Digital Commons Network^™

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

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

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 …

An Examination Of High-Entropy Alternatives Of Connectionist Temporal Classification Loss For Optical Music Recognition Using Convolutional Recurrent Neural Networks, Hritik Saynganthone

Theses

The Connectionist Temporal Classification (CTC) loss function is the most commonly used loss function in the field of Optical Music Recognition (OMR). However, OMR suffers from a massive class imbalance problem, exacerbated by the fact that CTC loss is subject to the spiky distribution problem, wherein the blank token introduced by CTC is vastly overpredicted and appears in timesteps where it would make more sense to predict a non-blank token, since CTC will collapse repeated tokens into a single token. This work posits that alternative loss functions to CTC that optimize for an increase in entropy of the prior probability …

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 …

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 …

Meshed Trees: A Framework For Resilient Network Algorithms And Protocols, Peter Willis

Theses

The continued growth of computer networks in both size and complexity presents challenges for the control protocols that are tasked with maintaining optimal data-forwarding paths. These protocols commonly rely on shortest-path algorithms to determine where network data should be propagated and how it should be analyzed for inclusion in forwarding tables. However, problems arise during reconvergence, the process in which devices rediscover optimal paths following a network failure. Because control protocols were built decades ago for the networks of that era, the existing shortest-path algorithms utilized were designed for problems before the advent of modern computer networks. This resulted in …

Exploring Human Perception And Cognition In Expressing And Understanding Mechanical Designs, Yan-Ting Chen

Theses

Effective communication of mechanical designs through technical drawings requires geometric accuracy, efficiency, and an understanding of human perception and cognition. Although advances in computer-aided design (CAD) software have improved drawing precision and automation, current tools often overlook the spatial reasoning processes that users employ to interpret these representations. This study seeks to improve the accuracy and efficiency of human-computer interaction in CAD environments by examining how individuals perceive and interpret three-dimensional mechanical components within the context of technical drawings. A key focus is the identification of canonical and optimal views that align with intuitive human understanding. Through a series of …

The World Is Beautiful, Chelsea Demott Wildey

Theses

My thesis, The World is Beautiful is a short, digitally animated film that follows the journey of a jaded employee working at a dystopian fishery. The intention of this piece was to tell a well-paced, coherent story that also served as an allegory to spark verbal discourse in an audience. As a surface level story, the film portrays a working protagonist who has been forced to remain seated at her desk by a mechanical chair. She, presumably, has been monotonously pressing icons on a screen for her entire life until something catches her eye and prompts her to break free …

Advancing Security Safeguards In Large Language Models Through Multi-Agent Systems, Mohammed Rashed Alnuaimi

Theses

This thesis focused on enhancing the safe use of Large Language Model (LLM) through the innovative use of a Multi-Agent System (MAS). As LLMs like ChatGPT became essential to our everyday interactions, the need to maintain the safe use of these systems increased. This research thoroughly assessed the current security measures in place for LLM, pointed out their limitations and developed new and more effective security strategies. The core of the proposed solution was a MAS designed to ensure that all data processed by LLM met guidelines including Privacy, Confidentiality, and Ethical standards before reaching the user. The system involved …

Startup Success Forecasting Through Machine Learning: A Comprehensive Analysis Of It Startups, Khaled Abdulla Alhassani

Theses

Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success?

This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), …

Extremism Governance In The United Arab Emirates: A Case Study Using Qualitative Policy Framework, Haya Saleh Almansoori

Theses

This thesis investigates how the United Arab Emirates governs extremism using various models of governance and Gareth Morgan’s metaphorical organizational models. This research goes beyond a focus on security and looks at extremism as a governance and policy problem related to social cohesion, institutional trust, and legitimacy. The research utilizes a qualitative case study design through analyzing federal laws and national strategies, as well as institutional frameworks to understand how authority, coordination and meaning are organized in the UAE’s governance of extremism.

The results show that the UAE’s governance of extremism is characterized as a hybrid governance model with hierarchical …

Enhancing It Security Management With An Advanced Intrusion Detection System Based On Machine Learning And Explainable Ai, Hanan Aslam Al Zubaidi

Theses

The fast changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today's advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.

The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, …

Evaluating Large Language Models For Automated Cv Ranking: A Hybrid Embedding Approach For Enhanced Recruitment, Sarah Mohamed Alhindaassi

Theses

Increasing numbers of applications have revealed limitations in legacy keyword-filtering-based Applicant Tracking Systems (ATS), which commonly overlook candidate potential and ignore contextual or transferable skills. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) offer an exhilarating alternative, supporting context-sensitive and human-crafted reasoning in candidate evaluation. This thesis systematically evaluates four classes of approaches, lexical models, embedding-based methods, Large Language Models (LLMs), and hybrid ensembles, for automation of Curriculum Vitae (CV) to Job Description (JD) matching without exploiting prior annotations or annotations at match time. Using a combination of publicly available datasets and real-world sample data covering three …

Enhancing The Acceptability Of Decision-Making System Using Xai Case: Crime Profiling System, Mohamed Saeed Al Shamsi

Theses

In the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle …

باستخدام مجموعة بيانات متعددة من الطائرات بدون طيار Yolo البحث والإنقاذ البحري القائم على في ظروف الطقس الصعبة, Aysha Ali Alshebli

Theses

Object detection models, powered by deep learning and computer vision, are revolutionizing marine search and rescue (SAR). By analyzing aerial imagery and live drone footage, these systems automatically identify critical targets like survivors, life rafts, and debris across vast and treacherous ocean areas. This capability enhances operational efficiency by reducing human workload and accelerating response times, even in challenging conditions such as poor light, high seas, or cluttered backgrounds. The result is continuous monitoring, faster decision-making, and a significantly improved probability of successful rescue.

Departing from prior methodologies, YOLO introduced a paradigm shift through its single-shot architecture, which concurrently predicts …

Investigation Of Mechanical Performance And Metallurgical Characteristics Of Titanium Grade 2 Tube-To-Tubesheet Joints, Ahmad Hussein Al Tamimi

Theses

Tube-to-tubesheet joints, an integral component of shell and tube heat exchangers, are known for their vulnerability to leakage problems at the joint region due to mechanical and chemical factors coupled with fabrication techniques involved in creating these joints. An ideal condition of manufacturing process parameters and geometrical variables required to produce tube-to-tubesheet joints is highly desired. One of the most promising candidate materials to fabricate tube-to-tubesheet joints is titanium or titanium alloys due to their excellent strength and resistance to corrosion. The present study investigates the structural integrity of three categories of Titanium Grade 2 based tube-to-tubesheet joints comprising of …

Enhanced Privacy Preserving Healthcare Data Management With Federated Learning Using Homomorphic Encryption, Omar Abdulla Ali

Theses

Federated Learning (FL) is a decentralized approach of machine learning on multiple clients jointly training models without sharing their raw data, which drastically improves privacy and enhance protection against security breach. This is particularly critical in the healthcare sector, where hospitals and medical institutions are often unable to exchange patient records due to strict privacy regulations and data-management policies. However, there is still a risk of privacy breach when clients send their model updates to the central server, because if a model update is intercepted or analyzed by a malicious entity, it could be used to recover sensitive data using …

Enhanced Air Hockey Robot Performance Through Adaptive Control Algorithim Using Ai-Based Hierarechal Decicion Archeticture, Ayham Majed Salim

Theses

This thesis presents the development of an intelligent air hockey robot that combines precise mechanical design, computer vision, and adaptive control within an AI-based hierarchical decision architecture. The system integrates synchronized stepper motors, high-speed image processing, and a real-time decision framework to achieve competitive gameplay performance. The robot detects the puck using adaptive HSV color segmentation, supported by dynamic calibration that maintains accuracy under different lighting conditions. A two-stage trajectory prediction model, based on exponential decay velocity estimation, enables anticipation of puck motion and improves response time during fast gameplay.

At the decision level, a fuzzy-logic supervisor governs the robot’s …

Detecting Data Poisoning Attacks In Medical Imaging: A Study On Chest X-Ray Classification Tasks, Akhila Abdulla Asgar

Theses

This thesis examines the vulnerability of AI medical imaging models to adversarial threats, with a specific focus on data poisoning attacks in chest X-ray classification. The study begins with a Systematic Literature Review (SLR) to assess the existing adversarial attacks and defenses in medical imaging, revealing a significant research gap in studies exploring data poisoning attacks in the medical domain. Based on our literature search, an efficient and lightweight defense, namely friendly noise defense, against data poisoning has not been investigated in medical imaging classification tasks. Hence, in this work, we investigated its effectiveness on the chest X-ray dataset, and …

The Simulation Of Hyperspectral Observations By The Upcoming Spacecraft "Arab Satellite 813", With The Radiative Transfer Model Sciatran, Sara Maher Alhasan

Theses

The Arab Satellite 813, which is scheduled to be launched in 2025/2026 by the UAE Space Agency, is set to increase earth observation and monitor the climate across the middle East and North Africa (MENA) region. This study uses the SCIATRAN radiative transfer model which was originally developed for the Envisat mission to simulate the hyperspectral observation specifically tailored for the satellite's payload. Using calibrations that were used by the previous research in their SCIATRAN V4.6 study and adjusting parameters like the Leaf Area Index (LAI), a simulation which agreed robustly with the benchmarks set by the previous research was …

Understanding Behavioral Patterns: A Case Study Of Emirati Ex-Offenders In United Arab Emirates, Salim Ali Al Naqbi

Theses

Research on desistance has helped criminologists to better understand how people change their criminal identity and exercise personal choice. Yet in the UAE, these ideas has not often been applied to prisoners serving one or multiple offenses, even though incarceration and reoffending remain important social concerns. Because of this, desistance theory has not been fully used to study how rehabilitation is managed in UAE prisons, which means that some useful lessons may be missed. This study begins to close that gap by looking at the personal stories of 15 Emirati participants that were formerly incarcerated by adapting a qualitative and …

High-Energy And On The Move: Exploring The Kinematic And X-Ray Properties Of Young Stars Near Earth With Gaia, Chandra, And Erosita, Attila Vincent Varga

Theses

Young stars in the solar neighborhood provide a unique and insightful window into stellar evolution during the first few hundred million years after formation. Leveraging archival photometric and astrometric data from ESA’s Gaia Space Astrometry mission, as well as photometric data from NASA (2MASS and WISE) archives, I have identified over 50 new members of the nearby (D ∼ 100 pc), 3–8 Myr-old Epsilon Cha association (ECA), including six new proto-brown dwarf candidates. By modeling the kinematics and photospheric properties of both newly identified and previously known members of the ECA and Lower Centaurus Crux (LCC), I further established the …

It Hurts To Become, Ana Maria Joyce

Theses

Using the medium of oil painting, this thesis will investigate the ways that memories of my past inform and distort my perception of reality. Using domestic spaces as a framework in combination with my own body as the subject matter, I am creating compositions that redefine the meaning of home as a concept. Rather than a physical place of comfort, home is a cemetery for past selves that I revisit in my mind and grieve, no matter the time that has passed. By expressing this internal decay through physically scraping and sludging paint onto large scale surfaces, I aim to …

Three-Dimensional Cfd Analyses Of Natural Circulation In A Pressurized Water Reactor During A Loss Of Coolant Accident, Arad Dehestany

Theses

In this research, ANSYS FLUENT simulations will be performed on natural circulation cooling performance during loss of coolant accident on the semi-scale Mod-2A geometry. Natural circulation experiments were performed in the semi-scale Mod-2A test facility which is a small-scale model of the primary system of a four-loop PWR nuclear power generating plant. The CFD results are validated using the tests done in the facility. The scope of the research is to understand the flow, temperature distribution, inventory coolant losses, and pressure drop in the system. These findings can provide insights into the performance of such systems and identify potential improvements …

Examining Children’S Intuitive Understanding Of Mechanical Systems With A Gears Assembly Task, Nicole Taboada

Theses

Children’s causal reasoning about machines and their components is crucial for acquiring knowledge about science and math. Mechanistic understanding involves knowing how parts can be effectively connected to work together as a functional mechanical system. Little is known about how children’s understanding of mechanical systems develops across the lifespan. Legare and Lombrozo (2014) developed a gears assembly task to assess children’s understanding and learning of mechanics but did not track this skill across ages. The present study adapted the gears assembly task to measure mechanistic understanding in children between the ages of 4 to 11 years and adults. The goal …

Generation And Detection Of Photo-Thermal And Photo-Acoustic Waves In Solids For Advanced Near-Field Ir Imaging, Yide Zhang

Theses

Atomic force microscopy-infrared (AFM-IR) spectroscopy has become an essential tool for nanoscale chemical imaging, offering spatial resolution beyond the diffraction limit of conventional infrared spectroscopy. A typical AFM-IR setup directs a pulsed IR laser onto the sample, precisely at the tip of an AFM cantilever, detecting local absorption indirectly as photothermal expansion via the tip. This combination of AFM’s high spatial resolution and infrared spectroscopy’s chemical specificity enables detailed studies of a wide range of materials, from polymers and biological tissues to nanomaterials. However, the factors influencing AFM-IR signal generation and spatial resolution-especially in complex and inhomogeneous samples-remain challenging to …

Uncovering The Efficacy Of Unlabelled Data In Anomaly Detection, Andrew Shields

Theses

Anomaly detection is an important area of research within data mining and has applications in many domains, including cybersecurity, finance, and healthcare. Conventional methods depend on labelled training data and assume an even class distribution, restricting their 'real-world' application, where rare anomalies and unlabelled data are the norm. This research addresses these limitations by investigating how unlabelled data can support anomaly detection in imbalanced, data-scarce, complex environments. It addresses three research questions: how to integrate unlabelled data into anomaly detection models, how to improve performance when labelled anomalies are scarce, and how graph-based methods capture complex relational anomalies. Three empirical …

Addressing Training Challenges Of Generative Adversarial Networks For Biomedical Image Synthesis, Muhammad Muneeb Saad

Theses

Artificial intelligence (AI) plays a pivotal role in computer-aided diagnosis. In image-based diagnosis, AI systems rely on biomedical images containing sensitive disease-oriented information. AI systems utilize machine learning models to automate this diagnosis. Machine learning models such as deep neural networks require large training datasets to enhance the diagnostic precision of these models. Models can underperform when they are trained on imbalanced datasets. Imbalance in datasets refers to the skewed numbers of images per image class. There is a need to enhance the training of these models via data augmentation. In the context of biomedical imagery, where acquiring large, balanced …

Ruthless: The Revolution Of Gangsta Rap, Laci D. Lavoy

Theses

The popularization of gangsta rap in the United States was pivotal to the advancement of Black America following the close of the Civil Rights Movement. The ruthlessness of gangsta rap created a distaste of authority that became a catalyst for Black American unity and change. Artists quickly started writing songs that spoke out against issues plaguing Black and poor Americans, realizing the power of the microphone and the radio. These rappers created an awareness for White audiences that had not been present before and even created a space for White artists with shared experiences. Female rappers took this activism a …