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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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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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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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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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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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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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Beyond Detection: A Batch-Based Ai Framework For Temporal And Event-Correlated Trend Analysis Of Misinformation On Social Media, Vishnu Tejas Vijayaraghavan Dec 2025

Beyond Detection: A Batch-Based Ai Framework For Temporal And Event-Correlated Trend Analysis Of Misinformation On Social Media, Vishnu Tejas Vijayaraghavan

Theses

The rapid spread of misleading information on social media influences public behaviour and complicates crisis communication. Although transformer models such as BERT accurately detect misinformation at the post level, most studies analyse posts in isolation and overlook howmisinformation fluctuates over time or responds to major events. This study addresses that gap by developing an end-to-end analytical workflow that integrates BERT-based classification with temporal aggregation, topic clustering, anomaly detection, and event alignment. The analysis uses 10,700 COVID-19–related tweets (6,420 training, 2,140 validation, and 2,140 testing). Because timestamps were unavailable, synthetic timestamps were assigned using an evenly spaced date range between 1 …

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Mixed-Integer Linear Programming (Milp) Model For Transportation Cost Optimization, Yasmen Ghonim Dec 2025

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 …

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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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Desiccation Tolerance In Tetradesmus Green Algae, Kristen Patten Dec 2025

Desiccation Tolerance In Tetradesmus Green Algae, Kristen Patten

Theses

In the face of the global climate change threat, understanding the adaptations that organisms have evolved to handle environmental variation is of particular interest to scientists. With climate change impacting global water availability and increasing the risk of drought for many traditional agricultural areas in the United States (IPCC, 2014, IPCC, 2021, and Kuwayama et al., 2019), desiccation tolerance in vegetative states is one adaptation that is currently receiving a lot of attention. Green algae are particularly useful organisms for understanding this adaptation due to their ubiquity across environments, which has given rise to independently evolved organisms displaying different levels …

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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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Hyaluronic Acid Loaded With Aloe Vera-Derived Extracellular Vesicles: A New Bioactive Approach For Fibrotic Scarring, Maria Camila Ceballos Santa Dec 2025

Hyaluronic Acid Loaded With Aloe Vera-Derived Extracellular Vesicles: A New Bioactive Approach For Fibrotic Scarring, Maria Camila Ceballos Santa

Theses

Disruptions in wound healing can result in pathological outcomes ranging from chronic nonhealing wounds to excessive fibrotic scarring. Hypertrophic scars and keloids are hallmarks of this dysregulation, characterized by abnormal collagen deposition, persistent myofibroblast activation, and disorganized extracellular matrix (ECM) remodeling. Chronic inflammation and oxidative stress play central roles by increasing reactive oxygen species (ROS), which damage essential macromolecules and sustain pro-inflammatory signaling, while promoting the transdifferentiation of fibroblasts into myofibroblasts and the excessive production of ECM proteins, such as collagen and α-smooth muscle actin (α-SMA). This fibrotic cascade leads to tissue stiffness, contractility, and disfigurement, affecting the well-being of …

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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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Unmasking Corruption And Bribery Using Predictive Analytics, Maha Salem Dec 2025

Unmasking Corruption And Bribery Using Predictive Analytics, Maha Salem

Theses

The initiative (Unmasking Corruption and Bribery Using Predictive Analytics) aims to identify employees in government sectors who commit corruption and bribery by addressing issues such as the lack of transparency, fairness, equality, and public trust, as well as weakened loyalty and poor reputation. This project focuses on data analytics and predictive modeling to detect suspicious or high-risk employee behaviors related to corruption and bribery. The main objective is to reduce these unethical activities in government sectors, strengthen integrity, and promote transparency and public trust through predictive capabilities. It also seeks to raise awareness about how serious and harmful these crimes …

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Predictive Maintenance For It Equipment Using Machine Learning Models, Rashed Khalid Alshirawi Dec 2025

Predictive Maintenance For It Equipment Using Machine Learning Models, Rashed Khalid Alshirawi

Theses

Predictive maintenance was recently introduced as a paradigm shift in the field of smart manufacturing, and it allows organizations to predict equipment failures and streamline maintenance planning based on the data-driven insights. The present work addresses the concept of the implementation of a decision tree-based predictive maintenance model based on AI4I 2020 Predictive Maintenance Dataset, a simulated environment of an actual industry through sensor data including temperature, torque, rotational velocity, and toolwear. The study uses a quantitative approach that is based on positivism philosophy, which is the focus on objectivity, empirical validation, and statistical testing of the model performance. Synthetic …

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Predicting Inmate Overcrowding To Improve Facility Management, Mayed Ali Alameri Dec 2025

Predicting Inmate Overcrowding To Improve Facility Management, Mayed Ali Alameri

Theses

Overcrowding is a major challenge to correction systems because the conventional forecasting techniques are inaccurate and inadequate in most cases. This paper will solve this by constructing and testing a machine learning-based model to predict facility-level overcrowding. With the use of the XGBoost Regressor model on a dataset comprising of U.S. correctional facilities, the research identified key structural drivers but showed that the static facility attributes alone have limited predictive power (r-square approx 0.18) The discussion shows that overcrowding is non-linear, and a complicated problem not confined to the facility characteristics but to the larger, non-measurable regional influences, of which …

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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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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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Exploring The Perception Of Dynamic Electrotactile Patterns On The Fingertips, Vedika Nimbalkar Dec 2025

Exploring The Perception Of Dynamic Electrotactile Patterns On The Fingertips, Vedika Nimbalkar

Theses

The goal of this study is to evaluate the effectiveness of electrotactile feedback on the fingertips by examining how accurately different dynamic patterns can be perceived. We designed three pattern types (Single Line, Double Line, and Box) and programmed each to move in eight distinct directions: bottom to top, top to bottom, left to right, right to left, bottom-left to top-right, top-right to bottom-left, bottom-right to top- left, and top-left to bottom-right. A study was conducted to assess these patterns’ perceived accuracy and response time on fingertips. The study results revealed that the Single Line and Double Line patterns had …

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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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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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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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The Klik Chair - Designed To Move, Built To Last, Neeraj Sugumaran Menon Dec 2025

The Klik Chair - Designed To Move, Built To Last, Neeraj Sugumaran Menon

Theses

For the modern individual who moves housing frequently, buying furniture has become an increasingly tedious and frustrating process. Traditional furniture often presents challenges due to its bulk, weight, and complex assembly requirements, making it difficult to transport and reassemble when moving to a new living space. As a result, the true value of a piece of furniture is no longer determined solely by its price tag or aesthetic appeal but by its ability to adapt to a nomadic lifestyle. Additionally, furniture that holds long-term sentimental or functional value has become even more significant in an era where disposability is routine. …

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Predicting Loan Defaults: A Behavioral Scoring Approach For Portfolio Risk Monitoring In Banking And P2p Lending, Humaid Sultan Almarri Dec 2025

Predicting Loan Defaults: A Behavioral Scoring Approach For Portfolio Risk Monitoring In Banking And P2p Lending, Humaid Sultan Almarri

Theses

The critical challenge of lending institutions and peer-to-peer investors is to single out high-risk active borrowers in order to avoid defaults and corresponding losses. This thesis is devoted to the development of a data-driven approach to predict loan defaults, which balances predictive accuracy with interpretability and cost-sensitive evaluation. Using a large dataset of loans from LendingClub, we applied the CRISP-DM methodology, performing extensive data preprocessing and exploratory analysis before training several machine learning models-logistic regression, decision tree, random forest, and XGBoost-to classify loans as default or non-default. Class imbalance was addressed through resampling, and models were tuned via cross-validation with …

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Play Of Curiosity: A Table-Top Game That Cultivates A Creative Attitude By Encouraging Curiosity In Young Adults, Kajal Shah Dec 2025

Play Of Curiosity: A Table-Top Game That Cultivates A Creative Attitude By Encouraging Curiosity In Young Adults, Kajal Shah

Theses

In a time that increasingly demands instant creativity, adaptability, and innovative thinking on a constant basis, nurturing a curious attitude among young adults must become a critical focus. Creativity and curiosity can be encouraged throughout various stages of life, and for myriads of personality types, the focus demographic of this paper is young adults, ages 18 to 26 years old. Young adulthood is when one starts to recognize post-formal thoughts. This stage of cognitive development helps to approach concepts beyond binary thinking, developing the unique abilities of ambiguous thinking and relativism. The enigmatic, ever-changing, and easygoing mind will often try …

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Macroscale Thermal Modelling For The Molten Metal Droplet Jetting Additive Manufacturing Process, Khushbu Zope Dec 2025

Macroscale Thermal Modelling For The Molten Metal Droplet Jetting Additive Manufacturing Process, Khushbu Zope

Theses

Molten Metal Jetting (MMJ) has become a promising pathway for next-generation metal additive manufacturing because it avoids the need for powders or high-power energy sources and enables precise, drop-on-demand deposition of structural alloys. Despite this promise, the field lacks a predictive thermal modeling framework that can describe the full temperature history of a part as thousands of droplets accumulate. Without such a model, it is difficult to control heat buildup, to understand how bonding conditions evolve across layers, and to avoid defects such as incomplete fusion, porosity, and geometrical distortion. This dissertation presents the first validated macroscale thermal modeling framework …

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Predictive Analysis Of Residential Property In Urban Areas, Hamad Adnan Alzarooni Dec 2025

Predictive Analysis Of Residential Property In Urban Areas, Hamad Adnan Alzarooni

Theses

This paper aims to determine the predictive modelling of residential property prices in urban Scotland with the help of an integrated framework in which machine learning methods are applied in combination with detailed socio-economic, health, housing, and geographic indicators. Conventional valuation methods tend to be based on the concept of few structural variables and ignore the effect of multidimensional variables in determining spatial variation in housing markets. To cope with this, the study uses Linear Regression, random forest, Multi-layer perceptron, and XGBoost models, which are assisted by a broad range of feature engineering, outlier management, and data preprocessing. Compared with …

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