SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted The role and use of Artificial Intelligence (Al tools) in audits of financial statements(Aston university, 2024-09) Alsaedi, Amal; George ,SalijenIntegrating artificial intelligence (AI) in the auditing function holds significant potential to transform the industry. As firms and stakeholders increasingly recognise the value of and demand audit quality, the accuracy, validity, and integrity of information generated by audit processes have become a vital consideration. Integrating AI into audit processes would be viewed as advancing audit techniques. However, the current limited adoption of this technology by audit firms raises concerns about their awareness of its transformative potential. This study aims to identify AI tools used in auditing and their impact on the audit process and quality. The study bridges the existing gap using a secondary exploratory method. Qualitative data was collected from transparency reports by the Big Four audit firms, i.e., KPMG, Deloitte, EY and PwC, and audit quality inspection reports for the four firms by FRC. For recency purposes, only reports published between 2020 and 2023 were considered. A thematic analysis of the data collected reveals that adoption of AI and data analytics in auditing is still low, and the Big Four firms are actively promoting increased adoption. The results demonstrate a notable disparity between potential and current applications, as shown by a clear gap between the publicised potential of AI and data analytics and their implementation within audit processes.20 0Item Restricted Artificial Intelligence Systems: Exploring AI Systems’ Patentability in The United Kingdom And The European Patent Office(University of Liverpool, 2024) Alarawi, Khalid; Jacques, SabineThe topic of Artificial Intelligence (AI) has become a common interest for the public and corporations on a global level. Through a legal analysis of case law between the United Kingdom and the European Patent Office (EPO), this paper will argue that while AI systems are indeed patentable in both the United Kingdom and the EPO and that despite the differences, the result of patenting AI is likely to be the same across both jurisdictions, there still needs to be further clarification in regards to different AI systems. Given that they are addressed as a hybrid between computer programs and mathematical methods, there needs to be deeper exploration towards AI system patentability across different types, and different technical applications.1 0Item Restricted Leveraging Brain-Computer Interface Technology to Interpret Intentions and Enable Cognitive Human-Computer Interaction(Univeristy of Manchester, 2024) Alsaddique, Luay; Breitling, RainerIn this paper, I present the developed, integration, and evaluation of a Brain–Computer Interface (BCI) system which showcases the accessibility and usability of a BCI head- set to interact external devices and services. The paper initially provides a detailed survey of the history of BCI technology and gives a comprehensive overview of BCI paradigms and the underpinning biology of the brain, current BCI technologies, recent advances in the field, the BCI headset market, and prospective applications of the technology. The research focuses on leveraging BCI headsets within a BCI platform to interface with these external end-points through the Motor Imagery BCI paradigm. I present the design, implementation, and evaluation of a fully functioning, efficient, and versatile BCI system which can trigger real-world commands in devices and digital services. The BCI system demonstrates its versatility through use cases such as control- ling IoT devices, infrared (IR) based devices, and interacting with advanced language models. The system’s performance was quantified across various conditions, achiev- ing detection probabilities exceeding 95%, with latency as low as 1.4 seconds when hosted on a laptop and 2.1 seconds when hosted on a Raspberry Pi. The paper concludes with a detailed analysis of the limitations and potential im- provements of the newly developed system, and its implications for possible appli- cations. It also includes a comparative evaluation of latency, power efficiency, and usability, when hosting the BCI system on a laptop versus a Raspberry Pi.16 0Item Restricted Integration of Artificial Intelligence in Supply Chain Management: A Case Study of Toyota Motor Corporation(University of Gloucestershire, 2024-08) AlQuwaie, Thamer Adeeb; Plummer, David; Rasheed, Muhammad Babar; Zhang, ShujunThis study examines AI deployment in Toyota Motor Corporation's supply chain management. By analysing the literature and interviewing key workers from Toyota, the research illustrates how AI technologies enhance logistics, demand forecasting, inventory management, and procurement. AI-driven predictive analytics and automation improved decision-making accuracy, operational efficiency, and cost savings. The research notes low data quality, expensive initial costs, and staff unwillingness to change as important impediments. The research suggests continual training, robust data management rules, and gradual AI deployment to solve these issues. The research also emphasises the importance of human factors in AI integration, including open communication and worker engagement for smooth adjustments. The research found that high management and departmental collaboration are needed to use AI technology successfully. Future research should include cross-sector and cross-regional comparisons, longitudinal studies to track impacts, and more work on social and ethical concerns. This research analyses Toyota's AI integration to provide supply chain AI users information and advice.9 0Item Restricted Generative AI for Mitosis Synthesis in Histopathology Images(University of Surrey, 2024-09) Alkhadra, Rahaf; Rai, Taran; Wells, KevinIdentifying mitotic figures has been established as an effective method of fighting cancer at its most vulnerable stage. Traditional methods rely on manual, slow, and invasive detection methods obtained from sectioned tissue samples to acquire histopathological images. Currently, Artificial Intelligence (AI) in oncology has produced a paradigm shift in the fight against cancer, also known as computational oncology. This is heavily reliant on the availability of mitotic figure datasets to train models; however, such datasets are limited in size, type, and may infringe on patient privacy. It is hypothesised that the potential of computational oncology can be realised by synthesising realistic and diverse histopathological datasets using Generative Artificial Intelligence (GenAI). This report demonstrates a comparison of Denoising Probabilistic Diffusion Models (DDPM) and StyleGAN3 in generating synthetic histopathology images, with mitotic figures. The MIDOG++ dataset containing human and canine samples with 7 types of tumours was used to train the models. Quality and similarity of generated and real images was evaluated using as Frechet Inception Distance (FID), Mean Square Error (MSE), Structural Similarity Index (SSIM), and Area Under the Curve (AUC) as a part of Receiver Operating Characteristic (ROC) study were incorporated. Our results suggests that the DDPM model is superior in terms of structural accuracy, however, StyleGAN3 capture the colour scheme better.19 0Item Restricted The Role of Artificial Intelligence in Personalising the Recruitment Process in Saudi Arabia: A Systematic Literature Review(Swansea University, 2024-09-29) Alotaibi, Mohammed; Balaussa, ShaimakhanovaArtificial intelligence (AI) has revolutionised various industry sectors, including human re- sources (HR),by enhancing decision-making, automating tasks, and improving efficiency. In the Kingdom of Saudi Arabia, the adoption of AI in HR is increasing, particularly in recruitment processes. This study explores how AI is transforming recruitment in Saudi Arabian organisations, highlighting the benefits and challenges associated with its im- plementation. AI-driven recruitment tools can streamline candidate screening, improve decision-making by analysing large datasets, and enhance the overall candidate experi- ence through personalisation. However, the study also identifies significant challenges, such as the need for AI systems to align with local cultural norms, legal requirements, and data privacy regulations. Moreover, the limited availability of skilled professionals to manage AI technologies and concerns about bias in AI-driven decisions are notable barriers. The research emphasises the importance of understanding employees’ and HR professionals’ perceptions of AI, particularly in terms of trust, acceptance, and effec- tiveness. By applying frameworks such as the technology acceptance model (TAM) and employee engagement theory, this study aims to assess AI’s impact on recruitment, fo- cusing on personalised onboarding experiences and strategic workforce planning in Saudi Arabia. The findings suggest that despite existing challenges, AI holds significant po- tential to optimise HR operations and contribute to organisational success, aligning with Saudi Arabia’s Vision 2030 goals. Future research should address the ethical implications, long-term impacts, and cultural adaptations necessary for successful AI integration in re- cruitment.By bridging these gaps, AI can play a pivotal role in modernising recruitment practices, enhancing efficiency, and driving competitive advantage in the evolving Saudi employment market.7 0Item 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 Triple-Negative, Digital Biomarkers, Survival Analysis, Neoadjuvant Chemotherapy, Histology Images Analysis(Univeristy of Warwick, 2024-04) Albusayli, Rawan; Rajpoot, Nasir; Minhaz, FayyazTimely detection, precise diagnosis, and effective risk stratification play a pivotal role in optimising treatment decisions and enhancing outcomes for individuals battling cancer. The advent of Digital Pathology (DP) introduces a revolutionary potential to elevate cancer detection methods and refine treatment management by improving diagnostics and prognostics. This thesis endeavours to harness the power of deep-learning techniques for analysing histology images in triple-negative breast cancer (TNBC), with the ultimate goal of extracting digital biomarkers to enrich the study of patients' outcomes. The analysis of whole slide images commences with a robust tissue classification, followed by intricate computational examinations to extract spatial features of the tumour microenvironment. This inquiry unveils correlations and relationships between the studied features, patients' treatment responses, and survival outcomes. The refined tissue classification model emphasises the significance of tumour-associated stroma and stromal tumour-infiltrating lymphocytes in predicting patients' responses to neoadjuvant chemotherapy. Spatial quantitative measures derived from the computational analysis serve as invaluable digital biomarkers, providing crucial insights into risk outcomes for individuals with TNBC. Moreover, delving into NanoString data and exploring digital basal and non-basal subtyping of TNBC extends the scope of this thesis and augments the comprehension of the disease. This broadening of perspective opens avenues for potential connections among histopathological characteristics, molecular profiles, and disease subtypes, thereby enhancing the prospects for personalised treatment strategies to advance.7 0Item Restricted Adaptive Resilience of Intelligent Distributed Applications in the Edge-Cloud Environment(Cardiff University, 2024-04) Almurshed, Osama; Rana, OmerThis thesis navigates the complexities of Internet of Things (IoT) application placement in hybrid fog-cloud environments to improve Quality of Service (QoS) in IoT applications. It investigates the optimal distribution of a Service Function Chain (SFC), the building blocks of an IoT application, across the fog-cloud infrastructure, taking into account the intricate nature of IoT and fog-cloud environments. The primary objectives are to define a platform architecture capable of operating IoT applications efficiently and to model the placement problem comprehensively. These objectives involve detailing the infrastructure's current state, execution requirements, and deployment goals to enable adaptive system management. The research proposes optimal placement methods for IoT applications, aiming to reduce execution time, enhance dependability, and minimise operation costs. It introduces an approach to effectively manage trade-offs through the measurement and analysis of QoS metrics and requires the implementation of specialised scheduling and placement strategies. These strategies employ concurrency to accelerate the planning process and reduce latency, underscoring the need for an algorithm that best corresponds to the specific requirements of the IoT application domain. The study's methodology begins with a comprehensive literature review in the area of IoT application deployment in hybrid fog-cloud environments. The insights gained inform the development of novel solutions that address the identified limitations, ensuring the proposal of robust and efficient solutions.19 0Item Restricted Feature Selection for High Dimensional Healthcare Data(University of Surrey, 2024-01) Alayed, Abdulrahman; Kouchaki, SamanehIn today’s digital landscape, researchers frequently encounter the complexity of handling highdimensional datasets. At times, data mining and machine learning methods struggle when confronted with immense datasets, leading to inefficiencies. The presence of extensive raw data with numerous features can negatively impact machine learning algorithms, affecting accuracy, increasing overfitting, and amplifying complexity. This is primarily due to the inclusion of redundant and irrelevant data, which hampers the learning process. However, employing feature selection techniques can effectively address these challenges. By selectively choosing relevant features, these techniques enable machine learning algorithms to operate more efficiently. They contribute to faster training, reduce model complexity, enhance accuracy, and mitigate overfitting issues. The primary objective of this project is to create an automatic variable selection pipeline by choosing the best features among various innovative feature selection techniques. The pipeline incorporates different categories of variable selection methods: Filter methods, Wrapper methods, Embedded methods, and Hybrid Method. The variable selection techniques are applied to the MIMIC-III (Medical Information Mart for Intensive Care) dataset, which is reachable at no cost. This database is well-suited for the project's goals, as it is a centralized database containing details about patients admitted to the critical care unit of a vast regional hospital. The dataset is particularly useful for forecasting the likelihood of death pst-ICU admission during hospital stay. To achieve this goal, the project employs six classification techniques: Logistic Regression (LR), K-nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The project systematically evaluates and compares the model's performance using various assessment metrics.34 0