Saudi Cultural Missions Theses & Dissertations
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Item Restricted Investigating the Influence of Knowledge and Attitudes on AI Practices in English Language Teaching: A Mixed-Methods Study of New Zealand Secondary School ESOL Teachers(Victoria University of Wellington, 2024) Khalil, Daya; Siyanova-Chanturia, AnnaThe rapid integration of Artificial Intelligence (AI) in education has transformed the teaching landscape, offering new opportunities but also posing challenges for teachers (Rahman et al., 2024; Karataş et al., 2024; Kartal & Yeşilyurt, 2024). Previous studies, such as those by Zhang et al. (2023) and Wang et al. (2023), have highlighted the potential benefits of AI for streamlining teaching practices and enhancing instructional efficiency. However, the effective use of AI depends on teachers’ knowledge and attitudes, which shape how they adopt and implement AI tools (Chiu et al., 2024; Kim & Kwon, 2023). Despite the increasing focus on AI in research, no empirical evidence to date has directly investigated how secondary school English for Speakers of Other Languages (ESOL) teachers’ knowledge and attitudes influence their AI practices in New Zealand. This study aims to fill that gap by exploring these relationships. This mixed-methods study involved survey data from 35 secondary school ESOL teachers and semi-structured interviews with four participants. Quantitative results showed that 68.6% of teachers reported low use of AI in English language teaching (ELT), while 31.4% demonstrated moderate use. Knowledge levels varied, with 40% having low knowledge and only 17.1% possessing advanced knowledge. Attitudes were mixed, with 22.9% showing positive attitudes and 25.7% expressing negative attitudes. Regression analysis revealed that attitudes (β = 0.560) were a stronger predictor of practices than knowledge (β = 0.379). Qualitative themes highlighted cautious exploration, the perceived need for robust verification methods of AI content, and the influence of both confidence and familiarity on AI use. Teachers with positive attitudes were more inclined to integrate AI meaningfully, while those with limited knowledge or negative attitudes restricted their use to simpler applications. These results emphasize the need for professional development that strengthens both technical knowledge and critical perspectives, supporting responsible and effective AI integration in ELT.13 0Item Restricted Integration of Artificial Intelligence in Supply Chain Management: A Case Study of Toyota Motor Corporation(University of Gloucestershire, 2024-08) AlQuwaie, Thamer Adeeb; Plummer, David; Rasheed, Muhammad Babar; Zhang, ShujunThis study examines AI deployment in Toyota Motor Corporation's supply chain management. By analysing the literature and interviewing key workers from Toyota, the research illustrates how AI technologies enhance logistics, demand forecasting, inventory management, and procurement. AI-driven predictive analytics and automation improved decision-making accuracy, operational efficiency, and cost savings. The research notes low data quality, expensive initial costs, and staff unwillingness to change as important impediments. The research suggests continual training, robust data management rules, and gradual AI deployment to solve these issues. The research also emphasises the importance of human factors in AI integration, including open communication and worker engagement for smooth adjustments. The research found that high management and departmental collaboration are needed to use AI technology successfully. Future research should include cross-sector and cross-regional comparisons, longitudinal studies to track impacts, and more work on social and ethical concerns. This research analyses Toyota's AI integration to provide supply chain AI users information and advice.9 0Item Restricted Generative AI for Mitosis Synthesis in Histopathology Images(University of Surrey, 2024-09) Alkhadra, Rahaf; Rai, Taran; Wells, KevinIdentifying mitotic figures has been established as an effective method of fighting cancer at its most vulnerable stage. Traditional methods rely on manual, slow, and invasive detection methods obtained from sectioned tissue samples to acquire histopathological images. Currently, Artificial Intelligence (AI) in oncology has produced a paradigm shift in the fight against cancer, also known as computational oncology. This is heavily reliant on the availability of mitotic figure datasets to train models; however, such datasets are limited in size, type, and may infringe on patient privacy. It is hypothesised that the potential of computational oncology can be realised by synthesising realistic and diverse histopathological datasets using Generative Artificial Intelligence (GenAI). This report demonstrates a comparison of Denoising Probabilistic Diffusion Models (DDPM) and StyleGAN3 in generating synthetic histopathology images, with mitotic figures. The MIDOG++ dataset containing human and canine samples with 7 types of tumours was used to train the models. Quality and similarity of generated and real images was evaluated using as Frechet Inception Distance (FID), Mean Square Error (MSE), Structural Similarity Index (SSIM), and Area Under the Curve (AUC) as a part of Receiver Operating Characteristic (ROC) study were incorporated. Our results suggests that the DDPM model is superior in terms of structural accuracy, however, StyleGAN3 capture the colour scheme better.16 0Item Restricted The Role of Artificial Intelligence in Personalising the Recruitment Process in Saudi Arabia: A Systematic Literature Review(Swansea University, 2024-09-29) Alotaibi, Mohammed; Balaussa, ShaimakhanovaArtificial intelligence (AI) has revolutionised various industry sectors, including human re- sources (HR),by enhancing decision-making, automating tasks, and improving efficiency. In the Kingdom of Saudi Arabia, the adoption of AI in HR is increasing, particularly in recruitment processes. This study explores how AI is transforming recruitment in Saudi Arabian organisations, highlighting the benefits and challenges associated with its im- plementation. AI-driven recruitment tools can streamline candidate screening, improve decision-making by analysing large datasets, and enhance the overall candidate experi- ence through personalisation. However, the study also identifies significant challenges, such as the need for AI systems to align with local cultural norms, legal requirements, and data privacy regulations. Moreover, the limited availability of skilled professionals to manage AI technologies and concerns about bias in AI-driven decisions are notable barriers. The research emphasises the importance of understanding employees’ and HR professionals’ perceptions of AI, particularly in terms of trust, acceptance, and effec- tiveness. By applying frameworks such as the technology acceptance model (TAM) and employee engagement theory, this study aims to assess AI’s impact on recruitment, fo- cusing on personalised onboarding experiences and strategic workforce planning in Saudi Arabia. The findings suggest that despite existing challenges, AI holds significant po- tential to optimise HR operations and contribute to organisational success, aligning with Saudi Arabia’s Vision 2030 goals. Future research should address the ethical implications, long-term impacts, and cultural adaptations necessary for successful AI integration in re- cruitment.By bridging these gaps, AI can play a pivotal role in modernising recruitment practices, enhancing efficiency, and driving competitive advantage in the evolving Saudi employment market.7 0Item Restricted Diagnosis of Oral and maxillofacial cysts using artificial intelligence: a literature review(University of Manchester, 2024) Almohawis, Alhaitham; Yong, SinAbstract Oral and maxillofacial cysts are cavities that can pose significant risks if not detected and treated promptly. Many of these cysts are asymptomatic, often going unnoticed until complications arise. The introduction of artificial intelligence (AI) presents a promising opportunity for early detection and management of these cysts. Aim: To explore current studies on the use of artificial intelligence in diagnosing oral and maxillofacial cysts. Objectives: To examine the existing literature in this field, assess the accuracy, effectiveness, and limitations of AI models, and identify challenges in implementing AI in clinical practice. Methods: This literature review followed a systematic approach, identifying 223 studies from PUBMED and SCOPUS databases between 1975 and 2024. After applying inclusion and exclusion criteria, 26 retrospective cohort studies were included in the final analysis. A risk of bias assessment was conducted using the ROBINS I tool. Results: The investigation revealed that AI models consistently demonstrate high accuracy in detecting oral cysts in both radiographs and digital histopathology. The ROBINS I tool indicated a moderate risk of bias in most of the included studies. Notable limitations include limited datasets, variable data quality, and a lack of explainability in AI models results. Conclusion: AI models have shown considerable effectiveness and speed in detecting both simple and complex cysts. However, to fully leverage AI's potential in clinical settings, further rigorous studies are needed to evaluate its risks, benefits, and feasibility, ensuring compliance with governmental health regulations on AI.11 0Item Restricted Triple-Negative, Digital Biomarkers, Survival Analysis, Neoadjuvant Chemotherapy, Histology Images Analysis(Univeristy of Warwick, 2024-04) Albusayli, Rawan; Rajpoot, Nasir; Minhaz, FayyazTimely detection, precise diagnosis, and effective risk stratification play a pivotal role in optimising treatment decisions and enhancing outcomes for individuals battling cancer. The advent of Digital Pathology (DP) introduces a revolutionary potential to elevate cancer detection methods and refine treatment management by improving diagnostics and prognostics. This thesis endeavours to harness the power of deep-learning techniques for analysing histology images in triple-negative breast cancer (TNBC), with the ultimate goal of extracting digital biomarkers to enrich the study of patients' outcomes. The analysis of whole slide images commences with a robust tissue classification, followed by intricate computational examinations to extract spatial features of the tumour microenvironment. This inquiry unveils correlations and relationships between the studied features, patients' treatment responses, and survival outcomes. The refined tissue classification model emphasises the significance of tumour-associated stroma and stromal tumour-infiltrating lymphocytes in predicting patients' responses to neoadjuvant chemotherapy. Spatial quantitative measures derived from the computational analysis serve as invaluable digital biomarkers, providing crucial insights into risk outcomes for individuals with TNBC. Moreover, delving into NanoString data and exploring digital basal and non-basal subtyping of TNBC extends the scope of this thesis and augments the comprehension of the disease. This broadening of perspective opens avenues for potential connections among histopathological characteristics, molecular profiles, and disease subtypes, thereby enhancing the prospects for personalised treatment strategies to advance.7 0Item Restricted Adaptive Resilience of Intelligent Distributed Applications in the Edge-Cloud Environment(Cardiff University, 2024-04) Almurshed, Osama; Rana, OmerThis thesis navigates the complexities of Internet of Things (IoT) application placement in hybrid fog-cloud environments to improve Quality of Service (QoS) in IoT applications. It investigates the optimal distribution of a Service Function Chain (SFC), the building blocks of an IoT application, across the fog-cloud infrastructure, taking into account the intricate nature of IoT and fog-cloud environments. The primary objectives are to define a platform architecture capable of operating IoT applications efficiently and to model the placement problem comprehensively. These objectives involve detailing the infrastructure's current state, execution requirements, and deployment goals to enable adaptive system management. The research proposes optimal placement methods for IoT applications, aiming to reduce execution time, enhance dependability, and minimise operation costs. It introduces an approach to effectively manage trade-offs through the measurement and analysis of QoS metrics and requires the implementation of specialised scheduling and placement strategies. These strategies employ concurrency to accelerate the planning process and reduce latency, underscoring the need for an algorithm that best corresponds to the specific requirements of the IoT application domain. The study's methodology begins with a comprehensive literature review in the area of IoT application deployment in hybrid fog-cloud environments. The insights gained inform the development of novel solutions that address the identified limitations, ensuring the proposal of robust and efficient solutions.18 0Item Restricted Feature Selection for High Dimensional Healthcare Data(University of Surrey, 2024-01) Alayed, Abdulrahman; Kouchaki, SamanehIn today’s digital landscape, researchers frequently encounter the complexity of handling highdimensional datasets. At times, data mining and machine learning methods struggle when confronted with immense datasets, leading to inefficiencies. The presence of extensive raw data with numerous features can negatively impact machine learning algorithms, affecting accuracy, increasing overfitting, and amplifying complexity. This is primarily due to the inclusion of redundant and irrelevant data, which hampers the learning process. However, employing feature selection techniques can effectively address these challenges. By selectively choosing relevant features, these techniques enable machine learning algorithms to operate more efficiently. They contribute to faster training, reduce model complexity, enhance accuracy, and mitigate overfitting issues. The primary objective of this project is to create an automatic variable selection pipeline by choosing the best features among various innovative feature selection techniques. The pipeline incorporates different categories of variable selection methods: Filter methods, Wrapper methods, Embedded methods, and Hybrid Method. The variable selection techniques are applied to the MIMIC-III (Medical Information Mart for Intensive Care) dataset, which is reachable at no cost. This database is well-suited for the project's goals, as it is a centralized database containing details about patients admitted to the critical care unit of a vast regional hospital. The dataset is particularly useful for forecasting the likelihood of death pst-ICU admission during hospital stay. To achieve this goal, the project employs six classification techniques: Logistic Regression (LR), K-nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The project systematically evaluates and compares the model's performance using various assessment metrics.34 0Item Restricted Exploring the Landscape of Artificial Intelligence Application in Mathematics Education: A Scoping Review(University of Dundee, 2024-07) Luay, Sultan; Louise, CampbellThis thesis explores the integration of artificial intelligence (AI) in mathematics education, highlighting both its potential benefits and inherent complexities. Through a scoping review of existing literature and AI applications, key findings reveal that tools such as Intelligent Tutoring Systems and Adaptive Learning Platforms enhance personalized learning, engagement, and student performance. However, significant challenges such as technology access disparities, the need for extensive teacher training, data privacy concerns, and ethical implications impede widespread adoption. The study emphasizes the importance of ongoing research, particularly longitudinal studies on AI's long-term effects and its role in developing higher-order cognitive skills. It also calls for strategic efforts from educators, policymakers, and technologists to ensure equitable access, effective professional development, and robust ethical guidelines. This research contributes to the growing body of knowledge on AI in education, offering insights that can inform future research, practice, and policy, ultimately advancing the integration of AI in mathematics education.47 0Item Restricted Home Monitoring in Interstitial Lung Disease(University College London, 2024) Althobiani, Malik Abdulmalik; Hurst, John R; Porter, Joanna; Russell, Anne-Marie; Folarin, AmosIntroduction: Interstitial lung disease (ILD) comprises a variety of conditions affecting the parenchyma of the lung, with a diverse incidence. Some patients are prone to rapid progression, while others are susceptible to exacerbations. Forced vital capacity (FVC) is used as an endpoint in clinical trials for novel idiopathic pulmonary fibrosis (IPF) therapies. However, it is often measured every three months, resulting in lengthy monitoring periods to identify meaningful treatment responses or disease trajectories. Home spirometry may enable more regular monitoring, potentially allowing for faster detection of ineffective treatment and reductions in clinical trial size, duration, and cost. Individuals with ILD often experience cough, shortness of breath, anxiety, exercise limitation, and fatigue, impacting their quality-of-life (QoL). Conventional indicators of disease progression, such as pulmonary function tests (PFT), may not completely capture the severity of symptoms experienced by patients. Continuous remote patient monitoring involving more than FVC may provide a more complete and real-time assessment of physiological parameters and symptoms. However, the views of clinicians and patients are poorly understood, as is the feasibility and utility of delivering such an approach. Aim: To systematically gather, summarise and evaluate the evidence from clinical trials for feasibility, reliability, and detection of exacerbations and/or disease progression in patients with ILD. To understand the views of clinicians and patients about home monitoring in patients with ILD. To investigate the feasibility and utility of a 4 contemporary approach to patient care using commercially available technology to detect disease progression in patients with ILD through continuous monitoring of physiological parameters and symptoms. Methods: A systematic review was conducted assessing studies on home monitoring of physiological parameters and symptoms to detect ILD exacerbations and progression. This was followed by an international survey of clinicians to explore their perspectives on using telehealth for remote ILD health care support. A patient survey was then conducted to quantify patients’ use of and experiences with digital devices. These preliminary studies informed the development of the research question and main PhD hypotheses. To test these hypothesis, two subsequent studies were conducted. Firstly, a feasibility study that assessed the feasibility, acceptability, and value of remote monitoring using commercially available technologies over 6 months period. Secondly, a prospective observational cohort study that evaluated a real-time multimodal program using commercially available technology to detect disease progression in patients with ILD through continuous monitoring of physiological parameters and symptoms. Results: The systematic review provided supportive evidence for the feasibility and acceptability of home monitoring in patients with ILD and identified priorities for future research. The findings of the follow-up studies indicated that although health care professionals recognised the potential benefits of home monitoring, their adoption rate was low due to barriers like lack of organisational support, technical issues, and 5 workload constraints. Although the findings of the mixed-methods study have demonstrated that digital devices are widely used among patients with ILD, the views and perspectives regarding the use of these devices is varied. The prospective multi- centre observational cohort study provided evidence supporting the feasibility and acceptability of remote monitoring to capture both subjective and objective data from varied sources in patients with respiratory diseases. The high engagement level observed from the passively collected data suggests the potential value of wearables for long-term, user-friendly remote monitoring in chronic respiratory disease management. The main study is one of the first to employ a comprehensive multimodal remote monitoring system to investigate the potential of home-monitoring to detect progression in patients with ILD. The results demonstrate the potential of multimodal home-monitoring to assess associations between physiological parameters and symptoms with disease progression, and to detect disease progression in patients with ILD. Moreover, the results suggest a strong correlation between hospital and home measurements of forced vital capacity in patients with ILD. Conclusion: Taken collectively, the findings presented in this thesis supports the use of a multimodal home-monitoring system, and the potential role for physiological parameters and symptoms to detect ILD progression. It provides a contemporary, personalised approach to patient management. These results provide a critical initial step towards further evaluating the value of home-monitoring for ILD management. However, larger, longitudinal validation studies are required. Future research could explore the potential of machine learning algorithms on this data for real-time detection of ILD disease progression. Machine learning models could provide early detection of changes in lung function and alert patients and healthcare providers to acute and chronic changes and empower patients to better self-manage their disease. This could allow for timely interventions and more personalised management of ILD.22 0