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Segment-Level Machine Learning For Detecting Partial Deepfake Audio: An Rnn–Svm Hybrid Approach For Real-World Adversarial Environments, Ahmad Alhelli Dec 2025

Segment-Level Machine Learning For Detecting Partial Deepfake Audio: An Rnn–Svm Hybrid Approach For Real-World Adversarial Environments, Ahmad Alhelli

Theses

This study investigates the detection of real, fully fake, and partially fake (PF) audio using classical machine-learning models as well as a segment-level analysis model. Unlike most existing research, which focuses solely on binary real-vs-fake classification, this work introduces a three-class detection framework and constructs realistic PF samples by inserting short synthetic speech segments into otherwise genuine audio recordings. Its method combines MFCC and spectral feature engineering,Wav2Vec2 embeddings, controlled PF synthesis and various models such as SVM, Random Forest, XGBoost and an attention based RNN. Segment level windowing allows fine-grained study of transition of time and breaks of manipulation. The …

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Integrating Climate And Geospatial Features Into Machine Learning Models, Abdulaziz Ahmed Aljaziri Dec 2025

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 …

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Multilingual Identity Document Information Extraction Via Dynamic Templates And Hybrid Ocr, Khalifa Rashed Dec 2025

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 …

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Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali Dec 2025

Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali

Theses

This study investigates how artificial intelligence can enhance telecom network management by forecasting internet usage, predicting congestion, and identifying user behavior patterns from mobile phone activity data. The study made use of anonymized logs for calls, SMS and internet, and put up a multi-model analytical pipeline, which was composed of time-series forecasting (ARIMA, LSTM), clustering (K-Means), and classification (XGBoost), to perform the analysis. Among the time-series methods, ARIMA ranked first in the forecast performance (RMSE=0.31) and gave LSTM a convincing defeat in the case of this particular short and stable dataset. Based on K-Means segmentation, users were sorted into five …

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Predicting Athlete Performance Using Machine Learning Models, Mohammad Abdulbasit Mohammad Alabdulla Dec 2025

Predicting Athlete Performance Using Machine Learning Models, Mohammad Abdulbasit Mohammad Alabdulla

Theses

The purpose of this research is to develop and assess multi-modal machine learning for robust performance analysis to predict athletic performance and evaluate injury risk. The study employed Data Analytics approach, where composite features, i.e., Training Stress and Recovery Score,were modelled to characterize training-recovery correlations. It had four different regression models (MLR, RFR, SVR, DNN) and four different classification models (Logistic Regression, RFC, SVM, DNN). DNN exhibited an increased degree of effectiveness in the forecasting Synthetic Performance Score (R2 =0.9998). Notably, the Random Forest Classifier (RFC) turned out to be the most valid predictor of injuries risk (F1-Score 0.7736; AUC-ROC …

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Optimizing Waste Management And Recycling Patterns Using Data Analytics, Mohammad Omar Almarri Dec 2025

Optimizing Waste Management And Recycling Patterns Using Data Analytics, Mohammad Omar Almarri

Theses

The increasing pace of urbanisation and consumption has increased the burden of waste generation in the world and it is a huge burden on the current waste management systems. The United Arab Emirates (UAE) is a region that is intensifying this challenge through the accelerated urbanization, high sustainability targets, and the necessity of effective recycling policies. The thesis that is being examined explores the ways in which the data analytics can be utilized to streamline waste management and recycling trends, emphasizing the enhancement of the collection process, forecasting the trends in the waste generation as well as facilitating the use …

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Leveraging Ai In Traffic Monitoring For Improved Accident Prediction In Uae, Mahra Alattar Dec 2025

Leveraging Ai In Traffic Monitoring For Improved Accident Prediction In Uae, Mahra Alattar

Theses

This paper investigates how Machine Learning (ML) models can be used in traffic surveillance and predict accidents in the United Arab Emirates (UAE). In spite of large infrastructure developments and stringent traffic laws, traffic accidents continue to be a major problem with an increase in fatalities and injuries. There is a gap in effective detection and responses since traditional monitoring and forecasting techniques are unable to capture the intricate, nonlinear, and time-dependent character of traffic patterns. This thesis fills that void by utilizing sophisticated the potential use of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), …

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Integrated Machine Learning For Smart Home Resource Optimization, Ahmed Almazrouei Dec 2025

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 …

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Biodegradation Of Common Post-Consumer Plastic, Laura Krebs Dec 2025

Biodegradation Of Common Post-Consumer Plastic, Laura Krebs

Theses

Plastic waste is widespread throughout the Laurentian Great Lakes watershed. During environmental exposure, plastic undergoes abiotic and biotic degradation. The presence of novel plastic substrates coupled with rapid microbial turnover may lead to selection for or evolution of metabolic pathways and enzymes with enhanced biodegradation capabilities. This study investigated the biodegradation potential of bacteria isolated from debris accumulation hotspots - stormwater retention ponds, storm drains, and tributaries - in the Lake Ontario watershed within Rochester, New York. Bacterial isolates were exposed to commonly littered plastics for 240 d: cellulose acetate from smoked cigarette filters, high-density polyethylene from takeout shopping bags, …

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Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi Dec 2025

Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi

Theses

The paper is research exploring the importance of Artificial Intelligence (AI) and Data Analytics to optimize airport operations and the passenger experience in the environment of the expanding air traffic in the world and the smart city movement. With increasing tasks that airports currently experience, congestion, flight delays, mishandling of baggage, and limited capacity, nowadays AI-based technologies integration is crucial to the operational efficiency, sustainability, and customer satisfaction. This study discusses the use of predictive analytics, machine learning, and clustering models to enhance passenger flows, resource allocation, and performance in general at airports. The research is based on the working …

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Optimizing Delivery Time Predictions Using Machine Learning: A Data-Driven Approach To Last-Mile Logistics, Mohamed Burqaiba Dec 2025

Optimizing Delivery Time Predictions Using Machine Learning: A Data-Driven Approach To Last-Mile Logistics, Mohamed Burqaiba

Theses

This paper addresses how machine learning can be utilized to improve the prediction of delivery times during the last mile, specifically in Dubai urban logistics issues whereby the traffic congestion and weather circumstances usually contribute to unpredictable delivery times. The main goal was to evolve machine learning models that can predict properly delivery time depending on several parameters, i.e., speed of traffic, weather, length of delivery, and geography. The reason why three machine learning models were chosen [Random Forest, Gradient Boosting, and XGBoost] in this analysis is that they have the opportunity to work with non-linear relationships in the data. …

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A Machine Learning Framework For Forecasting Passenger Flow At Dubai International Airport, Khalifa Alfalasi Dec 2025

A Machine Learning Framework For Forecasting Passenger Flow At Dubai International Airport, Khalifa Alfalasi

Theses

The fast growth of global air travel made the accurate prediction of passenger flow a challenge for airport operations. Traditional forecasting models, like regression and SARIMA, have been useful in stable conditions but usually do not show the non-linear and dynamic changes driven by weather, infrastructure, and behaviour of passengers. Therefore, recent studies showed the potential of machine learning and big data to improve the accuracy of forecasting. However, most of the work is still limited to specific airports, datasets, or operations. This study develops a machine learning–based predictive framework for short-interval passenger throughput at Dubai International Airport. Using available …

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Procedurally Textured Gaussians, Benson Haley Dec 2025

Procedurally Textured Gaussians, Benson Haley

Theses

Gaussian Splatting is rapidly establishing itself as the leading methodology for Novel View Synthesis—the process of generating arbitrary views of a 3D scene from sparse 2D input images—and from its popularity, various geometric and texture-based improvements have emerged.  We propose a novel texture-based modification to Gaussian Splatting that draws inspiration from the advent of procedural textures in classical rendering, superseding previous texel-based approaches by training a continuous texture per splat that improves view fidelity while also lending itself to be easily combined with geometric improvements to baseline Gaussian Splatting.

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Optimizing Power Grids In Uae Using Data Analytics For Improved Efficiency And Reliability, Khalid Bukhashem Dec 2025

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 …

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Predictive Modelling Of Long-Term Outcomes In Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Machine Learning, Neha Malik Dec 2025

Predictive Modelling Of Long-Term Outcomes In Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Machine Learning, Neha Malik

Theses

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome is a complex chronic illness characterized by debilitating, heterogeneous symptoms. The condition exhibits highly variable long-term outcomes, posing significant challenges for patient management, research, and clinical practice. Currently, there is a lack of tools for predicting individual patient trajectories, creating a substantial prognostic gap that is compounded by the prevailing research focus on diagnostics over prognosis. This study addresses that gap by investigating whether multidimensional baseline patient-reported outcome measures (PROMs), analysed using advanced and explainable machine learning methods, can meaningfully predict heterogeneous 12-month outcomes in ME/CFS, and by examining what patterns of predictability themselves reveal about …

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Labor Transportation Simulation And Optimization - A Case Study, Tala Taha Dec 2025

Labor Transportation Simulation And Optimization - A Case Study, Tala Taha

Theses

Transportation systems play a critical role in supporting economic activity, workforce mobility, and service delivery in urban and industrial environments. In many organizations, fixed-time transportation systems are essential for ensuring that employees are transferred from accommodation facilities to work locations within strict operational time windows. The case study company in this research operates a large-scale labour transport service between multiple accommodation camps and client sites in Dubai. Historically, buses were dispatched in an ad hoc manner, relying on supervisors’ experience rather than a systematic planning approach. This often led to low seat utilisation, unnecessarily long routes, higher fuel costs, and …

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Supervised Learning For Predicting Mental Health And Burnout In Healthcare Workers, Afra Alfalasi Dec 2025

Supervised Learning For Predicting Mental Health And Burnout In Healthcare Workers, Afra Alfalasi

Theses

This study aims to predict and analyze burnout among healthcare workers using supervised machine learning techniques and Exploratory Data Analysis (EDA). Leveraging the Healthcare Workforce Mental Health Dataset, the research identifies key demographic, occupational, and psychological factors most strongly associated with burnout. The methodology involves data preprocessing, feature selection, and model training using algorithms such as logistic regression, decision trees, random forests, and gradient boosting. Model performance will be evaluated through standard metrics, including accuracy, precision, recall, and ROC-AUC. The expected outcome is a predictive framework that highlights high-impact burnout predictors and generates actionable insights to support early intervention and …

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Chatgpt As A Mental Health Ally: A Study On College Students’ Adoption Of Ai For Therapy, Alya Albastaki Dec 2025

Chatgpt As A Mental Health Ally: A Study On College Students’ Adoption Of Ai For Therapy, Alya Albastaki

Theses

This research investigates college students’ use of ChatGPT for mental health support, addressing a population with high unmet mental health needs due to barriers like accessibility and cost. Through a mixed-methods study, which included a survey of 126 students and sentiment analysis of 1,200+ social media posts, the research examined adoption prevalence, gender influences, and perceived benefits and limitations. Survey findings show 40.5% of students use ChatGPT for mental health, especially those with self-reported challenges. Female students reported higher adoption, linked to greater mental health needs and openness to supplementary support. Key benefits included 24/7 access, anonymity, and low cost, …

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Assessing Large Language Models As An Interpretive Layer In Marketing Mix Modeling: Implications For Marketing Analytics, Mohammad Bin Haider Dec 2025

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 …

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Out To Pasture, Amandeep Singh Dec 2025

Out To Pasture, Amandeep Singh

Theses

With a wildly successful acting career approaching its final days, Marcel T. Cow looks back at his rise to fame, as well as a rollercoaster of a career. A toxic relationship, substance abuse, and living a lavish lifestyle all lead to his downfall, ultimately making him face one final decision: Leave the vices behind and save his career, or give in and lose everything. Out To Pasture is an animated biopic parody about Marcel’s life as an actor and how his career comes to an end. The film starts with a flashback to Marcel’s humble beginnings, moving to the Big …

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Asymptote, Aybuke Yilmazer Dec 2025

Asymptote, Aybuke Yilmazer

Theses

Asymptote is a short film that is an expression of mankind’s emotional consumption through confusing love with sex. This film is a frame-by-frame hand-drawn animation that combines digital and analog techniques. It was produced between August 2024 and April 2025, and its music was re-produced in August 2025. This paper is a written declaration of the motives and intentions of the director, Aybuke Yilmazer, in making this film, and analyzes the filmmaking process. The text of the thesis is in third person except for the self-evaluation section.

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Early Diabetes Prediction Using Machine Learning: A Comparative Study Of Classification Models, Ahmed Abdelqadir Bawazir Dec 2025

Early Diabetes Prediction Using Machine Learning: A Comparative Study Of Classification Models, Ahmed Abdelqadir Bawazir

Theses

The current dissertation investigates the application of machine learning in diabetes prediction at early stages through a comparison of the performances of three classification models, including Logistic Regression, Decision Tree, and Random Forest. Inspired by the increased and spread cases of diabetes worldwide, the research objective is to facilitate early diagnosis by providing interpretable and accurate predictive models. Based on Pima Indians Diabetes Dataset containing 768 clinical records, the research implemented data preprocessing including KNN imputation and outlier processing, feature scaling, and formation of interaction features. Quantitative and comparative approach was made to train and test the models based on …

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Advancing 3d Infrastructure Utilities Mapping Through The Integration Of Multi-Method Digital Modelling Techniques, Adil Abdulla Aldashti Dec 2025

Advancing 3d Infrastructure Utilities Mapping Through The Integration Of Multi-Method Digital Modelling Techniques, Adil Abdulla Aldashti

Theses

With urban growth accelerating, cities increasingly need precise 3D information about their underground service networks to support sustainability goals and digital systems. Older 2D maps are often outdated, incomplete, and misaligned, making them difficult to use within 3D GIS environments. Moreover, conventional surveying tools may fall short in accuracy, struggle to reach buried assets, and require high costs when dealing with existing utilities. This thesis aims to develop and evaluate a field to 3D methodology for mapping underground utilities in Dubai, integrating multi-sensor and surveying approaches, and GIS 3D models in alignment with ASCE 38-22(American Society of Civil, 2022a), PAS …

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Adaptive Reuse Of Underutilized Strip Malls: Creating Mixed-Use Continuum Care Campuses For Seniors, Julie Le Nguyen Dec 2025

Adaptive Reuse Of Underutilized Strip Malls: Creating Mixed-Use Continuum Care Campuses For Seniors, Julie Le Nguyen

Theses

As suburban commercial landscapes continue to decline, underutilized strip malls present an opportunity to reimagine aging-supportive environments through adaptive reuse. This thesis investigates how these sites can be transformed into mixed-use senior housing and continuum-care campuses through adaptive reuse strategies. Using a comparative case study methodology, the research examines a range of precedents—including senior housing retrofits, dementia villages, and community-based mixed-use developments—to identify key spatial, programmatic, and environmental principles that support aging populations. A five-part evaluative framework assesses project performance across site integration and green space, social and intergenerational engagement, programmatic and service integration, mixed-use activation, and cognitive and environmental …

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Machine Learning Approaches For Predicting And Preventing Vehicle Theft, Hamad Almutawa Dec 2025

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 …

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Three Forks, Virginia Van Enckevort Dec 2025

Three Forks, Virginia Van Enckevort

Theses

Three Forks is a thesis drama series about a 19th-century woman sent West to live with her estranged family in Muscogee, a lawless town where justice bends to power and survival means learning the game — or being buried by it. As we follow our lead, Eleanor Davis, out West and eventually her settlement into town, we gain a look into her new world, one where tribal council contend with encroaching U.S. jurisdiction and where settlers, freedmen, and railroad men jostle for influence in a town on the brink of statehood. The many fabled stories and myths about the West …

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Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry Dec 2025

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 …

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Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi Dec 2025

Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi

Theses

The rapid evolution of AI heightens the need for learning systems that are efficient, robust, and explainable. This dissertation advances these three pillars through innovations in classification, generative modeling, domain adaptation under data-scarce conditions, and multimodal retrieval. Collectively, the methods reduce dependence on large, labeled datasets, improve adaptability under distribution shifts, enable deployment on resource-constrained platforms, and enhance interpretability. For classification, the Iterative Maximum Likelihood Classifier (IMLC) recasts regularized maximum likelihood training as a fixed-point contraction with convergence guarantees, enabling faster and more stable optimization. Results on synthetic data and MNIST validate its efficiency. For generative models, we introduce Parametric …

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Trust Calibration In Human-Ai Teaming: Within-Session Dynamics, Transparency, And Performance Effects, Mohamed Ahmed Aljaziri Dec 2025

Trust Calibration In Human-Ai Teaming: Within-Session Dynamics, Transparency, And Performance Effects, Mohamed Ahmed Aljaziri

Theses

Trust plays a decisive role in the effectiveness of human-AI teams, particularly in tasks that depend on coordinated decision-making under uncertainty. While prior research acknowledges that trust in automation is dynamic, current work provides limited insight into how trust evolves within an interaction, what causes it to become miscalibrated, and how transparency affects these processes. This thesis examines trust calibration in a controlled 2-D grid-world search-and-rescue environment, where 54 participants collaborated with an AI teammate presented through four communication modes based on the Ability, Benevolence, and Integrity (ABI) framework. The study uses secondary analysis of experimental data to observe: (1) …

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Practice?, Pei Chen Dec 2025

Practice?, Pei Chen

Theses

3D animation is a combination of art and technique. Art serves to convey your perspective, express beauty, tell stories, and more. However, to accomplish these goals, we need solid technical support. Only by paying attention to both, can we achieve excellent results. The title of this film is “Practice?”, which is a deliberate pun. On one hand, "practice" refers to the martial arts training sequences of the character, during which his master provides guidance, intervenes in his movements, and progressively increases the difficulty. On the other hand, "practice" also symbolizes my own artistic and technical experimentation—exploring and refining skills in …

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