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Predicting Loan Defaults: A Behavioral Scoring Approach For Portfolio Risk Monitoring In Banking And P2p Lending, Humaid Sultan Almarri
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 …
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 …
Early Electrical Fault Detection In Power Systems Using Data Analytics, Hessa Alraeesi
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 …
Macroscale Thermal Modelling For The Molten Metal Droplet Jetting Additive Manufacturing Process, Khushbu Zope
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 …
Predictive Analysis Of Residential Property In Urban Areas, Hamad Adnan Alzarooni
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 …
Segment-Level Machine Learning For Detecting Partial Deepfake Audio: An Rnn–Svm Hybrid Approach For Real-World Adversarial Environments, Ahmad Alhelli
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 …
Integrating Climate And Geospatial Features Into Machine Learning Models, Abdulaziz Ahmed Aljaziri
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 …
Multilingual Identity Document Information Extraction Via Dynamic Templates And Hybrid Ocr, Khalifa Rashed
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 …
Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali
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 …
Predicting Athlete Performance Using Machine Learning Models, Mohammad Abdulbasit Mohammad Alabdulla
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 …
Work And Play In Tandem: A Brief Study Of Familiar Silhouettes In The Contemporary Workplace, Tanisa Bose Samaddar
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 …
Optimizing Human Resource Decisions: Predicting Promotions Using Data Analytics, Mohammad Khalid A Mohammad Abdulrahim
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), …
Deepfake Audio Detection, Rashed Alfalasi
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. …
Security Vulnerabilities In Cloud Storage: A Comparative Study Of Google Drive, Dropbox, And Onedrive, Sultan Majid Alshamsi
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 …
Integrated Machine Learning For Smart Home Resource Optimization, Ahmed Almazrouei
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 …
A Comparative Analysis Of Machine Learning And Deep Learning Models For Human Activity Recognition Using Wearable Sensor Data, Fares Shaban
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 …
Biodegradation Of Common Post-Consumer Plastic, Laura Krebs
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, …
Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi
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 …
Ai-Driven Cyber Threat Detection, Humaid Thani Almheiri
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 …
Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi
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 …
Optimizing Delivery Time Predictions Using Machine Learning: A Data-Driven Approach To Last-Mile Logistics, Mohamed Burqaiba
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. …
A Machine Learning Framework For Forecasting Passenger Flow At Dubai International Airport, Khalifa Alfalasi
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 …
Optimizing Power Grids In Uae Using Data Analytics For Improved Efficiency And Reliability, Khalid Bukhashem
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 …
Beyond Detection: A Batch-Based Ai Framework For Temporal And Event-Correlated Trend Analysis Of Misinformation On Social Media, Vishnu Tejas Vijayaraghavan
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 …
Labor Transportation Simulation And Optimization - A Case Study, Tala Taha
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 …
Supervised Learning For Predicting Mental Health And Burnout In Healthcare Workers, Afra Alfalasi
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 …
Assessing Large Language Models As An Interpretive Layer In Marketing Mix Modeling: Implications For Marketing Analytics, Mohammad Bin Haider
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 …
Mixed-Integer Linear Programming (Milp) Model For Transportation Cost Optimization, Yasmen Ghonim
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 …
Hyaluronic Acid Loaded With Aloe Vera-Derived Extracellular Vesicles: A New Bioactive Approach For Fibrotic Scarring, Maria Camila Ceballos Santa
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 …
Microgrid Approach With Data-Driven Monitoring To Enhance The Resilience Of Water Distribution Systems, Binod Ale Magar
Microgrid Approach With Data-Driven Monitoring To Enhance The Resilience Of Water Distribution Systems, Binod Ale Magar
Theses
Existing centralized water supply systems are critically threatened by the joint effects of climate and socioeconomic changes, extreme weather events, and physical degradation due to aging, and their changeability. The uncertain and changing drivers pose challenges for water supply systems in providing sustainable water services during disruptions. The energy field dealing related issues has endorsed the microgrid approach with a decentralized energy supply with dispersed local energy sources. The application of energy microgrids has demonstrated their resiliency for sustainable energy supply. For improved resiliency of a water distribution system, this study investigated the application of a microgrid approach and the …