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Articles 391 - 420 of 11540
Full-Text Articles in Entire DC Network
Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti
Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti
Theses
Urban infrastructure delivery is a complex process influenced by project type, spatial distribution, stakeholder coordination, and data quality. This thesis examines the application of Microsoft Power BI, a no-code visual analytics tool, as a means to analyze performance patterns in urban projects and inform decision-making in smart city governance. The study uses a structured secondary dataset simulating over 600 infrastructure projects across various departments and neighborhoods. By transforming raw data into an interactive dashboard, the research explores relationships between delay rates, customer satisfaction, budget compliance, and technology adoption. The analysis revealed that project categories such as metal furniture and police …
Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula
Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula
Theses
Social media has evolved into a critical channel for communication, expression, and public influence, but it has also become a prevalent avenue for cybercrime, particularly in digitally advanced nations such as the United Arab Emirates (UAE). The rising complexity of online offences, coupled with anonymisation tools and cross-border digital behaviour, has made the attribution of social-media-based cyber incidents increasingly challenging for law enforcement. In this context, artificial intelligence (AI) offers the potential to strengthen digital investigations by providing intelligent, scalable, and evidence-driven attribution capabilities. This research develops an AI-assisted Internet Protocol (IP) attribution framework tailored specifically for UAE law enforcement …
The Environmental Impact Of Rapid Construction In Australia, Ahmad Alsubousi
The Environmental Impact Of Rapid Construction In Australia, Ahmad Alsubousi
Theses
This research examines the degrading effect of the high-rate of construction in Australia especially on the generation of waste, effectiveness of policies to be applied regarding waste and sustainability implication. The research utilizes regression analysis, clustering, and forecasting based on ARIMA with data provided by National Waste Report (2006 07 to 2022 23) to analyse the magnitude and the factors affected by construction and demolition (C&D) waste that ended up being the major factor of overall waste production in Australia. Findings depict an observable upward trend in C&D waste along with landfill use and variations in the performance of waste …
Predictive Ai Models For Phishing Attack Detection: A Data-Driven And Statistical Analysis Approach, Suhail Othman Alfalasi
Predictive Ai Models For Phishing Attack Detection: A Data-Driven And Statistical Analysis Approach, Suhail Othman Alfalasi
Theses
Phishing has remained a serious threat to cybersecurity, as this type of attack can easily bypass detection systems that are either rule-based or blacklist-based. The proposed thesis work will present a solution that will make use of both statistical analysis and machine learning to correctly identify a phishing website. The solution will make use of a hybrid approach comprising statistical-based preprocessing methodologies, such as PCA and decision tree-based feature selection, to filter the crucial URL features that a website may possess. A carefully balanced dataset has been utilized, as well as a non-parametric approach utilizing the Mann-Whitney U-test to validate …
The Role Of Ai And Predictive Policing In Crime Prevention, Saif Salem Mohammad Hassan Abdulla
The Role Of Ai And Predictive Policing In Crime Prevention, Saif Salem Mohammad Hassan Abdulla
Theses
The current thesis examines the application of Artificial Intelligence (AI) in the arena of predictive policing and crime forecasting using an integrated case study based on the empirical approach supported by a narrative review of literature. Due to the growing use of digital data by the law-enforcement agencies, AI techniques, including machine learning and spatio-temporal modelling, are implemented to detect patterns of crimes, predict high-risk areas, and assist law-enforcement decision-making. Though these technologies have the potential to make the processes of accuracy and resource allocation better, they also bring up the issue of the fairness, transparency, and disproportionate effects on …
Defaking Deepfakes: Designing And Evaluating Ai-Powered Digital Media, Saniat Javid Sohrawardi
Defaking Deepfakes: Designing And Evaluating Ai-Powered Digital Media, Saniat Javid Sohrawardi
Theses
The rapid spread of generative AI has revolutionized media production, creating new challenges for information integrity as convincing deepfakes proliferate. Journalists play a critical role in upholding credible public information, yet existing deepfake detection technologies often overlook their specific workflows and requirements. This dissertation addresses these needs by identifying what journalists require from detection tools and evaluating usability in realistic scenarios through the following works: Journalists' Needs and Tool Design: Through qualitative user studies, we uncover journalists' preferences for tools that provide transparent, explainable evidence and context-aware analysis integrated into news verification routines. These insights drive the design of DeFake, …
Predictive Modeling Of Tokenized Real Estate Prices Using Machine Learning, Mohammed Alteneiji
Predictive Modeling Of Tokenized Real Estate Prices Using Machine Learning, Mohammed Alteneiji
Theses
In this paper, we propose a machine learning-based model to predict the prices of tokenized real-estate assets, combining blockchain data, real-estate data, and sentiment data. Tokenization refers to partitioning a physical asset into fractional tokens on a blockchain, where each token represents a fraction of that asset. These include token liquidity, trading volume, platform activity, and the on-chain activity of investors. These new variables require data-based analytics that are capable of accounting for more complex relationships than customary real estate valuation approaches. To address this, we propose a multimodal dataset that incorporates on-chain data (token supply, number of wallet-holders, transaction …
Detecting Illicit Bitcoin Transactions On The Dark Web, Abdulla Alsuwaidi
Detecting Illicit Bitcoin Transactions On The Dark Web, Abdulla Alsuwaidi
Theses
Dark web markets use Bitcoin and related cryptocurrencies to transfer and launder proceeds of crime while obscuring the real-world identities of operators. This creates an increasingly challenging environment for regulators and law-enforcement agencies, who must analyse suspi- cious transactions on decentralised, public, pseudonymous blockchains and increasingly dense and noisy Bitcoin transaction graphs. This thesis investigates whether, and how, blockchain analytics and machine learning can help detect illicit transactions on Bitcoin networks. The first part of the thesis is a systematic literature review of recent contributions from academia and industry that examine major solution approaches for identifying illicit flows on public …
Data-Driven Crime Prediction: Toward Smarter Reduction Strategies, Zayed Almarri
Data-Driven Crime Prediction: Toward Smarter Reduction Strategies, Zayed Almarri
Theses
This study explores how machine learning and weather data can be used for the prediction of crime more accurately in the city of Seattle. Predictive policing is a method in law enforcement that uses data and computer algorithms to forecast the locations that crimes are likely to happen. Although many studies focused on using past crime data alone, this research also includes weather conditions like temperature, rainfall, and humidity, which may influence when and where crimes occur. Several machine learning models, including Random Forest (RF) and Support Vector Machines (SVM), were used to classify areas of the city into high-risk …
An Interpretable Machine Learning Framework For Detecting Phishing Urls Based On Lexical Features, Khalid Alrokhaimi
An Interpretable Machine Learning Framework For Detecting Phishing Urls Based On Lexical Features, Khalid Alrokhaimi
Theses
Phishing attacks represent one of the most significant and persistent threats in the cybersecu- rity landscape, with attackers increasingly using sophisticated URL manipulation techniques to deceive users and steal sensitive information. Traditional detection methods, which rely primarily on blacklists and heuristic rules, struggle to identify zero-day phishing URLs that have not yet been catalogued in security databases. This research addresses this critical gap by developing an interpretable machine learning framework for detecting phishing URLs usingclexical, structural, content-based, and domain metadata features. The study employs a comprehensive dataset of 11,430 labeled URLs (5,715 legitimate and 5,715 phishing) with 87 extracted features, …
Physics Meets Data: Merging Physics-Based Methods With Deep Learning To Model Complex Systems, Maryam Toloubidokhti
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 …
Persuasive Pathways In Digital Apologies: The Role Of Persuasion Routes In Engagement With Youtuber Apology Videos, Darrin Majocha
Persuasive Pathways In Digital Apologies: The Role Of Persuasion Routes In Engagement With Youtuber Apology Videos, Darrin Majocha
Theses
This study explores how viewers process YouTube apology videos and how those processing methods relate to their judgments of the creator and their willingness to continue engaging with the channel. Guided by the Elaboration Likelihood Model, a single-session online survey experiment randomly assigned 413 adult participants to watch one of five publicly available apology videos from well-known YouTubers. After viewing, participants reported the extent to which they engaged in central versus peripheral processing, rated the creator's credibility and the apology's sincerity, and indicated their intentions to engage. Central processing was positively associated with engagement intentions, whereas peripheral processing was weakly …
Predicting Violent Crime Hotspots, Mohammad Alhammadi
Predicting Violent Crime Hotspots, Mohammad Alhammadi
Theses
This thesis presents a systematic literature review and an empirical demonstration focused on predicting violent-crime hotspots. Drawing on 50 studies published between 2010 and 2025, the review synthesises methodological developments across hotspot mapping, spatio-temporal modelling, risk terrain analysis, and machine-learning approaches. The review highlights a clear evolution from retrospective density maps to more dynamic, data-driven techniques, while also identifying persistent challenges related to data bias, temporal granularity, environmental context, fairness, and operational implementation. To complement the review, the thesis applies kernel density estimation (KDE) and three ensemble machine-learning models—Random Forest, Gradient Boosting, and XGBoost—to 769,680 geocoded violent-crime incidents recorded in …
Predicting Employee Attrition With Machine Learning: Data-Driven Strategies For Enhancingworkforce Retention, Ali Almheiri
Predicting Employee Attrition With Machine Learning: Data-Driven Strategies For Enhancingworkforce Retention, Ali Almheiri
Theses
In this research, the dual prediction and prescription model is developed and validated so that this model can not only predict employee turnover risk, but it also proposes the appropriate retention interventions, which apply across industries. On the basis of the IBM HR Analytics Attrition dataset (n=1,470), we preprocessed demography, job and satisfaction variables and trained three machine-learning classifiers, Random Forest, Logistic Regression and XGBoost to predict voluntary turnover. XGBoost model recorded the best discrimination (AUC=0.87), sensitivity (0.76), and specificity (0.81), which signifies strong predictive power. Analysis of feature-importance was conclusive with time, rate of frequent business travel and compensation …
Potential And Low-Cost Football Talents For Uae Clubs Based On Data-Driven Analysis, Rashid Alqemzi
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 …
Investigating The Effect Of Build Direction On Mechanical Properties In Pellet-Based 3d Printing, Yahya Mohammed
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 …
Exploring Engineering Students' Utilization Of Resources In Calculus Using Self-Regulated Learning, Kriz George
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 …
Event-Driven Traffic Management, Abdulla Humaid Alhosani
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 …
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 …
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 …
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. …
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 …
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 …
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 …
Modeling Economic Effects On Climate Change, Ahmed Abdulqader
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 …
A Ux Approach To Improving Patient Experience In Healthcare, Lingxin Sun
A Ux Approach To Improving Patient Experience In Healthcare, Lingxin Sun
Theses
Doc Ease is a UX research and design project that addresses the emotional and functional difficulties patients experience before visiting a doctor. Through user interviews, competitive analysis, and design iteration, this project identified key user pain points such as unclear appointment processes, lack of trust, and pre-visit anxiety. The resulting mobile app offers clear booking tools, visual doctor information, medication reminders, and an emotional wellness module to help patients prepare with confidence. The project contributes to the field of healthcare UX by integrating functional usability with emotional care, proposing a design framework that centers patient experience in both logic and …