Saudi Cultural Missions Theses & Dissertations
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Item Restricted Making Linked Data Discoverable In The Context Of Wildlife Data Observatories(Saudi Digital Library, 2025) Mussa, Omar; Perera, Charith; Rana, Omer; Goossens, Benoit; Orozco-terWengel, PabloIn recent years, Linked Data (LD) and Semantic Web technologies have gained traction as powerful frameworks for integrating and querying distributed datasets across disciplines. Despite their potential, the complexity of technologies such as RDF triplestores and SPARQL query languages continues to hinder adoption among non-expert users, particularly within bioscience and wildlife research, where observational data is prevalent. This thesis addresses the usability gap by exploring how LD technologies can be made more accessible for domain specialists without technical expertise in Semantic Web technologies. A mixed-methods approach was adopted, combining a systematic literature review, ethnographic fieldwork, and iterative interface design. Findings highlighted key limitations in existing LD access techniques, particularly their inability to support observational data patterns and spatiotemporal querying needs. In response, this research presents an integrated approach that combines graphical and conversational user interfaces to assist domain specialists in constructing queries without prior technical knowledge. This approach enables users to formulate complex semantic queries either through visual interactions or by describing their information needs in natural language, with the system translating these into executable queries, supporting key features that include dynamic filter generation, spatial selection, and ontology-aware entity linking. The approach was evaluated through a task-based user study involving bioscience researchers. Results demonstrate that the integrated interface significantly improves usability, task accuracy, task completion time (over 50% improvement across most tasks) and user satisfaction compared to graphical or conversational UIs. Furthermore, this thesis explores the integration of Large Language Models (LLMs) via a Retrieval-Augmented Generation (RAG) approach to enhance semantic interpretation and user support. Across multiple use cases, integrating LLMs enhances the expressivity of natural language queries, allowing previously unsupported queries to be answered with over 89% accuracy and up to 100% for many tasks. Overall, this research contributes to the field by introducing accessible LD retrieval methods for non-experts in ecological data observatories.27 0Item Restricted How Large Language Models are Reshaping Skills and Job Requirements for Public Health Professionals in Saudi Arabia(Saudi Digital Library, 2025) Alkhinjar, Mulfi; Palmer, PaulaContext: Large Language Models (LLMs) such as ChatGPT, Gemini, and DeepSeek are transforming professional work across sectors by enhancing information processing and decision support. In public health, these technologies offer the potential to improve efficiency, analytical capacity, and data-driven decision-making. Yet, their integration raises concerns about workforce preparedness, evolving skill requirements, and ethical oversight. In Saudi Arabia, where Vision 2030 prioritizes digital transformation in healthcare, understanding how public health professionals adapt to these technologies is vital for workforce and policy planning. Method: This exploratory mixed-methods study examined the professional impact of LLMs and the preparedness of public health professionals for their integration. The validated Shinners Artificial Intelligence Perception (SHAIP) survey, adapted for LLMs and public health, was distributed to employees of the Saudi Public Health Authority, yielding 32 complete responses. Ten semi-structured interviews further explored four constructs: professional impact, preparedness, new essential skills, and obsolete skills. Quantitative data were analyzed descriptively, and qualitative data were coded using thematic analysis. Findings: Survey results indicated that LLMs positively influence efficiency and workflow but revealed gaps in training and ethical guidance. Interview themes reinforced these findings, identifying new essential skills such as prompt engineering, digital literacy, and critical oversight, while traditional tasks like manual data entry and report drafting were viewed as increasingly automated. Conclusion: LLMs are transforming the roles of public health professionals. Successful adoption requires structured training, institutional readiness, and ethical governance. The study offers actionable recommendations to align workforce development and recruitment strategies with Saudi Vision 2030, emphasizing capacity building and responsible AI integration in public health practice.18 0Item Restricted The Impact of LLMs Usage on Learning Outcomes for Software Development Students: A Focus on Prompt Engineering(Saudi Digital Library, 2025) Aljohani, Mohammed; Itamar Shabtai; June K. Hilton; Chinazunwa UwaomaThis study investigates the impact of large language model (LLM) usage, specifically ChatGPT, on student learning outcomes in programming education. The research adopts a mixed-methods approach, combining quantitative survey data from students and qualitative interviews with instructors. The study addresses three research questions: (1) the effect of LLM usage on undergraduate students' learning outcomes, (2) the influence of prompt engineering skills on this relationship, and (3) instructors' perceptions on these relationships. Quantitative data were collected from 159 students across two Saudi universities using a structured online survey with sections covering demographic information, LLM usage, self-reported programming understanding, and prompt engineering skills. Qualitative data were obtained through semi-structured interviews with programming instructors, covering LLM usage, prompt engineering skills, and their impact on student learning outcomes. The quantitative analysis utilized Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the measurement and structural models, including path coefficients, model explanatory power (R²), and predictive power (PLSpredict). Qualitative data were thematically analyzed using Atlas.ti to identify key themes related to instructor perspectives on the model. LLM usage positively impacts learning outcomes. While quantitative results did not show a significant moderating effect of prompt engineering skills, qualitative findings highlight its critical role in determining the positive effect of LLM usage on learning outcomes. The study emphasizes the importance of clear LLM usage policies and early prompt engineering training to promote meaningful engagement and maintain academic integrity in programming courses.15 0Item Restricted Improving Feature Location in Source Code via Large Language Model-Based Descriptive Annotations(Arizona State University, 2025-05) Alneif, Sultan; Alhindawi, NouhFeature location is a crucial task in software maintenance, aiding developers in identifying the precise segments of code responsible for specific functionalities. Traditional feature location methods, such as grep and static analysis, often result in high false-positive rates and inadequate ranking accuracy, increasing developer effort and reducing productivity. Information Retrieval (IR) techniques like Latent Semantic Indexing (LSI) have improved precision and recall but still struggle with lexical mismatches and semantic ambiguities. This research introduces an innovative method to enhance feature location by augmenting source code corpora with descriptive annotations generated by Large Language Models (LLMs), specifically Code Llama. The enriched corpora provide deeper semantic contexts, improving the alignment between developer queries and relevant source code components. Empirical evaluations were conducted on two open-source systems, HippoDraw and Qt, using standard IR performance metrics: precision, recall, First Relevant Position (FRP), and Last Relevant Position (LRP). Results showed significant performance gains; a 40% precision improvement in HippoDraw, and a 26% improvement in Qt. Recall improved by 32% in HippoDraw and 24% in Qt. The findings highlight the efficacy of incorporating LLM-generated annotations, significantly reducing developer effort and enhancing software comprehension and maintainability. This research provides a practical and scalable solution for software maintenance and evolution tasks.21 0Item Restricted Adversarial Machine Learning: Safeguarding Al models from Attacks(Lancaster University, 2025-01-10) Alammar, Ghaida; Bilal, MuhammadThe field of AML has gained considerable popularity over the years with researchers seeking to explore gaps and new opportunities for growth. This goal of this report is to offer an in-depth survey of adversary attacks and defences in machine learning by examining existing gaps in current algorithms and understanding the implications for systems. By exploring evasion, poisoning, extraction, and inference attacks, the paper reveals the weaknesses of the existing methodologies such as adversarial training, data sanitization, and differential privacy. These techniques are usually not versatile to newer threats and have raised concerns about how effective they are in practical use. The research contributes to the field by conducting an extensive literature review of 35 articles and highlighting the need to implement adaptive and diverse defence strategies as well as empirical studies to evaluate the effectiveness of AML mechanisms. Some of the strategic suggestions are to incorporate continuous training frameworks, optimise real-time monitoring processes, and improve privacy-preserving methods to safeguard confidential information. This analysis is envisaged to offer practical data to foster the development of AML to help in the development of robust AI systems that will remain impregnable to various kinds of adversarial threats in numerous vital sectors. The study examines the basic design and consequences of various attacks in addition to the impact of subtle manipulation of input data on patterns and privacy. The report further addresses the modern challenges of large language models (LLMs) and autonomous systems. Furthermore, this research emphasises the significance of robust protection against enemy attack in strategic areas. The studies additionally evaluate present day protection mechanisms inclusive of antagonistic training, enter preprocessing, and making models stronger and more reliable. By evaluating the efficiency of these defences and evaluating key areas for improvement, the dissertation provides invaluable insights into enhancing the security and reliability of systems. The results of addressing the attacks and defences expose the need for unremitting advancements in data protection in various systems.31 0Item Restricted Automating the Formulation of Competency Questions in Ontology Engineering(University of Liverpool, 2025) Alharbi, Reham; Tamma, Valentina; Grasso, Floriana; Payne, TerryOntology reuse is a fundamental aspect of ontology development, ensuring that new ontologies align with established models to facilitate seamless integration and interoperability across systems. Despite decades of research promoting ontology reuse, practical solutions for semi-automatically assessing the suitability of candidate ontologies remain limited. A key challenge is the lack of explicit requirement representations that allow for meaningful comparisons between ontologies. Competency Questions (CQs) , which define functional requirements in the form of natural language questions, offer a promising means of evaluating ontology reuse potential. However, in practice, CQs are often not published alongside their ontology, making it difficult to assess whether an existing ontology aligns with new requirements, ultimately hindering reuse. This thesis tackles the challenge of ontology reuse by introducing an automated approach to retrofitting CQs into existing ontologies. Leveraging Generative AI, specifically Large Language Models (LLMs), this approach generates CQs from ontological statements, enabling the systematic extraction of functional requirements even when they were not explicitly documented. The performance of both open-source and closed-source LLMs is evaluated, with key parameters such as prompt specificity and temperature explored to control hallucinations and improve the quality of retrofitted CQs. Results indicate high recall and stability, demonstrating that CQs can be reliably retrofitted and aligned with an ontology’s intended design. However, precision varies due to long-tail data effects, and potential data leakage may artificially inflate recall, necessitating further research. By enabling the reconstruction of CQs, this approach provides a foundation for assessing ontology reuse based on requirement similarity. Specifically, CQ similarity can serve as an indicator of how well an existing ontology aligns with the needs of a new ontology development effort. To operationalize this idea, this thesis proposes a reuse recommendation phase within ontology development methodologies. This phase systematically identifies candidate ontologies based on requirement overlap, offering a structured approach to reuse assessment. The methodology is validated through a practical case study, demonstrating its effectiveness in real-world ontology design. By embedding an explicit reuse recommendation step in the ontology engineering process, this approach provides ontology engineers with a systematic method to identify suitable candidate ontologies, enhancing the overall design process.27 0Item Restricted Evaluating Chess Moves by Analysing Sentiments in Teaching Textbooks(the University of Manchester, 2025) Alrdahi, Haifa Saleh T; Batista-navarro, RizaThe rules of playing chess are simple to comprehend, and yet it is challenging to make accurate decisions in the game. Hence, chess lends itself well to the development of an artificial intelligence (AI) system that simulates real-life problems, such as in decision-making processes. Learning chess strategies has been widely investigated, with most studies focused on learning from previous games using search algorithms. Chess textbooks encapsulate grandmaster knowledge, which explains playing strategies. This thesis investigates three research questions on the possibility of unlocking hidden knowledge in chess teaching textbooks. Firstly, we contribute to the chess domain with a new heterogeneous chess dataset “LEAP”, consists of structured data that represents the environment “board state”, and unstructured data that represent explanation of strategic moves. Additionally, we build a larger unstructured synthetic chess dataset to improve large language models familiarity with the chess teaching context. With the LEAP dataset, we examined the characteristics of chess teaching textbooks and the challenges of using such a data source for training Natural Language (NL)-based chess agent. We show by empirical experiments that following the common approach of sentence-level evaluation of moves are not insightful. Secondly, we observed that chess teaching textbooks are focused on explanation of the move’s outcome for both players alongside discussing multiple moves in one sentence, which confused the models in move evaluation. To address this, we introduce an auxiliary task by using verb phrase-level to evaluate the individual moves. Furthermore, we show by empirical experiments the usefulness of adopting the Aspect-based Sentiment Analysis (ABSA) approach as an evaluation method of chess moves expressed in free-text. With this, we have developed a fine-grained annotation and a small-scale dataset for the chess-ABSA domain “ASSESS”. Finally we examined the performance of a fine-tuned LLM encoder model for chess-ABSA and showed that the performance of the model for evaluating chess moves is comparable to scores obtained from a chess engine, Stockfish. Thirdly, we developed an instruction-based explanation framework, using prompt engineering with zero-shot learning to generate an explanation text of the move outcome. The framework also used a chess ABSA decoder model that uses an instructions format and evaluated its performance on the ASSESS dataset, which shows an overall improvement performance. Finally, we evaluate the performance of the framework and discuss the possibilities and current challenges of generating large-scale unstructured data for the chess, and the effect on the chess-ABSA decoder model.12 0Item Restricted Evaluating Text Summarization with Goal-Oriented Metrics: A Case Study using Large Language Models (LLMs) and Empowered GQM(University of Birmingham, 2024-09) Altamimi, Rana; Bahsoon, RamiThis study evaluates the performance of Large Language Models (LLMs) in dialogue summarization tasks, focusing on Gemma and Flan-T5. Employing a mixed-methods approach, we utilized the SAMSum dataset and developed an enhanced Goal-Question-Metric (GQM) framework for comprehensive assessment. Our evaluation combined traditional quantitative metrics (ROUGE, BLEU) with qualitative assessments performed by GPT-4, addressing multiple dimensions of summary quality. Results revealed that Flan-T5 consistently outperformed Gemma across both quantitative and qualitative metrics. Flan-T5 excelled in lexical overlap measures (ROUGE-1: 53.03, BLEU: 13.91) and demonstrated superior performance in qualitative assessments, particularly in conciseness (81.84/100) and coherence (77.89/100). Gemma, while showing competence, lagged behind Flan-T5 in most metrics. This study highlights the effectiveness of Flan-T5 in dialogue summarization tasks and underscores the importance of a multi-faceted evaluation approach in assessing LLM performance. Our findings suggest that future developments in this field should focus on enhancing lexical fidelity and higher-level qualities such as coherence and conciseness. This study contributes to the growing body of research on LLM evaluation and offers insights for improving dialogue summarization techniques.32 0Item Restricted An In-Depth Analysis of the Adoption of Large Language Models in Clinical Settings: A Fuzzy Multi-Criteria Decision-Making Approach(University of Bridgeport, 2024-08-05) Aldwean, Abdullah; Tenney, DanThe growing capabilities of large language models (LLMs) in the medical field hold promising transformational change. The evolution of LLMs, such as BioBERT and MedGPT, has created new opportunities for enhancing the quality of healthcare services, improving clinical operational efficiency, and addressing numerous existing healthcare challenges. However, the adoption of these innovative technologies into clinical settings is a complex, multifaceted decision problem influenced by various factors. This dissertation aims to identify and rank the challenges facing the integration of LLMs into healthcare clinical settings and evaluate different adoption solutions. To achieve this goal, a combined approach based on the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) has been employed to prioritize these challenges and then use them to rank potential LLM adoption solutions based on experts’ opinion. However, utilizing LLMs technologies in clinical settings faces several challenges across societal, technological, organizational, regulatory, and economic (STORE) perspectives. The findings indicate that regulatory concerns, such as accountability and compliance, are considered the most critical challenges facing LLMs adoption decision. This research provides a thorough and evidence-based assessment of LLMs in the clinical settings. It offers a structured framework that helps decision-makers navigate the complexities of leveraging such disruptive innovations in clinical practice.40 0Item Restricted A Method for Formal Analysis and Simulation of Standard Operating Procedures (SOPs) to Meet Safety Standards(George Mason University, 2024) Bashatah, Jomana; Sherry, LanceStandard Operating Procedures (SOPs) are the “glue” that holds the command-and-control center together. They are step-by-step instructions to guide the operators on how to control complex human-machine systems. While the machine is certified and the operators are licensed, SOPs are loosely regulated. Time and cost constraints limit the testing of SOPs to account for the variability noted in human performance, i.e., SOP execution time and the variability in the operational environment. Additionally, SOPs mainly exist as static text documents, i.e., Word documents, hindering the ability to revise SOPs and maintain configuration integrity consistently. To address these limitations, this dissertation developed a framework for a digital SOP representation, metrics, and a simulation model to aid in creating, revising, and evaluating SOPs. A canonical structure, the extended Procedure Representation Language (e-PRL), was developed to decompose SOP steps into perceptual, cognitive, and motor elements. A method for using Large Language Models (LLMs) to generate SOP Steps from Owners Manuals, and a method to classify the text in the SOP steps into e-PRL components was developed. Techniques, including Monte-Carlo simulations to assess human performance and quantitative metrics that evaluate SOP content and training requirements, were developed for the e-PRL representation. Three case studies demonstrating the applicability of the methods are presented from the following domains: (1) aviation operational SOPs, (2) International Space Station (ISS) Habitable Airlock (HAL) SOPs, and (3) semi-autonomous vehicle SOPs. The implications of the results for each case study and the limitations and future work for the methods are discussed.31 0