Saudi Cultural Missions Theses & Dissertations
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Item Restricted Machine Learning for Radiotherapy Treatment of Prostate Cancer(Saudi Digital Library, 2026) Alqarni, Maram; Teresa, Guerrero Urbano; Andrew, KingExternal beam radiotherapy (EBRT) and brachytherapy (BT) are both forms of radiation treatment used for prostate cancer to destroy cancer cells. EBRT applies the radiation externally while BT involves placing radioactive seeds inside the prostate. At Guy’s Cancer Centre, both treatment modalities are performed depending on various factors. Each of the treatment modalities involves different imaging modalities used for treatment planning, delivery and follow-up. However, both have some overlapped clinical tasks such as defining the clinical target volume (CTV) and organs at risk (OARs) from imaging data. The work described in this thesis aims to perform research to promote clinical translation of machine learning (ML) techniques to streamline workflows in EBRT and BT. The first piece of work in this thesis focuses on an ML-based segmentation model for prostate MRI. One of the main challenges affecting clinical adoption of ML in MRI segmentation is the domain shift problem. The findings of this piece of work reveal for the first time the significant impact on model performance of using different acquisition/annotation protocols, even if using the same scanner vendor/field strength. It is shown that training an ML model with data that covers the important sources of domain shift can produce a robust model with good generalisability performance. The next piece of work investigates the possibility of race bias in ML-based prostate MRI segmentation. Through experiments on a controlled dataset of White and Black patients, it is shown that the model performance gap between Black and White subjects is dependent on the level of (im)balance between Black and White subjects in the training data. Again, it is shown that training using demographically balanced data can produce a fair and robust model. The conclusion from both of these pieces of work is that model performance can be robust if the training data is sufficiently diverse, both in terms of image characteristics and patient demographics. Building upon these analyses, the thesis next investigates the clinical utility of a diagnostic prostate MRI model trained on diverse data and externally validates it on in-house clinical data. The evaluation of this model encompasses not only standard quantitative metrics but also measurement of inter-observer variability in manual segmentation and assessments of performance on downstream clinical tasks. Next, the thesis investigates the clinical utility of multi-organ ML-based segmentation models. Here, two models are investigated: one for planning MRI called the “FIMRAa-P” model and another radiotherapy CT model called the “PelvisMA-CT” model. Both models are extensively evaluated quantitatively and qualitatively by five observers. The agreement between the quantitative metrics and the qualitative clinical metrics is also investigated for each clinical structure, revealing generally poor agreement between the two. It is also shown that this agreement is dependent on the structure being segmented and the profession of the clinicians who perform the evaluations. One of the main clinical translation outcomes of this thesis is the deployment of PelvisMA-CT by the Clinical Scientific Computing (CSC) group at GSTFT, and its integration into a contouring application called GSTTAutoSeg. This model is currently being used clinically at Guy’s Cancer Centre and the thesis presents the results of a monitoring and enhancement study based on its ongoing clinical use. Overall, the thesis presents a number of key contributions, all aimed at promoting clinical translation of ML in EBRT and BT. It is hoped that the work performed will accelerate the benefits of ML in radiotherapy treatment planning and delivery and ensure that all patients benefit from the introduction of the thoroughly evaluated new technology.6 0Item Restricted Predicting Carbon Credit Prices Using Advanced Machine Learning Techniques(Saudi Digital Library, 2026) Rayan, Najdi; Wang, HaiAccurate forecasting of carbon credit prices supports risk management, investment decisions, and policy assessment in the context of climate action. EU ETS carbon prices exhibit volatility, non-linearity, and non-stationarity, which reduces the effectiveness of traditional forecasting models. This dissertation proposes and evaluates a three-stage hybrid machine learning model for one-day-ahead forecasting of EU Emissions Trading System (EU ETS) carbon prices. The architecture follows a divide-and-conquer strategy. First, Wavelet Packet Decomposition (WPD) decomposes the carbon price signal into multiple frequency components. Second, a Gated Recurrent Unit (GRU) network models temporal dependencies and forecasts the trend component. Third, an Extreme Gradient Boosting (XGBoost) model predicts and corrects the GRU residual errors using wavelet-derived detail components as input features. The model was trained and tested on a dataset covering January 2018 to December 2024. The dataset includes EU ETS carbon prices, Brent crude oil prices, and electricity prices, while the forecasting model is univariate and uses the carbon price series only. On an unseen test set of 510 days, the model achieved a Mean Absolute Percentage Error (MAPE) of 1.66%, a Root Mean Squared Error (RMSE) of 4.86 EUR/ton, and a Mean Absolute Error (MAE) of 4.41 EUR/ton. The results indicate that combining signal decomposition, deep learning, and gradient boosting provides stable forecasting performance for EU ETS carbon prices under realistic evaluation conditions.6 0Item Restricted Artificial Intelligence (AI)-based Multi-criteria Shipping Industry Provider Selection(Saudi Digital Library, 2026) Khan, Ibraheem Abdulhafiz Q; Hussain, Farookh KhadeerThis thesis highlights that automating the selection of maritime shipping service providers is pivotal to supply-chain performance. By replacing fragmented and subjective practices with transparent analytics, automation reduces cost and time, improves reliability, and ensures decisions are reproducible at scale. To achieve this, the thesis introduces an intelligent multi-criteria search engine (MC-SE) that integrates artificial intelligence (AI) and multi-criteria decision-making (MCDM) to support both shippers and freight companies in identifying reliable, cost-effective providers. The objectives are to (i) develop an AI-based predictive classifier for offshore shipping decisions; (ii) systematically map provider criteria to the service quality framework (SERVQUAL); (iii) propose an AI-assisted approach for criteria weighting; (iv) conduct a SERVQUAL survey for provider-side assessment; and (v) validate the framework through an Australian case study. Methodologically, criteria were extracted from provider websites and benchmark datasets, then clustered into decision attributes using semantic similarity techniques. These clusters were aligned with SERVQUAL dimensions to ensure construct validity. AI-based weighting and supervised learning were applied within an MCDM pipeline to calculate attribute importance, integrate cost as a complementary decision factor, and rank providers objectively. This dual use of structured datasets and unstructured textual content ensures that the framework adapts to both traditional logistics data and dynamic, web-based information sources. Provider-side service quality is structured via SERVQUAL, while cost is modelled as a complementary decision attribute within the overall MC-SE multi-criteria framework. Validation demonstrates strong agreement between the proposed MC-SE weighting and the SERVQUAL survey (mean absolute error (MAE), MAE = 0.014), with dimension-level differences typically within 2–3%. The optimisation classifier, based on a voting ensemble, achieves 82.3% accuracy on held-out test data. These findings show that data-driven weighting, combined with supervised learning, can robustly support provider selection in practice. Overall, this thesis develops a novel, AI-driven framework to support the automated selection of maritime shipping service providers, bridging gaps between academic models and industry practice. Future research will refine the MC-SE framework, evaluate its portability across diverse contexts, and extend the evaluation to incorporate customer-experience evidence that complements provider-side quality and explicit cost trade-offs. Importantly, providers’ clusters are consistently aligned with canonical SERVQUAL dimensions to preserve theoretical and empirical coherence.19 0Item Restricted Integrating Educational Data Mining and Artificial Intelligence to Enhance ICT User Satisfaction and Administrative Efficiency in Saudi Educational Institutions(Saudi Digital Library, 2026) Almaghrabi, Hamad; Soh, BenThe integration of Information and Communication Technology (ICT) in educational administration offers transformative opportunities to enhance efficiency and user satisfaction, but also presents significant challenges. Despite the potential of ICT systems to stream- line processes and support data-driven decision-making, their implementation is often hindered by fragmented infrastructures, inconsistent adoption, and limited alignment with user needs. This thesis addresses these challenges through the design and evaluation of the AI-integrated IiCE framework, developed to strengthen ICT adoption and administrative performance in educational institutions. Educational administrative environments are inherently complex, characterised by mul- tidimensional data, dynamic workflows, and overlapping responsibilities that often expose systemic inefficiencies. The proposed IiCE framework leverages predictive analytics and user-centred design principles to generate actionable insights for optimising ICT utilisa- tion. Its key objectives include identifying the determinants of user satisfaction, enhancing decision-making processes, and fostering an organisational culture that supports technolo- gical innovation and acceptance. Employing a mixed-methods research approach, this study investigates current ICT ad- option practices in Saudi educational institutions. Quantitative and qualitative analyses, incorporating stakeholder perceptions and institutional data, were conducted to uncover adoption barriers and performance gaps. Machine learning (ML) models were applied to predict user satisfaction trends, while SHAP (Shapley Additive Explanations) techniques provided interpretability by highlighting the most influential factors affecting adoption. The framework also integrates adaptive training modules, modular deployment strategies, and continuous feedback mechanisms to ensure sustainability and contextual adaptability. Grounded in Saudi Arabia’s Vision 2030 for digital transformation, the evaluation of the IiCE framework demonstrates its ability to enhance administrative workflows, optim- ise resource allocation, and strengthen stakeholder engagement. Expert validation con- firms its effectiveness in mitigating inefficiencies, promoting collaboration, and supporting evidence-based management practices. This research contributes to the fields of educational administration and ICT innova- tion by presenting an adaptable, AI-driven framework that bridges the gap between tech- nological potential and practical implementation. The findings underscore the value of advanced AI techniques in managing ICT complexity, driving user satisfaction, and im- proving institutional efficiency. Future work may extend this framework through real-time analytics, greater model interpretability, and cross-domain applications for broader educational impact7 0Item Restricted Analysing Large-Scale Attacks in IoT Environments using ML/DL(Saudi Digital Library, 2025) Bokhari, Mohammed Ibrahim K; Neetesh, SexenaThe fine-grained classification of malicious network traffic presents a significant and persistent challenge in cybersecurity, primarily due to the extreme class imbalance inherent in real-world network data. Conventional machine learning approaches, which apply a single, unitary model to the problem, have demonstrated limited success, often failing to effectively identify rare but critical minority attack classes. This dissertation argues that the conventional model paradigm is fundamentally flawed for this problem space and proposes a hierarchical, multi-stage classification framework as a more robust alternative. This research presents a comprehensive, multi-faceted investigation into this problem, using the 34-class CICIoT2023 dataset as a benchmark. The study was conducted across four distinct experimental paths, comparing two ensemble methods (XGBoost and Random Forest) and two class-handling strategies (a "Grouped" approach that manually merges similar classes and an "Ungrouped" approach that tackles all 34 classes directly). Within this structure, we designed and implemented a 4-tier hierarchical framework that employs a "divide and conquer" strategy, using an initial classifier to handle majority traffic and a class-level routing mechanism to delegate ambiguous samples to specialised recovery tiers. An adaptive resampling strategy was deployed within these tiers, concentrating aggressive SMOTE only where required. The empirical results provide a holistic validation of the proposed architecture. The optimal configuration—an Ungrouped, XGBoost-led hierarchical framework—achieved a final accuracy of 0.9228 and Macro-F1 score of 0.7948, a substantial improvement over all other experimental paths and conventional baselines. More significantly, this approach demonstrated a more than 800% increase in the F1-score for some of the under-represented minority classes. The analysis also revealed a key architectural principle: classifier performance is role-dependent, with different ensemble methods excelling in different roles within the hierarchy, highlighting the importance of managing the bias-variance trade-off at a systemic level. Finally, this work provides a rigorous, data-centric analysis that distinguishes between model limitations and the inherent limitations of the dataset, identifying a "dataset-induced ceiling" on performance for 5 of the 34 classes. The primary contribution of this dissertation is, therefore, a methodologically robust and architecturally novel framework, validated through a comprehensive, multi-path experimental design. The principles of hierarchical decomposition and adaptive resource allocation are domain-agnostic and offer a promising direction for future research into extreme imbalance problems.10 0Item Restricted AI-Powered Multimodel Detection System for Cybersecurity Attacks: Design, Implementation, and Evaluation(Saudi Digital Library, 2025) Alhazmi, Marwan; Nguyen, HoangAs cyber threats have become increasingly complex, so too has the need for advanced detection methods to be able to analyze different types of data. Historically, traditional intrusion detection systems (IDS), have relied on analyzing one form of data, either a statistical analysis of network traffic or an alert log written in text format. These limitations restrict the capability of IDSs to detect the many complexities associated with modern attacks. Therefore, this dissertation proposes an AI powered, multimodel detection system that utilizes a combination of both structured network data, and unstructured alert text, to improve the performance of intrusion detection systems. The methodologies include preprocessing and feature extraction on the CICIDS2017 dataset, machine learning algorithms for the analysis of structured data and Natural Language Processing (NLP) algorithms for the analysis of text data. The multimodel fusion method used late fusion where the predictions from each modality are combined to produce a single prediction. In addition, several classification algorithms were trained and tested including Random Forest, Logistic Regression, and Text Classification. Results showed that the multimodel system significantly outperformed the single-modality systems based on the evaluation metrics of Accuracy, Precision, Recall, and F1-Score. Furthermore, the multimodel fusion strategy enhanced the context of the detection by reducing false positive detections; this addresses a major challenge that is commonly experienced by researchers in the field of Intrusion Detection Systems (IDS). Therefore, this dissertation provides a practical, scalable, multimodel AI-based framework for detecting cybersecurity threats and demonstrates the effectiveness of using a combination of structured and unstructured data sources, along with providing direction for further advancements in Intelligent Intrusion Detection Systems.28 0Item Restricted Evaluating Static, Contextual, and End-to-End Embedding Techniques for Malware Detection on Dynamic API Call Data(Saudi Digital Library, 2026) Basfar, Mohammed Raed; Joey, LamThe rate of malware development continues to challenge cybersecurity, with traditional signature- and heuristic-based techniques overwhelmed by polymorphic and zero-day attacks. Natural language processing (NLP) offers a promising direction by modeling dynamic API call sequences as semantic linguistic data, enabling sophisticated embedding and sequence-learning methods to be used for malware detection. This dissertation contrasts and analyzes three typical embedding methods static, contextual, and end-to-end task-learned representations—under a shared experimental framework. Specifically, it employs Word2Vec embeddings with a Convolutional Neural Network (CNN), contextual BERT embeddings with a CNN, and a Bidirectional Long Short-Term Memory (BiLSTM) network with a trainable embedding layer and weighted loss function to address class imbalance. The experiments were conducted on a dynamic API call dataset of around 44,000 malware and 1,000 benign samples, summarized by the first 100 API calls executed under sandboxed conditions. Results indicate that the Word2Vec + CNN pipeline had the highest overall accuracy and malware detection precision but the lowest benign recall. The BERT + CNN model provided more balanced class performance, but at the expense of added computational overhead. The BiLSTM had the highest benign recall, as it was able to easily distinguish from non-malicious activity, but the lowest precision and hugely added resource use. The findings point out the competing trade-offs among detection accuracy, benign recall, and processing efficiency, highlighting the issue of aligning model selection with actual security contexts' resource constraints and priorities. The study contributes by reporting a comparative systematic review of the embedding approaches for malware detection and offering informative insights into performance vs. efficiency trade-offs. Apart from its scientific significance, it proves the larger potential of NLP-based approaches to supporting malware detection systems and to informing the design of responsive, resource-aware cybersecurity systems.13 0Item Restricted Advances in Artificial Intelligence for Energy Forecasting and Performance Management in Buildings(Saudi Digital Library, 2026) Alkhatani, Nasser; Petri, IoanAccurate energy forecasting is essential for intelligent building management, supporting operational optimisation, strategic planning, and demand-side flexibility. However, existing forecasting methods often struggle to remain accurate across multiple time horizons and to generalise across different building types with limited data. This thesis addresses these challenges by developing a modular modelling framework that advances both multi-horizon forecasting and cross-building adaptability. The first contribution is a hybrid forecasting model (SVR → XGBoost → LSTM) designed to deliver stable prediction performance across four horizons: 24 hours, one week, one month, and one year. The hybrid design leverages the complementary strengths of its components SVR for noise reduction, XGBoost for nonlinear feature learning, and LSTM for long-range temporal modelling resulting in improved robustness and generalisation compared with single-model approaches. The second contribution introduces a deep hybrid model (CNN → GRU → LSTM) within a transfer learning framework. Pretrained on multi-building datasets and fine-tuned using limited data from new buildings, this approach enhances cross-domain adaptability while reducing training time and data requirements, demonstrating the practical value of transfer learning for scalable energy forecasting. A third contribution integrates statistical peak detection to support the identification of high- demand events, enabling forecasting outputs to inform grid-interactive building operations. Rigorous evaluation including multi-metric assessment, residual diagnostics, ablation testing, and statistical significance analysis confirms the reliability and robustness of the proposed models. Overall, the thesis provides methodological and empirical advances that strengthen data-driven building energy management. The results show that hybridisation and transfer learning, when carefully designed, can enhance accuracy, stability, and generalisation, offering a scalable pathway toward more efficient and sustainable smart building operations.16 0Item Restricted Advanced Machine Learning Approaches for Comprehensive Cardiovascular Disease Risk Prediction Using Synthetic Data and Dynamic Feature Selection(Saudi Digital Library, 2025) Alqulaity, Malak; Yang, PoCardiovascular diseases (CVD) are a leading cause of global mortality, highlighting the need for accurate and reliable risk prediction models. Traditional CVD risk assessment tools, such as Framingham, SCORE, and QRISK, have several limitations that affect their accuracy and applicability. These tools typically focus on a narrow set of major risk factors, potentially overlooking important non-traditional factors, resulting in a less comprehensive risk assessment. Additionally, they often rely on linear models, which may fail to capture complex, non-linear interactions within the data. This thesis addresses the limitations of traditional CVD risk assessment tools by developing a hybrid predictive framework that integrates advanced machine learning (ML) techniques to enhance the accuracy of Coronary Artery Calcium (CAC) score prediction and CVD risk assessment using both traditional and non-traditional risk factors. The research is structured around three key objectives: generating synthetic data, enhancing feature selection, and developing a hybrid approach. To address data limitations, a Tabular Generative Adversarial Network (GAN) was enhanced to generate high-quality synthetic data, effectively expanding the training dataset and improving model robustness. Feature selection was further refined through an adaptive SHAP-based method, which dynamically adjusts feature importance thresholds to capture both traditional and non-traditional CVD risk factors more accurately. Finally, a hybrid approach combining hyperparameter tuning algorithms (Genetic Algorithms, Particle Swarm Optimisation, and Bayesian Optimisation) with Gradient Boosting algorithms (XGBoost, LightGBM, and CatBoost) was implemented to maximise predictive accuracy. This two-stage model first predicts CAC scores and then uses these predictions, alongside additional risk factors, to assess the likelihood of CVD events. Results demonstrate that the hybrid approach consistently enhances prediction accuracy across multiple metrics, with the CatBoost model particularly outperforming in both CAC score prediction and CVD classification.13 0Item Restricted INTELLIGENT ROBOTICS WITH DIGITAL-TWIN ALIGNMENT: SEMANTIC NAVIGATION, MANIPULATION, PLANNING, AND HUMAN-TO-ROBOT ACTION TRANSFORMATION(Saudi Digital Library, 2025) Alanazi, Ahmed Hamdan; Lee, YugyungThis dissertation advances AI-empowered indoor robotics through four interconnected contributions that unify navigation, manipulation, semantic planning, and human-to-robot action transformation within a digital-twin-aligned framework. GRIP, a grid-aware semantic navigation module, integrates symbolic scene understanding with hybrid search-and-policy execution to achieve robust and context-aware ObjectNav. PathFormer, a transformer-based manipulation model structured around a 3D spatial--semantic grid, generates smooth, interpretable, and physically consistent trajectories that remain tightly aligned with digital-twin simulation. KG-Transformer, a knowledge-guided semantic planner, leverages a lightweight digital twin to calibrate execution, veto unsafe behaviors, and autonomously repair failing plans across diverse indoor environments. ActionFormer, an action-generation transformer, introduces a unified imitation-learning pipeline that integrates human-activity recognition, human-motion generation, and robot-motion generation. ActionFormer supports more than twenty complex human activities, producing robot-ready demonstrations that generalize across platforms and enable end-to-end imitation learning from video and landmark sequences. Collectively, these contributions establish a coherent foundation for AI-empowered robotics grounded in digital-twin intelligence. Across benchmarks and real-world deployments, GRIP yields up to 9.6\% higher success rate and more than $2\times$ gains in path efficiency (SPL, SAE). PathFormer produces digitally consistent manipulation trajectories validated through robust sim-to-real transfer. KG-Transformer achieves 99.6\% executability, delivers a +4.6-point improvement on unseen-scene tasks, and eliminates safety violations in both simulated and multi-robot execution. ActionFormer attains state-of-the-art performance in human-activity recognition and high execution accuracy across more than 20 activities, generating realistic human-motion traces and corresponding robot-motion trajectories for embodied robotic demonstration. Together, these advances deliver a trustworthy, semantically aligned, and high-performance simulation-to-reality pipeline that significantly enhances the adaptability, reliability, and real-world readiness of autonomous indoor robotic systems.42 0