Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted AI-Enabled Autonomous Knowledge Extraction from Large-Scale Textual Data(Saudi Digital Library, 2026) Alharbi, Abdulrahman; Obradovic, ZoranThe rapid growth of large-scale textual data across social media platforms, news media, and scientific repositories presents both unprecedented opportunities and significant challenges for extracting meaningful insights. During global events such as the COVID-19 pandemic, understanding public discourse requires analyzing vast amounts of noisy, heterogeneous, dynamic, and geographically distributed data. At the same time, the exponential increase in scientific publications has made traditional evidence synthesis methods increasingly labor-intensive, time-consuming, and difficult to scale. Existing approaches to textual knowledge extraction often operate in isolation, lack interpretability, fail to integrate heterogeneous data sources, and do not support scalable end-to-end automation. This dissertation addresses these limitations by proposing a unified framework for AI-enabled autonomous knowledge extraction from large-scale textual data. The research introduces a comprehensive pipeline that integrate sentiment analysis, topic modeling, semantic interpretation, spatiotemporal reasoning, and multi-agent automation for scalable, robust and reproducible text analysis across heterogeneous domains. First, this work introduces TriLex, a novel unsupervised sentiment analysis framework that combines multiple lexicon-based sentiment analysis methods through weighted aggregation, majority voting, and dynamic thresholding technique to improve robustness and accuracy for short and noisy textual data. Building on this foundation, a hierarchical spatiotemporal framework is developed to capture the evolution of public sentiment across global, national, and regional scales. The framework integrates over 7 million social media posts and thousands of news articles to analyze COVID-19 vaccine discourse across time, geographic regions, and platforms. To enhance topic interpretability, this research integrates BERTopic with large language models (LLMs), enabling automated generation of coherent and context-aware topic representations for large-scale textual discourse. A cross-platform analytical framework is further introduced to examine temporal relationships between social media and news media discourse, demonstrating a bidirectional relationship in which news coverage and public discourse influence each other over time. Extending beyond discourse analysis, this dissertation introduces an Agentic AI framework that automates the end-to-end process of large-scale multilingual knowledge extraction and evidence synthesis. The proposed multi-agent system coordinates specialized agents for query generation, multilingual retrieval, metadata harmonization, title and abstract screening, and full-text analysis. Evaluated on a multilingual corpus of over 52,000 scientific records, the framework achieves high screening performance while substantially reducing processing time from months to hours, demonstrating significant improvements in scalability, robustness, and reproducibility. Collectively, this dissertation bridges the gap between analytical understanding and autonomous knowledge extraction from large-scale textual data. By integrating robust sentiment analysis, interpretable topic modeling, spatiotemporal discourse analysis, and autonomous AI systems within a unified framework, this work establishes a scalable and extensible paradigm for transforming heterogeneous textual data into actionable knowledge. The proposed methodologies are validated using real-world datasets spanning social media, news media, and scientific literature across diverse application domains.13 0Item Restricted ENHANCING TRAFFIC SAFETY THROUGH AI-DRIVEN, PRIVACY-PRESERVING, AND SECURE IMPAIRED DRIVING DETECTION SYSTEMS(Saudi Digital Library, 2026) Alsulieman, Razan; Sherif, AhmedDrunk driving remains a major threat to road safety worldwide, contributing significantly to traffic injuries and fatalities each year. Traditional detection approaches are largely reactive and vehicle-centric, relying on in-vehicle sensors, breathalyzers, or post-incident enforcement. These methods often depend on driver cooperation, intrusive hardware installations, or limited monitoring environments, restricting their scalability and effectiveness in large transportation systems. At the same time, modern cities increasingly deploy roadside cameras, surveillance networks, and drone based monitoring systems, creating new opportunities for proactive intoxication detection at the infrastructure level. However, leveraging such external monitoring introduces challenges related to secure data collection, reliable AI-based analysis, privacy protection, and real-world deployment. This dissertation proposes a secure, privacy-preserving Artificial Intelligence framework for proactive drunk driving detection using out-of-vehicle surveillance data. The framework addresses three key aspects required for reliable infrastructure-level monitoring. First, a lightweight authentication scheme is developed to ensure secure data collection from distributed monitoring platforms such as drones and surveillance devices. The proposed design employs physically unclonable functions and symmetric cryptographic primitives to provide protection against impersonation, replay attacks, and device cloning while maintaining low computational overhead for resource-constrainedenvironments. Second, AI-based intoxication detection models are developed using Machine Learning and Deep Learning techniques to analyze facial imagery captured under real-world surveillance conditions. Extensive experiments evaluate multiple models under varying noise and disruption scenarios to ensure robustness across both low- and high-resource computational environments. The framework also incorporates explainable AI methods to improve transparency and verify that model decisions rely on meaningful facial features. Finally, the framework integrates privacy-preserving learning mechanisms through federated learning, enabling distributed model training without transferring sensitive facial images to centralized servers. This approach protects user privacy while maintaining strong detection performance across distributed monitoring nodes. These contributions establish a secure, scalable, and privacy-aware infrastructure-level system for proactive intoxication detection, supporting intelligent transportation systems aimed at improving traffic safety.9 0