Saudi Cultural Missions Theses & Dissertations
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Item Restricted Impact of Artificial Intelligence Integration in Emergency Department Triage on Waiting Times: A Systematic Review Compared to Conventional Practices in ED Triage.(The University of Sheffield, 2024-09) Alhazmi, Mohammed; Miles, JemieBackground: The global issue of increased patient waiting times in healthcare facilities is a pressing concern, as it can lead to significant patient harm due to delayed access to healthcare. This research proposes the integration of artificial intelligence into emergency department triage systems as a solution to mitigate this issue. Aims: To evaluate the impact of integrating Artificial Intelligence (AI) support tools on waiting times in Emergency Departments through a systematic review of existing literature. Design: A thorough systematic review of the literature was conducted by searching electronic databases and internet search engines, including ScienceDirect, Springer, and PubMed, as well as reference lists. Studies published from January 1, 2019, to May 25, 2024, were included. Articles that did not pertain to AI, interventions that were irrelevant to emergency departments (EDs) or did not provide outcomes related to reducing waiting times either directly or indirectly, or evaluation data were excluded to ensure the quality and relevance of the included studies. Results: The analysis included ten peer-reviewed journals published after January 2019 on integrated Artificial Intelligence (AI) with emergency department triage. Recent findings suggest that integrating artificial Intelligence (AI) models into the emergency department (ED) triage processes can hold significant potential for reducing overcrowding and minimising wait times. Some studies have found that AI reduces waiting times by between 20 seconds and 30 minutes. However, a study found AI to increase waiting times for categories 3 to 5 by 2.75 to 5.33 minutes. Conclusions: This review has highlighted AI's potential to bring innovative solutions to emergency department settings. Implementing these AI-driven solutions has shown promise in enhancing healthcare delivery in the emergency department. However, further research is crucial to refine these models and ensure their practical application, underscoring the importance of continued involvement in the field.44 0Item Restricted The Use of Artificial Intelligence and Machine Learning in Zero Trust Networks(Newcastle University, 2024) Alnadhari, Sultan Majid; Shepherd, CarltonThis paper focuses on the application of Artificial Intelligence (AI) and Machine Learning (ML) within the context of the Zero Trust (ZT) security model to improve Cybersecurity within the ever-evolving digital landscape. Conventional security models that focus on proactively protecting the perimeter and assuming trust within internal networks are often inadequate against these threats. Zero trust can be characterised as a modern approach resulting from the "never trust, always verify" principle; thus, it implies an unceasing process of the users' authentication and access authorisation. Regarding Zero Trust security, this research builds upon the concept by incorporating AI/ML techniques to enhance threat, anomaly, and predictive detection. The first and foremost is the implementation of deep learning models using an optimised Keras framework better suited for the unique dynamics of the Zero Trust environments. Some of these models successfully differentiate and filter network traffic into normal and malicious categories using state-of-the-art features like dropout characters and dense layers. Briefly discuss some problems and solutions, for instance, data shift and model performance decline in conditions that change with time: transfer learning and periodically, for example, perform retraining of the model. Real-world assessments clearly show that incorporating Artificial Intelligence and Machine Learning into the Zero Trust Architectures enhances the capability to identify and mitigate advanced persistent threats and zero-day attacks. Therefore, this research will form a basis for more work in the area of Artificial Intelligence and Cybersecurity by presenting the knowledge required to establish intelligent security systems that can learn to handle new threats as they emerge effectively in real-time. Specifically, the results highlight how these speeds strengthen Zero Trust security solutions against emerging threats.27 0Item Restricted The Feasibility of Implementing AI in Bid/No-Bid decision-making(University of Bath, 2024-09-02) Alowairdhi, Saleh Mohammed; Jesty, James; Liyanage, NavodThis report explores the feasibility of implementing an Artificial Intelligence (AI)-based Benchmarking Tool to enhance the bid/no-bid decision-making process at Cundall, a multi-disciplinary consultancy firm. The study addresses critical inefficiencies in the current decision-making system, including data inconsistencies, subjective judgments, and underutilization of historical data, which hinder alignment with Cundall's Blue Ocean Strategy. Through a combination of qualitative and quantitative research methods, the report identifies key decision factors, evaluates data readiness, and proposes an AI solution designed to provide data-driven insights, improve decision-making consistency, and align bid decisions with strategic objectives. The proposed AI Benchmarking Tool integrates historical data analysis, machine learning algorithms, and an interactive dashboard to deliver explainable, user-friendly recommendations. The report includes a proof-of-concept using PowerBI and Random Forest machine learning models, demonstrating the tool's potential to improve bid success rates and operational efficiency. Ethical considerations, stakeholder engagement, and implementation challenges, such as data quality and system integration, are thoroughly addressed. The findings highlight the transformative potential of AI in construction decision-making, positioning Cundall as an industry leader in innovation and strategic differentiation. Recommendations emphasize a phased implementation approach, robust governance, and continuous refinement to ensure alignment with Cundall's sustainability and innovation goals. This study contributes to the growing discourse on AI adoption in the construction industry, providing actionable insights for leveraging technology to create competitive advantages.18 0Item Restricted Integrating Industry 4.0 in Project Management: A Systematic Literature Review(De MontFort University, 2024-09-20) almehaize, Ghannam nasser; Oyinlola, AdewaleThis thesis investigates Industry 4.0 technologies with the aim of integrating them into project management methodologies to improve efficiency, decision-making, and overall project success. The study investigates the existing studies on the influence of these technologies on project management processes and evaluates the present status of their integration across a variety of sectors. This is accomplished via a comprehensive examination of the available literature and studies. Industry 4.0 technologies have the potential to revolutionise project management by enabling the sharing and analysis of real-time data, according to the results. In addition, they present challenges regarding organisational culture, communication, and skill limitations. This thesis shows that project managers need technical understanding, leadership, and flexibility. This thesis ultimately emphasises the potential of Industry 4.0 technologies to enhance project performance, while also emphasising the need for organisations to modify their project management frameworks in order to prosper in a digital environment that is swiftly evolving. In order to enable organisations to fully realise the promise of these technologies for successful and sustainable development, the study's conclusion calls for further research to develop frameworks that facilitate the effective integration of these technologies.11 0Item Restricted The Future of Indirect Marketing in a Digital World(University of Sussex, 2024-09-27) Aldowais, Raed Fahad; Ye Yang, NicoleThis dissertation explores the transformative impact of digital technologies on indirect marketing strategies, focusing on how businesses adapt to the evolving digital landscape. The study aims to assess the roles of artificial intelligence (AI), big data, and the Internet of Things (IoT) in enhancing indirect marketing, with a specific focus on consumer behaviour and brand loyalty. The research methodology is based on secondary data analysis, drawing from academic journals, industry reports, and case studies. This approach allows for a comprehensive examination of current trends and technologies affecting marketing practices across various industries. The main findings indicate that AI, big data, and IoT significantly enhance marketing effectiveness by improving customer targeting, personalization, and real-time engagement. These technologies have enabled businesses to create more meaningful consumer interactions, leading to increased customer loyalty and higher marketing ROI. The integration of these technologies provides a synergistic effect that amplifies their individual benefits. In conclusion, the dissertation underscores the critical role of digital technologies in shaping modern marketing strategies. The study highlights the importance of adopting these technologies to remain competitive in a digital-first marketplace while addressing challenges such as data privacy and the need for skilled personnel. These insights offer valuable guidance for future research and practical application in the field of digital marketing.12 0Item Restricted Evolving and Co-Evolving Meta-Level Reasoning Strategies for Multi-Agent Collaboration(Massey University, 2024) Alshehri, Mona Abdulrahman M; Reyes, Napoleon; Barczak, AndreThis research presents a novel hybrid evolutionary algorithm for generating meta-level reasoning strategies through computational graphs to solve multi-agent planning and collaboration problems in dynamic environments using only a sparse training set. We enhanced Genetic Network Programming (GNP) by reducing its reliance on randomness, using conflict extractions and optimal search in computational mechanisms to explore nodes more systematically. We incorporated three algorithms into the GNP core. Firstly, we used private conflict kernels to extract conflict-generating structures from graph solutions, which enhances selection, crossover, and mutation operations. Secondly, we enhanced the GNP algorithm by incorporating optimal search and merged Conflict Directed A* with GNP to reduce the search branching factor. We call our novel algorithm Conflict-Directed A* with Genetic Network Programming (CDA*-GNP), which identifies the most effective combination of processing nodes within the graph solution. Additionally, we investigated the use of a chromosome structure with multiple subprograms of varying sizes that the algorithm automatically adjusts. Thirdly, we applied Conflict-Directed A* to a genetically co-evolving heterogeneous cooperative system. A set of agents with diversified computational node composition is evolved to identify the best collection of team members and to efficiently prevent conflicting members from being in the same team. Also, we incorporated methods to enhance the population diversity in each proposed algorithm. We tested the proposed algorithms using four cooperative multi-agent testbeds, including the prey and predator problem and the original tile world problem. More complex multi-agent and multi-task benchmarking testbeds were also introduced for further evaluation. As compared to existing variants of GNP, experimental results show that our algorithm has smoother and more stable fitness improvements across generations. Using the popular tile world problem as a benchmarking testbed, CDA*-GNP achieved better performance results than the best existing variant of GNP for solving the problem. Our algorithm returned 100% accuracy on the test set compared to only 83% reported in the literature. Moreover, CDA*-GNP is 78% faster in terms of the average number of generations and 74% faster in terms of the average number of fitness evaluations. In general, our findings suggest that a hybrid algorithm that balances the utilization of Genetic Network Programming and Optimal strategies leads to the evolution of high-quality solutions faster.26 0Item Restricted AN INTEGRATED DIGITAL TWIN FRAMEWORK AND EVACUATION SIMULATION SYSTEM FOR ENHANCED SAFETY IN SMART BUILDINGS(Western Michigan University, 2024-06-29) Almatared, Manea Mohammed S; Liu, HexuFire hazards in buildings continue to pose a substantial risk to human life and property safety despite declining deaths, injuries, and damages over the past decade. Consequently, fire safety management (FSM) is crucial to effectively preventing and controlling fire hazards. However, several challenges need to be addressed to ensure optimal FSM in buildings, such as the lack of effective integration of advanced technologies such as Internet of Things (IoT) sensors, fire detection systems, and automated response mechanisms, the reliance on insufficient fire safety equipment (FSE) maintenance and a lack of operational skills among occupants. In particular, traditional manual methods of searching for information, such as using two-dimensional drawings and relying on paper documents, have become inefficient and costly as buildings have become larger and more complex. This leaves room for improvement in current FSM practices— specifically, high-efficiency evacuation- the best approach for minimizing mortality and property loss. Digital twin (DT) technologies have been widely used in other industries, such as manufacturing and transportation, to improve efficiency, reduce costs, and enhance safety. However, the FSM sector has been a slow adopter of DT technology. This study investigated the adoption of DT technologies in the FSM sector. This research aims to explore the limitations, opportunities, and challenges associated with adopting DT technology in the FSM sector and further develop a DT-based FSM framework towards smart facility management (FM). This framework lets decision-makers obtain comprehensive information about the building's communication and safety systems. It can also enable the real time monitoring of FSE and provide predictive maintenance. Toward this objective, several DTs for FSM were first reviewed, including building information modeling (BIM), the Internet of Things (IoT), artificial intelligence (AI), and augmented reality (AR). These technologies can be used to enhance the efficiency and safety of FSM in smart buildings. The framework was then synthesized based on the literature review, application requirements, and industry needs. A questionnaire survey was conducted for FM professionals to evaluate the framework and identify the challenges of adopting DT and the proposed framework in the FSM sector. The survey results identify the current state of DT technology in the FSM sector, provide insights into the perception of DT technology among FM practitioners, and validate its expected benefits and potential challenges. The main barriers to adopting DTs in FSM are a lack of knowledge about DTs, their initial costs, user acceptance, difficulties in systems integration and data management, education training costs, a lack of competence, development complexity, and data security. Furthermore, the research develops a building fire evacuation simulation system based on the validated framework, i.e., smart lighting. This system integrates the data from the BIM platform, Fire Dynamic Simulator (FDS), and Agent-Based Simulation (ABS) platform for evacuation through customized developments. Real-time fire situation is transmitted to the evacuation simulation platform to assess the impact of dynamic fire spread on the evacuation of people. A model for optimizing evacuation route planning is designed to improve the utilization of each evacuation exit and provide a visualization of evacuation routes as smart lighting in Dynamo. This proposed system was validated by conducting a case study on three fire evacuation scenarios. An average of 20.9 % increases the evacuation efficiency in three scenarios. The main contributions of this research include (1) Developing a DT-based FSM framework for smart buildings, (2) Developing a fire emergency evacuation simulation system for buildings by integrating DT technologies, and 3) Achieving the integration and interoperability of BIM data, fire data, and evacuation data from different platforms.30 0