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The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi Dec 2025

The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi

Theses

This thesis will examine how the traditional crime and cy- were affected by the 2008 financial crisis. United States are also experiencing a rise in crime between 2005 and 2012. Based on the FBI data at the national level. The Internet Crime Complaint Center (IC3), Uniform Crime Reports and important economic indicators. The study, which involves tors, including unemployment, GDP, rates of foreclosures, and mortgage rates, is a combination. correlationbased interpretation supported by exploratory descriptive trend analysis. model-fit checks. The results indicate that contrary to the conventional expectations, violent and property crime also maintained their long-term reduction during the period …

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Potential And Low-Cost Football Talents For Uae Clubs Based On Data-Driven Analysis, Rashid Alqemzi Dec 2025

Potential And Low-Cost Football Talents For Uae Clubs Based On Data-Driven Analysis, Rashid Alqemzi

Theses

The use of data analytics in professional football has changed the way clubs view, value and invest in players. Meanwhile, elite European teams have consistently exploited the data-driven strategies that offer them a competitive edge, but in up-and-coming football markets, such as the UAE Pro League, nepotism has remained strong, with reputation-based and agentdriven recruitment being the transformation choice of even the biggest profile moves. This work explores whether predictive analytics can discover hidden high-quality football talent from ”hotbeds” of talent including Brazil and Argentina, in a way that matches the economic capabilities of clubs in the UAE, given the …

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Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar Dec 2025

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

Theses

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

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Exploring Engineering Students' Utilization Of Resources In Calculus Using Self-Regulated Learning, Kriz George Dec 2025

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 …

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Event-Driven Traffic Management, Abdulla Humaid Alhosani Dec 2025

Event-Driven Traffic Management, Abdulla Humaid Alhosani

Theses

Traffic congestion during peak hours and large public events is a persistent challenge in urban areas, affecting mobility, economic productivity, and quality of life. While many cities are moving towards smart, data-driven traffic management, the practical effectiveness of predictive models for event-driven traffic control remains uncertain. This thesis presents an offline, data-driven feasibility study that investigates whether ma- chine learning and time-series models can predict traffic volume patterns under different conditions, including weather and the presence of events. Using a historical traffic dataset with derived trend variables, the study applies exploratory data analysis (EDA) and two predictive approaches: ARIMA for …

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Ai-Powered Multimodal Tour Guide: Enhancing Cultural Tourism With Image Recognition, And Personalized Storytelling, Abdulla Ibrahim Aljawi Dec 2025

Ai-Powered Multimodal Tour Guide: Enhancing Cultural Tourism With Image Recognition, And Personalized Storytelling, Abdulla Ibrahim Aljawi

Theses

This thesis presents the design and evaluation of an AI-powered multimodal tour guide that uses image recognition and personalised storytelling to enhance cultural heritage experiences. Traditional approaches to learning about monuments rely on static plaques, generic tour content, or manual web searches, which limit personalisation, interactivity, and accessibility. To address these limitations, the study implemented a “Multimodal Monument Explorer” that allows users to upload a photo of a landmark or describe it in natural language and then receive rich, context-aware explanations in both text and audio form. The system integrates a persistent vector database of monument images, OpenCLIP-based visual embeddings, …

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Mercurial, Bhavana Neti Dec 2025

Mercurial, Bhavana Neti

Theses

Mercurial is a groundbreaking visual journey through typography that charts the fluid and often volatile passage, from the depths of sadness, eventually finding the light in the end, inspired by the song title Louder than Bombs by BTS, a Korean boyband. Over a carefully observed period, this book captures the subtle and dramatic transformations of emotion through a unique lens. "Mercurial" represents a unique and multi-layered approach to understanding and visualizing emotions. Initially, the exploration of shapes as a representational tool proved inadequate for capturing this complexity. This led to a pivotal shift towards typography, where the inherent qualities of …

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Physics Meets Data: Merging Physics-Based Methods With Deep Learning To Model Complex Systems, Maryam Toloubidokhti Dec 2025

Physics Meets Data: Merging Physics-Based Methods With Deep Learning To Model Complex Systems, Maryam Toloubidokhti

Theses

Accurate modeling of complex systems is crucial in domains such as healthcare, where personalized diagnosis and treatment planning are essential. Traditional physics-based models provide structured, theoretically grounded insights but are often computationally intensive and constrained by simplified assumptions that limit adaptability to patient-specific conditions. In contrast, data-driven models are computationally efficient and capable of capturing complex patterns, yet they often lack interpretability and fail to incorporate essential physical principles, reducing robustness and generalization. This disconnect between mechanistic understanding and computational practicality presents significant challenges in critical applications such as healthcare, where both physical accuracy and real-time performance are vital. To …

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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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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 Human Resource Decisions: Predicting Promotions Using Data Analytics, Mohammad Khalid A Mohammad Abdulrahim Dec 2025

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

Theses

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

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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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Investigating The Effect Of Build Direction On Mechanical Properties In Pellet-Based 3d Printing, Yahya Mohammed Dec 2025

Investigating The Effect Of Build Direction On Mechanical Properties In Pellet-Based 3d Printing, Yahya Mohammed

Theses

This study investigates the influence of build orientation on the mechanical performance of Polylactic Acid (PLA) components fabricated using Fused Granular Fabrication (FGF), a pellet-based additive manufacturing method, and benchmarks these results against Fused Deposition Modeling (FDM). Although orientation-dependent mechanical behavior has been widely studied in filament-based extrusion, there remains a critical gap in understanding how these effects appeared in pellet-based fabrication. To address this gap, PLA tensile and compression specimens were fabricated using both FGF and FDM under identical geometries, processing parameters, and preparation procedures, ensuring that any differences in performance arise solely from the manufacturing method and build …

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Deepfake Audio Detection, Rashed Alfalasi Dec 2025

Deepfake Audio Detection, Rashed Alfalasi

Theses

The rise of deepfake audio technology has introduced a serious threat to information credibility, personal security, and media integrity. This thesis investigates the application of machine learning techniques for detecting synthetic audio through the analysis of acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, chroma_stft, and zero-crossing rate. The dataset used in this study was sourced from Kaggle and contains labeled samples of real and fake audio clips. The research aimed to train and evaluate multiple machine learning models—Support Vector Machines (SVM), Random Forest, XGBoost, Logistic Regression, and Neural Networks—to determine the most effective approach for deepfake audio classification. …

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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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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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Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi Dec 2025

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

Theses

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

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A Comparative Analysis Of Machine Learning And Deep Learning Models For Human Activity Recognition Using Wearable Sensor Data, Fares Shaban Dec 2025

A Comparative Analysis Of Machine Learning And Deep Learning Models For Human Activity Recognition Using Wearable Sensor Data, Fares Shaban

Theses

Wearable sensors have become vital for health monitoring, evaluation of sport performance, and recognition of activities of daily life in Human Activity Recognition (HAR). With the combination of machine learning and inertial sensing, new possibilities for the collection of data pertaining to human motion in daily life have emerged. Nevertheless, problems remain to be resolved in the field of modeling in relation to tradeoffs in classification. The focus remains on imaging, signal variability, and sensor noise. This study aims to determine the usefulness of Inertial Measurement Unit (IMU) data for activity recognition and compares classical machine learning methods with a …

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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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Ai-Driven Cyber Threat Detection, Humaid Thani Almheiri Dec 2025

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 …

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Early Electrical Fault Detection In Power Systems Using Data Analytics, Hessa Alraeesi Dec 2025

Early Electrical Fault Detection In Power Systems Using Data Analytics, Hessa Alraeesi

Theses

The increasing penetration of renewable energy, especially solar generation, has introduced higher variability into power system loading, making early detection of electrical faults more challenging yet more essential for maintaining network reliability. This research presents a machine-learning–based framework for early electrical fault detection using a Random Forest model developed in DataRobot. Due to confidentiality constraints on DEWA operational data, a Kaggle dataset was adopted and enriched with simulated solar irradiance variability to mirror real network conditions in the UAE. After comprehensive data preprocessing and feature engineering, the Random Forest model demonstrated strong generalization performance, accurately distinguishing early fault signatures from …

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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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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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Predicting Residential Real Estate Prices In Dubai Using Integrated Data Analytics: A Machine Learning Approach, Mohammad Nasser Dec 2025

Predicting Residential Real Estate Prices In Dubai Using Integrated Data Analytics: A Machine Learning Approach, Mohammad Nasser

Theses

Dubai’s rapidly evolving real estate market attracts substantial global investment yet remains characterized by information asymmetry, pricing volatility, and fragmented data sources. This study examines how integrated data analytics and ensemble machine learning can be used not primarily for precise price prediction, but rather to identify and quantify the key behavioral and transactional drivers of residential property prices in Dubai. A unified dataset was constructed by integrating over 100,000 residential property transactions from the Dubai Land Department with macroeconomic indicators, web-scraped listing attributes, and sentiment measures derived from online reviews and social media discussions. Supervised learning models including Linear Regression, …

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Strainx : A Technical Exploration Of Cinema4d'S Native Particle System In An Experimental Title Sequence, Farah Ahmad Dec 2025

Strainx : A Technical Exploration Of Cinema4d'S Native Particle System In An Experimental Title Sequence, Farah Ahmad

Theses

This paper presents a practice-based investigation into the capabilities of Cinema 4D’s native particle system through the creation of an experimental title sequence titled StrainX. The project was conceived as a technical and aesthetic exploration, prioritizing abstraction, form, and motion over narrative content. Through iterative testing and simulation, the study examines how Cinema 4D’s built-in particle, geometry, and material tools can generate visually complex results without relying on third-party plugins, such as X-Particles. All assets, simulations, and textures were developed natively within Cinema 4D, allowing for a unified and efficient workflow. The methodology emphasizes procedural experimentation, simulation caching using Alembic …

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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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Security Vulnerabilities In Cloud Storage: A Comparative Study Of Google Drive, Dropbox, And Onedrive, Sultan Majid Alshamsi Dec 2025

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

Theses

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

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Modeling Economic Effects On Climate Change, Ahmed Abdulqader Dec 2025

Modeling Economic Effects On Climate Change, Ahmed Abdulqader

Theses

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

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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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Work And Play In Tandem: A Brief Study Of Familiar Silhouettes In The Contemporary Workplace, Tanisa Bose Samaddar Dec 2025

Work And Play In Tandem: A Brief Study Of Familiar Silhouettes In The Contemporary Workplace, Tanisa Bose Samaddar

Theses

This thesis addresses the limitations posed by conventional office furniture, and its impact on human psychological and physiological well-being. Drawing upon literature that delves into the topics of neuroscience of play, environmental psychology, and emotional sustainability, the thesis seeks to solve these issues through design modifications. It aims to understand how brief, movement-focused breaks can help rejuvenate executive functioning, uplift mood, and encourage bursts of social interaction. A five-stage prototyping process, thumbnail sketches, clay models, 3D modeling, full-scale drawings, and iterative Fusion360 refinements, later Tandem was born. By requiring at least two users to operate its see-saw mode, Tandem counteracts …

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