Juhaida Abu Bakar

Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries.

This research investigates trends and advances in the field of Lexical Simplification (LS), namely efforts to simplify text. We used data from the Scopus database to see the types of publications that appeared most frequently. As a result, conference papers were the largest (65.6%), followed by journal articles and conference reviews. We also found that the number of publications fluctuated every year, with 2016 and 2020 being the peaks, indicating that there is a need to be more consistent and produce more publications in the future. This research also highlights the important contributions of several researchers and universities. From a language perspective, LS research mostly uses English, but some also uses Spanish and Mandarin. The main publication source is Lecture Notes in Computer Science. Keyword analysis shows that the main topics of this research are Lexical Simplification (18.34%), natural language processing systems (15.65%), computational linguistics (9.54%), semantics (7.85%), and Complex Word Identification (6.85%). These findings help understand the current state and future directions of LS research, emphasizing the importance of language variety and improving the quality of publications in this field.

Automatic text summarisation involves creating a concise and coherent description while preserving essential information and context. Time constraints faced by educators prompted this study. Writing is a crucial skill for students, especially in English; however, many need more feedback on their mistakes. Writing, akin to scaffolding, necessitates the involvement of proficient essay writers to assess and review students' work. Scaffolding facilitates student engagement and improves learning outcomes, making it an effective instructional strategy. To address this issue, Lecturer Feedback Tools (LeFT), a Natural Language Processing web application system, is introduced to educators to provide feedback to enhance students' writing skills. It utilises four summarisations SpaCy, Gensim, Natural Language Toolkit, and Sumy. What sets this tool apart is its ability to record all operations in a database, ultimately boosting educators' productivity and students' work. The evaluation results show a consensus among participants, with 66.7% expressing the system's usefulness and 83.3% finding it easy to use, reflecting their satisfaction with the study's outcomes.

With the postponement of face-to-face learning activities due to the spread of COVID-19 in Malaysia, there was a rapid shift to distance learning in higher education. Accordingly, students must be digitally literate to enhance their distance learning sessions. On the other hand, some distance learning students are less aware of the importance of digital literacy competencies. Therefore, a pilot study was conducted to assess the digital literacy competencies of distance learning students. A survey was conducted by distributing an online questionnaire during the academic session from May to November 2021. A total of two hundred and thirty-two students participated in the survey. The outcomes revealed that the students demonstrated adequate digital literacy competencies throughout the distance learning sessions. Nevertheless, students are encouraged to upskill for understanding the most recent digital technologies better, allowing them to use the most recent digital technologies in their learning and workplace.