SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item 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.21 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.10 0Item Embargo Comparative Study of the Performance of an Artificial Intelligence Platform in Detecting Periapical Radiolucencies Across Different Imaging Modalities(Saudi Digital Library, 2025) Allihaibi, Marwa; Koller, Garrit; Mannocci, FrancescoAim: This thesis aimed to evaluate the diagnostic accuracy of a commercial artificial intelligence (AI) platform in detecting periapical radiolucencies (PARLs) across different imaging modalities. The evaluation included preoperative assessment of teeth requiring primary endodontic treatment with comparison against dental professionals, radiographic healing assessment at follow-up, and assessment of teeth referred for apical microsurgery. Methods: Five retrospective diagnostic accuracy studies were conducted to evaluate the commercial AI platform Diagnocat (versions 1.0 and 2.0) for PARL detection across multiple imaging modalities. The studies utilised radiographic data from patients treated at Guy's and St Thomas' NHS Foundation Trust between 2012-2023. The study sample included: (1) 339 teeth indicated for primary root canal treatment, assessed on periapical radiographs (PARs) and compared with two experienced endodontists; (2) 376 teeth assessed at minimum one-year follow-up on PARs for radiographic healing outcomes, compared with two endodontists; (3) 134 molars evaluated on cone-beam computed tomography (CBCT) for preoperative and postoperative assessment; (4) 177 posterior teeth requiring primary endodontic treatment, assessed on PARs and compared with eleven general dental practitioners (GDPs); and (5) 116 anterior teeth referred for apical microsurgery, evaluated on both PARs and CBCT. Reference standards varied by study design: CBCT for PAR validation, expert consensus for CBCT assessment, and histopathology for cases referred for apical microsurgery. Statistical analyses included calculation of sensitivity, specificity and accuracy with 95% confidence intervals. McNemar's test assessed diagnostic performance differences. Subgroup analyses examined performance across anatomical variables. Results: Across five retrospective studies, Diagnocat demonstrated significant performance variability dependent on imaging modality, anatomical location, and treatment status. On PARs, for non-root-filled teeth requiring primary root canal treatment, sensitivity was 47.9% and specificity 95.4%, indicating reliable exclusion of disease but missing over half of actual lesions. In root-filled teeth assessed at one-year follow-up, sensitivity increased to 67.3% while specificity decreased to 82.3%, suggesting altered diagnostic thresholds based on treatment status. Performance on CBCT scans of molars showed marked improvement, achieving 93.9% sensitivity and 65.2% specificity in preoperative cases, and 88.6% sensitivity and 63.3% specificity in follow-up cases. While three-dimensional (3D) imaging substantially enhanced sensitivity for posterior teeth, it was accompanied by reduced specificity, indicating potential for overdiagnosis. Anatomical analysis revealed consistent underperformance in maxillary teeth and specific roots on PARs, limitations that were largely resolved on CBCT for posterior teeth. In contrast, anterior teeth demonstrated persistently poor performance regardless of imaging modality, achieving only 63.8% sensitivity on PARs and 57.5% on CBCT despite histopathological confirmation of periapical pathology. Cross-modality consistency was poor, with only 43.8% of lesions detected on both imaging modalities. Compared to clinicians, Diagnocat showed lower sensitivity (47.9% vs 65.3%) but comparable specificity (95.4% vs 97.7%) when assessed against endodontists in non-root-filled teeth. In root-filled teeth, this pattern reversed, with the AI achieving higher sensitivity (67.3% vs 49.3%) but lower specificity (82.3% vs 92.5%). When compared with GDPs, Diagnocat demonstrated lower sensitivity (44.9% vs 80.8%) but markedly superior specificity (94.3% vs 47.5%). Re-evaluation with version 2.0 showed no improvement in PARL detection across 1,308 PARs and 268 CBCT scans. Conclusion: This thesis demonstrated that multiple factors critically determine AI diagnostic accuracy for PARL detection, including imaging modality, anatomical location, and treatment status, thus highlighting fundamental limitations in training data representation and model development. AI platforms require comprehensive training on datasets with balanced anatomical representation and the incorporation of three-dimensional imaging before being considered for reliable implementation in endodontic diagnosis.14 0Item Restricted AI-Based Analysis of Magnetic Nanoparticle Relaxometry Curves for Structure-Specific Cancer Detection and Classification(Saudi Digital Library, 2025) AlHumam, Malack; Hovorka, OndrejCancer remains one of the world’s leading causes of death, and the key to successful treatment relies heavily on early and accurate diagnosis. This thesis explores a minimally invasive diagnostic method by combining magnetorelaxometry (MRX) with artificial intelligence (AI). Magnetorelaxometry measures how magnetic nanoparticles relax after being excited by an external magnetic field, producing relaxation curves that depend on anisotropy orientation and variation, particle number, structure geometry. Among magnetic nanoparticles, superparamagnetic iron oxide nanoparticles (SPIONs) are particularly suited for biomedical applications due to their biocompatibility and tunable relaxation properties. However, these curves often overlap and appear indistinguishable to the human eye, making traditional analysis challenging. The central research question of this thesis is whether AI can classify nanoparticle ensembles by structure and particle number from their relaxation curves, using them as unique markers for cancer detection and classification. To address this, five simulated datasets were generated, each incorporating multiple structures with different particle numbers under varying anisotropy conditions. After preprocessing, the data were analyzed with supervised, semi-supervised, and unsupervised models, supported by dimensionality reduction visualizations (PCA, t-SNE, UMAP). Supervised models achieved the strongest performance, with multiclass logistic regression reaching an accuracy of 0.89 in the dataset with aligned anisotropy and no variation. ZChains consistently emerged as the most distinguishable ensembles, relaxing roughly twice as long as YChains and providing clearer separability in both geometry and particle number, as confirmed by PCA scatter plots. In contrast, YChains frequently collapsed under z-axis anisotropy alignment, while Triangles and Rings were distinguishable only under controlled anisotropy variation. Arkus structures degraded rapidly when anisotropy variation increased. Semi-supervised pseudo-labeling maintained comparable accuracy of 0.817 under limited labeling, while unsupervised KMeans clustering, although non-predictive, provided insights into ensemble overlap and natural similarity groupings. The main contribution of this work is the demonstration that AI can classify nanoparticle ensembles through relaxation curve morphology rather than biomarker binding assays. This represents a shift from proof of detection toward structure-based classification, bridging magnetic physics with biomedical AI applications. Future directions include aligning anisotropy axes experimentally, exploring relaxation saturation for cancer staging, and translating AI pipelines to real biological magnetorelaxometry data.4 0Item Restricted Artificial Intelligence in Armed Conflict: Responsibility and Legal Reforms to Ensure Compliance with International Humanitarian Law(Saudi Digital Library, 2025) AlJerais, Najd; Master’s, degreeThe growing integration of artificial intelligence (AI) into military operations presents significant and unique challenges for international humanitarian law (IHL). This dissertation aims to answer the question of the extent to which AI-enabled weapons challenge compliance with IHL, and how effective potential reforms are in addressing these challenges. It first explores current and emerging military AI technologies and the application of IHL, analysing the risks posed to the core principles of distinction, proportionality, and precaution. It then identifies the main compliance challenges introduced by the deployment of AI-enabled weapons in the conduct of hostilities. These include the weakening of human judgment, responsibility gaps in both state and individual responsibility within the chain of command, and the fragmentation caused by the absence of international consensus. Finally, the dissertation considers potential responses, focusing on strengthening Article 36 weapons review procedures under Additional Protocol I, implementing human rights and ethical oversight, and developing enforceable international standards to ensure meaningful human control. It argues that while IHL remains the central legal framework governing armed conflict, it requires urgent strengthening through binding legal, ethical, and governance reforms. Ultimately, the discussion contributes to ongoing debates on the future of warfare by clarifying the limits of the IHL framework, evaluating the effectiveness of proposed reforms, and highlighting the need for a coherent regulatory approach capable of responding effectively to rapid technological change.12 0Item Restricted MAPPING ELIXIR COMMUNITIES: EVALUATING CONTENT-BASED AND NETWORK-BASED APPROACHES FOR SYSTEMATIC EXPERT DISCOVERY IN UK BIOMEDICAL RESEARCH(Saudi Digital Library, 2025) Shafi, Suha; Goble, CaroleELIXIR, Europe’s distributed life sciences infrastructure coordinating 18 communities, currently lacks systematic methods for identifying UK researchers whose work aligns with its community goals. This dissertation develops and evaluates two computational pipelines to address this challenge: a content-based approach using semantic matching of publication content, and a network-based approach using co-authorship expansion from verified UK ELIXIR members. The content-based pipeline processed 80,849 UK-affiliated publications retrieved through structured PubMed queries derived from ELIXIR communities’ descriptions, generating BioBERT embeddings for semantic similarity search. The network-based pipeline expanded from 86 verified ELIXIR UK authors through co-authorship networks, processing 370,282 publications and assigning themes using a hybrid keyword-embedding approach. Both systems were integrated into Retrieval-Augmented Generation (RAG) architectures enabling complex expert discovery queries. Evaluation employed four complementary frameworks: coverage validation of the content-based approach achieving 100% success in identifying known UK ELIXIR authors; systematic overlap analysis revealing 24.4% author overlap but only 3.6% publication overlap between systems; literature-based expert evaluation using evidence-based bibliometrics criteria; and parameter sensitivity testing across more than 600 configurations confirming robustness of the system across different thresholds and expert scoring methods. When rigorous evaluation criteria (ten or more publications, multi-institutional collaboration, recent activity since 2020) were applied, the content-based system identified 26,111 experts with 100% confidence, while the network-based system identified 28,567 experts with 99.97% confidence. Critically, only 924 experts (5.6%) were validated by both methods using the literature-based expert evaluation, demonstrating that the approaches identify fundamentally different expert populations. Network-based discovery excelled at finding collaborative, early-career researchers (70.9% versus 49.6%) in established computational domains like Galaxy workflows. Content-based discovery excelled in finding focused specialists (99.1% single-theme) and mid-career researchers in emerging interdisciplinary areas like Rare Diseases. The 924 overlapping experts proved to be cross-domain bridges, appearing in different ELIXIR communities 93% of the time and discovered through entirely different evidence by each method. The investigation demonstrates that content-based and network-based approaches access different dimensions of expertise: intellectual contribution versus social integration, validating the need for combined deployment. Together, the systems identify over 17,333 unique UK experts across more than 54,500 researcher-community mappings, providing ELIXIR with comprehensive, quality-assessed mappings for strategic community development and engagement.21 0Item Restricted Generating biodegradable molecular composites with MolGPT : A transformer based approach(Saudi Digital Library, 2025) AlJeldah, Futoon M; Hosni, ZiedThis work presents the development of biodegradable polymer composites using the MolGPT generative transformer model. MolGPT was trained on the GuacaMol dataset and fine-tuned on the COCONUT datasets to produce valid, unique, and novel molecules. The model achieved 98.7%, 96.4%, and 94.1% in validity, uniqueness, and novelty, respectively. confirming its capability to generate chemically diverse structures. A Random Forest classifier trained on a QSAR biodegradation dataset was used to classify candidates as readily or non-readily biodegradable. Readily biodegradable molecules were selected for further evaluation and validation. AutoDock Vina was employed to dock these candidates onto a polyethylene (PE) fragment, with the lowest-energy mode subjected to DFT calculations at the B3LYP/6-31G(d) level. The docked PE–biodegradable complex exhibited HOMO–LUMO gaps of 2.1 eV, together with a binding energy of –17.6 kcal/mol. These results demonstrate that MolGPT can generate novel biodegradable candidates and that their interactions with polyethylene enhance electronic reactivity, providing a foundation for understanding how biodegradable molecules can promote polymer degradation and a basis for future laboratory validation and material design.6 0Item Restricted AI and Human Judgment in Financial Reporting: A Comparative Study of Accuracy, IFRS Compliance, and Professional Perceptions(Saudi Digital Library, 2025) Alammari, Shada; Bosa, IrisThe accelerating integration of artificial intelligence (AI) into financial reporting processes has generated both promise and unease within the accounting profession. Contemporary developments in natural language processing (NLP) and generative AI, including large language models (LLMs), have made it feasible to produce complex financial outputs, such as statements of profit or loss (P&L) and statements of financial position (SFP), directly from structured or semi-structured data inputs. This technological shift raises fundamental questions about the quality, reliability, and compliance of AI-generated financial statements, particularly in contexts governed by International Financial Reporting Standards (IFRS) or national adaptations such as Saudi Arabia’s SOCPA-endorsed framework. While human accountants operate with judgement, professional scepticism, and regulatory awareness, AI operates through probabilistic pattern recognition. The central issue is whether such outputs can be relied upon for decision-useful financial reporting without undermining accountability and compliance. This dissertation addresses these concerns through a mixed-methods approach, combining (i) a simulation-based comparison of AI-generated and manually prepared financial statements derived from a reconstructed trial balance of Saudi Telecom Company (STC), and (ii) a survey of Saudi accounting professionals evaluating the perceived reliability, compliance, and adoption potential of AI in financial reporting. The simulation evaluates three key dimensions: numerical accuracy, IFRS compliance with respect to presentation and classification, and structural integrity, all benchmarked against a human-prepared standard. The survey extends this technical assessment by capturing professional perceptions of AI’s ability to complement or substitute for human judgement, as well as perceived risks such as regulatory non-compliance, loss of auditability, and the potential for novel misstatements.13 0Item Restricted Exploring the Use of Artificial Intelligence in Financial and Administrative Departments in Saudi Arabia and the United Kingdom(Saudi Digital Library, 2025) Alotaibi, Wafa; Ioannidis, ChristosArtificial Intelligence (AI) is becoming increasingly common in organisations. Many companies use it to finish tasks more quickly, to support decision-making, and to improve accuracy. Most of the academic writing about AI focuses on the technical systems or the economic effects, but fewer studies look at how employees themselves feel and experience AI in their daily work, especially in financial and administrative departments. My research addresses this gap, as I believe it is important to understand how employees view AI, what benefits they see, and what challenges they face. To answer this, I designed an online questionnaire that asked participants about their use of AI, its benefits, challenges, and ethical concerns. The survey was shared through email, LinkedIn, and professional networks. In total, 307 responses were received, of which 296 participants completed the demographic section. The respondents were employees working in finance, administration, or both. The questionnaire included multiple-choice, Likert scale, and open-ended questions. The answers were analysed using descriptive statistics to highlight trends, and simple qualitative analysis was applied to the open comments to provide further insights. The findings show that a majority of employees already use AI tools in their departments, with chatbots, automated reporting, and predictive analytics being the most common applications. The main benefits reported were time-saving, cost reduction, and increased accuracy. However, participants also highlighted challenges such as lack of training, technical errors, and concerns about job security and data privacy. Despite these difficulties, most respondents supported further adoption of AI in their departments, provided that organisations offer sufficient training, clear communication, and attention to ethical considerations. This study adds to the current knowledge on AI by focusing on the employee perspective. It suggests that successful AI adoption requires not only investment in technology but also careful consideration of people, their skills, and their concerns.23 0Item Restricted The Additional Regulatory Challenges Posed by AI In Financial Trading(Saudi Digital Library, 2025) Almutairi, Nasser; Alessio, AzzuttiAlgorithmic trading has shifted from rule-based speed to adaptive autonomy, with deep learning and reinforcement learning agents that learn, re-parameterize, and redeploy in near real time, amplifying opacity, correlated behaviours, and flash-crash dynamics. Against this backdrop, the dissertation asks whether existing EU and US legal frameworks can keep pace with new generations of AI trading systems. It adopts a doctrinal and comparative method, reading MiFID II and MAR, the EU AI Act, SEC and CFTC regimes, and global soft law (IOSCO, NIST) through an engineering lens of AI lifecycles and value chains to test functional adequacy. Chapter 1 maps the evolution from deterministic code to self-optimizing agents and locates the shrinking space for real-time human oversight. Chapter 2 reframes technical attributes as risk vectors, such as herding, feedback loops, and brittle liquidity, and illustrates enforcement and stability implications. Chapter 3 exposes human-centric assumptions (intent, explainability, “kill switches”) embedded in current rules and the gaps they create for attribution, auditing, and cross-border supervision. Chapter 4 proposes a hybrid, lifecycle-based model of oversight that combines value-chain accountability, tiered AI-agent licensing, mandatory pre-deployment verification, explainability XAI requirements, cryptographically sealed audit trails, human-in-the-loop controls, continuous monitoring, and sandboxed co-regulation. The contribution is threefold: (1) a technology-aware risk typology linking engineering realities to market integrity outcomes; (2) a comparative map of EU and US regimes that surfaces avenues for regulatory arbitrage; and (3) a practicable governance toolkit that restores traceable accountability without stifling beneficial innovation. Overall, the thesis argues for moving from incremental, disclosure-centric tweaks to proactive, lifecycle governance that embeds accountability at design, deployment, and post-trade, aligning next-generation trading technology with the enduring goals of fair, orderly, and resilient markets.11 0