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 Diagnosis of Oral and maxillofacial cysts using artificial intelligence: a literature review(University of Manchester, 2024) Almohawis, Alhaitham; Yong, SinAbstract Oral and maxillofacial cysts are cavities that can pose significant risks if not detected and treated promptly. Many of these cysts are asymptomatic, often going unnoticed until complications arise. The introduction of artificial intelligence (AI) presents a promising opportunity for early detection and management of these cysts. Aim: To explore current studies on the use of artificial intelligence in diagnosing oral and maxillofacial cysts. Objectives: To examine the existing literature in this field, assess the accuracy, effectiveness, and limitations of AI models, and identify challenges in implementing AI in clinical practice. Methods: This literature review followed a systematic approach, identifying 223 studies from PUBMED and SCOPUS databases between 1975 and 2024. After applying inclusion and exclusion criteria, 26 retrospective cohort studies were included in the final analysis. A risk of bias assessment was conducted using the ROBINS I tool. Results: The investigation revealed that AI models consistently demonstrate high accuracy in detecting oral cysts in both radiographs and digital histopathology. The ROBINS I tool indicated a moderate risk of bias in most of the included studies. Notable limitations include limited datasets, variable data quality, and a lack of explainability in AI models results. Conclusion: AI models have shown considerable effectiveness and speed in detecting both simple and complex cysts. However, to fully leverage AI's potential in clinical settings, further rigorous studies are needed to evaluate its risks, benefits, and feasibility, ensuring compliance with governmental health regulations on AI.12 0Item Restricted Classification of Unresolved Target Based on Specular Reflection(University of Arizona, 2024-05-08) Alghamdi, Ahmed; Elkabbash, MohamedThis thesis explores using specular reflections to enhance remote sensing capabilities for identifying unresolved targets. Traditional remote sensing methods often struggle with the resolution limitations imposed by distance and target size, making distinguishing and classifying distant objects difficult. This research proposes a novel approach to overcome these constraints by harnessing the unique properties of specular reflections. Through a series of methodically designed experiments conducted in laboratory settings and real-world scenarios, this study demonstrates the potential of specular reflections to act as optical 'fingerprints.' These experiments validate theoretical models and show the practical applicability of specular reflections for long-range identification and classification tasks. Key experiments included detailed analyses over 27 kilometers, revealing how specular reflections can be captured and analyzed to provide critical data beyond traditional imaging capabilities. The findings of this research have significant implications for military surveillance, environmental monitoring, and space debris tracking, offering a new tool for enhanced observation and identification of distant objects. This thesis proves that specular reflections can extend the visual reach of remote sensing technologies, paving the way for more precise and reliable long-distance optical sensing.33 0Item Restricted Religious Hatred in Arabic Social Media: Analysis, Detection, and Personalization(2023-05) Albadi, Nuha; Mishra, ShivakantMiddle Eastern societies have long suffered from civil wars and domestic tensions that are partly caused by conflicting religious beliefs. This thesis examines the extent of religious hate in Arabic social media, evaluates the impact of automated accounts (i.e., bots) and personalized recommendation algorithms on its spread, and investigates social computing methods for automatically recognizing Arabic-language content and bots promoting religious hatred. First, the thesis addresses the scarcity of Arabic resources in the field by creating two publicly available, annotated Arabic datasets for Twitter and YouTube through crowdsourcing. It then presents a comprehensive analysis highlighting the prevalence of religious hatred on Arabic social networks, the most targeted religious groups, the unique characteristics of perpetrators, and the distinctions between Twitter and YouTube in terms of hate speech volume and targeted groups. Based on gathered insights, it then develops and evaluates several supervised machine learning models to automatically and efficiently detect hateful content. This thesis also contributes new insights into the role of Arabic-language bots in spreading religious hatred on Twitter and introduces a novel regression model tailored to detect Arabic-tweeting bots. Finally, the thesis audits YouTube’s recommendation algorithm to assess the effect of personalization based on demographics and watch history on the extent of hateful content recommended to users. The research presented in this thesis offers practical implications for platform designers to facilitate enforcing their policy against hate and malicious automation and contributes to the broader effort to combat online radicalization.32 0