Saudi Cultural Missions Theses & Dissertations
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Item Restricted An Ontology-based Framework for the Modelling and Online Detection of Obsessive Compulsive Disorder(Cardiff University, 2024-11) muhajab, Areej; Abdelmoty, AliaIn the contemporary digital landscape, the prevalence and impact of Obsessive- Com- pulsive Disorder (OCD) discourse in online platforms have garnered increasing signif- icance. This thesis presents an integrated framework aimed at detecting and classi- fying OCD in online discourse by harnessing the synergy between ontology develop- ment and machine learning. The primary objective is to enhance the understanding and identification of OCD-related content within the vast and varied landscape of on- line forums. The research begins with the construction of a comprehensive ontology, named OCD, specifically designed to encapsulate the multifaceted aspects of OCD. This ontology is developed to represent the complex interplay of OCD symptoms, behaviors, and related mental health concepts. Drawing upon insights from medical literature, psy- chological studies, and existing biomedical ontologies, the OCD ontology provides a structured, hierarchical representation of OCD, enabling systematic identification and categorisation of OCD-related terms. Consequently, it furnishes a rich semantic framework that facilitates accurate interpretation of online discourse. In addition to ontology development, the thesis explores machine learning method- ologies, particularly focusing on the classification of OCD-related posts on online plat- form. A variety of classification models are employed to analyse and categorise online content. Leveraging the OCD ontology as a foundational reference for feature extrac- tion and semantic analysis, these models are trained and evaluated on a corpus of OCD forum posts. The classification process is designed to discern various OCD manifestations, such as obsessions and compulsions, thereby offering a granular un- derstanding of the disorder’s portrayal in digital communication. The outcomes of this thesis carry significant implications for mental health profes- sionals, online community moderators, and researchers. The developed framework and methodologies represent a pioneering tool for monitoring, understanding, and addressing OCD in the digital space.15 0Item Restricted IBBRB: Intelligent Blockchain-based Reputation Broker for Robot Selection(University of Technology Sydney, 2023-12) Alharbi, Wafa; Hussain, Farookh KhadeerRobot as a service (RAAS) is a cloud-based subscription service that enables robotic devices to be leased instead of purchased. RAAS has recently increased in popularity due to the numerous advantages that it offers to robotic service requesters such as flexibility and the lower cost of entry and maintenance compared to owning the equipment, and the ease of implementation. The concept of RAAS has contributed to the increased use of robots in different disciplines, such as industry, education, health and agriculture. Robotic service requesters may face difficulties in searching for the most suitable robot for their required tasks based on their preferences. Robot selection has attracted the interest of many researchers and it has been widely discussed in the literature. Robot selection is based on ranking the available robotic alternatives after they have been assessed by robotic experts. The assessment process is based on customer requirements as well as the task’s functional and non-functional requirements. However, through a systematic literature review, it has been identified that selecting a robot based on its previous performance in similar tasks has not been discussed yet. Furthermore, all the proposed robot selection methods require robotic experts to determine the requirements and robotic alternatives. To address these issues, this research aims to propose and develop an intelligent blockchain-based reputation broker for robot selection termed IBBRB. IBBRB is an intelligent reputation system that allows robotic service requesters (customers) to rate the performance of robots after hiring them. To avoid data manipulation, which is a common issue with reputation systems, blockchain technology is used to store and secure all trust values in IBBRB. IBBRB is built to provide novel and intelligent mechanisms to: (i) standardise robotic knowledge across all robotic service requesters, suppliers and manufacturers by encapsulating all the robotic attributes and their relationships into an ontological manifestation called Robotic Attribute Ontology (RAO), and then to propose a blockchain-based method for RAO evolution using a crowdsourcing approach, (ii) develop a comprehensive method to carry out robotic reputation computations termed Reliable Reputation Computation Method for Robotics (RRCM). RRCM incorporates building: (a) a reputation model that produces reputation values for robots based on previous customers’ ratings, and (b) a prediction model that predicts reputation values for non-reviewed robots to bootstrap new robots and overcome the cold start issue, (iii) develop a method to infer reputation values for all non-reviewed contexts of multi-purpose robots based on their similarities to the reviewed contexts. Finally, this research uses software prototyping to validate the performance and accuracy of the aforementioned proposed methods.19 0