Saudi Cultural Missions Theses & Dissertations

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Adversarial Robustness of Intrusion Detection Systems for the In-Vehicle Networks of Connected and Autonomous Vehicles
(Saudi Digital Library, 2026) ALORAINI, FATIMAH SULAIMAN; Javed, Amir
Connected and autonomous vehicles (CAVs) rely on machine learning (ML)-based intrusion detection systems (IDSs) to secure in-vehicle network (IVN) communications. However, ML models are inherently vulnerable to adversarial attacks. While prior adversarial research in CAVs has predominantly focused on perception models, particularly object detection, the robustness of IVN-based IDSs remains largely underexplored. This thesis addresses this gap by investigating the adversarial robustness of IVN-based IDSs, introducing an IVN-specific threat taxonomy, and developing an attack method capable of generating adversarial IVN frames under varying levels of attacker knowledge of the deployed IDS model. Experimental results demonstrate that adversarial manipulation poses a severe threat to IVN-based IDSs. Under complete attacker knowledge of the deployed IDS model, detection performance drops from an F1-score of 99%toaslowas19%, withattacksuccess rates reaching up to 89%. Even under limited knowledge, detection performance decreases from 95% to 38%, with success rates of up to 60%. To mitigate these vulnerabilities, this thesis proposes Explainability guided Counterfactual Adversarial Training (EXCAT), a novel defense mechanism that leverages model explainability to generate more representative adversarial training examples. EXCAT restores detection performance to up to 94% and reduces attack success rates to as low as 7.55%, demonstrating that explainability-guided training offers a promising direction for strengthening IVN-based IDS robustness and improving the safety of deployed CAV systems.
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Evaluation of Modified AI-Enhanced Radiographic Images of Artificial Teeth for Caries Removal Decision-Making in Predoctoral Dental Students
(Saudi Digital Library, 2026) Aldandan, Sukaina; Fontana, Margherita; Neiva, Gisele
Background: Accurate radiographic interpretation is essential for caries removal decision-making but remains challenging for early dental learners. Artificial intelligence (AI) has demonstrated promise in improving caries detection; however, its role in supporting operative decision-making during preclinical training remains unclear. Objective: To evaluate the effect of modified AI-enhanced radiographic images on the quality of caries removal performed by first-year dental students and to determine whether the effect of modified AI-enhanced images differs between shallow and deeper lesions. Methods: A two-period crossover study was conducted involving first-year dental students in a preclinical operative dentistry course. Participants performed caries removal on standardized 3D-printed teeth containing either shallow or deeper carious lesions. Students completed procedures using either standard bitewing radiographs or modified AI-enhanced radiographic images. Caries removal quality was assessed using the Composite Caries Removal Quality Score (CRQS), which incorporated convenience form, caries removal at the dentinoenamel junction (DEJ), caries removal at the pulpal floor. Completion time was also recorded. Results: Modified AI-enhanced radiographic images significantly improved overall CRQS compared with standard radiographs (p = 0.002). This improvement was primarily observed in shallow lesions, which demonstrated significantly higher CRQS scores under the modified AI-enhanced condition (p = 0.001), whereas no significant difference was found for deeper lesions (p = 0.727). The greatest improvement was observed in convenience form for shallow lesions (p < 0.001). No significant differences were detected for caries removal at the DEJ or pulpal floor. Lesion depth significantly influenced several outcomes, with deeper lesions demonstrating lower DEJ scores and requiring longer completion times. Modified AI-enhanced images did not significantly affect completion time. Conclusions: Modified AI-enhanced radiographic images improved the quality of caries removal performed by first-year dental students, particularly for shallow lesions where radiographic interpretation is more challenging. The benefits were primarily related to improved convenience form rather than caries removal at the DEJ or pulpal floor. These findings suggest that modified AI-enhanced radiographic images may be a valuable adjunct in preclinical dental education and may support the development of diagnostic and operative decision-making skills in early learners.
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Toward Robust Mental Health Classification Systems Across Genres and Languages
(Saudi Digital Library, 2026) Alqahtani, Amal Abdullah; Diab, Mona; Hwa, Rebecca
Mental health conditions are a major global public health challenge, yet many individuals do not receive appropriate care because of stigma, limited access to services, and the difficulty of accurate assessment. Natural Language Processing (NLP) has shown growing promise for identifying mental health conditions through language, but existing systems often struggle to generalize across modalities, domains, conditions, and languages. Existing approaches leave critical gaps in condition specificity, cross-genre robustness, and multilingual coverage. Prior work often studies isolated features or a single modality, leaving open how language markers behave across both writing and speech for the same condition. Condition-specific continual pretraining remains underexplored relative to generic mental health adaptation. Cross-condition transfer from clinically comorbid disorders has been proposed but rarely validated. And the field remains overwhelmingly English-centric, with Arabic among the most underserved languages despite its more than 400 million speakers. This dissertation addresses these gaps through a progression from interpretable linguistic analysis to multilingual evaluation, using schizophrenia as a core case study. We first present an integrated analysis of cohesion features, pragmatic cues, and language model-based measures across clinical speech and writing, showing that patients exhibit heightened fear, higher neuroticism, reduced specificity, and lower cohesion, with effects generally stronger in writing. We then evaluate these signals through supervised classification, finding that cohesion is the strongest standalone structured feature view in writing, while a TF-IDF lexical baseline dominates in speech. Moving to neural modeling, we show that progressive multi-stage continual training of BERT on patient-generated social media achieves an 11.7% relative F1 improvement over base BERT and outperforms MentalBERT and ClinicalBERT for schizophrenia detection. We then demonstrate that focused cross-condition transfer outperforms broad mental health pretraining, with StressRoBERTa achieving 82% F1 on the SMM4H 2022 stress detection benchmark. To extend mental health NLP beyond English, we introduce ArMHC, a large-scale Arabic mental health corpus from X (formerly Twitter) constructed through a dialect-aware extraction pipeline with LLM-based validation, covering 18 conditions across 1,911 users. Using the ArMHC schizophrenia subset, we evaluate cross-lingual and cross-genre transfer from English clinical data to Arabic social media, finding that both language and genre mismatch contribute substantially to transfer degradation, with genre mismatch being qualitatively more destructive: cross-lingual same-genre transfer still permits partial detection, while cross-genre transfer falls below chance. Overall, this dissertation demonstrates that robust mental health NLP benefits from combining interpretable linguistic analysis with domain-adaptive and transfer-based modeling, while expanding into low-resource multilingual settings. The findings contribute new linguistic evidence, modeling strategies, and dataset resources for building more inclusive and clinically relevant computational approaches to mental health assessment.
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Artificial Intelligence through Machine Learning techniques to enhance the application of 3D body scanning in apparel shape and sizing
(Saudi Digital Library, 2026) Alhassawi, Ruqey Ali; Simeon, Gill; Steve, Hayes; Kristina, Brubacher
Significant challenges persist in realising the full potential of technology related to accurate and inclusive body dimension variation and garment sizing and fit. Traditional methods often fail to capture the complexity of human body morphology, highlighting the value of more detailed approaches to analysing body dimension variation. This doctoral research aims to support the visual analysis of anthropometric population data through the integration of artificial intelligence (AI) and machine learning (ML) techniques, addressing limitations in traditional anthropometric methods used for apparel sizing and body–to–pattern mapping. A mixed–methods approach was employed across five interconnected phases, leveraging 3D body scanning (3DBS) technology to analyse and compare real–world body dimensions, classical garment sizing classifications and garment patterns. The research involved: (1) a comprehensive analysis of 3DBS data to establish body dimension diversity, (2) a critical reassessment of the traditional 8–head figure ratio, (3) clustering algorithms (Hierarchical, self–organizing map (SOM), k–means) to classify body types, (4) application of support vector machine (SVM) and principal component analysis–SVM (PCA–SVM) models for accurate size prediction, and (5) enhanced regression analysis to develop a data–driven approach for garment pattern adjustment. A dataset of 677 female participants from a range of ethnic backgrounds was utilised. Significant dimensional variations within conventional size groups were identified, revealing limitations in traditional measurement-based sizing systems within the study sample. Key findings demonstrate frequent deviations from the classical 8–head figure proportion model, emphasising the need for a more comprehensive approach. Clustering algorithms successfully delineated distinct morphological categories, while SVM modelling exposed trade–offs between predictive accuracy and computational complexity. Regression analysis established quantitative relationships between body measurements and pattern block parameters, offering a means of examining how body dimension variation relates to patternmaking practice. This research makes several theoretical, methodological and practical contributions. Theoretically, it provides data-based evidence of body proportion variability within standard size categories, challenges the classical 8-head figure proportion model using measured data, and identifies distinct body shape clusters within the study sample. Methodologically, it applies an integrated analytical framework – combining 3D body scan data, statistical analysis, ML clustering and classification, and regression analysis – to examine body dimension variation and body-to-pattern relationships. Practically, it provides how data-driven analysis of anthropometric variation may inform patternmaking considerations, subject to further applied investigation. This research examines the integration of 3D body scanning and computational techniques within anthropometric analysis. The use of data-based derived visual tools provides a means of representing and exploring body variation within the study sample. The findings highlight the potential relevance of data-driven approaches to sizing and may inform further investigation into how body diversity is represented within garment sizing systems.
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Assessing the Accuracy of Artificial Intelligence Synthetic CT Generation for Liver and Brain MRI-Only Radiotherapy
(Saudi Digital Library, 2025) Aljaafari, Lamyaa; SPEIGHT, Richard; BIRD, David; Buckley, David; ALQAISIEH, Bashar
Background: Magnetic resonance imaging (MRI) is increasingly integrated into radiotherapy because of its superior soft-tissue contrast compared with computed tomography (CT). This has prompted interest in four-dimensional (4D) MRI for motion management and MRI-only radiotherapy using synthetic CT (sCT) for dose calculation and patient positioning verification. This thesis aimed to provide clinical evidence for the technical feasibility and clinical implementation of MRI-only radiotherapy for liver and brain cancer. Methods: (i) A PRISMA-guided systematic review of the 4D MRI literature for abdominal radiotherapy was conducted. (ii) A deep-learning sCT model was developed using clinical MRI and CT data to generate liver MRI-only radiotherapy. (iii) The performance of a commercial sCT solution (Philips MRCAT) was assessed for brain MRI-only radiotherapy. For both liver and brain, dosimetric accuracy was evaluated using dose volume histogram (DVH) analysis. In addition, image-guided patient positioning was verified using the clinical XVI system. Results: (i) The systematic review, encompassing 39 studies, indicated that 4D MRI had the potential to improve abdominal radiotherapy by enabling accurate tumour definition and motion characterisation compared to 4D CT. (ii) For the liver sCT model, relative mean dose differences between CT and sCT were 0.0% for the planning target volume (PTV) and <0.5% for all organs at risk (OARs). Positioning verification revealed mean translational and rotational differences of <0.5 mm and <0.5°, respectively. (iii) For the brain MRCAT, relative mean dose differences were <0.4% for the PTV and <0.3% for OARs, with positioning accuracy maintained within ±1 mm and ±1°. Conclusion: 4D MRI shows considerable promise for motion management, but its clinical implementation remains limited, by lack of robust clinical validation or standardisation. Both liver and brain sCT models demonstrated dosimetric and positioning accuracy comparable to CT, confirming the technical feasibility of MRI-only radiotherapy for the liver and its clinical applicability for the brain.
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AI-Powered Multimodel Detection System for Cybersecurity Attacks: Design, Implementation, and Evaluation
(Saudi Digital Library, 2025) Alhazmi, Marwan; Nguyen, Hoang
As cyber threats have become increasingly complex, so too has the need for advanced detection methods to be able to analyze different types of data. Historically, traditional intrusion detection systems (IDS), have relied on analyzing one form of data, either a statistical analysis of network traffic or an alert log written in text format. These limitations restrict the capability of IDSs to detect the many complexities associated with modern attacks. Therefore, this dissertation proposes an AI powered, multimodel detection system that utilizes a combination of both structured network data, and unstructured alert text, to improve the performance of intrusion detection systems. The methodologies include preprocessing and feature extraction on the CICIDS2017 dataset, machine learning algorithms for the analysis of structured data and Natural Language Processing (NLP) algorithms for the analysis of text data. The multimodel fusion method used late fusion where the predictions from each modality are combined to produce a single prediction. In addition, several classification algorithms were trained and tested including Random Forest, Logistic Regression, and Text Classification. Results showed that the multimodel system significantly outperformed the single-modality systems based on the evaluation metrics of Accuracy, Precision, Recall, and F1-Score. Furthermore, the multimodel fusion strategy enhanced the context of the detection by reducing false positive detections; this addresses a major challenge that is commonly experienced by researchers in the field of Intrusion Detection Systems (IDS). Therefore, this dissertation provides a practical, scalable, multimodel AI-based framework for detecting cybersecurity threats and demonstrates the effectiveness of using a combination of structured and unstructured data sources, along with providing direction for further advancements in Intelligent Intrusion Detection Systems.
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The Influence of Artificial Intelligence on EAP Learners’ Oral Fluency
(Saudi Digital Library, 2026) Alwadaeen, Norah Bakheet; Abbuhl, Rebekha
There is ongoing debate on how AI speaking tools can support the development of oral fluency in second language (L2) instruction. Despite the widespread usage of these tools, such as AI chatbots and Automated Speech Recognition (ASR), questions persist about how well they will work to improve oral fluency, reduce speaking anxiety, and foster learner autonomy. This study investigates how an AI-mediated speaking partner influences English for Academic Purposes (EAP) learners’ oral fluency, speaking anxiety, and autonomy over a short, intensive practice cycle. Five upper-intermediate ESL students at a California community college completed nine EAP Talk chatbot sessions across 3 weeks, framed by pre- and post-intervention IELTS-style monologic speaking tasks. Acoustic analyses of the pre/post tasks in PRAAT targeted three utterance-fluency indices (speaking ratio, repair phenomena, and pause placement). Session-by-session Likert questionnaires captured perceived fluency gains, anxiety, and autonomy, and post-intervention semi-structured interviews explored learners’ experiences with the AI-mediated practice. Oral fluency findings indicated that the speaking ratio increased, whereas pause and repair indices generally shifted in favorable directions. Anxiety, which was scaled so higher scores indicated less anxiety, exhibited clear gains. Autonomy trajectories were positive at the group level. Furthermore, the study highlights both the promise and limitations of AI chatbots for EAP speaking. It emphasizes the value of multi-indicator fluency assessment, explicit autonomy supports, and longer comparative designs in future work.
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An Analysis of Face Synthesis Methods and Their Influence on Human Perception
(Saudi Digital Library, 2025) Almaimani, Maha; Patterson, Eric
Synthetic faces (e.g., computer-generated characters) have been increasingly utilized across various fields, including entertainment, healthcare, and education. Perceptual studies are often con- ducted to understand how synthetic faces are perceived by humans, aiming to enhance both quality and user experience in these domains. Over the years, numerous methods have been developed to create synthetic faces, ranging from traditional techniques such as image composites, Active Ap- pearance Models, and 3D Morphable Models to more recent machine-learning-based frameworks like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Despite the growing adoption of synthetic face generation and the variety of algorithms available for their creation, cognitive scientists have underutilized these advanced techniques. Re- search in this area has remained largely dependent on earlier approaches, and even when Artificial- Intelligence-based (e.g., AI-based) methods are employed, there is an overreliance on GANs, particu- larly StyleGAN2. This trend highlights a significant gap in the exploration of alternative generative architectures in perceptual studies. Furthermore, existing studies have primarily focused on the technical performance of GANs and VAEs, while human perception of their outputs has remained underexplored. This dissertation seeks to address this gap by first providing a comprehensive review of syn- thetic face generation methods from a perceptual standpoint. Second, it analyzes and perceptually compares two prominent AI-based models: StyleGAN2 (a GAN variant) and NVAE (a VAE flavor) across multiple contexts (e.g., full scenes vs. isolated faces without background, and animated vs. static faces) to determine how these conditions influence perceived realism and trustworthiness. This comparison supports the development of cognitive research that advances the generation of percep- tually engaging and practically useful synthetic faces. Finally, conducting a study to investigate how truthful versus misleading medical information about dementia influenced participants’ perceptions when viewing videos of synthetic faces generated by StyleGAN2. The outcomes of this dissertation provide insights into the perceptual differences between GAN-based and VAE-based synthetic faces across diverse contexts. Understanding these distinctions will contribute to the responsible and effective application of synthetic faces in real-life applications.
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INVESTIGATING NOVEL ANALYSIS APPROACHES FOR STRUCTURAL CONDITION ASSESSMENT USING ULTRASOUND AND INFRARED DATA
(Saudi Digital Library, 2025) Alqurashi, Inad; Catbas, Necati
Aging civil infrastructure, particularly reinforced concrete bridges, is experiencing progressive deterioration that threatens safety, serviceability, and long-term performance. Traditional inspection methods such as visual examination and hammer sounding are limited in their ability to detect subsurface defects and are prone to subjectivity. This dissertation develops and validates an integrated, multi-modal structural condition assessment framework that combines rapid Infrared Thermography (IRT), high-resolution Ultrasound Tomography (UT), Artificial Intelligence (AI)-driven anomaly detection, and immersive Digital Twin (DT) visualization to overcome these limitations. The research advances three main areas: (1) a dual-mode IR–UT workflow exploiting the complementary strengths of each modality, enabling rapid surface screening with IRT and in-depth defect characterization with UT; (2) optimized deep learning (DL) models tailored to each modality, with a transformer-based Grounding DINO model applied to raw Infrared (IR) imagery for automated detection of thermal anomalies, and a lightweight You Only Look Once (YOLO)-v8n model applied to UT volumetric slices for detecting internal delaminations, voids, ducts, and rebar, both trained on large, segmentation-assisted, color-standardized datasets to ensure robust performance under diverse field conditions; and (3) integration of Unmanned Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR), photogrammetry, and multi-modal non-destructive testing (NDT) data into a geo-referenced Virtual Reality (VR) environment to support real-time, collaborative decision-making. Laboratory testing on engineered specimens with embedded defects and field deployment on multiple in-service bridges, including the NASA Causeway Bridge, achieved high detection accuracy (mAP@0.5 up to 0.93 for UT using YOLOv8n and 0.80 for IRT using Grounding DINO), strong localization (Average IoU ≈ 0.80–0.90), and significant efficiency gains through targeted UT scanning. The VR-based DT enabled inspectors to seamlessly review thermal anomalies, volumetric UT slices, and 3D geometry in a single immersive scene, reducing defect confirmation time from several minutes to approximately one minute per location. By fusing complementary NDT modalities with AI models purpose-built for each data type and immersive visualization, this research delivers a scalable, repeatable, and field-validated methodology for rapid, objective, and data-rich condition assessment of reinforced concrete structures, with potential for broader application to other infrastructure types to enable proactive maintenance strategies and improved lifecycle management.
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Healthcare Professionals’ Initial Perceptions of Integrating Artificial Intelligence into Amputation and Prosthetic Rehabilitation: A Pilot Survey
(Saudi Digital Library, 2025) Alghamdi, Rahaf; Donovan-Hall, Maggie
Background: Artificial intelligence (AI) demonstrates potential to transform prosthetic rehabilitation through enhanced personalisation, real-time adaptability, and improved functional outcomes. In prosthetic care, AI can optimise socket design, interpret biosignals for intuitive limb control, and analyse gait. Despite promising technological advances, clinical adoption remains limited due to concerns regarding system reliability, data privacy, and insufficient clinician preparedness. Understanding healthcare professionals' perspectives is critical for effective implementation. Methods: A pilot online survey utilising open and closed-ended questions was administered to physiotherapists, occupational therapists, and prosthetists involved in amputation care. The adapted survey contained 23 closed-ended items (demographic background, awareness, benefits, concerns, trust of AI) and two open-ended questions. Data analysis employed descriptive statistics and content analysis. Results: Among 43 participants, results revealed high theoretical AI awareness (84% familiarity) but limited practical exposure (37% encountered AI tools). Strong optimism existed regarding AI's benefits for care quality, clinical decision-support, and treatment personalisation. Significant concerns included insufficient training/resources, data privacy, and system reliability. Importantly, 95% believed AI should complement, not replace, clinicians. Conclusion: This pilot study offers insights into rehabilitation professionals’ perceptions of AI in prosthetic rehabilitation and confirms the adapted survey as feasible for large-scale research. Clinicians demonstrated cautious optimism toward AI integration, viewing it as an augmentative tool rather than a replacement for expertise. The primary adoption barrier is not resistance but a critical training and resource gap. Future implementation must prioritise professional education, address data security concerns, and develop AI systems aligned with distinct professional values within multidisciplinary teams.
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Saudi Cultural Missions Theses & Dissertations

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