Saudi Cultural Missions Theses & Dissertations
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Item Restricted The Integration of Artificial Intelligence (AI) In Business Operations(La Trobe University, 2022) Alqahtani, Raed Ayidh; Soh, BenThis research investigates the integration of Artificial Intelligence (AI) in business operations. AI has become increasingly prevalent in various industries due to its potential to enhance efficiency, improve decision-making, and drive innovation. However, there is a lack of comprehensive understanding of how AI integration has been implemented in the business context. Therefore, this study utilizes a systematic review approach, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, to examine and synthesize existing literature on AI integration in business operations. The primary outcomes of this research will provide insights into the current state of AI integration in businesses, identify common challenges and benefits, and highlight potential areas for future research. This research contributes to the understanding of the impact of AI on business operations, paving the way for the effective and successful implementation of AI in organizations.11 0Item Restricted Human Action Recognition Based on Convolutional Neural Networks and Vision Transformers(University of Southampton, 2025-05) Alomar, Khaled Abdulaziz; Xiaohao, CaiThis thesis explores the impact of deep learning on human action recognition (HAR), addressing challenges in feature extraction and model optimization through three interconnected studies. The second chapter surveys data augmentation techniques in classification and segmentation, emphasizing their role in improving HAR by mitigating dataset limitations and class imbalance. The third chapter introduces TransNet, a transfer learning-based model, and its enhanced version, TransNet+, which utilizes autoencoders for improved feature extraction, demonstrating superior performance over existing models. The fourth chapter reviews CNNs, RNNs, and Vision Transformers, proposing a novel CNN-ViT hybrid model and comparing its effectiveness against state-of-the-art HAR methods, while also discussing future research directions.20 0Item Restricted Mental Health on Social Media: AI-Driven Detection and Response(Arizona State University, 2025) Alghamdi, Zeyad; Liu, HuanMental health issues are increasingly prevalent, with stress playing a critical role in the development of severe mental and physical health conditions. Early detection and effective intervention are essential for mitigating these challenges. In an increasingly digital world, social media serves as a valuable repository of large-scale data on how individuals vent and express stress. This data source captures two critical dimensions or perspectives: the individual and the social. The individual dimension is revealed through direct expressions of stress in users’ posts, where emotional states and linguistic patterns provide important indicators. In a synergistic manner, the social dimension is discerned from the reactions of others, offering contextual cues that reflect the broader environment’s influence on the user’s mental state. My dissertation builds on this dual perspective by integrating social science and psychological theories to inform a methodologies,that strengthens AI’s capacity to recognize stress-related cues and also to engage with mental health discourse in a refined and contextaware manner. To achieve this, I propose three innovative detection strategies that capture the individual and social dimensions. The first strategy focuses on analyzing the finegrained linguistic and emotional features to identify stress within individual posts, directly addressing the individual perspective. The second strategy extends this analysis by examining the broader contextual nuances embedded in these posts, thereby deepening the understanding of individual stress expressions. The third strategy shifts attention to the social perspective by incorporating emotional cues from community responses as auxiliary signals to enhance the stress classification. Finally, drawing on the insights from these works, I established a data-supported refinement process that improves AI’s ability to produce more supportive responses that are both contextually aware and socially attuned. This research exemplifies how interdisciplinary innovation can redefine AI’s role in addressing complex challenges in mental health.24 0Item Restricted The Role of Artificial Intelligence in Project Management(University of Technology Sydney, 2024-11-11) Muryif Alshehri, Mohammed; Abdo, PeterThe increasing complexity of global projects has elevated the challenges in project management, necessitating the adoption of innovative solutions. This study investigates the transformative potential of Artificial Intelligence (AI) in project management, emphasizing its role in enhancing decision-making, risk management, and operational efficiency. Employing a systematic literature review methodology, the research synthesizes findings from 13 high-index journal articles to evaluate AI techniques, including machine learning, decision trees, and advanced predictive analytics. The study identifies AI’s ability to improve resource allocation, forecasting accuracy, and stakeholder engagement while mitigating risks and optimizing sustainability. Findings highlight the integration challenges such as data quality, system compatibility, and resistance to change, which hinder the widespread adoption of AI tools. Despite these obstacles, AI demonstrates considerable benefits, including automation of routine tasks, enhanced cost estimation, and improved project timelines. Notably, AI-driven tools have achieved a 20% reduction in project completion times and a 15% decrease in costs due to proactive risk mitigation. This research provides actionable insights into the effective implementation of AI within the framework of traditional project management methodologies. It concludes that while AI presents significant opportunities to redefine project management practices, its successful adoption requires addressing technical and organizational challenges, along with fostering an adaptive cultural mindset. This study lays the groundwork for future research aimed at leveraging AI to create sustainable, efficient, and resilient project management ecosystems.63 0Item Restricted Quantifying and Profiling Echo Chambers on Social Media(Arizona State University, 2024) Alatawi, Faisal; Liu, Huan; Sen, Arunabha; Davulcu, Hasan; Shu, KaiEcho chambers on social media have become a critical focus in the study of online behavior and public discourse. These environments, characterized by the ideological homogeneity of users and limited exposure to opposing viewpoints, contribute to polarization, the spread of misinformation, and the entrenchment of biases. While significant research has been devoted to proving the existence of echo chambers, less attention has been given to understanding their internal dynamics. This dissertation addresses this gap by developing novel methodologies for quantifying and profiling echo chambers, with the goal of providing deeper insights into how these communities function and how they can be measured. The first core contribution of this work is the introduction of the Echo Chamber Score (ECS), a new metric for measuring the degree of ideological segregation in social media interaction networks. The ECS captures both the cohesion within communities and the separation between them, offering a more nuanced approach to assessing polarization. By using a self-supervised Graph Auto-Encoder (EchoGAE), the ECS bypasses the need for explicit ideological labeling, instead embedding users based on their interactions and linguistic patterns. The second contribution is a Heterogeneous Information Network (HIN)-based framework for profiling echo chambers. This framework integrates social and linguistic features, allowing for a comprehensive analysis of the relationships between users, topics, and language within echo chambers. By combining community detection, topic modeling, and language analysis, the profiling method reveals how discourse and group behavior reinforce ideological boundaries. Through the application of these methods to real-world social media datasets, this dissertation demonstrates their effectiveness in identifying polarized communities and profiling their internal discourse. The findings highlight how linguistic homophily and social identity theory shape echo chambers and contribute to polarization. Overall, this research advances the understanding of echo chambers by moving beyond detection to explore their structural and linguistic complexities, offering new tools for measuring and addressing polarization on social media platforms.24 0Item Restricted LIGHTREFINENET-SFMLEARNER: SEMI-SUPERVISED VISUAL DEPTH, EGO-MOTION AND SEMANTIC MAPPING(Newcastle University, 2024) Alshadadi, Abdullah Turki; Holder, ChrisThe advancement of autonomous vehicles has garnered significant attention, particularly in the development of complex software stacks that enable navigation, decision-making, and planning. Among these, the Perception [1] component is critical, allowing vehicles to understand their surroundings and maintain localisation. Simultaneous Localisation and Mapping (SLAM) plays a key role by enabling vehicles to map unknown environments while tracking their positions. Historically, SLAM has relied on heuristic techniques, but with the advent of the "Perception Age," [2] research has shifted towards more robust, high-level environmental awareness driven by advancements in computer vision and deep learning. In this context, MLRefineNet [3] has demonstrated superior robustness and faster convergence in supervised learning tasks. However, despite its improvements, MLRefineNet struggled to fully converge within 200 epochs when integrated into SfmLearner. Nevertheless, clear improvements were observed with each epoch, indicating its potential for enhancing performance. SfmLearner [4] is a state-of-the-art deep learning model for visual odometry, known for its competitive depth and pose estimation. However, it lacks high-level understanding of the environment, which is essential for comprehensive perception in autonomous systems. This paper addresses this limitation by introducing a multi-modal shared encoder-decoder architecture that integrates both semantic segmentation and depth estimation. The inclusion of high-level environmental understanding not only enhances scene interpretation—such as identifying roads, vehicles, and pedestrians—but also improves the depth estimation of SfmLearner. This multi-task learning approach strengthens the model’s overall robustness, marking a significant step forward in the development of autonomous vehicle perception systems.43 0Item Restricted Balancing Innovation and Protection: Is AI Regulation the Future of Saudi FinTech?(King's College London, 2024-09) Alkhathlan, Alaa Saad; Keller, AnatThis study investigates the implications of artificial intelligence in the Saudi FinTech sector, focusing on the evolving regulatory landscape. While AI holds substantial promise for driving innovation, it also poses ethical and practical challenges such as data privacy, algorithmic transparency, and fairness. This study examines the current regulatory framework in Saudi Arabia, highlighting efforts like the AI Ethics Principles and the Personal Data Protection Law. Despite these measures, significant gaps remain due to the voluntary nature of the AI Ethics Principles and Generative AI Guidelines, resulting in inconsistent implementation. The primary aim of this study is to guide policymakers on regulating AI in the Saudi FinTech sector while preserving innovation. Key recommendations urge policymakers to develop regulations based on international best practices, addressing issues such as data privacy, algorithmic biases, and systemic risks. Emphasising the need for continuous dialogue among regulators, FinTech companies, and international partners, the study also calls for enhancing human-machine collaboration, establishing regulatory sandboxes, creating an AI Oversight Committee, and supporting research to better understand AI's implications. By aligning with Saudi Vision 2030 goals, these recommendations aim to strengthen Saudi Arabia's AI regulatory framework, support sustainable growth in the FinTech sector, and build public trust in AI-driven financial services.49 0Item Restricted AI in Telehealth for Cardiac Care: A Literature Review(University of technology sydney, 2024-03) Alzahrani, Amwaj; Li, lifuThis literature review investigates the integration of artificial intelligence (AI) in telehealth, with a specific focus on its applications in cardiac care. The review explores how AI enhances remote patient monitoring, facilitates personalized treatment plans, and improves healthcare accessibility for patients with cardiac conditions. AI-driven tools, such as wearable devices and implantable medical devices, have demonstrated significant potential in tracking critical health parameters, enabling timely interventions, and fostering proactive patient care. Additionally, AI-powered chatbots and telehealth platforms provide patients with real-time support and guidance, enhancing engagement and adherence to treatment regimens. The findings reveal that AI contributes to improving healthcare outcomes by enabling early detection of cardiac events, tailoring treatment plans to individual patient needs, and expanding access to care for underserved populations. However, the integration of AI in telehealth is not without challenges. Ethical considerations, such as ensuring data privacy, managing biases in AI algorithms, and addressing regulatory complexities, emerge as critical areas requiring attention. Furthermore, technological limitations, including the need for robust validation and patient acceptance of AI technologies, underscore the importance of bridging the gap between research and real-world implementation. This review also examines future trends, including the integration of blockchain technology with AI to enhance data security and privacy in telehealth systems. Advancements in machine learning and the Internet of Things (IoT) are paving the way for innovative solutions, such as secure remote monitoring and personalized rehabilitation programs. While AI holds transformative potential in revolutionizing telehealth services for cardiac patients, addressing these challenges is imperative to ensure equitable, effective, and patient-centered care. This review underscores the need for interdisciplinary collaboration and regulatory oversight to unlock the full potential of AI in telehealth and improve outcomes for cardiac patients globally.29 0Item Restricted Utilizing Artificial Intelligence to Develop Machine Learning Techniques for Enhancing Academic Performance and Education Delivery(University of Technology Sydney, 2024) Allotaibi, Sultan; Alnajjar, HusamArtificial Intelligence (AI) and particularly the related sub-discipline of Machine Learning (ML), have impacted many industries, and the education industry is no exception because of its high-level data handling capacities. This paper discusses the various AI technologies coupled with ML models that enhance learners' performance and the delivery of education systems. The research aims to help solve the current problems of the growing need for individualized education interventions arising from student needs, high dropout rates and fluctuating academic performance. AI and ML can then analyze large data sets to recognize students who are at risk academically, gauge course completion and learning retention rates, and suggest interventions to students who may require them. The study occurs in a growing Computer-Enhanced Learning (CED) environment characterized by elearning, blended learning, and intelligent tutelage. These technologies present innovative concepts to enhance administrative procedures, deliver individualized tutorials, and capture students' attention. Using predictive analytics and intelligent tutors, AI tools can bring real-time student data into the classroom so that educators can enhance the yields by reducing dropout rates while increasing performance. Not only does this research illustrate the current hope and promise of AI/ML in the context of education, but it also includes relevant problems that arise in data privacy and ethics, as well as technology equality. To eliminate the social imbalance in its use, the study seeks to build efficient and accountable AI models and architectures to make these available to all students as a foundation of practical education. The students’ ideas also indicate that to prepare the learning environments of schools for further changes, it is necessary to increase the use of AI/ML in learning processes41 0Item Restricted The role and use of Artificial Intelligence (Al tools) in audits of financial statements(Aston university, 2024-09) Alsaedi, Amal; George ,SalijenIntegrating artificial intelligence (AI) in the auditing function holds significant potential to transform the industry. As firms and stakeholders increasingly recognise the value of and demand audit quality, the accuracy, validity, and integrity of information generated by audit processes have become a vital consideration. Integrating AI into audit processes would be viewed as advancing audit techniques. However, the current limited adoption of this technology by audit firms raises concerns about their awareness of its transformative potential. This study aims to identify AI tools used in auditing and their impact on the audit process and quality. The study bridges the existing gap using a secondary exploratory method. Qualitative data was collected from transparency reports by the Big Four audit firms, i.e., KPMG, Deloitte, EY and PwC, and audit quality inspection reports for the four firms by FRC. For recency purposes, only reports published between 2020 and 2023 were considered. A thematic analysis of the data collected reveals that adoption of AI and data analytics in auditing is still low, and the Big Four firms are actively promoting increased adoption. The results demonstrate a notable disparity between potential and current applications, as shown by a clear gap between the publicised potential of AI and data analytics and their implementation within audit processes.30 0