Digital Commons Network^™

Exploring Engineering Students' Utilization Of Resources In Calculus Using Self-Regulated Learning, Kriz George

Theses

In response to high failure rates of engineering students in introductory math courses such as calculus, a wide variety of interventions have been implemented. A common intervention is targeted at modifying the curricula. Additionally, a major initiative to improve pass rates is to provide resources to students to help them better learn the concepts and get continual support as they complete assignments and other coursework. Despite these interventions, pass rates continue to remain low. I posit that merely the availability of resources is not enough for student success in mathematics courses. Students who lack knowledge of how to use these …

Integrating Climate And Geospatial Features Into Machine Learning Models, Abdulaziz Ahmed Aljaziri

Theses

This thesis addresses a significant gap in real estate valuation models by investigating the economic impact of localized climate conditions and granular geospatial amenities. An abstract summarizes the following: - The main themes, ideas or areas of theory being investigated: This research investigates the integration of localized climate conditions and granular geospatial amenities into machine learning (ML) frameworks for residential real estate valuation. - The background and context of the research: In dynamic urban markets like Dubai, traditional valuation models often rely on broad location labels and structural attributes, overlooking the tangible economic impact of environmental comfort and micro-climates in …

Integrated Machine Learning For Smart Home Resource Optimization, Ahmed Almazrouei

Theses

This thesis explores howmachine learning can be used to support better energy management in smart homes. Many smart home systems today collect a large amount of data through sensors and smart meters, but they still depend on simple rules and do not make predictive or automatic decisions. In the academic field, energy forecasting and energy optimization are often studied separately, which creates a gap in understanding how the two components can work together in a real setting. Because of this, there is a need to test an integrated approach that uses both forecasting and optimization in one framework. In this …

Ai-Driven Cyber Threat Detection, Humaid Thani Almheiri

Theses

Bycreating an AI-driven method using deep learning and statistical analysis tools, this study seeks to fill important security holes in conventional intrusion detection systems. Current signature-based systems miss new and complex cyberattacks, which have significant financial and operational consequences for companies. The suggested approach detects unusual network activity in real-time by combining statistical analysis with long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and statistical analysis. This study will create and test hybrid models that can identify both known and zero-day threats while reducing false positives using publicly accessible datasets like UNSW-NB15, CIC-IDS2017, and NSL-KDD. Expected results are a …

Optimizing Power Grids In Uae Using Data Analytics For Improved Efficiency And Reliability, Khalid Bukhashem

Theses

The United Arab Emirates (UAE) has aims to triple its renewable energy capacity to 14 GW to attain the objective of 30% contribution of clean energy by 2031. Energy consumption, on the other hand, will increase from 5.2 to 5.8 exajoules by 2028. The grid infrastructure for which was developed to facilitate the centralized generation of power, is not conducive to dealing with the variability of the decentralized renewable resources that lead to inefficiencies in operation and reliability problems. The Dubai power grid is analyzed using historical data of 105,120 residential, commercial and industrial supplies. In this analysis machine learning …

An Examination Of High-Entropy Alternatives Of Connectionist Temporal Classification Loss For Optical Music Recognition Using Convolutional Recurrent Neural Networks, Hritik Saynganthone

Theses

The Connectionist Temporal Classification (CTC) loss function is the most commonly used loss function in the field of Optical Music Recognition (OMR). However, OMR suffers from a massive class imbalance problem, exacerbated by the fact that CTC loss is subject to the spiky distribution problem, wherein the blank token introduced by CTC is vastly overpredicted and appears in timesteps where it would make more sense to predict a non-blank token, since CTC will collapse repeated tokens into a single token. This work posits that alternative loss functions to CTC that optimize for an increase in entropy of the prior probability …

Mixed-Integer Linear Programming (Milp) Model For Transportation Cost Optimization, Yasmen Ghonim

Theses

Motivated by a request from a real company, this study presents a mixed integer linear programming (MILP) model for labor transportation at real company located in Dubai (BSG). The study integrates routing, assignment, and environmental pricing into one model, with the focus on fixed shift worker transport and featuring several dorms and sites. It also tests the model with the real company data from the UAE service sector. The model assigns workers from dormitories to the client sites by busses in a manner that minimizes daily transport cost and monetized CO₂ emissions, subject to capacity, routing, and utilization constraints. The …

Assessing Large Language Models As An Interpretive Layer In Marketing Mix Modeling: Implications For Marketing Analytics, Mohammad Bin Haider

Theses

Marketing mix modelling (MMM) remains a core technique for guiding budget allocation, yet its outputs are often difficult for non-technical planners to interpret and govern. At the same time, large language models (LLMs) offer new possibilities for translating complex model artefacts into narrative guidance, but raise concerns about hallucination, reproducibility, and alignment with model-risk governance. This thesis examines whether an open-source MMM framework can be engineered as a repro- ducible, governance-ready pipeline and then augmented with a tightly constrained LLM interpretive layer. The empirical setting is a multi-brand, multi-country retail portfolio with several years of digital marketing and outcome data …

Meshed Trees: A Framework For Resilient Network Algorithms And Protocols, Peter Willis

Theses

The continued growth of computer networks in both size and complexity presents challenges for the control protocols that are tasked with maintaining optimal data-forwarding paths. These protocols commonly rely on shortest-path algorithms to determine where network data should be propagated and how it should be analyzed for inclusion in forwarding tables. However, problems arise during reconvergence, the process in which devices rediscover optimal paths following a network failure. Because control protocols were built decades ago for the networks of that era, the existing shortest-path algorithms utilized were designed for problems before the advent of modern computer networks. This resulted in …

Exploring Human Perception And Cognition In Expressing And Understanding Mechanical Designs, Yan-Ting Chen

Theses

Effective communication of mechanical designs through technical drawings requires geometric accuracy, efficiency, and an understanding of human perception and cognition. Although advances in computer-aided design (CAD) software have improved drawing precision and automation, current tools often overlook the spatial reasoning processes that users employ to interpret these representations. This study seeks to improve the accuracy and efficiency of human-computer interaction in CAD environments by examining how individuals perceive and interpret three-dimensional mechanical components within the context of technical drawings. A key focus is the identification of canonical and optimal views that align with intuitive human understanding. Through a series of …

Playa Sediment Bioaccessibility Presents Previously Unknown Risks To Population Health In A Water-Stressed Region Undergoing Lake Desiccation: Salton Sea, Usa, Jordan Jaeger

Theses

Globally, drylands are expanding, and endorheic basins are in rapid decline. Saline lake desiccation exposes lakebed playa likely to become dust, posing potential health risks to surrounding communities. Studies investigating health risks from playa dust exposure often focus on health effects associated with the presence of airborne particulates, but fewer studies investigate health risks associated with dust source chemistry. This study examines dust source particle composition in a desiccating inland sea in a water-stressed agricultural region of California. Specifically, chemical bioaccessibility of bound trace elements uncovers risks to children, adults, and agricultural worker health. Arsenic exposure via sediment ingestion poses …

Experimental Investigation: Performance Of Ranque–Hilsch Vortex Tubes Under Various Surface Boundary Conditions, Ahmad Al-Najjar

Theses

The Ranque–Hilsch vortex tube (RHVT) is a passive thermofluid device that splits compressed gas into simultaneous cold and hot streams through strong swirling flow inside a slender tube, without moving parts. This study experimentally examines how external boundary conditions applied to the tube wall influence RHVT performance. Three wall conditions were imposed: (i) externally cooled (cooling), (ii) externally heated (heating), and (iii) adiabatic (insulated). Tests were conducted with air over an inlet pressure range of 2–6 bar. The primary objective was to understand the extent to which wall thermal condition modifies temperature separation and energy efficiency. Performance was quantified using …

Advancing Security Safeguards In Large Language Models Through Multi-Agent Systems, Mohammed Rashed Alnuaimi

Theses

This thesis focused on enhancing the safe use of Large Language Model (LLM) through the innovative use of a Multi-Agent System (MAS). As LLMs like ChatGPT became essential to our everyday interactions, the need to maintain the safe use of these systems increased. This research thoroughly assessed the current security measures in place for LLM, pointed out their limitations and developed new and more effective security strategies. The core of the proposed solution was a MAS designed to ensure that all data processed by LLM met guidelines including Privacy, Confidentiality, and Ethical standards before reaching the user. The system involved …

Cobalt Layered Double Hydroxide As Advanced Electrode Materials For Supercapacitors And Electrocatalysis, Wafa Sultan Alaryani

Theses

Due to the high demand for sustainable energy storage options and efficient energy conversion devices, extensive research has been conducted on various electrode materials. This thesis is concerned with the fabrication of electrode material mainly using cobalt-based material, particularly cobalt layered double hydroxides (Co-LDH) with the combination of nitrogen-doped carbon nanotubes (N-CNTs) to be used in supercapacitors and oxygen evolution reactions (OERs) applications. The main objective is to enhance the electrochemical performance of supercapacitors and to develop a novel electrode material for a more stable and efficient OER in alkaline medium.

The methodology involved a one-step hydrothermal procedure to synthesize …

Startup Success Forecasting Through Machine Learning: A Comprehensive Analysis Of It Startups, Khaled Abdulla Alhassani

Theses

Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success?

This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), …

Enhancing It Security Management With An Advanced Intrusion Detection System Based On Machine Learning And Explainable Ai, Hanan Aslam Al Zubaidi

Theses

The fast changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today's advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.

The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, …

Assessing Teachers’ Practices In Supporting Gifted/Talented And Twice-Exceptional Students In Schools And Centers In Abu Dhabi, Zayed Jaber

Theses

Gifted/talented and twice-exceptional (2e) students, especially those whose needs are often overlooked in mainstream classrooms, are more likely to benefit from consistently implemented Differentiated Instructional Practices (DIPs). This study aimed to examine implelemntation of DIPs for gifted/talented and 2e students and to compare the practices implemented between of Special Educational Needs (SEN) teachers and General Education teachers in schools and centers within Abu Dhabi. This research used a quantitative, cross-sectional survey design to collect data from eighty-six teachers from Abu Dhabi. A questionnaire was designed to rate teachers’ self-reported implementation of DIPs across the four domains of content, process, product, …

Evaluating Large Language Models For Automated Cv Ranking: A Hybrid Embedding Approach For Enhanced Recruitment, Sarah Mohamed Alhindaassi

Theses

Increasing numbers of applications have revealed limitations in legacy keyword-filtering-based Applicant Tracking Systems (ATS), which commonly overlook candidate potential and ignore contextual or transferable skills. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) offer an exhilarating alternative, supporting context-sensitive and human-crafted reasoning in candidate evaluation. This thesis systematically evaluates four classes of approaches, lexical models, embedding-based methods, Large Language Models (LLMs), and hybrid ensembles, for automation of Curriculum Vitae (CV) to Job Description (JD) matching without exploiting prior annotations or annotations at match time. Using a combination of publicly available datasets and real-world sample data covering three …

Enhancing The Acceptability Of Decision-Making System Using Xai Case: Crime Profiling System, Mohamed Saeed Al Shamsi

Theses

In the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle …

Groundwater Quality And Health Risk Assessment In The Fujairah Region Using Hydrogeochemical, Statistical, And Spatial Approaches, Mohammed Khaled Alawlaqi

Theses

Groundwater is a valuable resource in the arid climate of the United Arab Emirates, where growing population, agricultural, and industrial activities are putting increasing pressure on the limited groundwater reserves. This work investigates the hydrogeochemical characteristics of groundwater in the Fujairah region to assess water quality and potential health risks. A total of 64 groundwater samples were collected from the Eastern Shallow Aquifer (ESA) and the Ophiolite aquifer in the study area and were analyzed for physicochemical parameters, major ions, and trace metals. Hydrogeochemical methods such as Piper, Gibbs, Chadha, and Schoeller diagrams were used to identify water types and …

Enhanced Privacy Preserving Healthcare Data Management With Federated Learning Using Homomorphic Encryption, Omar Abdulla Ali

Theses

Federated Learning (FL) is a decentralized approach of machine learning on multiple clients jointly training models without sharing their raw data, which drastically improves privacy and enhance protection against security breach. This is particularly critical in the healthcare sector, where hospitals and medical institutions are often unable to exchange patient records due to strict privacy regulations and data-management policies. However, there is still a risk of privacy breach when clients send their model updates to the central server, because if a model update is intercepted or analyzed by a malicious entity, it could be used to recover sensitive data using …

Detecting Data Poisoning Attacks In Medical Imaging: A Study On Chest X-Ray Classification Tasks, Akhila Abdulla Asgar

Theses

This thesis examines the vulnerability of AI medical imaging models to adversarial threats, with a specific focus on data poisoning attacks in chest X-ray classification. The study begins with a Systematic Literature Review (SLR) to assess the existing adversarial attacks and defenses in medical imaging, revealing a significant research gap in studies exploring data poisoning attacks in the medical domain. Based on our literature search, an efficient and lightweight defense, namely friendly noise defense, against data poisoning has not been investigated in medical imaging classification tasks. Hence, in this work, we investigated its effectiveness on the chest X-ray dataset, and …

Determination Of Variations In Orbital Parameters Of Sample Of Satellites Through Spacecraft Tracking, Asma Ali Alasmari

Theses

The continues increasing density of Resident Space Objects (RSOs) in Earth’s orbit highlights the crucial need for Space Situational Awareness (SSA). This thesis studies the orbital changes in two regions, active low Earth orbit (LEO) satellites and inactive geostationary Earth orbit (GEO) satellites. For LEO satellites we used publicly available data (two line element TLE from Space-Track) and ASTRIAGraph. We verified the orbital elements series and time ordered them, applied a Hampel (median+MAD) mask to semi-major axis, eccentricity vector and inclination to find outliers and exclude them from event detection. The events are then detected using time normalized slopes and …

The Simulation Of Hyperspectral Observations By The Upcoming Spacecraft "Arab Satellite 813", With The Radiative Transfer Model Sciatran, Sara Maher Alhasan

Theses

The Arab Satellite 813, which is scheduled to be launched in 2025/2026 by the UAE Space Agency, is set to increase earth observation and monitor the climate across the middle East and North Africa (MENA) region. This study uses the SCIATRAN radiative transfer model which was originally developed for the Envisat mission to simulate the hyperspectral observation specifically tailored for the satellite's payload. Using calibrations that were used by the previous research in their SCIATRAN V4.6 study and adjusting parameters like the Leaf Area Index (LAI), a simulation which agreed robustly with the benchmarks set by the previous research was …

Three-Dimensional Cfd Analyses Of Natural Circulation In A Pressurized Water Reactor During A Loss Of Coolant Accident, Arad Dehestany

Theses

In this research, ANSYS FLUENT simulations will be performed on natural circulation cooling performance during loss of coolant accident on the semi-scale Mod-2A geometry. Natural circulation experiments were performed in the semi-scale Mod-2A test facility which is a small-scale model of the primary system of a four-loop PWR nuclear power generating plant. The CFD results are validated using the tests done in the facility. The scope of the research is to understand the flow, temperature distribution, inventory coolant losses, and pressure drop in the system. These findings can provide insights into the performance of such systems and identify potential improvements …

Examining Children’S Intuitive Understanding Of Mechanical Systems With A Gears Assembly Task, Nicole Taboada

Theses

Children’s causal reasoning about machines and their components is crucial for acquiring knowledge about science and math. Mechanistic understanding involves knowing how parts can be effectively connected to work together as a functional mechanical system. Little is known about how children’s understanding of mechanical systems develops across the lifespan. Legare and Lombrozo (2014) developed a gears assembly task to assess children’s understanding and learning of mechanics but did not track this skill across ages. The present study adapted the gears assembly task to measure mechanistic understanding in children between the ages of 4 to 11 years and adults. The goal …

Generation And Detection Of Photo-Thermal And Photo-Acoustic Waves In Solids For Advanced Near-Field Ir Imaging, Yide Zhang

Theses

Atomic force microscopy-infrared (AFM-IR) spectroscopy has become an essential tool for nanoscale chemical imaging, offering spatial resolution beyond the diffraction limit of conventional infrared spectroscopy. A typical AFM-IR setup directs a pulsed IR laser onto the sample, precisely at the tip of an AFM cantilever, detecting local absorption indirectly as photothermal expansion via the tip. This combination of AFM’s high spatial resolution and infrared spectroscopy’s chemical specificity enables detailed studies of a wide range of materials, from polymers and biological tissues to nanomaterials. However, the factors influencing AFM-IR signal generation and spatial resolution-especially in complex and inhomogeneous samples-remain challenging to …

Uncovering The Efficacy Of Unlabelled Data In Anomaly Detection, Andrew Shields

Theses

Anomaly detection is an important area of research within data mining and has applications in many domains, including cybersecurity, finance, and healthcare. Conventional methods depend on labelled training data and assume an even class distribution, restricting their 'real-world' application, where rare anomalies and unlabelled data are the norm. This research addresses these limitations by investigating how unlabelled data can support anomaly detection in imbalanced, data-scarce, complex environments. It addresses three research questions: how to integrate unlabelled data into anomaly detection models, how to improve performance when labelled anomalies are scarce, and how graph-based methods capture complex relational anomalies. Three empirical …

A Framework For Adaptive Ride-Sharing And Management Of Electric Vehicles, Avinash Nagarajan

Theses

Europe aims for carbon neutrality by 2050, with transportation currently amount- ing 20% of its total emissions. Motivated by the sustained mass transition to Autonomous Electric Vehicles (AEVs) and by the distributed energy generation and storage of local Smart Energy Communities (SECs), this thesis presents the design, implementation and evaluation of a carbon-neutral, community-based, scalable ride-sharing service. Its models formalise the partition of an AEVs fleet over the SECs of a city, as well as the allocation of trip petitions (TPs) to AEVs, their routing and charging scheduling over a simulated time horizon. These models integrate energy generation, allocation and …

Functional Foods For Older Adults: Comparing The Bioactive Properties Of Cheese To Identify Products That Promote Healthy Ageing, Aimee Plante

Theses

Functional foods for the elderly: comparing the bioactive properties of cheese to identify products that promote healthy ageing.

People are living longer, and a healthy ageing population is a key goal of our National Positive Ageing Strategy. One approach to support this initiative is to promote and develop functional foods that can support healthy ageing by reducing age-related conditions, including cardiovascular disease, diabetes, obesity and cancer. Cheese particularly aged and fermented varieties like cheddar show great potential as a functional food with inherent and established health properties. It naturally contains bioactive peptides, vitamins, minerals, protein and possible cultures that confer …