Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
29 results
Search Results
Item Restricted AI-Powered Multimodel Detection System for Cybersecurity Attacks: Design, Implementation, and Evaluation(Saudi Digital Library, 2025) Alhazmi, Marwan; Nguyen, HoangAs 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.26 0Item Restricted The Influence of Artificial Intelligence on EAP Learners’ Oral Fluency(Saudi Digital Library, 2026) Alwadaeen, Norah Bakheet; Abbuhl, RebekhaThere 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.22 0Item Restricted An Analysis of Face Synthesis Methods and Their Influence on Human Perception(Saudi Digital Library, 2025) Almaimani, Maha; Patterson, EricSynthetic 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.31 0Item Restricted INVESTIGATING NOVEL ANALYSIS APPROACHES FOR STRUCTURAL CONDITION ASSESSMENT USING ULTRASOUND AND INFRARED DATA(Saudi Digital Library, 2025) Alqurashi, Inad; Catbas, NecatiAging 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.8 0Item Restricted Healthcare Professionals’ Initial Perceptions of Integrating Artificial Intelligence into Amputation and Prosthetic Rehabilitation: A Pilot Survey(Saudi Digital Library, 2025) Alghamdi, Rahaf; Donovan-Hall, MaggieBackground: 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.12 0Item Restricted Graph Neural Networks for Drug Screening(Saudi Digital Library, 2025) Aqeeli, Noura Eissa; Panas, DagaDrug discovery is a lengthy and costly process that often involves small, noisy, and imbalanced datasets. In our study, we investigate the use of graph neural networks (GNNs) for predicting molecular homeostatic activity in neuronal cells through transfer learning. We evaluate Graph Convolutional Networks (GCNs) and Message Passing Neural Networks (MPNNs) with transfer learning, comparing their performance to Random Forest and non-transfer GNN baselines. To guide the selection of source datasets for pre-training, we implement a molecular latent representation similarity framework across nine MoleculeNet datasets. Additionally, we fine-tune a foundational molecular model on our target dataset. We evaluate the models using five-fold cross-validation, using the Area Under the Receiver Operating Characteristic curve (AUC-ROC) and the Area Under the Precision-Recall curve (AUC-PR) as metrics. Our results indicate that transferring knowledge from high-similarity source datasets outperforms the baseline models. Moreover, source-to-target transfer is more effective than fine-tuning the foundation model; however, the foundation model exhibits superior generalisation capabilities. Finally, we employ a selected set of models to rank an unlabelled molecular dataset. Our findings demonstrate that GNNs, combined with similarity-guided transfer learning, enhance performance in predicting bioactivity within low-data and imbalanced settings, highlighting the importance of carefully selecting source datasets to avoid negative transfer.9 0Item Embargo Organisational Readiness for AI in the Front-end Planning of Public Construction Projects(Saudi Digital Library, 2025) Felemban, Haneen Mohammedhassan; Khan, M SohailThe Kingdom of Saudi Arabia (KSA) Vision 2030 initiatives aim to diversify the economy and enhance the public sector. This led to an increase in public projects. However, many projects suffer from underperformance and failure, with these issues frequently arising during Front-end Planning (FEP), which is a crucial initial phase of project definition. Artificial Intelligence (AI) has been identified as having the potential to lower the barrier of carrying out FEP and improve decision- making for better overall project outcomes. The adoption of AI at the FEP stage can significantly improve practices, however, the readiness of public construction organisations to adopt AI remains under-explored. This research established foundational knowledge (exploratory) and test hypotheses (explanatory), defined as collective capability, culture, and governance structure required for AI integration in KSA public construction. The research employed a sequential exploratory mixed-methods approach grounded in the Technology-Organisation-Environment (TOE) Framework and Theory of Planned Behaviour (TPB). The research first conducted 30 semi-structured interviews with key stakeholders (government officials, engineers, and industry experts) to explore their perspectives on FEP and enablers and barriers to adopt AI, while an online survey of 234 professionals validated these insights. Purposive and snowball sampling ensured relevance, while the demographic profile reflected the structure of the Saudi construction workforce, enhancing the representativeness of the sample. Findings revealed a robust model (R²=0.995, p<.001) where organisational absorptive capacity (β =0.261) and organisational maturity (β= 0.235) emerged as key factors for this readiness. Followed by technological readiness (β= 0.216), environmental support (β= 0.143), and senior management support (β= 0.126). reinforced by government support, senior management engagement, and technological readiness. Survey results showed 82.9% identified team competence as the most critical failure factor at FEP. These insights extended the theory by integrating TOE with TPB, showing that structural enablers such as process and resources, must align with behavioural dimensions to achieve readiness. Overall, this research makes a novel theoretical contribution by demonstrating how the intersection of mixed-methods, context specific (KSA), multi-level frameworks (TOE+TPB), specific project phases (FEP), and industry specificity (construction) creates unique adoption dynamics absent from Western- centric models. This research contributes to knowledge by identifying the interconnected role of organisational absorptive capacity and organisational maturity in determining organisational readiness to adopt AI. Theoretically, it extends the TOE framework by integrating individual-level behavioural factors, offering a contextualised perspective and provides the first empirical examination of AI adoption in FEP in the KSA construction industry. This framework provides a contextualised approach to AI adoption tailored to the KSA public construction sector, highlighting the need to reduce bureaucratic rigidity, enhance managerial communication, and promote learning in organisational culture. It also addresses employee concerns related to job security to ensure readiness for successful AI adoption. Future research should explore the integration of dynamic capabilities theory to address aspects of readiness development, which can guide policymakers and industry practitioners in improving organisational readiness for AI.24 0Item Restricted Deep Learning based Cancer Classification and Segmentation in Medical Images(Saudi Digital Library, 2025) Alharbi, Afaf; Zhang, QianniCancer has significantly threatened human life and health for many years. In the clinic, medical images analysis is the golden stand for evaluating the prediction of patient prog- nosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of medical images is time- consuming and expensive for pathologists, radiologists and CT scans experts. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become main stream to segment tumours automatically, significantly reducing the workload of healthcare professionals. However, there still remain many challenging tasks towards medical images such as auto- mated cancer categorisation, tumour area segmentation, and relying on large-scale labeled images. Therefore, this research studies theses challenges tasks in medical images proposing novel deep-learning paradigms that can support healthcare professionals in cancer diagnosis and treatment plans. Chapter 3 proposes automated tissue classification framework called Multiple Instance Learning (MIL) in whole slide histology images. To overcome the limitations of weak super- vision in tissue classification, we incorporate the attention mechanism into the MIL frame- work. This integration allows us to effectively address the challenges associated with the inadequate labeling of training data and improve the accuracy and reliability of the tissue classification process. Chapter 4 proposes a novel approach for histopathology image classification with MIL model that combines an adaptive attention mechanism into an end-to-end deep CNN as well as transfer learning pre-trained models (Trans-AMIL). Well-known Transfer Learning architectures of VGGNet [14], DenseNet [15] and ResNet[16] are leverage in our framework implementation. Experiment and deep analysis have been conducted on public histopathol- ogy breast cancer dataset. The results show that our Trans-AMIL proposed approach with VGG pre- trained model demonstrates excellent improvement over the state-of-the-art. Chapter 5 proposes a self-supervised learning for Magnetic resonance imaging (MRI) tu- mour segmentation. A self-supervised cancer segmentation framework is proposed to re- duce label dependency. An innovative Barlow-Twins technique scheme combined with swin transformer is developed to perform this self supervised method in MRI brain medical im- ages. Additionally, data augmentation are applied to improve the discriminability of tumour features. Experimental results show that the proposed method achieves better tumour seg- mentation performance than other popular self- supervised methods. Chapter 6 proposes an innovative Barlow Twins self supervised technique combined with Regularised variational auto-encoder for MRI tumour images as well as CT scans images segmentation task. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative Barlow-Twins technique scheme is developed to represent tumour features based on unlabeled images. Additionally, data augmentation are applied to improve the discriminability of tumour features. Experimental results show that the pro- posed method achieves better tumour segmentation performance than other existing state of the art methods. The thesis presents four approaches for classifying and segmenting cancer images from his- tology images, MRI images and CT scans images: unsupervised, and weakly supervised methods. This research effectively classifies histopathology images tumour regions based on histopathological annotations and well-designed modules. The research additionally comprehensively segments MRI and CT images. Our studies comprehensively demonstrate label-effective automatic on various types of medical image classification and segmentation. Experimental results prove that our works achieve state-of-the-art performances on both classification and segmentation tasks on real world datasets24 0Item Restricted Detecting Supply Chain Threats(Saudi Digital Library, 2025) Akash Aravindan Paul Rajan; Nor Iman Binti Abdul Rashid; Ayham Al-Kilani; Alexandru-Aurel Constantin; Ashley Doel; Dr Erisa Karafili; Marwan Mousa Altamimi; Dr Erisa KarafiliThis study investigates the detection of supply chain threats in open-source software by developing an innovative system that integrates scraping techniques and artificial intelligence (AI) for intent analysis. The project aims to address critical vulnerabilities by analysing git commit messages and corresponding code changes, ensuring enhanced transparency and security in the software supply chain. The proposed system comprises a GitHub scraper that retrieves structured data using GraphQL and REST APIs, over- coming API rate limitations for efficient data collection. The collected data is processed by an AI model, ”Baymax,” which employs large language models (LLMs) to evaluate the alignment between commit messages and code changes. The system is designed with scalability and modularity to accommodate repositories of varying sizes and com- plexities. The project was implemented using Agile Scrum methodologies, employing iterative development practices with tasks prioritised through the MoSCoW framework. Collaboration within the development team was structured through specialised roles, and progress was monitored via sprints, stand-ups, and retrospectives. The results indicate that the system effectively enhances the integrity of open-source software by identi- fying discrepancies indicative of potentially malicious changes. Future work includes expanding platform compatibility, improving system performance, and incorporating user feedback to improve accuracy. This research contributes to the growing field of software supply chain security, with implications for broader applications in software development and beyond.15 0Item Embargo AI-Enabled Bioresponsive Clinical Decision Support Systems for Chronic Pain: User-Centered Approach(Saudi Digital Library, 0025) Alrefaei, Doaa; Soussan, DjamasbiThe advancement of eye-tracking technologies has enabled the development of systems capable of detecting attention and cognitive states objectively and in real time. Biometric technologies that capture psychological measures, such as eye movements (EMs), have allowed user experience (UX) research to expand toward building smart bioresponsive tools. One area that may benefit from these advancements is chronic pain, where self-report methods are often limited in capturing the complex phenomenon of chronic pain experience in both research and practice. This has established a need for objective biomarkers that can support pain assessment. Pain literature suggests the use of EMs as potential biomarkers, as they reflect pain-related attentional patterns. This dissertation adopts a bioresponsive, UX research approach to explore the efficacy of using EMs to detect pain experience in individuals with and without chronic pain. A proof-of-concept AI tool was developed to detect chronic pain using only EMs from individuals with and without chronic pain, achieving an accuracy of 81%, thereby demonstrating the robustness of EMs as a potential biomarker for pain. To successfully evolve this proof of concept into a fully developed and effective Clinical Decision Support System (CDSS) for chronic pain treatment and management, it is essential to understand the needs of the healthcare professionals who will use the system. As a first step, traditional UX research methods were employed to conduct interviews with healthcare professionals involved in the treatment and management of chronic pain. Based on this research, six user personas, four representing doctors and two representing nurses, were developed to serve as a foundational guideline for the design of an initial CDSS prototype. The findings of this dissertation contribute to both UX research and pain science by presenting a comprehensive methodology for using eye movements (EMs) as input signals to an AI tool capable of detecting differences in attentional patterns toward pain-related stimuli. It also contributes to clinical practice by outlining design guidelines for developing an initial prototype of such an AI-based CDSS, grounded in the needs and workflows of healthcare professionals.20 0
- «
- 1 (current)
- 2
- 3
- »