SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted AI in Telehealth for Cardiac Care: A Literature Review(University of technology sydney, 2024-03) Alzahrani, Amwaj; Li, lifuThis literature review investigates the integration of artificial intelligence (AI) in telehealth, with a specific focus on its applications in cardiac care. The review explores how AI enhances remote patient monitoring, facilitates personalized treatment plans, and improves healthcare accessibility for patients with cardiac conditions. AI-driven tools, such as wearable devices and implantable medical devices, have demonstrated significant potential in tracking critical health parameters, enabling timely interventions, and fostering proactive patient care. Additionally, AI-powered chatbots and telehealth platforms provide patients with real-time support and guidance, enhancing engagement and adherence to treatment regimens. The findings reveal that AI contributes to improving healthcare outcomes by enabling early detection of cardiac events, tailoring treatment plans to individual patient needs, and expanding access to care for underserved populations. However, the integration of AI in telehealth is not without challenges. Ethical considerations, such as ensuring data privacy, managing biases in AI algorithms, and addressing regulatory complexities, emerge as critical areas requiring attention. Furthermore, technological limitations, including the need for robust validation and patient acceptance of AI technologies, underscore the importance of bridging the gap between research and real-world implementation. This review also examines future trends, including the integration of blockchain technology with AI to enhance data security and privacy in telehealth systems. Advancements in machine learning and the Internet of Things (IoT) are paving the way for innovative solutions, such as secure remote monitoring and personalized rehabilitation programs. While AI holds transformative potential in revolutionizing telehealth services for cardiac patients, addressing these challenges is imperative to ensure equitable, effective, and patient-centered care. This review underscores the need for interdisciplinary collaboration and regulatory oversight to unlock the full potential of AI in telehealth and improve outcomes for cardiac patients globally.19 0Item Restricted Utilizing Artificial Intelligence to Develop Machine Learning Techniques for Enhancing Academic Performance and Education Delivery(University of Technology Sydney, 2024) Allotaibi, Sultan; Alnajjar, HusamArtificial Intelligence (AI) and particularly the related sub-discipline of Machine Learning (ML), have impacted many industries, and the education industry is no exception because of its high-level data handling capacities. This paper discusses the various AI technologies coupled with ML models that enhance learners' performance and the delivery of education systems. The research aims to help solve the current problems of the growing need for individualized education interventions arising from student needs, high dropout rates and fluctuating academic performance. AI and ML can then analyze large data sets to recognize students who are at risk academically, gauge course completion and learning retention rates, and suggest interventions to students who may require them. The study occurs in a growing Computer-Enhanced Learning (CED) environment characterized by elearning, blended learning, and intelligent tutelage. These technologies present innovative concepts to enhance administrative procedures, deliver individualized tutorials, and capture students' attention. Using predictive analytics and intelligent tutors, AI tools can bring real-time student data into the classroom so that educators can enhance the yields by reducing dropout rates while increasing performance. Not only does this research illustrate the current hope and promise of AI/ML in the context of education, but it also includes relevant problems that arise in data privacy and ethics, as well as technology equality. To eliminate the social imbalance in its use, the study seeks to build efficient and accountable AI models and architectures to make these available to all students as a foundation of practical education. The students’ ideas also indicate that to prepare the learning environments of schools for further changes, it is necessary to increase the use of AI/ML in learning processes11 0Item Restricted Factors Driving Individuals’ Usage Intention of Artificial Intelligence (AI) Assistants in E-commerce: Perspectives of Users and Non-Users(University of Technology Sydney, 2024) Alnefaie, Ahlam Eid Awad; Kang, Kyeong; Sohaib, OsamaThe ongoing revolution of e-commerce has brought about significant transformations in the global retail landscape, redefining how consumers interact with online platforms. In response to this transformative trend, businesses increasingly adopt and integrate artificial intelligence (AI) technologies, particularly AI assistants. AI assistants have gained significant traction to enhance customer engagement, improve personalised experience, and streamline various aspects of the e-commerce process. Companies across diverse industries and geographical regions have recognised the potential of AI assistants in fostering more profound connections with customers, providing real-time support, and bolstering sales through intelligent recommendations. Consequently, investment in AI research and development has surged, leading to remarkable advancements in AI assistants’ features and functionalities. Despite the growing interest of the scientific community and business stakeholders in the topic, scholarly research on the factors influencing e-commerce consumers’ attitudes and intentions toward using AI assistants is still limited and provides contradictory evidence regarding some factors. Moreover, no comparative studies in the e-commerce context empirically investigated the attitudes of non-users and users toward AI assistant use. Also, several consumers' demographics have been excluded from prior research, with no previous empirical research on AI assistant use across different cultural backgrounds. For these reasons, the study aimed to comprehend the factors influencing consumers' behavioural intention to utilise AI assistants and to recognise the significant user differences based on multiple perspectives. This study employed a unique research model based on the technology acceptance model. It extended it with external factors of AI assistants’ capabilities that still need to be tested together in AI assistant adoption for e-commerce consumers. This research conducted a mixed-method approach. In the first phase (Phase A), a quantitative method was employed to investigate the relationships between the constructs in the study model, and the Partial Least Square Structural Equation Modelling (PLS-SEM) and several statistical techniques were adopted. Furthermore, to account for cross-cultural differences and identify potential variations in usage intentions towards using AI assistants between Eastern and Western cultures, a multi-group analysis (MGA) was conducted. In the second phase (Phase B), a qualitative approach was conducted by applying machine learning and natural language processing techniques to analyse reviews of the Louis Vuitton brand's e-commerce applications. The objective of this stage was to obtain supporting evidence for the results of the VI first study and to gain deeper insights into consumer attitudes and experiences. Subsequently, the results were integrated to provide multiple insights to answer the research questions and strengthen the findings. This study has confirmed some previous studies' results and provided new findings. The attitude factor was the significant predictor of the intention to use AI assistants in non-users and users, with a direct and positive effect. Perceived usefulness was found to be the statistically significant predictor of attitudes in both non-users and users of AI assistants. The additions to the original TAM model, specifically incorporating interactive communication and personalisation, were statistically significant predictors of the attitudes of non-users and users to use AI assistants with positive effects. In contrast, perceived ease of use was a nonsignificant predictor of the non-users’ attitudes and positively impacted the users’ attitudes towards using AI assistants. Furthermore, no significant differences existed in the relationships among the primary factors influencing the intention to utilise AI assistants in e-commerce when comparing Western and Eastern cultural groups. This study contributes to both theory and practice by extending the TAM model with two external factors enabling the assessment of the factors affecting the intention to use AI assistants from consumer, social, and marketing perspectives and providing new empirical data on this topic in technology adoption studies. The study also enables further research on this topic and comparing study results, thus improving understanding of the phenomenon. It also provides various e-commerce practitioners with valuable information and recommendations regarding AI assistant use, enabling them to make better decisions in developing and implementing AI assistant technologies.7 0Item Restricted Developing AI-Powered Support for Improving Software Quality(University of Wollongong, 2024-01-12) Alhefdhi, Abdulaziz Hasan M.; Dam, Hoa Khanh; Ghose, AdityaThe modern scene of software development experiences an exponential growth in the number of software projects, applications and code-bases. As software increases substantially in both size and complexity, software engineers face significant challenges in developing and maintaining high-quality software applications. Therefore, support in the form of automated techniques and tools is much needed to accelerate development productivity and improve software quality. The rise of Artificial Intelligence (AI) has the potential to bring such support and significantly transform the practices of software development. This thesis explores the use of AI in developing automated support for improving three aspects of software quality: software documentation, technical debt and software defects. We leverage a large amount of data from software projects and repositories to provide actionable insights and reliable support. Using cutting-edge machine/deep learning technologies, we develop a novel suite of automated techniques and models for pseudo-code documentation generation, technical debt identification, description and repayment, and patch generation for software defects. We conducted several intensive empirical evaluations which show the high effectiveness of our approach.30 0Item Restricted Leveraging Potentials of Big Data for Better Decision-Making and Value Creation in Nonprofit Organisations(Saudi Digital Library, 2023-07-21) Alsolbi, Idrees; Prasad, Mukesh; Agarwal, Renu; Unkelhar, BhuvanIn Nonprofit Organisations, analysing and understanding donor behaviour remain critical and challenging. While big data and machine learning techniques promise technical solutions to address this problem, how to design and build an intelligent decision support system based on these technologies remains unclear. The literature reveals certain challenges for analysing donor behaviour. The researcher adopted a design science framework which helped to create an artefact (an intelligent decision support system) to analyse donor behaviour. The results show that (by analysing public big data sets of donors from different sources) certain variables are essential to analyse donor behaviour in nonprofit organisations. These variables are the total amount of donations, the number of donations, gender, age, social level of income, educational level, and the frequency of donations which assist the researcher in choosing the appropriate analysis model, from classification to predictions, and deciding the most beneficial machine learning techniques. The researcher aims to provide a theoretical foundation design for developing an intelligent decision support system for analysing donor behaviour. The research contributes to decision support and data analytics research by presenting the capabilities of data analytics and machine learning techniques in the context that face the difficulty of understanding donor behaviour.71 0