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 Assessing the Regulation of Medical Artificial Intelligence in Clinical Settings: Considerations for Form, Process, Authority, and Timing(University of Pittsburgh, 2025) Alotaibi, Hazim; Crossley, MaryThe rapid integration of artificial intelligence (AI) into healthcare has introduced a transformative category of technologies known as Medical Artificial Intelligence (MAI). These tools, often approved by the U.S. Food and Drug Administration (FDA), are now used to assist in diagnosis, treatment planning, patient monitoring, and clinical decision-making. However, MAI poses novel regulatory challenges due to its dynamic, adaptive, and sometimes opaque nature. This dissertation critically examines the current regulatory frameworks governing MAI in the United States, focusing on tools used by health professionals in clinical settings. Drawing on legal theory, empirical data, and interdisciplinary analysis, the study explores how MAI fits—or fails to fit—within existing regulatory categories designed for conventional medical devices. It analyzes FDA approval trends, clearance pathways, medical specialties, and manufacturer profiles for over 1,000 FDA-approved AI/ML-enabled devices. The study also investigates gaps in post-market surveillance, algorithmic transparency, and the role of third-party evaluators. Chapters in this dissertation evaluate the scope and limits of current regulatory mechanisms, such as the FDA’s 510(k), De Novo, and PMA pathways, and discuss the involvement of other regulatory bodies including the Federal Trade Commission and Department of Health and Human Services. Particular attention is given to unresolved legal questions, such as liability in AI-induced errors, the classification of MAI as a “system” versus a “device,” and the role of evolving real-world performance in determining regulatory adequacy. Ultimately, this work proposes a tailored regulatory framework that is adaptive, risk-based, and harmonized across international borders. It advocates for collaborative governance involving public agencies, private innovators, and global partners to ensure that regulation keeps pace with technological advancement while protecting patients, supporting clinicians, and promoting innovation.18 0Item 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.25 0Item Restricted The ethical implications of using Multimodal Learning Analytics: a framework for research and practice(University College London (UCL), 2025) Alwahaby, Hifa; Cukurova, MutluA growing number of multimodal data (MMD) streams and complex artificial intelligence (AI) models are being used in learning analytics research to allow us to better understand, model and support learning, together with teaching processes. Considering MMDs’ potentially more invasive, extremely granular and temporal nature compared to log files, they may present additional ethical challenges in comparison to more traditional learning activity data. The systematic review undertaken during this study revealed a dearth of ethical considerations in previous multimodal learning analytics (MMLA) literature. Consequently, this study aims to identify the ethical issues associated with the use of MMLA and propose a practical framework to assist end-users to become more aware of these issues and potentially mitigate them. To gain a better understanding of the ethical issues and how they may be mitigated, the study aims to investigate the ethical concerns associated with the use of MMLA in higher education by collecting the opinions and experiences of appropriate stakeholders. Accordingly, structured individual interviews were conducted via Microsoft Teams, a video conferencing software, due to COVID-19 restrictions. In total, 60 interviews were conducted with educational stakeholders (39 higher education students, 12 researchers, eight educators and one representative of an educational technology company). Based on the thematic coding of verbatim transcriptions, nine distinct themes were identified. In response to the themes and accompanying probing questions presented to the MMLA stakeholders, and based on the ethical guidance and recommendations identified from previous literature, a first draft of the MMLA ethical framework was prepared. Subsequently, the draft was evaluated by 27 evaluators (seven higher-education students, 13 researchers–practitioners, four teachers, one ethics expert and two policymakers) by means of structured interviews. Additionally, a group of researchers adopted the framework in their research and provided constructive feedback. Based on the thematic analysis of the interviews, the framework was continually improved for three rounds until data saturation was achieved. This resulted in the presentation of the first MMLA ethical framework, which was the principal goal of this study. This thesis delivers three key contributions: (1) a systematic review of previous MMLA literature that confirms the lack of ethical considerations in the literature; (2) an examination of the ethical issues connected with MMLA from the perspective of different stakeholders; and (3) an ethical MMLA framework for higher education. By developing the framework, this thesis aims to increase awareness of the potential ethical issues and therefore, alleviate them by promoting a more ethical design, along with the development and use of MMLA in a higher education setting.13 0Item Restricted Design, analysis, and evaluation of highly secure smart city infrastructures and services(University of Arizona, 2025) Almazyad, Ibrahim; Hariri, SalimCritical infrastructure resources and services, such as energy networks, water treatment facilities, and 5G telecommunications, form the backbone of national security and public welfare. However, many of these infrastructures rely on outdated technologies, rendering them increasingly vulnerable to evolving cyber threats. As these infrastructures become increasingly digitized and integrated under Industry 4.0 - integrating cloud computing, Artificial Intelligence (AI), and the Industrial Internet of Things (IIoT) - they simultaneously introduce a broader attack surface susceptible to threats such as sensor spoofing, Denial-of-Service (DoS), and man-in-the-middle attacks. Existing critical infrastructure testbeds are isolated and limited in their ability to replicate cross-domain dependencies and security vulnerabilities inherent in modern smart cities. To address this gap, this dissertation developed a Federated Cybersecurity Testbed as a Service (FCTaaS) environment, an innovative approach that integrates geographically dispersed critical infrastructure testbeds to enable the development and experimentation of effective algorithms to secure the normal operations of critical infrastructures against a wide range of cyberattacks. The research specifically focused on two critical infrastructures: The Water Treatment Facility Testbed (WTFT) and the 5G Telecommunication Testbed (5GTT). It begins with a comprehensive threat modeling across Industrial Control Systems (ICS) and 5G architecture to identify vulnerabilities, followed by designing and implementing security detection and mitigation algorithms. Specifically, we have developed an edge-deployed anomaly detection algorithm that is based on an autoencoder that achieved 98.3% accuracy in detecting cyberattacks against water treatment infrastructure. We have also demonstrated the effectiveness of our security defense algorithms in detecting cyberattacks against 5G networks with an accuracy of 98.9% against various cellular network attacks. This dissertation developed a unified and scalable cybersecurity research environment that significantly facilitates the development of realistic critical infrastructure experimentations and AI-driven security algorithms to secure and protect their normal operations against any type of cyberattacks known or unknown, regardless of their origins, insider or outsider.12 0Item Restricted Human Activity Monitoring for Telemedicine Using an Intelligent Millimeter-Wave System(University of Dayton, 2025) Alhazmi, Abdullah; Chodavarapu, VamsyThe growing aging population requires innovative solutions in the healthcare industry. Telemedicine is one such innovation that can improve healthcare access and delivery to diverse and aging populations. It uses various sensors to facilitate remote monitoring of physiological measures of people, such as heart rate, oxygen saturation, blood glucose, and blood pressure. Similarly, it is capable of monitoring critical events, such as falls. The key challenges in telemonitoring are ensuring accurate remote monitoring of physical activity or falls by preserving privacy and avoiding excessive reliance on expensive and/or obtrusive devices. Our approach initially addressed the need for secure, portable, and low-cost solutions specifically for fall detection. Our proposed system integrates a low-power millimeter-wave (mmWave) sensor with a NVIDIA Jetson Nano system and uses machine learning to accurately and remotely detect falls. Our initial work focused on processing the mmWave sensor's output by using neural network models, mainly employing Doppler signatures and a Long Short-Term Memory (LSTM) architecture. The proposed system achieved 79% accuracy in detecting three classes of human activities. In addition to reasonable accuracy, the system protected privacy by not recording camera images, ensuring real-time fall detection and Human Activity Recognition (HAR) for both single and multiple individuals at the same time. Building on this foundation, we developed an advanced system to enhance accuracy and robustness in continuous monitoring of human activities. This enhanced system also utilized a mmWave radar sensor (IWR6843ISK-ODS) connected to a NVIDIA Jetson Nano board, and focused on improving the accuracy and robustness of the monitoring process. This integration facilitated effective data processing and inference at the edge, making it suitable for telemedicine systems in both residential and institutional settings. By developing a PointNet neural network for real-time human activity monitoring, we achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Furthermore, the proposed system provided activity data reports, tracking maps, and fall alerts and significantly enhanced telemedicine applcations by enabling more timely and targeted interventions based on objective data. The final proposed system facilitates the ability to detect falls and monitor physical activity at both home and institutional settings, demonstrating the potential of Artificial Intelligence (AI) algorithms and mmWave sensors for HAR. In conclusion, our system enhances therapeutic adherence and optimizes healthcare resources by enabling patients to receive physical therapy services remotely. Furthermore, it could reduce the need for hospital visits and improve in-home nursing care, thus saving time and money and improving patient outcomes.13 0Item Restricted PerfectHR: Using AI to Reduce Candidate-Job Mismatch and Improve Recruitment Efficiency(Queen Mary University of London, 2025) Baraheem, Ghadeer; Wijetunge, PiyajithThe recruitment process is critical for organizations to find the right talent. However, existing recruitment software often faces issues like candidate-job mismatches and biases, leading to inefficient hiring processes. This paper presents PerfectHR, a recruitment software solution designed to reduce candidate-job mismatches and improve recruitment efficiency using artificial intelligence. The software integrates a logistic regression model for candidate classification and OpenAI’s GPT-4 language model for CV summarization. PerfectHR addresses bias in the dataset and algorithm by excluding sensitive features such as age and gender to ensure that they do not influence the model predictions. The application was developed using React.js for the frontend, Node.js for the backend, MongoDB for database management, and deployed on Vercel. Initial testing indicates that PerfectHR provides a reliable and user-friendly experience, effectively supporting job postings, candidate evaluations, and communication. Future work will focus on expanding the training dataset to cover a broader range of job types and further refining the application to improve performance and scalability.24 0Item Restricted AI Conversational Agents in Healthcare for Type-2 Diabetes(University of Technology Sydney, 2024-09-12) Sawad, Abdullah Bin; Kocaballi, Baki; Prasad, Mukesh; Narayan, Bhuva; Lin, ShanshanType 2 diabetes (T2D) is a global health crisis with significant impacts on individuals and healthcare systems. This thesis develops AI conversational agents (CAs) to promote physical activity and lifestyle changes for those at risk of T2D through a multi-phase study, including a systematic review, a design framework, and empirical testing. The systematic review identified gaps in digital interventions, particularly the limited use of CAs in T2D prevention. A standardised framework was then developed, focusing on personalisation, user engagement, and proactive health management. This framework guided the iterative design and refinement of a CA prototype, tested across diverse populations in Sydney and Jeddah. The thesis integrated real-time activity tracking via Fitbit and enhanced conversational capabilities using large language models. Findings demonstrated that AI-driven, personalised interactions significantly encouraged physical activity, a key factor in preventing T2D progression. This thesis contributes to health informatics by demonstrating AI’s role in preventive healthcare. It highlights the importance of a user-centred design approach, ensuring that digital health tools are effective and align with the users’ needs and preferences. Future research should focus on long-term engagement strategies and integrating conversational agents with broader healthcare systems to enhance their effectiveness and reach.14 0Item Restricted APPLYING MACHINE LEARNING (THE K-MEANS ALGORITHM) TO CLUSTERING AND ANALYZING SYNOVIAL FLUID CONTENTS AMONG DIFFERENT AGES AND GENDERS IN HEALTHY AND OSTEOARTHRITIS PATIENTS(Oakland University, 2024) Alabkary, Bader Eid; Zohdy, Mohamed AMachine learning, a subset of AI, has made a significant impact on the medical field by improving the speed and accuracy of test results. Among the many discrete ML tools, k-means is a type of data clustering that uses unsupervised ML to divide unclassified data into different groups with similar variances. This dissertation applied the k-means clustering algorithm to analyze synovial fluid compositions of healthy people and osteoarthritis (OA) patients, focusing on four components: hyaluronic acid (HA), chondroitin sulfate (C6S, C4S), and the C6S ratio. The main objective was to identify distinct patterns and clusters within these datasets based on age and gender. Data was extracted from two previously published research studies. The first dataset comprised 187 healthy participants, with ages ranging from 10 to 90 years. The second dataset consisted of 133 OA participants with ages ranging from 55 to 90 years. Applying ML algorithms, specifically k-means clustering, the MATLAB program was used for data analysis. The findings showed the k-means clustering successfully highlighted age- and gender-related synovial fluid concentration patterns. In addition, for both healthy and OA groups, younger people had higher levels of synovial fluid components, which decreased with age. In healthy people, HA levels were high among younger people but decreased with age. In the OA group, HA levels increased in older patients. These findings confirmed the potential of synovial fluid concentration in diagnosing joint health. These findings also asserted the utility of ML techniques, such as k-means clustering, in medical data analysis.10 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 0