Saudi Cultural Missions Theses & Dissertations
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Item Restricted A Systematic Review of User Consent, Transparency, and Secure Data Transmission and Storage(University of Technology Sydney (UTS), 2024-11-03) Alharbi, Sultanah; Hussain, Farookh KhadeerSmart home technology is revolutionizing residential environments by connecting devices to enhance comfort, safety, and energy efficiency. However, these advancements raise significant privacy concerns, particularly in data collection, transmission, and storage. This systematic review examines user consent, transparency, and secure data handling in smart homes, identifying challenges and innovative solutions such as blockchain and AI integration. The review highlights deficiencies in current consent mechanisms, the complexity of GDPR compliance, and practical barriers to implementation, offering insights for future research and practical privacy frameworks.19 0Item Restricted Epigenetic Habitats : Mimesis and Living Architecture in Light of Catharine Malabou’s Meditation About Synaptic Chips(Univeristy College London, 2024) Alangari, Nujud; Vivaldi, Jordi4 Mimesis has been integrated with architecture for a long time—from ancient civilisations e.g. ancient Greece and the Renaissance to the modern and postmodern eras. These architectural eras tend to respond to Platonic or Kantian schemes, illustrating the evolution of architectural mimesis. For Plato, mimesis meant copying and reproducing nature through art; for Kant, however, it was more about harmonising beauty and function than copying from nature. Kant believed that art is a creation of genius which does not copy nature directly but rather reinvents nature’s rules into artistic expression. While rich in their interpretation of imitation, both concepts lack the dynamic meaning of mimesis when it comes to mimicking human intelligence. In this context, I would like to address the following question: Is the arrival of AI and robotics in architecture demanding a new epigenetic scheme for thinking about mimesis? I would like to address this question by considering Catherine Malabou’s interpretation of the concept of ‘synaptic chips’ that has been discussed in her work on epigenetic mimesis—an idea that transforms the entire picture of AI in architecture. The discussion of synaptic chips as presented by Malabou serves as a metaphorical basis for the evolution and adaptation of architectural design. Architectural designs may similarly evolve through the influence of connections that are synaptic-like; such structures respond to changes in their environments based on environmental stimuli. This approach—which is epigenetic—to mimesis suggests a shift more profound from just replicating forms to creating architectures that learn from their surroundings, thus adapting to them. This reveals a more complex interplay between form, function and environment than what is traditionally understood under Platonic or Kantian mimesis. Through this extension of mimesis by Malabou using neuroscience plus epigenetics, one can infer an avenue towards dynamic designs: designs that are more responsive and, in turn, enhance mimetic capabilities of AI systems within architecture—thereby also enhancing the architectural design’s adaptability and functionality.7 0Item Restricted THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING KPIS AND OPTIMIZATION OF HUMAN RESOURCE MANAGEMENT(The Hague University, 2024-09-28) Alsamhan, Khulud; Le Fever, HansMANAGEMENT SUMMARY This thesis explores how artificial intelligence (AI) can enhance human resource management (HRM), particularly in recruitment and onboarding. The study focuses on LinkedIn's AI tools, aiming to understand their effectiveness in improving key performance indicators (KPIs) and optimizing HR processes. The research draws on a broad literature review, examining the evolution of AI in HR. AI has shown potential in automating tasks like candidate screening and onboarding, but there are challenges, including biases in AI systems and the need for continuous improvement. Using Saunders' research onion framework, a mixed-methods approach was adopted, combining surveys and interviews with HR professionals who use LinkedIn's AI tools. This approach provided a comprehensive view of AI's impact on HRM. The results indicate that AI tools significantly enhance effectiveness by automating repetitive tasks and improving candidate matching, thus reducing the time-to-hire and increasing accuracy. However, some challenges remain, such as occasional inaccuracies and the need for better user training. It's clear that refining AI algorithms and incorporating human oversight can help address these issues. In onboarding, AI tools have been successful in automating administrative tasks and personalizing the onboarding experience. Feedback suggests that AI-driven processes help new hires feel more supported and prepared. The study concludes with recommendations for further research and practical steps for implementation. It highlights the need for ongoing refinement of AI tools, better integration practices, and comprehensive training for HR professionals. Future research should focus on long-term impacts and best practices for AI in HRM. In summary, AI has the potential to transform HRM by enhancing KPIs and optimizing processes. However, a balanced approach that combines technology with human judgment is essential for maximizing these benefits. This thesis provides a foundation for future advancements in using AI in HRM.14 0Item Restricted Early Prediction of Cancer Using Supervised Machine Learning: A Study of Electronic Health Records From The Ministry of National Gurad Health Affairs(University College London (UCL), 2024-08) Alfayez, Asma; Lai, Alvina; Kunz, HolgerEarly detection and treatment of cancer can save lives; however, identifying those most at risk of developing cancer remains challenging. Electronic health records (EHR) provide a rich source of "big" data on large patient numbers. I hypothesised that in the period preceding a definitive cancer diagnosis, there exist healthcare events, such as a history of disease, captured within EHR data that characterise cancer progression and can be exploited to predict future cancer occurrence. Using longitudinal phenotype data from the EHR of the Ministry of National Guard Health Affairs, a large healthcare provider in Saudi Arabia, I aimed to discover health event patterns present in EHR data that predict cancer development in periods prior to diagnosis by developing predictive models using supervised machine learning (ML) algorithms. I used two different prediction periods: six months and one year prior to cancer diagnosis. Initially, the thesis focused on the prediction of both malignant and benign neoplasms, before moving on to predicting the future risk of malignant neoplasms (cancer), since predicting life-threatening illness remains the most important clinical challenge. To refine the approach for specific cancer types, predictive models were built for the top three malignancies in this population: breast, colon, and thyroid cancers. ML predictive models were developed using the following algorithms: (1) logistic regression; (2) penalised logistic regression; (3) decision trees; (4) random forests; (5) gradient boosting; (6) extreme gradient boosting; (7) k-nearest neighbours; and (8) support vector machine. Model performance was assessed using k-fold cross-validation and area under the curve—receiver operating characteristics (AUC-ROC). After developing different models, their performance was compared with and without hyperparameter tuning using tree-based pipeline optimization (TPOT) and GridSearch. This study provides novel proof-of-principle that ML algorithms can be applied to EHR data to develop models that can be used to predict future cancer occurrence.26 0Item Restricted TESTING COSMETIC PRODUCTS ON ANIMALS(Solent University, 2024-08) Almutiri, Hanan; Hegarty, SebastianeAbstract Technological innovation particularly the use of artificial intelligence (AI) has brought a revolution in the cosmetics and skincare industry. This paper aims to provide an understanding of how AI is being utilized in the formulation and tailoring of cosmetics and skincare products and its effects on creative product development, product safety, and consumer satisfaction. New generation technologies that include the use of artificial intelligence including machine learning, in silico modelling and virtual try-on have helped revolutionize product development and improve the delivery of customized skincare solutions and therefore customer experiences. These technologies facilitate accurate tuning of formulation and more accurate prediction of how the product will behave on different skin types, enhancing the product’s performance and hence the consumers’ confidence. The research follows the systematic review methodology and follows the PRISMA guidelines to conduct an exhaustive and transparent analysis. For the purpose of this study, a total of fifteen relevant articles were chosen in order to establish a strong basis for assessing the current and future advancements of AI in the cosmetics industry. The main issues that have been highlighted include ethical concerns, legal issues, the development of new AI methods, and the shift from animal testing to AI-based solutions. Ethical issues are mainly on the reduction of animal testing, which is viewed as both scientifically meaningless and ethically wrong. AI presents potential solutions that are compatible with the current ethical practices and emphasize in vitro and in silico experiments that are not only ethical but also effective. Regulatory issues and possibilities are discussed, with a focus on the need for new guidelines that can facilitate the implementation of AI solutions while maintaining safety and legal requirements. AI advancements in safety assessment are achieved through the establishment of models and simulations that provide improved accuracy and reliability. Based on the findings of this study, AI has the ability to revolutionize the cosmetics industry, however, more studies are required to improve on the current challenges and harness the potential of the technology. Further research should be directed towards the collection of primary data and the empirical assessment of the applicability of AI-based approaches in compliance with legal requirements.30 0Item Restricted USER MODELLING AND ADAPTIVE INTERACTION ON INTERACTIVE DASHBOARDS(University of Manchester, 2024-06-06) Alhamadi, Mohammed; Vigo, MarkelInteractive information dashboards are data visualisation tools that enable interaction with complex underlying datasets using visualisations such as charts and maps typically on a single display. The popularity of dashboards has grown across key sectors such as healthcare, education and energy, driven by the abundance of available data. Still, users face various challenges when interacting with dashboards, ranging from insufficient support for essential functionalities such as data-detail adjustment to problems with data presentation such as information overload. These problems subject users to high cognitive demands, complicate information retrieval and increase the risk of arriving at incorrect conclusions, ultimately leading to erroneous decision-making. Dashboard issues are sometimes due to developers prioritising aesthetics over functionality. At other times, they arise from a mismatch between users' visual literacy level expected by dashboard developers and the actual level of the users. When dashboard users encounter interaction problems, they exhibit certain interaction strategies as workarounds to overcome the problems. Modelling user behaviour on dashboards can shed light on these workarounds especially when applied in problematic situations. Strategies employed by users in response to interaction problems have, to a large extent, not been thoroughly explored. This thesis addresses this gap by identifying the interaction and information presentation problems faced by dashboard users, adaptation techniques that could address these problems and user strategies applied in response to problems. Results of a literature review and an interview study highlighted various problems faced by users, and at times, a disconnect between problems, adaptations and strategies. Subsequently, an experiment was conducted to identify user strategies indicative of problems when encountering four established interaction and information presentation problems: information overload, inappropriate data order \& grouping, ineffective data presentation and misaligned visual literacy expectations. These problems were prioritised based on their severity and the limited understanding of user strategies when encountering them. We found clear distinctions between the strategies applied on problematic and adapted dashboards. Then, we incorporated the strategies, along with graph literacy, in user models to predict usability. In a final user study, we ecologically validated the effect of the majority of the influential user strategies on usability in real-world dashboards. While filtering data was linked to negative outcomes, customisation made users more effective. Encouragingly, usability predictions were more accurate on problematic dashboards and challenging tasks. These promising results open up avenues for tailored interventions to address the problems in real time.23 0Item Restricted Impact of Strategic Knowledge Management Practices on ERP Systems in Saudi Arabia Business Organizations(Hunan University, 2024-06-15) Baslom, Mohammed Majdy M; Shu, TongMany organizations are currently implementing enterprise resource planning (ERP) to address their operational challenges. Despite its appeal, ERP implementation is fraught with obstacles and complications, particularly in developing nations. Recent studies indicate that the implementation of EM-ERP has significantly enhanced production, services, revenues, and employee well-being. Both developed and developing nations have witnessed the emergence of novel management levels and innovative concepts. The "Saudi Vision 2030" initiative is a significant national undertaking with substantial economic implications for Saudi Arabia. Knowledge management (KM) is assuming new, crucial responsibilities in advancing the industrial business environment, especially in the face of globalization and intense corporate competition. Organizations are increasingly focusing on the development and application of knowledge as a strategic asset. In 2017, the industrial sector contributed approximately 45% of Saudi Arabia's gross domestic product (GDP), a figure expected to rise as KM-ERP programs are integrated into Saudi business organizations, particularly in the manufacturing sector. This Dissertation investigates the critical factors influencing the adoption of ERP systems for effective KM in the Saudi Arabian manufacturing sector. The study aims to determine how KM can be utilized as a strategic resource to optimize ERP systems, consequently enhancing organizational competitiveness. Additionally, it assesses the role of support teams, providing a novel perspective on how human resources and team interactions can substantially influence ERP and KM processes. The integration of ERP systems and KM is essential for improving the performance, efficiency, and competitiveness of industrial businesses in Saudi Arabia. ERP systems automate and integrate business operations, including human resources, accounting, inventory, production, and sales. KM connects the generation, dissemination, and implementation of knowledge within an organization to achieve its goals. Thus, it is crucial for manufacturing companies to develop and implement contemporary strategies. Given the global impact of AI on research and implementation, expanding Saudi Arabia's research program is vital. By monitoring and analyzing data from machinery and shop floor processes, manufacturers can detect patterns to predict or prevent malfunctions. ERP systems are critical digital infrastructures that link operations throughout manufacturing enterprises. With the rapid development of AI capabilities, ERP platforms are poised for transformation. The integration of intelligent features can provide unprecedented connectivity, visibility, efficiency, and insight, revolutionizing the manufacturing sector in Saudi Arabia and enhancing the nation's economic status. This investigation achieves several essential contributions. First, it identifies critical factors influencing the success or failure of an ERP-KM environment within Saudi Arabian manufacturing organizations. The study focuses on organizational learning readiness, change management, ERP adoption scenarios, and KM methods used by Saudi enterprises. Second, it integrates information management and decision-making by examining knowledge alignment, collaboration, and communication. The study uses quantitative methods, including logistic regression and partial least squares SEM, followed by CFA and structural model assessment using Python. Third, it evaluates the impact of ERP and KM systems on support teams within business organizations, quantifying this impact with statistical metrics such as goodness of fit, R-squared, Chi-square, RMSEA, CFI, and TLI. The study identifies adoption barriers and explores how social, political, economic, and cultural factors influence KM and ERP implementation. Lastly, the research implements the PLS-SEM model and demonstrates that strategic business information distribution significantly impacts AI awareness in KM. It highlights the necessity of instruction and training in novel technologies and examines the role of learning environments and AI awareness in organizational structures. By exploring interdepartmental collaboration and information exchange, it provides a comprehensive perspective on organizational dynamics impacting ERP and KM systems. Incorporating strategic KM practices into the ERP systems of Saudi Arabian manufacturing companies will optimize ERP capabilities and reveal both financial and non-financial benefits. This Dissertation contributes to organizational learning readiness, change management, and technological adoption, providing insights into the optimization of ERP and KM systems in Saudi Arabia.23 0Item Restricted Digital Technologies in Accounting Firms: Adoption, Impact and New Avenues for Future Research(University of East Anglia, 2023-06) Alsahlawi, Saja; Guven-Uslu, Pinar; Dewing, IanPaper 1 Purpose – The purpose of this paper is to review the literature on the impact of digital technologies on accounting practice and accountants' roles in the context of accounting firms. Design/methodology/approach – A scoping review of academic studies was used to achieve the study's purpose. Findings – Only thirteen empirical papers on the impact of digital technologies on accounting practice and accountants' roles in the context of accounting firms were identified. Furthermore, the review revealed that discussion papers and anecdotal claims dominate the literature. It is important for future research to consider to what extent the accounting profession, and accounting firms in particular, are embracing digital technology and how it is impacting accounting practice and accountants' roles. The findings also reveal that the challenges and risks associated with digital technologies are unaccounted for and ignored in the literature. Originality/value – This paper contributes to the field of accounting research by providing an overview of emergent literature on the usage of digital technologies and its impact on accounting and accountants' roles in accounting firms context. It is also the first to synthesise and discuss the challenges/risks, as well as the opportunities/benefits associated with digital technologies within that context while also aiming to serve as a catalyst for future research.118 0Item Restricted Audit Data Analytics, the Transformation within the Audit Profession: Perspectives from the Kingdom of Saudi Arabia. A(Royal Melbourne Institute of Technology (RMIT) University, 2024-04-18) Alharbi, Yousef; Phan, Duc; Kend, MichaelEmerging and advanced audit data analytics (ADA) technologies such as big data analytics (BDA) and artificial intelligence (AI) algorithms that can analyse vast amounts of structured, semi-structured, and unstructured data are changing the auditing industry's practices, processes, and evidence collection processes. This research will investigate factors that encouraged or discouraged Saudi audit firms from investing capital in emerging and advanced (ADA) technologies such as BDA and AI tools. Also examined here are new forms of audit evidence generated by these emerging and advanced technologies and factors that facilitate or impede the collection of such audit evidence. Furthermore, this study will explore the differences between listed and unlisted audit clients regarding the audit processes when advanced or emerging technologies are deployed. Diffusion of innovation (DOI) theory, technological-organisational-environmental (TOE) framework, and socio-technical (ST) theory will serve as the basis for the theoretical framework for this study. The study will use two methodologies to collect data: firstly, conducting semi-structured interviews with participants who have knowledge and experience of emerging and advanced ADA technologies for auditing, and secondly, reviews of the documentary data sources generated by audit firms on this topic. In this study, empirical findings from Saudi Arabia will be presented on the transformation occurring in the audit profession where emerging and advanced ADA technologies are being used. This study presents four contributions. The present study is one of the first to reveal capital investment decisions in emerging and advanced ADA technologies since it provides empirical knowledge aimed at enhancing academic perceptions in this area. It is also one of the first studies to provide empirical evidence about the new forms of audit evidence and the differences in audit processes between listed and unlisted clients when using ADA tools. For practical contribution, this study provides a comprehensive capital investment decision-making model in ADA technologies that consists of three pillars (i.e., technological, organisational, and external environmental), which allows audit firms to make informed capital investment decisions in emerging and advanced ADA technologies. In terms of theoretical contributions, this study is among the first to combine diffusion of innovation (DOI) withtechnological-organisational-environmental (TOE) theories to interpret findings about RQ1 and RQ2. This study is the first to utilize ST system theory to investigate the phenomenon of RQ3 since this topic has not been addressed previously. As a methodological contribution, this study utilized two qualitative data collection methods (i.e., semi-structured interviews and documentary sources) in conjunction with interpretivism philosophy in order to support and strengthen its findings. The findings reveal that KSA audit firms have many reasons (i.e., technological, organisational, and external environmental) for investing or not investing in ADA technologies. Technological factors are relative advantages, compatibility, complexity, trialability, observability, uncertainty vs. certainty, and trust in such technologies. Several organisational factors lead KSA audit firms to invest in ADA technologies: improvements of their operations, leadership support, the readiness of KSA audit firms, and technological competencies of auditors and other relevant employees. External environmental factors that encourage or discourage KSA audit firms from investing in such technologies are the country’s regulations and regulators, international auditing standards, competitive pressures, and trading partners' or clients’ requirements. It is not necessary for each audit firm to consider all these factors before deciding to invest or delay investment in audit technologies. However, it is more beneficial that KSA audit firms consider all these factors before deciding to invest in modern audit techniques, as they can look at matters from many angles and in detail, which gives them a better opportunity to make informed decisions. The findings about the new forms of audit evidence have been interpreted based on DOI and TOE theories, and they KSA audit firms did generate new forms of audit evidence by analysing the entire population of clients' data using AI and BDA, Radio Frequency Identification (RFID), drones, and sensors. The technical-based factors that lead KSA audit firms to generate new forms of audit evidence are relative advantages, compatibility, complexity, and simplicity. Organisational aspects that simplify the generation of new forms of audit evidence are leadership commitment and support and seeking to improve how audit firms operate with reference to external auditing practices. Finally, collecting new forms of audit evidence has been influenced significantly by external environmental factors such as government regulations and audit standards, audited clients, and competitive pressures. The findings about the differences in audit processes between listed and unlisted audit clients to collect new forms of evidence using modern technologies are driven by the six elements that comprise the ST system framework. These six elements drive four factors that establish the differences in audit processes and the use of ADA technologies between listed and unlisted clients: risk levels, regulations, accounting standards, and differences in the quality and quantity of the data provided by each client category (i.e., listed, or unlisted).65 0Item Restricted Audio-to-Video Synthesis using Generative Adversarial Network(University of New South Wales, 2024-01-23) Aldausari, Nuha; Mohammadi, Gelareh; Sowmya, Arcot; Marcus, NadineVideo generation is often perceived as stringing several image generators. However, in addition to visual quality, video generators must also consider motion smoothness and synchronicity with audio and text. Audio plays a crucial role in guiding visual content, as even slight discrepancies between audio and motion can be noticeable to human eyes. Thus, audio can be a self-supervised signal for learning the motion and building correlations between the audio and motion. While there are attempts to build promising audio-to-video generation models, these models typically rely on supervised signals such as keypoints. However, annotating keypoints as supervised signals takes time and effort. Thus, this thesis focuses on audio-based pixel-level video generation without keypoints. The primary goal of this thesis is to build models that generate a temporally and spatially coherent video from audio inputs. The thesis proposes multiple audio-to-video generator frameworks. The first proposed model, PhonicsGAN, uses GRU units for audio to generate pixel-based videos. The subsequent frameworks, each, address particular challenges while still pursuing the main objective. To improve the spatial quality of the generated videos, a model that adapts the image fusion concept to video generation is proposed. This model incorporates a multiscale fusion model that combines images with video frames. While the spatial quality of the video is important, the temporal aspect of the video frames should also be considered. To address this, a shuffling technique is proposed presenting each dataset sample with varied permutations to improve the video's temporal learning. We propose a new model that learns motion trajectory from sparse motion frames. AdaIN is utilised to adjust the motion in the content frame to the target frame to enhance the learning of video motion. All the proposed models are compared with state-of-the-art models to demonstrate their ability to generate high-quality videos from audio inputs. This thesis contributes to the field of video generation in several ways: Firstly, by providing an extensive survey on GAN-based video generation techniques. Secondly, by proposing and evaluating four pixel-based frameworks for enhanced audio-to-video generation output that each addresses important challenges in the field. Lastly, by collecting and publishing a new audio/visual dataset that can be used by the research community for further investigations in this area.24 0
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