SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item 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 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 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 Towards Robust Cybersecurity Realm: An Exhaustive Evaluation of AI-Driven Approaches for Enhanced Insider Threat Detection(University of Warwick, 2024-01-08) Alyami, Rahf Yousif; Safa, Nader SohrabiToday, insider threats pose a significant risk to an organization's cybersecurity posture, often proving difficult to detect and causing substantial damage not only to an organization's financial resources but also to its reputation, mission, personnel, infrastructure, information, equipment, networks, or systems. Despite their critical importance, many organizations tend to primarily focus on external threats, unintentionally neglecting those that come from within. This study aims to explore the effectiveness of artificial intelligence in detecting insider threats in the cybersecurity landscape. It focuses on evaluating different algorithms and their ability to identify unusual behaviour patterns that indicate potential insider threats. To achieve this goal, the study involves developing a Python-based machine learning program in Jupyter Notebook to assess the performance of various anomaly-based and classification-based models such as One-Class Support Vector Machine (OCSVM), Isolation Forest (iForest), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Neural Network (NN). Additionally, the study will conduct a comprehensive examination and comparative analysis of three sophisticated techniques: SelectKBest, Principal Component Analysis, and Synthetic Minority Over-sampling to enhance and optimize the performance of the selected models. This will ultimately lead to identifying the most efficient, anomaly, and classification-based detection models that deliver outstanding performance results, as well as identifying the best techniques to optimize their performance. For anomaly-based detection, the study's results revealed that the iForest algorithm demonstrated superior performance over OCSVM, achieving remarkable metrics of 90% Precision, 93% Recall, 92% F1-Score, and 93% Accuracy. For the classification-based models, a variety of combinations produced impressive results. The integration of the SMOTE technique and SelectKBest proved to be effective in reducing the occurrence of false positives. For instance, the RF-SMOTE-SelectKBest model showcased a remarkable 100% Recall and 99% Accuracy. The SVM-SMOTE-SelectKBest model maintained consistent performance metrics, recording 97% in Precision, Recall, F1-Score, and 99% Accuracy. The AdaBoost-SMOTE-SelectKBest model achieved 99% Accuracy. The XGBoost-SMOTE-SelectKBest model delivered 95% Precision, 95% Recall, 95% F1-Score, and 99% Accuracy. The NN-SMOTE-SelectKBest model exhibited exceptional performance, achieving 99% Accuracy, 97% Precision, and 95% Recall. The results of this study provide important insights into the ability of AI to efficiently identify insider threats, as well as in helping to select appropriate methods to enhance the effectiveness of insider threat detection.12 0Item Restricted Utilising Technical Analysis, Commodities Data, and Market Indices to Predict Stock Price Movements with Deep Learning(Cardiff University, 2024) Aloraini, Osama Mohammed A; Sun, XianfangThis study investigates the efficacy of deep learning models, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), for forecasting stock price movements in the U.S. stock market. The dataset used includes 133 stocks across 19 different sectors and covers the period from 2010 to 2023. Moreover, to enrich the dataset, eleven technical indicators and their corresponding trading strategies, represented as vectors, were integrated along with market indices and commodities data. Consequently, various experiments were conducted to assess the effectiveness of different feature combinations. The findings reveal that the CNN model outperforms the LSTM model in both accuracy and profitability, achieving the highest accuracy of 59.7%. Furthermore, models demonstrated an ability to identify significant trend-changing points in stock price movements. Another finding shows that translating trading strategies into vector form plays a critical role in enhancing the performance of both models. However, it was observed that incorporating external features like market indices and commodities data led to model overfitting. Conversely, relying only on stock-specific features triggered a risk of model underfitting.64 0Item Restricted A cross-sectional survey: Radiographer's knowledge, attitude, and perception towards using artificial intelligence in medical imaging in Saudi Arabia.(Saudi Digital Library, 2023-12-31) Alotaibi, Asma; Linehan, MarkA cross-sectional survey: Radiographer's knowledge, attitude, and perception towards using artificial intelligence in medical imaging in Saudi Arabia Abstract Background: Radiography relies heavily on technology. Currently, clinical radiography practices incorporate artificial intelligence (AI) and associated applications to improve patient care. As radiographers are considered the end users of AI technology in radiography, research into their familiarity with and perceptions of AI application in medical imaging is needed in Saudi Arabia. To identify the requirements of effective implementation, proper understanding of radiographers' attitudes and perceptions regarding current and future AI applications is critical. Aim: The aim of this study was to assess the knowledge, perceptions, and attitudes of radiographers towards the use of AI in medical imaging. Methodology: A cross-sectional descriptive survey was conducted in the western region of the KSA using a self-developed questionnaire. The survey targeted 100 radiographers working in 10 hospitals. The data obtained from the questionnaire were analysed using Statistical Package for the Social Sciences version 28. Results: Fifty-two participants answered the questionnaire, achieving a response rate of 52%. Of the radiographers, 42.3% reported that they were aware of AI, and 48.1% expressed enthusiasm about AI integration in radiography. However, they also expressed concerns related to job security, errors or technical issues, the efficiency of AI, using AI in radiography, the role of AI in diagnosis and reporting, employees losing their skills, and the cost of the tools. Conclusion: The study findings indicate a generally favourable attitude towards the integration of AI in medical imaging among radiographers. However, it is important for professional leadership to address radiographers' concerns. Institutions and workplaces must train and educate radiographers about the benefits of AI. By alleviating their fears related to the use of AI and educating them about the benefits of AI, successful adoption may be ensured.60 0Item Restricted THE IMPACT OF ARTIFICIAL INTELLIGENCE ON AGRI-FOOD SUPPLY CHAIN SUSTAINABILITY: A SYSTEMATIC LITERATURE REVIEW(Saudi Digital Library, 2023-11-28) Abdulkarim, Bayan; Zhao, GuoqingPurpose: Considering the growth in artificial intelligence, the purpose of this paper is to review the impact of the use of artificial intelligence (AI) on agri-food supply chain (AFSC) sustainability. Specifically, the importance of AI technologies, along with their role and use in AFSC sustainability, has been analysed in this study. Design/methodology/: A systematic review has been adopted as the methodology in which precedent studies conducted on the topic have been collected using databases and search engines. The review is based on 60 papers published from 2018 to 2023 in international journals that have been collected. The analysis has been presented by using a thematic analysis approach. Findings: The paper indicates the use of AI technologies in the AFSC sector. It has been noticed that AI technologies such as machine learning, computer vision, robotics, blockchain, and others are being used in the agri-food sector to promote efficient performance. With the help of AI technologies, the sector has improved its operations and imposed control on problems such as food waste and losses. This contributes to promoting sustainability within the supply chain as the AI assist in managing the process from production to reaching the final consumer. Value: This research work has provided insights related to AI and its involvement in the agri-food supply chain. This paper is useful for agriculture, retailers, and policymakers as it provides valuable insights into the role of AI technologies and the benefits they offer to the agri-food sector.18 0Item Restricted Augmenting Decision-making in Human Resources Processes: the impact of task characteristics and algorithmic transparency on perceived trustworthiness and fairness of the decision-making process(Saudi Digital Library, 2022) Alturki, Hend Saud Abdulaziz; Hummel, JochemLeveraging artificial intelligence technologies for decision-making processes within an organization has various implications. While the technologies adoption is increasing due to benefits including efficiency and cost-saving, concerns are simultaneously growing in terms of the societal and individual impact of this phenomenon. It is especially pertinent to recognize those impacts in the functions of Human Resources Management where issues like benevolence and reductionism can be more permanent. Accordingly, calls for augmenting decision-making, by including a human factor in the process, are in the rise in an effort to limit the negative effects. This paper works to investigate (1) whether augmenting the decision-making process would improve the perception of individuals affected by the decisions in operative human resources functions, and also (2) assess the impact of systems’ transparency on individuals’ perception of trustworthiness and fairness of the decision-making process. Adopting an experimental approach, results from a scenario-based survey of mixed-factorial design (2x2x4) and a sample of (245) participants show that augmented decision making is perceived to be more trustworthy and fairer in operative human resources functions. Moreover, findings suggest that while task characteristics have a significant impact on the perceptions, transparency’s impact is only significant in specific cases (i.e., automated decision making and quantifiable tasks)36 0