Saudi Cultural Missions Theses & Dissertations
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Item Restricted REGULATING ALGORITHMIC DISCRIMINATION UNDER THE EU AI ACT: EVALUATING BIAS MITIGATION DUTIES FOR HIGH-RISK AND GENERAL-PURPOSE AI SYSTEMS(Saudi Digital Library, 2025) ALSOMALI, ABDULAZIZ; ZIHAO, LIAlgorithmic systems now allocate work, credit, welfare and even police attention (facial recognition systems). They are not ‘neutral’ instruments; they often reproduce and amplify structural disadvantage. This dissertation asks whether the European Union’s Artificial Intelligence Act, when coupled with the Charter of Fundamental Rights and the equality acquis, can prevent and redress such discrimination. This dissertation argues that the Act is normatively necessary but only conditionally sufficient. Its risk architecture, data‑governance duties, documentation and oversight requirements, and the upstream regime for general‑purpose models supply the right legal levers. Constitutional adequacy will materialise only if implementation embeds equality law into technical practice through three cumulative conditions: (i) standards that require context‑specific metric selection justified by proportionality and the availability of less discriminatory alternatives; (ii) supervision with genuine statistical and legal capacity across the system lifecycle; and (iii) remedial pathways that convert logs and technical files into proof under burden‑shifting rules. Thus this paper turns to a functional comparison with the United States and the United Kingdom shows how adverse‑impact doctrine, discovery, and regulator‑led guidance can be harnessed without sacrificing the coherence of the EU model. Followed by Chapter 5 which sets out a concise implementation blueprint and measurable indicators. On that basis, bias mitigation is framed not as ethics, but as a legal duty by which the Act’s success must be judged.6 0Item Restricted A Novel Emoji-Aware Computational Framework for Body-Shaming Detection in Gulf Arabic Social Media Discourse(Saudi Digital Library, 2026) Albluwi, Abeer; Rizk, DominickOnline body-shaming has become a prevalent form of appearance-based harm in Gulf Arabic social media, particularly on TikTok and Instagram. In these environments, harmful content is rarely explicit. It is carried through indirect and culturally embedded forms of expression, including sarcastic religious expressions and emojis whose meaning is often context-dependent and inverted. Despite the scale of the problem, no prior Arabic NLP work had addressed body-shaming as a standalone classification task, and no dataset existed to support it. This dissertation introduces GABSD-E (Gulf Arabic Body-Shaming Dataset, Emoji-Enriched), the first annotated corpus built specifically for this task. The dataset contains 24,988 comments from TikTok and Instagram, labeled under a three-class taxonomy: Body-Shaming (BS), General Bullying (B), and Not Bullying (NB), with Fleiss' kappa = 0.87. Exploratory analysis confirmed that 42.9% of comments contain at least one emoji, and that the BS class has the highest emoji density relative to class size, establishing that emojis are structurally embedded in how body-shaming is communicated, not incidental to it. Building on this, the dissertation proposes a novel emoji-aware representation framework that treats emojis as culturally grounded semiotic units rather than preprocessing noise. The framework consists of three components: a semantic emoji tagging layer based on a manually constructed Gulf Arabic emoji dictionary; a contextual lexicon injection layer for culturally specific expressions; and a preservation-over-deletion preprocessing pipeline that reverses the standard Arabic NLP practice of emoji removal. Five Arabic transformer models were fine-tuned and evaluated under this framework. SaudiBERT achieved the best performance, with a mean Macro-F1 of 0.9519 ± 0.0030 and accuracy of 95.27% ± 0.30 across five random seeds, substantially above all classical baselines (best classical Macro-F1: 0.6906).Ablation results confirmed that emoji removal caused the largest per-condition performance drop (−0.0589 Macro-F1), with consistent degradation observed across all classes, indicating that emoji signals provide complementary contextual cues that improve discrimination across categories. Semantic enrichment was the most critical component for the BS class (ΔF1-BS = +0.0775). An exploratory benchmark of four large language models showed that the best LLM under few-shot prompting (GPT-4o, Macro-F1 = 0.9278) still fell 0.0279 points below the fine-tuned SaudiBERT, confirming that model scale alone does not substitute for culturally grounded domain-specific modeling. The dissertation contributes GABSD-E as a reusable dataset and benchmark framework, an annotation methodology applicable to related harm categories, and a representation framework that generalizes to any Arabic NLP task where emoji pragmatics carry discriminative weight.10 0Item 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.7 0Item Restricted MIDDLE MESIAL CANAL DETECTION IN MANDIBULAR PERMANENT MOLARS USING MANUAL VERSUS SEMIAUTOMATIC SEGMENTATION OF CBCT SCANS (IN VITRO STUDY)(Saudi Digital Library, 2026) Albaradi, Abdulmajeed Abdullah Ibrahim; Moussa, Sybel Mokhtar*Background: Technological advancements are improving medical practices in dentistry, leading to favorable changes in dental operations. Cone beam computed tomography (CBCT) is a cutting-edge technique for creating three-dimensional images. It offers a noninvasive way to examine intricate root canal structures. Objectives: This study aimed to assess the accuracy of identifying the middle mesial canal (MMC) in mandibular molars using manual and semiautomated pulpal segmentation of CBCT scans, along with the clearing technique. *Methods: A diagnostic accuracy in vitro study was conducted on 48 extracted first and second mandibular permanent molars obtained from the Oral and Maxillofacial Surgery Department at Alexandria University. The molars were placed in a human cadaver mandible and analyzed to identify the MMC using periapical digital radiography, manual, semiautomated, and automated CBCT segmentation, and the clearing technique (gold standard). Sensitivity, specificity, and accuracy of all methods were collected and volumetric analysis and time were analyzed using repeated measures ANOVA, followed by multiple pairwise comparisons using Bonferroni correction, with a significance level set at P<0.05. *Results: A MMC was detected in two out of the 48 teeth. Digital radiography showed 0% sensitivity and 100% specificity, with an overall accuracy of 95.83%. Manual, semiautomated, and automated CBCT analyses achieved 100% sensitivity and specificity for detecting the two MMC compared to the clearing technique. Manual segmentation took significantly more time (71.56 minutes) compared to semiautomated (22.48 minutes) and automated (0.20 minutes) methods (p < 0.001). No significant differences in volume measurements were noted among the three methods. *Conclusion: CBCT provided greater sensitivity, specificity, and accuracy in identifying MMC compared to digital radiography. Semiautomated and automated segmentation offers significant time saving and accuracy over manual methods.12 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 impact10 0Item Restricted The Role of Artificial Intelligence in Optimising Demand Forecasting and Inventory Management Within Pharmaceutical Supply Chain Management - A case Study of Saudi Arabia(Saudi Digital Library, 2026) Albandari, Alenize; Mulyata, JohnThe study examined the application of Artificial Intelligence (AI) in optimising demand forecasting and inventory management within the pharmaceutical supply chain in Saudi Arabia. The literature review indicates that AI has significant potential to improve forecasting accuracy and inventory efficiency; however, its adoption is still hindered by technical, organisational, and regulatory challenges. The existing literature highlights important gaps; however, most studies have focused on developed markets, while research on emerging economies, such as Saudi Arabia, remains limited. As the nation undergoes a remarkable digital transformation with a set of Vision 2030 initiatives, issues with AI technologies are emerging in response to deeper systemic inefficiencies in healthcare logistics. The study is based on secondary data, employing a qualitative research design that involves a thematic analysis of literature, industry reports, and related case studies. The inquiry was guided by four research questions: what leads to the adoption of AI, how much AI can affect demand forecasting, whether it can be effective in enhancing inventory management, and what are the obstacles to successful implementation. The study revealed that AI improves the precision of demand forecasting by 30% and reduces the forecasting errors by 20-50%. In inventory management, AI is expected to lead to a 20% reduction in waste and a 25-35% decrease in inventory expenses. Challenges persist, including poor data, outdated systems, organisational resistance, cultural norms, and regulatory ambiguity. It is concluded that technological and strategic preparedness is high; nevertheless, to achieve real success, it is essential to address the organisational and regulatory barriers that run deep. Some of the recommendations include investing in data infrastructure, enhancing AI literacy, developing more transparent regulatory frameworks, and promoting intersectoral cooperation.20 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 The Impact of Digital Transformation on Enhancing the Medical Supply Chain(Saudi Digital Library, 2025) Abdulaziz, Albaraa; Theo, FotisThe current thesis aims to investigate how digital transformation (DT) technologies, artificial intelligence (AI), the Internet of Things (IoT), and blockchain can be used to improve the medical supply chain in Saudi Arabia through the Vision 2030 metric. A systematic review based on PRISMA was used to identify and screen the studies (n=6); narrative and thematic synthesis were performed as part of the study, and three general themes were created: operational excellence, economic viability, and implementation readiness. According to the findings, DT enhances visibility, predictability, responsiveness, and resilience, but the strength of evidence was not consistent, and some of the studies have indicated a high implementation cost and organisational barriers. It is important to note that the synthesis has revealed the convergence as well as tensions among studies that technical interoperability and workforce readiness are important in the effective adoption of DT. Some of these recommendations are gradual execution of technology, capacity building initiatives and sponsorship of national policy to enable equal adoption in all the facilities. Its implications go beyond Saudi Arabia, and the fact that DT serves as an agent of predictive and agile systems and the global discussions about how digital health transformation strategies are to be implemented.6 0Item Restricted The Impact of Artificial Intelligence on Supply Chain Optimization in Saudi Arabia(Saudi Digital Library, 2025) AlQahtani, Abdullah Saeed; Khobzi, HamidThis dissertation examines the impact of Artificial Intelligence (AI) on supply chain optimization in Saudi Arabia, with particular emphasis on its alignment with the Kingdom’s Vision 2030 objectives. AI technologies such as machine learning, predictive analytics, robotic process automation, and the Internet of Things are increasingly recognized for their potential to enhance efficiency, resilience, and sustainability within supply chain operations. However, despite growing national interest, empirical research focusing on AI adoption in the Saudi supply chain context remains limited. The study adopts a qualitative, interpretivist approach based on multiple secondary case studies drawn from peer-reviewed literature published between 2020 and 2025. The analysis is guided by the Technology–Organization–Environment (TOE) framework, supported by the Supply Chain Operations Reference (SCOR) model, to examine both adoption drivers and process-level applications across key sectors, including telecommunications, healthcare, manufacturing, and national mega-projects. Findings indicate that AI adoption in Saudi supply chains is most advanced in planning, forecasting, and logistics delivery, while challenges persist in system integration, data quality, workforce readiness, and organizational resistance to change. Environmental factors such as Vision 2030 initiatives and government support act as strong enablers, although adoption remains concentrated among large organizations and flagship projects. The study concludes that while AI has significant potential to transform Saudi supply chains, its full benefits depend on improved digital integration, skills development, and supportive policy frameworks.63 0Item Restricted Unblocking Operational Excellence: The Role of Digital Transformation in Optimising the Oil and Gas Supply Chains(Saudi Digital Library, 2025) AlObaidi, Danah; Jappie, AGDigital transformation is increasingly recognised as a systemic enabler of operational excellence in oil and gas supply chains, where volatility, and regulatory demands exceed traditional efficiency tools. This study investigates how four technologies -the Internet of Things (IoT), Artificial Intelligence (AI), blockchain, and digital twins- contribute through alignment. Using a qualitative, interpretivist approach, a systematic literature review and thematic analysis of 36 peer-reviewed studies identified five themes: predictive and AI-enabled performance, real-time visibility, integration bottlenecks, blockchain for compliance, and organisational fitness. Results show that integration maturity consistently outperforms novelty in driving value, while tensions such as explainability versus latency and governance versus agility shape adoption outcomes. The dissertation advances a conceptual framework linking digital enablers to Operational excellence through a “stacked capability” logic and offers practical recommendations for oil and gas leaders. Ultimately, digital transformation emerges not as a linear solution but as a discipline of orchestration that enables reliable, scalable, and trusted operational performance.17 0