Saudi Cultural Missions Theses & Dissertations
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Item Restricted Automatic Classification of Thyroid Tumors for Women Based on Artificial Intelligence Models for Ultrasound Scans(Saudi Digital Library, 2025) ALKHAMSAN، Hassan Saleh; Elsayed، Rezk Mostafa Ibrahim; Elgarayhi، Ahmed; Medhet، TamerThyroid cancer arises in the thyroid gland when its cells begin to grow uncontrollably. The thyroid gland is essential for producing hormones that regulate metabolism, heart rate, blood pressure, and body temperature. Thyroid cancer, characterized by uncontrolled cellular growth in the thyroid gland, poses significant health risks. This study presents a novel diagnostic model for distinguishing benign and malignant thyroid tumors in ultrasound images by integrating a transferred EfficientNetB0 model with a new parallel deep convolutional neural network (CNN). The methodology involves preprocessing using Anisotropic Diffusion Filtering (ADF) for noise reduction, followed by feature extraction via deep CNNs. A refined classification model, developed through feature selection and dimensionality reduction, is trained and validated using a dataset of 1137 ultrasound images. The proposed system achieves an accuracy of 92.28% and an F1-score of 92.76%, demonstrating its effectiveness in assisting clinical diagnosis. Comparative complexity analysis further validates its robustness in addition to visual analysis tool (spider graph) that provides additional insights. The results demonstrate the potential of deep learning (DL) models in improving the reliability of thyroid cancer diagnosis, aiding clinicians in decision-making processes and reducing the risk of misdiagnosis.2 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.4 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.11 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.7 0Item Restricted A Facial Expression-Aware Edge AI System For Driver Safety Monitoring(Saudi Digital Library, 2025) Almodhwahi, Maram; Wang, BinThis dissertation presents a driver monitoring system (DMS) that integrates emotion recognition to address critical issues in road safety. Road safety has become a global concern due to the significant increase in vehicle numbers and the rapid growth of transportation infrastructure. The number one cause of road accidents is human error, with a 90% ratio, with common contributing factors like distraction, drowsiness, panic, and fatigue. Traditional DMS approaches often fall short in identifying these emotional and cognitive states, limiting their effectiveness in accident prevention. To address these limitations, this research proposes a robust, deep-learning-based DMS framework designed to identify and respond to driver emotions and behaviors that may compromise safety. The proposed system utilizes advanced convolutional neural networks (CNN), specifically the inception module and Caffe-based ResNet-10 with a single-shot detector (SSD), to perform efficient facial detection and classification. These chosen model structures helped balance computational efficiency and accuracy. The DMS is trained on an extensive, diverse dataset comprising approximately 198,000 images and 1,600 videos sourced from multiple public and private datasets, ensuring the system’s robustness across a range of emotions and real-world driving scenarios. Emotions of interest include high-risk states such as drowsiness, distraction, and fear, alongside neutral conditions, and the model can perform well in different conditions, including low-light and foggy/blurry environments. Methodologically, the system incorporates essential data preprocessing techniques such as resizing, brightness normalization, pixel scaling, and noise reduction to optimize the model’s performance. On top of that, data augmentation and grayscale conversion improves the dataset’s variability, allowing the decrease of computational costs without sacrificing accuracy. This approach enabled the model to achieve high performance metrics, with an overall accuracy of 98.6% , an F1-score of 0.979, precision of 0.980, and recall of 0.979 across the four primary emotional states. This research contributes to the field by offering a less invasive, real-time solution for monitoring high-risk driver behaviors and providing insights for further advancements in automated driver assistance technologies. Future directions include optimizing the system for microcontrollers with low power consumption and implementing alerts for high-risk states to further mitigate accident risks, as well as including a multi-modal fusion of data from different sources (Infrared Camera, and a Microphone) to increase emotion recognition accuracy, which leads to taking better control and initiating more efficient proactive interventions.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.5 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.13 0Item Restricted Enhancing Demand Forecasting Accuracy, Inventory Performance, and Supply Chain Efficiency in Saudi Arabia’s Public Pharmaceutical Sector through Artificial Intelligence.(Saudi Digital Library, 2025) Alattas, Rawan Omar; Meriton, RoystonThis study examined the role of Artificial Intelligence (AI) in improving demand forecasting and inventory performance in pharmaceutical supply chains in Saudi Arabia’s public healthcare sector. A cross-sectional survey of 155 professionals was conducted, and data were analysed using descriptive statistics, correlation, regression, and mediation/moderation tests. Results showed that AI integration explained 71% of the variability in demand forecasting accuracy (β = 0.82, p < .001). AI adoption also predicted 69% of the variability in inventory performance (β=0.82, p < .001), with significant effects on stock turnover (β=0.83, p < .001), lead time reduction (β=0.81, p < .001), and waste minimisation (β=0.83, p < .001). Organisational capabilities mediated the link between AI adoption and supply chain performance, confirming the importance of digital infrastructure and analytics competency. Barriers such as resistance, regulatory issues, and data quality challenges were reported, but did not significantly moderate the relationship between AI integration and demand forecasting accuracy. These findings confirm that AI improves efficiency, reduces waste, and strengthens resilience in pharmaceutical supply chains. Therefore, AI adoption aligns with Saudi Arabia’s Vision 2030 healthcare reforms.18 0