Saudi Cultural Missions Theses & Dissertations
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Item Unknown 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 However, many of these infrastructures rely on outdated technologies, rendering them increasingly vulnerable to evolving cyber threats.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.3 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.8 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.5 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.13 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.50 0Item Restricted Sharper Swords, Tougher Shields The Impact of GenAI on the Offensive-Defensive Balance in Cyberspace(King’s College London, 2024-08-26) Abanumay, Sarah; Devanny, JosephThis dissertation investigates the relative advantages of generative artificial intelligence (GenAI) to cyber defensive and offensive operations. It examines how state and non-state actors can utilise GenAI, arguing that while GenAI can significantly enhance both offensive and defensive cyber operations, the extent of these benefits is determined by four interrelated factors: geostrategic priorities, economic resources, regulatory frameworks, and organisational capabilities. These factors collectively shape the cyber offensive-defensive balance, a central concept in this study for understanding GenAI's impact on cyber operations. The research follows a literature-based methodology guided by frameworks such as the NIST Cybersecurity Framework 2.0 and the Cyber Kill Chain. The dissertation is structured into three chapters: the evolution of GenAI in cybersecurity, an analysis of strategic debates and the offensive-defensive balance and an exploration of the factors shaping this balance. The findings provide valuable insights for maintaining cybersecurity in the GenAI era.14 0Item Restricted Integrating Artificial Intelligence Technologies in Sustainable Project Management(University of Exeter, 2024-07-04) Alqurashi, Abdullah; Roman, Jose MelenezSustainable project management encompassed the economic, environmental, and social aspects of a project to attain the project objectives in a sustainable manner. Nonetheless, the integration of AI technologies in sustainable project management was still low due to factors like inadequate knowledge of technical know-how, costs of implementing AI technologies, and resistance from the project team. This research aimed to identify the factors that hinder the application of AI in project management for sustainable practices and provided recommendations for enhanced application. The research sought to understand the status of AI adoption, challenges faced, and the impact of knowledge management practices on project performance based on the survey of 40 professionals in Saudi Arabia. The findings of this research enhanced the theoretical understanding of the topic by identifying that the level of awareness of AI is much higher than the level of its adoption. The research results show that although the level of awareness of AI technologies is relatively high, the implementation of the technologies is limited because of technical, financial, and organizational constraints. This research has also highlighted how knowledge management practice can be used to close this gap which can enhance increase in project performance, reduce costs and promote innovation. The research provided practical recommendations for organizations interested in using AI for sustainability and following best practices on a global level and in alignment with the vision of Saudi Arabia for the future. When applying these recommendations, professionals will be able to increase project efficiency, reduce costs, and promote innovation which contributes to sustainable development goals. This research presents a conceptual model that outlines how AI technologies can be applied in sustainable project management, fostering innovation and sustainable development. The research also highlights the necessity for future research to delve deeper into developing actionable frameworks and practical strategies for integrating AI into sustainable project management.66 0Item Restricted IS THE METAVERSEFAILING? ANALYSINGSENTIMENTS TOWARDSTHEMETAVERSE(The University of Manchester, 2024) Alharbi, Manal Dowaihi; Batista-navarro, RizaThis dissertation investigates Aspect-Based Sentiment Analysis (ABSA) within the context of the Metaverse to better understand opinions on this emerging digital environment, particularly from a news perspective. The Metaverse, a virtual space where users can engage in various experiences, has attracted both positive and negative opinions, making it crucial to explore these sentiments to gain insights into public perspectives. A novel dataset of news articles related to the Metaverse was created, and Target Aspect-Sentiment Detection (TASD) models were applied to analyze sentiments ex pressed toward various aspects of the Metaverse, such as device performance and user privacy. A key contribution of this research is the evaluation of the TASD architecture, TAS-BERT, and its enhanced version, Advanced TAS-BERT (ATAS-BERT), which performs each task separately, on two datasets: the newly created Metaverse dataset and the SemEval15 Restaurant dataset. They were tested with different Transformer based models, including BERT, DeBERTa, RoBERTa, and ALBERT, to assess performance, particularly in cases where the target is implicit. The findings demonstrate the ability of advanced Transformer models to handle complex tasks, even when the target is implicit. ALBERT performed well on the simpler Metaverse dataset, while DeBERTa and RoBERTa showed superior performance on both datasets. This dissertation also suggests several areas for improvement in future research, such as processing paragraphs instead of individual sentences, utilizing Meta AI models for dataset annotation to enhance accuracy, and designing architectures specifically for models like DeBERTa, RoBERTa, and ALBERT, rather than relying on architectures originally designed for BERT, to improve performance. Additionally, incorporating enriched context representations, such as Part-of-Speech tags, could further enhance model performance.9 0Item Restricted Leveraging Brain-Computer Interface Technology to Interpret Intentions and Enable Cognitive Human-Computer Interaction(Univeristy of Manchester, 2024) Alsaddique, Luay; Breitling, RainerIn this paper, I present the developed, integration, and evaluation of a Brain–Computer Interface (BCI) system which showcases the accessibility and usability of a BCI head- set to interact external devices and services. The paper initially provides a detailed survey of the history of BCI technology and gives a comprehensive overview of BCI paradigms and the underpinning biology of the brain, current BCI technologies, recent advances in the field, the BCI headset market, and prospective applications of the technology. The research focuses on leveraging BCI headsets within a BCI platform to interface with these external end-points through the Motor Imagery BCI paradigm. I present the design, implementation, and evaluation of a fully functioning, efficient, and versatile BCI system which can trigger real-world commands in devices and digital services. The BCI system demonstrates its versatility through use cases such as control- ling IoT devices, infrared (IR) based devices, and interacting with advanced language models. The system’s performance was quantified across various conditions, achiev- ing detection probabilities exceeding 95%, with latency as low as 1.4 seconds when hosted on a laptop and 2.1 seconds when hosted on a Raspberry Pi. The paper concludes with a detailed analysis of the limitations and potential im- provements of the newly developed system, and its implications for possible appli- cations. It also includes a comparative evaluation of latency, power efficiency, and usability, when hosting the BCI system on a laptop versus a Raspberry Pi.38 0