, sidewalks, and shared public spaces of cities, and Dubai Silicon Oasis (DSO) is at the forefront of this shift in the Gulf region. Through the Talabat ‘talabot’ autonomous delivery pilot, DSO has demonstrated that sidewalk robots can operate reliably in a hot-climate, mixed-use urban district, but this operational success has outpaced the governance frameworks, spatial design standards, and long-range planning foundations needed to scale deployment safely, inclusively, and sustainably to 2035. This thesis addresses that gap by asking how DSO can design and govern the integration of robots into its …

Hexahive: Simulating Vulnerability Bounty Economies Through Game-Theoretic Models, Lucille Blain

Theses

Bug bounty programs encourage security researchers to responsibly disclose software vulnerabilities by offering financial rewards, recognition, and opportunities to build long-term relationships with organizations. However, vulnerability discovery also creates competing incentives. A researcher may report a vulnerability through an official program, exploit it for passive income, trade information with other actors, delay disclosure, or seek compensation through alternative markets. These decisions are shaped by the expected value of rewards, perceived fairness of company responses, reputation benefits, competition, and the risk of penalties. Because directly observing these choices in real-world bug bounty programs presents ethical, legal, and practical challenges, controlled environments …

Optimizing Crime Response Time Using Predictive Analytics Models, Mohammad Hassan Bin Yaqoob

Theses

Timely and efficient emergency response is essential for public safety, particularly in high- risk urban environments. However, existing dispatch systems often rely on reactive decision- making, leading to delays and inefficient resource allocation. This thesis explores how artificial intelligence (AI) and data modeling can enhance police response times through predictive analytics. A systematic review of 17 peer-reviewed studies identified key predictors such as dispatch behavior, crime history, temporal characteristics, and geographic location. Using real-world emergency response and crime data, predictive models were developed and evaluated through algorithms including XGBoost, Linear Support Vector Machine (LSVM), Generalized Linear Model (GLM), and Neural …

The Economics Of Liability In Low Orbit Satellites, Mitchell Rieder

Student Work

This paper aims to answer three main questions regarding this proportional liability approach:

• What is the microeconomic perspective of utilizing proportional liability, and how can it be applied to the LEOS industry?
• What are the benefits of such an approach? How do these benefits differ from existing policy propositions?
• What steps are necessary before proportional liability can be implemented?

Through the evaluation of these questions, this paper seeks to demonstrate the policy viability of proportional liability, additionally showing that this policy will perform in much the same way as a standard Pigouvian tax, which existing research has evaluated though differing …

A Fish & A Bird, Deanna Moorehead

Theses

As a curtain of trees opens and dawn breaks over the glistening waters of Lake Ontario, a fish and a bird spot each other through the surface and fall madly in love. They dance and sing together from their respective atmospheres, yet each time the bird soars past the water's edge and onto the beach, the fish cannot follow. Night after night, by the light of the moon, the fish trains herself to leap out of the water, hoping to breach high enough to join her beau above. Late one afternoon, the fish approaches the shore, where the bird stands …

Scout 07, Austin Roberts

Theses

Throughout my tenure at RIT, I have produced multiple short films, undertaken gaming co-ops, and pursued an independent study centered on gaming assets to expand my technical and creative skills. For my thesis film, I sought to merge these experiences by concentrating on environmental storytelling—a deliberate challenge, given faculty concerns that narratives lacking strong character focus may struggle to engage audiences emotionally. With these considerations in mind, I crafted a film featuring a secondary character within a rich environment, designed to immerse viewers and provoke curiosity about the story’s temporal and spatial context.

Evaluating The Feasibility Of Digital Twin Adoption For Mep Systems, Tejas Adana

Theses

MEP systems dominate building operational costs, yet facility teams continue battling reactive maintenance cycles that result in unexpected equipment failures, emergency callouts, and continuous energy inefficiencies. Digital twin technology presents a practical solution by creating live virtual replicas that shift operations from firefighting breakdowns to anticipating issues before they escalate. However, adoption remains limited due to fragmented implementation guidance, short-term validation studies, and minimal research addressing emerging market contexts. This research employs a grounded approach combining systematic literature analysis, candid discussions with MEP engineers and facility managers across UAE commercial properties, and practical prioritization frameworks assessing real-world impact against implementation …

Disposal, Yihan Su

Theses

Disposal is a mixed-media animated short film created as a graduate thesis project. The story explores the protagonist’s search for meaning in life and his defiance of a predetermined fate. At the beginning, he is an ordinary, guileless figure without remarkable qualities. In his world, life revolves around constant competition, and one’s worth is measured by their position on a ranking board. Only the top three may pass through the golden gate, a symbol of transcendence. Others must continue the endless cycle of contests, while the lowest seven face incineration in the Disposal. Despite his efforts, the protagonist falls into …

Hugs In Hues, Shivani Pednekar

Theses

Hugs in Hues is an animated short film about social anxiety, emotional expression, and discomfort with physical touch. The story follows Tina, a young artist who struggles to express how she feels. When she pulls away from a hug from her friend Emily, Tina escapes into a painting-like 2D world made from her drawings. This imagined world reflects Tina’s emotions. The colors, textures, and spaces change based on how she feels. Her anxiety, fear of rejection, and need for safety take visual form. The real world feels sharp and rigid, while Tina’s illustrated world feels soft, imperfect, and expressive. Animation …

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 …

Content Aware Intra And Inter Prediction Using Texture And Motion Analysis, Yashaswi Karthik Reddy Danda

Theses

Modern video codecs rely on highly optimized block based intra and inter prediction frameworks,  in which block partitioning is determined through exhaustive rate distortion optimization (RDO)  searches. These exhaustive searches evaluate many possible block configurations to identify the  partitioning that minimizes overall rate distortion cost, and they have proven extremely effective  across diverse visual content. This thesis explores a complementary perspective by investigating  whether block partitioning can be guided more directly by content characteristics specifically,  texture complexity for intra prediction and motion complexity or motion density for inter  prediction.  For intra prediction, we introduce a texture adaptive approach that utilizes …

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 …

Planning For Power: The Role Of Architecture & Urban Form In Local Energy Generation In Dense Built-Up Areas, Chris Shing Wai Leung

Theses

This thesis explores how the urban form/building design, and a well-thought-out City Master Plan can significantly affect wind patterns and enhance on-site energy generation in dense urban areas. It will examine strategies such as modifying building heights, adjusting separations between structures, shaping urban landforms, and varying the roughness of the urban fabric to optimize wind energy generation. Additionally, the research will focus on the strategic placement of community power harvesting devices. The study of the urban forms and fabric with the impact of wind patterns is simulated by fluid mechanics with Computational Fluid Dynamics (CFD) software. The goal of this …

Basil_Dock: Development Of Accessible And Customizable Molecular Docking Procedures, Lee Schoneman

Theses

Molecular docking is a computational technique often used in drug design due to its ability to predict ligand binding potential, conformation, and location for a given drug target. Docking as a screening tool also comes at a greatly reduced cost compared to wet bench work. Despite its usefulness, molecular docking is often inaccessible to novice users, as to perform molecular docking, most programs require at least a basic knowledge of command line and computer programming for both installation and utilization. Additionally, tutorials for the most popular programs tend to be inflexible, instructing how to bind predetermined molecules to a specified …

A Decision Support System For Global Vaccine Funding: Data-Driven Proposal Scoring, Epidemiological Risk Modeling, And Portfolio Optimization, Ashutosh Kumar

Theses

Global health financing must continually stretch limited immunization resources across competing priorities, and Gavi, the Vaccine Alliance, plays a central role in this landscape. This work reframes Gavi’s funding decisions as a multi-objective portfolio optimization problem that jointly considers health impact, equity, cost-effectiveness, sustainability, and epidemiological interdependence. To operationalize this approach, this study develops a decision support system grounded in Modern Portfolio Theory, treating each proposal as an asset characterized by expected return and risk. Proposal returns are estimated through a heterogeneous data-fusion pipeline that generates quantitative, multi-objective scores aligned with Gavi’s strategic priorities, while epidemiological risk is quantified using …

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 …