Saudi Cultural Missions Theses & Dissertations
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Item Restricted DOES AI INTEGRATION MODERATE THE RELATIONSHIP BETWEEN FIRM GROWTH AND PERFORMANCE IN SMES: THE INFLUENCE OF DECISION-MAKING AND OPERATIONAL PERFORMANCE(University of South Alabama, 2025-05) AlQahtani, Dalal T; Butler, Frank C; Gillis, William E; Hair Jr, Joe F; Scott, Justin TToday’s dynamic business environment requires small and medium-sized enterprises (SMEs) to keep up with technological advancements in order to remain competitive. Business growth creates more challenges for SMEs since they possess fewer available resources than big organizations. Since the introduction of artificial intelligence (AI), several SMEs have been able to compete more effectively and deliver better performance. As part of this research, I examine the possibility that AI integration (AII) will moderate the relationship between firm growth and both decision-making and operational performance, ultimately affecting the performance of SMEs. The aim of this research is to provide practical implications for AI as a strategic resource for improving decision-making capabilities, performance and growth by utilizing the resource-based view (RBV) and information processing theory (IPT). A partial least squares structural equation model (PLS-SEM) was used to analyze data from 338 SME business strategy decision-makers in the United States. In order to verify the measurement model’s reliability and validity, a Confirmatory Composite Analysis (CCA) was performed, followed by the evaluation of the structural model in order to test the hypotheses. In contrast to initial hypotheses, this study found that firm growth is positively related to both decision-making and operational performance. Nevertheless, the study results support the original hypothesis that both decision-making performance (DMP) and operational performance (OPP) positively affect a firm’s performance. Furthermore, AII significantly moderated the relationship between FG and OPP, while it did not significantly moderate the relationship between FG and DMP. This indicates the complexity of the role AI integration plays in SMEs. The paper concludes with recommendations for future research, as well as guidance for practitioners regarding how SMEs can improve their decision-making capabilities and performance using AI.7 0Item Restricted ENHANCING SAFETY AND EFFICIENCY FOR CONTROL SYSTEMS BASED ON REINFORCEMENT LEARNING(The University of Arizona, 2025) Alanazi, Almutasim Billa Abdullah Alanazi; Tharp, Hal StanleyThis dissertation explores applying Artificial Intelligence (AI) techniques, specifically Reinforcement Learning (RL), to develop new control strategies that enhance safety and efficiency across various control systems. RL, recognized for its ability to adapt in dynamic environments without precise knowledge of system dynamics, offers a promising alternative to traditional (classical) control methods, such as Proportional-Integral-Derivative (PID) control. In this research, RL models are applied to four different applications to assess the efficacy of RL in terms of safety and efficiency, namely hydroponic systems, navigation systems, power management, and autonomous vehicles; all RL models are based on model-free RL method, specifically Q-learning and policy optimization algorithms (PPO) approach. The reason for selecting the applications is that safety and efficiency are important keys to such applications. Thus, for our study, we examine the safety and efficiency of model performance and output objectives more closely and note the advantages introduced by the RL models. For the first application, the RL model in the hydroponics system outperforms traditional methods in maintaining optimal pH and Electrical Conductivity (EC) levels, achieving accuracies of 96% and 99%, respectively, and demonstrating reduced settling times under disturbance conditions. In a high-disturbance study over 1,000 episodes, on average, the RL model improved safety by up to 15% by monitoring the system's success and observing the return rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Also, it achieved 35-40% energy savings by enabling the idle mode when the controlled reference was within the desired operating zone. Highlighting its potential to enhance agriculture technologies in terms of safety and efficiency. For the second application, the RL model enhances the navigation system's path planning by incorporating environmental factors when navigating through risk zones, such as poor weather conditions or sandstorms. The strategy involves updating the reward matrix based on safety and efficiency specifications to generate optimal routes that prioritize driver safety. Further improvements are achieved by minimizing energy consumption through these optimal routes, thereby enhancing overall efficiency. For the third application, the RL model in the power management controlled hybrid solar system limits grid reliance by up to 11.76% during peak hours, improving safety, enhancing energy stability, and offering a viable alternative to traditional methods like Time-of-Use programs. A financial analysis suggests a 14-year payback period, with projected profits of $8,500 over 25 years, indicating both economic and environmental benefits. In the fourth application, the RL model for autonomous vehicles employs continuous reward functions that demonstrate improved lane-following and obstacle avoidance, thereby enhancing safety. Compared to existing studies using the AWS DeepRacer simulator, this model reduced training time by 50%, resulting in more efficient learning. The continuous reward mechanism is ideal for real-time scenarios, as it provides the agent with enhanced feedback, leading to better performance and more learning efficiency. The findings highlight RL’s ability to address complex control challenges, enhancing safety and efficiency across examined cases, and underline its potential for broader application in control systems and real-world applications.10 0Item Restricted GRAPH-BASED APPROACH: BRIDGING INSIGHTS FROM STRUCTURED AND UNSTRUCTURED DATA(Temple University, 2025) Aljurbua, Rafaa; Obradovic, ZoranGraph-based methodologies provide powerful tools for uncovering intricate relationships and patterns in complex data, enabling the integration of structured and unstructured information for insightful decision-making across diverse domains. Our research focuses on constructing graphs from structured and unstructured data, demonstrating their applications in healthcare and power systems. In healthcare, we examine how social networks influence the attitudes of hemodialysis patients toward kidney transplantation. Using a network-based approach, we investigate how social networks within hemodialysis clinics affect patients' attitudes, contributing to a growing understanding of this dynamic. Our findings emphasize that social networks improve the performance of machine learning models, highlighting the importance of social interactions in clinical settings (Aljurbua et al., 2022). We further introduce Node2VecFuseClassifier, a graph-based model that combines patient interactions with patient characteristics. By comparing problem representations that focus on sociodemographics versus social interactions, we demonstrate that incorporating patient-to-patient and patient-to-staff interactions results in more accurate predictions. This multi-modal analysis, which merges patient experiences with staff expertise, underscores the role of social networks in influencing attitudes toward transplantation (Aljurbua et al., 2024b). In power systems, we explore the impact of severe weather events that lead to power outages, specifically focusing on predicting weather-induced outages three hours in advance at the county level in the Pacific Northwest of the United States. By utilizing a multi-model multiplex network that integrates data from multiple sources including weather, transmission lines, lightning, vegetation, and social media posts from two leading platforms (Twitter and Reddit), we show how multiplex networks offer valuable insights for predicting power outages. This integration of diverse data sources and network-based modeling emphasizes the importance of leveraging multiple perspectives to enhance the understanding and prediction of power disruptions (Aljurbua et al., 2023). We further present HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. The multiplex network layers of this framework gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. By incorporating multiplex network layers consisting of data collected over time and across regions, we demonstrate that HMN-RTS significantly improves the accuracy of predicting the duration of weather-related outages. This framework enables grid operators to make more reliable predictions up to 6 hours in advance, supporting early risk assessment and proactive mitigation (Aljurbua et al., 2024a, 2025a). Additionally, we introduce SMN-WVF, a spatiotemporal multiplex network designed to predict the duration of power outages in distribution grids. By integrating network-based approach and multi-modal data across space and time, SMN-WVF offers a novel method for predicting disruption durations in distribution grids, enhancing decision-making and mitigation efforts while highlighting the critical role of network-based approaches in forecasting (Aljurbua et al., 2025b). Overall, our research showcases the potential of graph-based models in tackling complex challenges in both power systems and healthcare. By combining the network-based approach with multi-modal data, we present innovative solutions for predicting power outages and understanding patient attitudes.13 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.23 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.25 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.30 0