SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Integrating Artificial Intelligence Technologies in Sustainable Project Management(University of Exeter, 2024-07-04) Alqurashi, Abdullah; Roman, Jose MelenezSustainable project management encompassed the economic, environmental, and social aspects of a project to attain the project objectives in a sustainable manner. Nonetheless, the integration of AI technologies in sustainable project management was still low due to factors like inadequate knowledge of technical know-how, costs of implementing AI technologies, and resistance from the project team. This research aimed to identify the factors that hinder the application of AI in project management for sustainable practices and provided recommendations for enhanced application. The research sought to understand the status of AI adoption, challenges faced, and the impact of knowledge management practices on project performance based on the survey of 40 professionals in Saudi Arabia. The findings of this research enhanced the theoretical understanding of the topic by identifying that the level of awareness of AI is much higher than the level of its adoption. The research results show that although the level of awareness of AI technologies is relatively high, the implementation of the technologies is limited because of technical, financial, and organizational constraints. This research has also highlighted how knowledge management practice can be used to close this gap which can enhance increase in project performance, reduce costs and promote innovation. The research provided practical recommendations for organizations interested in using AI for sustainability and following best practices on a global level and in alignment with the vision of Saudi Arabia for the future. When applying these recommendations, professionals will be able to increase project efficiency, reduce costs, and promote innovation which contributes to sustainable development goals. This research presents a conceptual model that outlines how AI technologies can be applied in sustainable project management, fostering innovation and sustainable development. The research also highlights the necessity for future research to delve deeper into developing actionable frameworks and practical strategies for integrating AI into sustainable project management.9 0Item Restricted IS THE METAVERSEFAILING? ANALYSINGSENTIMENTS TOWARDSTHEMETAVERSE(The University of Manchester, 2024) Alharbi, Manal Dowaihi; Batista-navarro, RizaThis dissertation investigates Aspect-Based Sentiment Analysis (ABSA) within the context of the Metaverse to better understand opinions on this emerging digital environment, particularly from a news perspective. The Metaverse, a virtual space where users can engage in various experiences, has attracted both positive and negative opinions, making it crucial to explore these sentiments to gain insights into public perspectives. A novel dataset of news articles related to the Metaverse was created, and Target Aspect-Sentiment Detection (TASD) models were applied to analyze sentiments ex pressed toward various aspects of the Metaverse, such as device performance and user privacy. A key contribution of this research is the evaluation of the TASD architecture, TAS-BERT, and its enhanced version, Advanced TAS-BERT (ATAS-BERT), which performs each task separately, on two datasets: the newly created Metaverse dataset and the SemEval15 Restaurant dataset. They were tested with different Transformer based models, including BERT, DeBERTa, RoBERTa, and ALBERT, to assess performance, particularly in cases where the target is implicit. The findings demonstrate the ability of advanced Transformer models to handle complex tasks, even when the target is implicit. ALBERT performed well on the simpler Metaverse dataset, while DeBERTa and RoBERTa showed superior performance on both datasets. This dissertation also suggests several areas for improvement in future research, such as processing paragraphs instead of individual sentences, utilizing Meta AI models for dataset annotation to enhance accuracy, and designing architectures specifically for models like DeBERTa, RoBERTa, and ALBERT, rather than relying on architectures originally designed for BERT, to improve performance. Additionally, incorporating enriched context representations, such as Part-of-Speech tags, could further enhance model performance.5 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 Regulatory and Social Acceptance Challenges in Using Artificial Intelligence in Genomic Diagnostics in Saudi Arabia: Applying the Responsive Regulation and Innovation Diffusion Model.(University College London (UCL), 2024-08-28) Alderaa, Khalid; Jong, SimchaThis study explores the regulatory and social acceptance challenges of integrating Artificial Intelligence (AI) into genomic diagnostics in Saudi Arabia, using the Responsive Regulation and Innovation Diffusion model as theoretical frameworks. Methodology: The research employs a narrative review methodology, emphasizing regulatory frameworks, public trust, and the cultural perceptions that influence the adoption of AI technologies. Findings: The study identifies that, although AI holds significant promise for advancing genomic diagnostics, its full integration is hindered by regulatory gaps and a low level of social acceptance. The research emphasises the importance of creating a flexible and dynamic regulatory framework that can evolve with AI advancements. It also highlights the crucial role of stakeholder engagement and public education in building trust and ensuring that innovation progresses without compromising public safety. Limitations: Key limitations of the study include the restricted scope of the literature review, which primarily focuses on the European Union and Saudi Arabia, and the fast-paced development of AI technology, which may limit the long-term applicability of the proposed models. Practical Implications: To improve the adoption of AI in healthcare, this study recommends the implementation of regulatory sandboxes, which would allow AI innovations to be tested in controlled environments. Additionally, fostering public trust through transparency and education is critical to ensuring the successful integration of AI technologies in genomic diagnostics.21 0Item Restricted AI and the data shadow(Royal Central School for Speach and Drama, 2024-07) Duhaithem, Iyas; Jarvis, LiamMy aim with this research is to provide a setting to study AI as an active participant in a theatrical process. In real-time interactivity, co-creating narratives with human participants while creating an opportunity for the participants to explore their data shadow. The encounter and research seeks to expose the inner workings of AI tools, laying bare their processes for participants to observe and judge. This, in turn, allows the participants to foster a deeper understanding of how their personal data is being utilised and transformed by these technologies. To facilitate this study, my goal was to create a space that facilitated a 'data self-encounter', where participants interact with their personal data transformed through algorithmic processes using AI generative tools. By design, this setting consists of two sides that create the full image: the personal and digital sides. The personal concept aspect is fundamental in the project, making interactivity crucial, especially in finding a structure that serves to explore the participant's input, such as dreams or memories. As for the digital side, it was important to include elements that transform data that is specific to the individual. This came in many forms, such as voice and imagery. AI tools such as Deepfakes, Eleven labs and ChatGPT have made such manipulations widely accessible allowing for quick turnarounds in turn, enabling a new way to modify and repurpose personal data. In the end, this allows the participant to experience the ways they could explore their digital identities in an interactive theatrical setting. To lay the groundwork for this examination, it is essential to define key concepts that will underpin the final analysis.3 0Item Restricted A Systemic Literature Review Exploring the Impact of Artificial Intelligence on Marketing(University of Liverpool, 2024-09) Aldihnayn, Anfal; Peter, GuentherThe incorporation of artificial intelligence (AI) in marketing has transformed the field in recent years. In this regard, companies are now increasingly deploying AI solutions to improve consumer engagement and operational efficiency. This study investigates the power of AI in marketing, particularly through the lenses of the Technology Acceptance Model and Innovation Diffusion Theory on how this technology accelerates corporate productivity and profitability, enhances customer satisfaction, and brings hyper-personalization into marketing processes. The study employs a systematic literature review (SLR) approach, drawing on 37 peer-reviewed publications published between 2018 and 2024 to examine AI's impact across the marketing domain. Results reveal that AI significantly improves marketing strategies, helping companies to improve their financial performance, automate tasks, personalise their interactions with consumers, and enrich the overall customer experience. The findings of this study also supported the view that perceived usefulness (PU) and perceived ease of use (PEOU) are critical determinants for the incorporation of AI into marketing practices. Moreover, the relative advantage of AI as compared to other traditional marketing methods, especially in terms of automation and personalisation, has become one of the major drivers leading to its adoption. However, this study also emphasising the importance of addressing the ethical challenges including data privacy and algorithmic biases associated with using AI in marketing. This work offers researchers a direct and comprehensive overview of the extant knowledge role of AI in marketing. It concludes that the future of AI in marketing will continue to grow whereby companies worldwide will be able to tap into the power of AI for innovation and efficiency. Companies using AI will be better equipped to respond to the dynamic market demands and ensure a competitive advantage.27 0Item Restricted Regulating Maritime Autonomous Surface Ships (MASS): Challenges and Prospects.(Swansea University, 2024-09-28) Almulhem, Mubarak Ahmed; Tettenborn, Andrew; Amaxilati, ZoumpouliaMaritime Autonomous Surface Ships (or ‘MASS’) are a revolutionary technology that in the near- to medium-term are set to transform the maritime industry. As this technology reaches maturation, it is expected to lower shipping costs and improve safety, because it will greatly reduce – and maybe even entirely eliminate – the need for human seafarers and with them the potential for human error. That said, MASS also present significant regulatory challenges, because the law of the sea was developed with manned ships in mind and, particularly in respect fully autonomous ships with no human oversight, the existing regime will therefore struggle to accommodate MASS. Against this backdrop, this study assesses the challenges and prospects which MASS present for maritime regulation. The commentary looks particularly at the classification and design of MASS, the criteria for what makes a ship a ship in international law, and in particular the role of the master, along with how liability questions are impacted by AI systems. Overall, the commentary argues that, while the introduction of MASS present many not insignificant challenges to current maritime regulation, there is also a good prospect for regulation to play a proactive role in shaping the technology in a two-way process which can seek to maximise the benefits of MASS while minimising the potential for harm.19 0Item Restricted Exploring the Applications of Artificial Intelligence in Enhancing Pre-Hospital Care: A Scoping Review(Queen’s University, Belfast, 2024) Alfaifi, Yahya; Clarke, SusanArtificial Intelligence (AI) has the potential to significantly improve pre-hospital care, especially in emergency medical services (EMS). However, its current application remains scattered, with varying integration levels across care stages. This scoping review aims to map and assess existing research on AI applications within pre-hospital care without focusing on specific AI technologies, such as machine learning (ML), deep learning (DL), or decision support systems (DSS). The review reflects the current research landscape, capturing how AI is utilised across critical stages such as call-taking, dispatch, and on-scene assessment. Using the framework developed by Arksey and O’Malley (2005), a systematic search was conducted across multiple databases to identify studies relevant to AI in pre-hospital care. The scope was deliberately broad to capture a comprehensive view of the available literature, focusing on identifying areas where further research is needed. The findings indicate that DSS is commonly used to support decision-making in call-taking and dispatch, while more advanced AI applications like ML and DL show potential in predictive analytics and real-time decision-making. However, these technologies are still in their early stages of real-world implementation. This review highlights the gaps in AI research, particularly in the later stages of prehospital care, such as transport and handover. Further exploration is necessary to unlock AI’s full potential in enhancing EMS operations and outcomes.37 0Item Restricted AI-Driven Approaches for Privacy Compliance: Enhancing Adherence to Privacy Regulations(Univeristy of Warwick, 2024-02) Alamri, Hamad; Maple, CarstenThis thesis investigates and explores some inherent limitations within the current privacy policy landscape, provides recommendations, and proposes potential solutions to address these issues. The first contribution of this thesis is a comprehensive study that addresses a significant gap in the literature. This study provides a detailed overview of the current landscape of privacy policies, covering both their limitations and proposed solutions, with the aim of identifying the most practical and applicable approaches for researchers in the field. Second, the thesis tackles the challenge of privacy policy accessibility in app stores by introducing the App Privacy Policy Extractor (APPE) system. The APPE pipeline consists of various components, each developed to perform a specific task and provide insightful information about the apps' privacy policies. By analysing over two million apps in the iOS App Store, APPE offers unprecedented and comprehensive store-wide insights into policy distribution and can act as a mechanism for enforcing privacy policy requirements in app stores automatically. Third, the thesis investigates the issue of privacy policy complexity. By establishing generalisability across app categories and drawing attention to associated matters of time and cost, the study demonstrates that the current situation requires immediate and effective solutions. It suggests several recommendations and potential solutions. Finally, to enhance user engagement with privacy policies, a novel framework utilising a cost-effective unsupervised approach, based on the latest AI innovations, has been developed. The comparison of the findings of this study with state-of-the-art methods suggests that this approach can produce outcomes that are on par with those of human experts, or even surpass them, yet in a more efficient and automated manner.21 0Item Restricted AI GENERATED TEXT VS. HUMAN GENERATED TEXT(University of East Anglia, 2024-09) Hadi, Nedaa; Misri, KazhanThe ability to distinguish between AI-generated and human-generated texts is becom- ing increasingly critical as AI technologies advance. This dissertation explores the development and evaluation of various machine learning models to accurately classify text as either AI-generated or human-generated. The research aims to identify the most effective classification techniques and preprocessing methods to enhance model performance and generalization across different text datasets. A range of machine learning and deep learning models, including Support Vec- tor Machine (SVM), Random Forest, Logistic Regression, Decision Tree, BERT, and LSTM, were employed to evaluate their effectiveness in distinguishing between the two types of texts. The study utilized a balanced and representative dataset through data sampling and augmentation techniques. Key preprocessing steps were implemented to refine the input data, and hyperparameter tuning was conducted to optimize model performance. The generalization capabilities of the models were further tested on additional datasets with varying text characteristics. The findings revealed that SVM and Random Forest models achieved the highest accuracy and reliability in classifying texts, demonstrating strong performance across multiple evaluation metrics. In contrast, deep learning models like BERT and LSTM were less effective under the given conditions, suggesting a need for more extensive datasets and computational resources to leverage their full potential. These results highlight the strengths and limitations of different approaches to text classification, providing a foundation for future research to enhance AI detection in diverse applications.21 0
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