SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item 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.7 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.7 0Item Restricted Artificial Intelligence for Islamic Ontology Linking Qur’an and Hadith Resources(Saudi Digital Library, 2026) Alshammari, Ibtisam Khalaf F; Atwell, Eic; Alsalka, Mohammad AmmarThe primary objective of this thesis is to integrate various Islamic resources, specifically the Qur’an and Hadith, building upon existing academic research. Although previous works have produced valuable contributions, they remain uncoordinated and heterogeneous, raising significant challenges for interoperability and integration. This thesis addresses these limitations by unifying these inconsistent resources into a coherent and comprehensive Islamic ontology, ensuring a more accurate and structured representation of Islamic knowledge and facilitating wider computational applications. The project employs a multi-stage methodology. A workflow combining RML, Cellfie plugin, and SDM-RDFizer interpreter is utilised to integrate Qur’anic resources, enabling data transformation and alignment. This framework successfully maps the data correctly, demonstrating the effectiveness of the Cellfie plugin tool. For the Hadith corpus, knowledge extraction approaches are applied to the LK-Hadith corpus. First experiment with Arabic named entity recognition (ANER) using a limited number of BERT-based Arabic pretrained language models (PLMs) employed on the Sahih Albukhari book produces insufficient results for semantic integration, leading to the adoption of topic modelling methods. The topic modelling and extraction involves Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), BERTopic with Arabic PLMs, and GPT-4. This experiment presented a bilingual Hadith_Teaching_Topics (HTT) dataset. Then, the semantic relationships between Qur’anic and Hadith topics are evaluated through the Quran_Hadith_Dataset (QH_Dataset) using multiple large language models (LLMs), enriching the construction of the Related Quran-Hadith Topics (RQHT) ontology. The ontology is subsequently extended to incorporate the QuranOntology, the entire six Hadith books derived from the LK-Hadith corpus, and their derived topics based on the HTT dataset. This thesis advances the field by establishing a methodological framework for integrating heterogeneous Islamic textual resources by involving multiple data transformations, knowledge extraction, and ontology engineering techniques. Within this framework, the Qur’anic resources are integrated to enhance the unification process, the bilingual HTT dataset is developed to augment the low-resource Arabic annotation and support the final integrated ontology, and the RQHT ontology is introduced to formally represent the semantic relationships between Qur’an and Hadith topics. Collectively, these contributions boost the unification process of Qur’an and Hadith knowledge within a comprehensive and logically consistent semantic Islamic ontology. Evaluation through quantitative and qualitative measures determines the Islamic ontology’s logical consistency, representational completeness, and suitability for advanced computational applications. This research provides a scalable semantic framework to support more knowledge representation and reasoning across core Islamic texts.18 0Item Restricted Enhancing Learner Engagement and Personalisation in AI-Powered Quiz Application through Adaptive Learning, Gamification, and Mobile Optimisation(Saudi Digital Library, 2025) Alnageeb, Moaz Omar; papazoglou, varvaraThis dissertation investigates the integration of adaptive learning techniques, gamification elements, and mobile optimisation into SkillsDotAI, an AI-powered educational platform that dynamically adjusts question difficulty based on real-time user performance. The research addresses three core questions concerning adaptive learning implementation, gamification’s impact on engagement, and mobile accessibility in educational technology. Thesystem employs a sophisticated architecture built on Node.js/Express.js with PostgreSQL database integration, featuring a multi-stage difficulty adjustment algorithm that adapts question complexity across discrete learning phases. Central to the platform is an AI-powered feedback system utilising Claude 3 Haiku, which provides personalised learning guidance based on comprehensive session data analysis. Gamification elements, including achievement badges, global leaderboards, and progress tracking, are implemented to enhance user motivation and engagement. A comprehensive evaluation was conducted with 100 participants who interacted with both adaptive and competitive learning modes. Results demonstrate strong user recognition of adaptive features, with 77% of participants perceiving intelligent difficulty adjustments. Statistical analysis revealed significant positive correlations between perceived adaptability and overall satisfaction (r = 0.305, p = .002), and between feedback helpfulness and satisfaction (r = 0.577, p ≤ .001). The mobile design approach proved highly successful, with 79% of participants using mobile devices and strong positive correlations between mobile preference and satisfaction (r = 0.348, p ≤ .001). Keycontributions include empirical validation of transparent adaptive learning mechanisms, demonstration of relationships between adaptive features and AI-powered feedback, and practical frameworks for mobile-optimised educational technology development. The research provides evidence that users who recognise adaptive system behaviours report higher satisfaction levels, challenging assumptions about transparent versus hidden adaptation strategies. This work advances the field of AI in education by providing a robust technical framework for adaptive learning implementation, comprehensive evaluation methodologies for complex educational systems, and practical insights for developing engaging, accessible learning platforms16 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.13 0Item Restricted Automated Synthetic Lung Tumor Generation for Training a U-Net Model on Lung CT Slices(Saudi Digital Library, 2025) AlJoher, Sarah; Blumensath, ThomasThis thesis presents an automated pipeline for generating synthetic lung tumor CT images and corresponding segmentation masks to improve deep learning–based tumor segmentation in low-data settings. Real tumor regions are extracted from annotated CT scans and inserted into healthy lung slices using a 2D Tukey window and Poisson image blending to preserve realistic texture and boundaries. Ground truth masks are generated automatically using the Segment Anything Model and refined through morphological operations. The synthetic and real images are used to train a 2D U-Net segmentation model, which is evaluated across multiple experimental trials on an external dataset composed entirely of real pathological CT scans. Results show that models trained with carefully curated synthetic data match or outperform models trained on real data alone, demonstrating improved generalization and robustness. This work highlights the potential of automated synthetic data generation to reduce reliance on large, manually annotated medical imaging datasets.20 0Item Restricted Artificial Intelligence in Government: How Organizational and Behavioural Drivers Shape the Use of AI Insights in Decision-Making(Saudi Digital Library, 2025) Alsadun, Dhuha; Trim, PeterThis research evaluates the key drivers that shape employee intention to use and recommend AI-driven insights in the process of making decisions within organizations operating in the public sector. Using survey data collected from 106 employees working in the Saudi Arabian public sector this study focuses on how the four constructs; trust, perceived usefulness, leadership support and misalignment with operational plans influence the intention to use and recommend AI-powered insights in decision-making processes. Findings from the study show that all four constructs are statistically significant, though they vary in their influence on AI insights utilization. Usefulness and leadership support are the strongest predictors particularly when it comes to recommending AI insights to other employees. Trust in AI outputs has a statistically significant impact on the dependent variable however its effect is selective where specific traits such as perceived competence and reliability in handling complex tasks appear as significant predictors. This shows the importance of evaluating trust as a multidimensional construct rather than a single uniform aspect. In the same manner, although respondents exhibited openness to change, the misalignment between AI recommendations and operational plans was found to possibly inhibit the integration of AI insights. Particularly, factors such as emotional discomfort and the preference of the traditional processes of making decisions highlights the psychological and structural aspects that affect AI insights integration. This research provides an important contribution to the growing volume of knowledge focusing on understanding factors influencing the integration of AI powered insights in decision making processes across the Saudi Arabian public sector. This discussion introduces human behaviour as a critical aspect to consider in evaluating the key drivers that promote the intention to use AI insights as well as to recommend to others in decision making processes. Recommendation, in particular, is viewed as a key behaviour that plays an important role in encouraging trust and normalizing the use of AI insights within organizations. By focusing on the Saudi Arabian public sector this research creates real-word data that can be used to develop practical, academic and formal recommendations to improve the use of AI insights in decision-making processes in the public sector.18 0Item Restricted Assessing The Combined Impact of Blockchain, AI, And IoT on Operational Efficiency in Pharmaceutical Supply Chains: A Multi-Case Study Approach(Saudi Digital Library, 2025) Alonayzan, Lama; Dowsn, AltriciaThis thematic study examines the integration of blockchain, artificial intelligence (AI), and the Internet of Things (IoT) in transforming pharmaceutical supply chains (PSCs) by enhancing their operational efficiency and resilience, based on three case studies. Aim: This study aims to critically investigate how the integrated application of these digital technologies promotes operational efficiency in PSCs. It concentrates on the adoption patterns, performance metrics, and strategic alignment of these integrated technologies with AstraZeneca, Pfizer, and Johnson & Johnson as case studies. Design: This study has employed a qualitative multi-case study approach, using secondary data sources encompassing peer-reviewed academic research articles, industry reports, and company data. Thematic text analysis has been carried out to identify patterns and extract insights systematically. The analysis is grounded in three relevant theories: the Resource-Based View (RBV), the Technology Acceptance Model (TAM), and the Supply Chain Resilience (SCR). Guided by these theories, the study interprets strategic resource management, technology adoption behaviours, and resilience enhancement in PSCs. Findings: The integrated use of blockchain, AI, and IoT has created a cyber-physical ecosystem in PSCs that enormously enhances practical visibility, traceability, inventory optimisation, lead-time reduction, risk mitigation, and regulatory compliance. These digital resources, in combination, have fostered productivity and supply chain resilience, especially witnessed amid the COVID-19 pandemic. Nevertheless, this integration also encounters barriers encompassing technical issues in the form of interoperability, cybersecurity, organisational hurdles in the form of required skills and change resistance, and regulatory challenges in the form of data privacy and complex compliance frameworks. Originality: This research is unique in that the present literature has gaps, and it fills them with a focus on the integrated impact of the simultaneous adoption of these technologies instead of adopting them in isolation, as other studies have. This study scientifically validates integrated technology advantages via three case studies, and hence, it offers real-time strategic and managerial recommendations. This study also reveals the significant role of harmonised policies and cross-sector coordination to overcome barriers toward this technological integration, and hence it enriches academia, besides the convergence of three digital technologies for resilient and efficient PSCs.14 0Item Restricted The Accuracy of Diagnosing Salivary Gland Diseases by Artificial Intelligence: Systematic Review(Saudi Digital Library, 2025) Aljohani, Wejdan; Seoudi, Noha1.1 Purpose Artificial intelligence (AI) is increasingly applied in the diagnosis of salivary gland diseases, particularly Sjögren’s syndrome (SS) and salivary gland tumours (SGTs). This review aimed to evaluate the diagnostic performance of AI models in these two disease categories and identify converging patterns, limitations, and research gaps. 1.2 Method A systematic literature search was conducted in PubMed, Scopus, and Google Scholar over the past two decades (2005-2025) using predefined inclusion and exclusion criteria. Data extraction captured study design, input modality, AI model type, performance metrics (sensitivity, specificity, accuracy, AUC). Quality analysis was performed using JBI tool. Results were stratified by disease group (SS vs SGTs) and AI model type (Machine learning vs Deep learning). 1.3 Results A total of 19 studies were included from the 221 initially retrieved. Most of the included studies were assessed as moderate risk of bias, with only three low-risk and one high-risk. In SS studies , ML models showed excellent performance when applied to structured data. Logistic Regression emerged as the best-performing architecture, achieving accuracies up to 94% with AUC values ranging from 0.88 to 0.96. DL models on histopathology ranged from weak performance in baseline Residual CNNs (ResNet) (50% accuracy) to excellent outcomes with custom architectures such as CTG-PAM (100% across sensitivity, specificity, and accuracy). In SGTs, ML models on imaging inputs showed moderate ability, with Logistic Regression achieving 78–84% accuracy (AUC up to 0.91) and ultrasound reporting lower sensitivity but good specificity. DL approaches outperformed ML, particularly hybrid CNN–Transformers on MRI (85% accuracy, AUC 0.96; Liu et al., 2023) and Vision Transformers on ultrasound (87% accuracy, AUC 0.93; He et al., 2025). CNNs were more variable: Inception showed consistent results (73–85% accuracy, AUC up to 0.91), while ResNet and Densely Connected CNN (DenseNet) performance fluctuated widely even within the same input modality. 1.4 Conclusion AI demonstrates high potential in salivary gland disease diagnosis, with structured data input and custom-made models and advanced DL architectures yielding the most promising results. However, heterogeneity in input modalities and model design limits comparability, underscoring the need for standardised, multicentre validation.9 0Item Restricted Employee Readiness for AI Adoption in Riyadh’s Healthcare Sector: Perceptions and Organizational Support(Saudi Digital Library, 2025) Almutairi, Hadeel; Cui, QinquanArtificial intelligence (AI) is widely recognized as a significant driver of digital transformation across several domains, with the healthcare sector identified as one of the most influenced sectors. This research assesses employee readiness for AI among healthcare professionals in Riyadh, Saudi Arabia, with particular attention paid to perceptions (perceived usefulness and ease of use) and organizational support, including training and management support. This study employed a quantitative, cross-sectional, and correlational design. A survey was administered to evaluate employee readiness levels and potential predictors of AI readiness. A total of 120 employees participated with overall readiness (M = 4.20, Var=0.64). The regression explained 39.4% of the variance in readiness, with perceived usefulness (B = 0.44, p < 0.001) and training (B = 0.40, p < 0.001) contributing positively to readiness, while management support contributed negatively (B = -0.17, p = 0.011), and ease of use was not significant (B = 0.05, p = 0.574). Independent t-tests and ANOVA confirmed no significant differences in readiness by gender (p = 0.40), job type (p = 0.44), or years of experience (p = 0.56). The results showed that perceived usefulness and training were the strongest predictors of employee readiness for AI. While ease of use was not significant, organizational support had a negative effect. This study contributes to the literature on AI readiness in Saudi healthcare, highlighting perceived usefulness and training as key drivers for AI adoption, while questioning assumptions about the management support role in AI adoption. Healthcare leaders and policymakers should prioritize training, communicate the practical benefits of AI, and ensure that managerial commitment is supported by resources.14 0