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Legal Challenges and Evidentiary Barriers in Determining Copyright Infringement by Generative AI: A Taiwan-Centred Analysis
No full textThis study examines the evidentiary challenges in copyright infringement litigation involving generative AI technologies under Taiwan's legal framework. Drawing from the operational mechanisms of large language and diffusion models, it explores the difficulty copyright holders face in proving substantial similarity or unauthorised reproduction when AI developers refuse to disclose training datasets. The paper analyzes two high-profile US cases: Andersen v Stability AI, where the court partially dismissed the plaintiffs' claims due to insufficient factual allegations regarding compressed copies and third-party use, and The New York Times v OpenAI and Microsoft, in which plaintiffs submitted outputs from ChatGPT and Browse with Bing that closely resembled original copyrighted articles, suggesting potential infringement of reproduction and derivative rights. These cases illustrate both the legal uncertainty and the potential for novel evidentiary strategies. The paper argues that prompt engineering--crafting input commands to provoke infringing outputs--may assist plaintiffs in building stronger prima facie cases. Finally, the paper proposes legislative reform by introducing a statutory licensing scheme specifically tailored to AI-related uses in Taiwan, aiming to reduce the evidentiary burden on authors and ensure fair compensation
“Share or Not”, The Relationship Between User Motivations and Fake News Sharing about Political Issues in Malaysia
No full textThis research investigates the psychological motivations underlying the sharing of fake news on social media concerning political issues in Malaysia. Despite the growing concern about fake news on social media platforms, gaps in psychological research and the relationship between social media use and fake news sharing remain unattended within the Malaysian context. The study aims to identify the primary motivations driving social media users to share fake news on social media platforms concerning Malaysian political issues and explores potential gender differences. Using a quantitative research design, this study collected data through a questionnaire comprising 32 items distributed among social media users. Results indicate a significant relationship between psychological motivations and the sharing of fake news on social media. However, the study found no significant gender-based differences in this relationship. Findings suggest that respondents are moderately motivated by psychological factors to share fake news regarding Malaysian political issues on social media. In conclusion, this study emphasises the influence of psychological motivations on sharing fake news. It contributes to the broader understanding of the relationship between social media and fake news. Future research in this area can further explore the nuances of psychological motivations and their implications for combating the dissemination of fake news in the digital landscape
A Study on Young Listeners’ Perceptions of English Usage on Hitz FM and TraXX FM in Klang Valley
No full textRadio has been a powerful medium for educating and informing audiences through various types of content, whether informative or entertaining. This study explores the relationship between the formality of English language usage on radio and audience perceptions, focusing on Hitz FM and TraXX FM. Examining the characteristics of each language style used by the two radio stations, associated perceptions, and their impact on listeners' station preferences sheds light on the language factor influencing listenership in this research. For data collection, the target population focuses on young adults aged 18 to 29 residing in the Klang Valley region who are listeners of both Hitz FM and TraXX FM. The data was analysed using SPSS software through three methods: descriptive analysis, Pearson Correlation Coefficient, and cross-tabulation to assess the relationships between language style, audience perception, and listenership. The findings indicate the relationship between language style, audience perception, and radio listenership. Results showed that listeners preferred Hitz FM's informal language style over Hitz FM's formal style of TraXX FM, leading to higher listenership for Hitz FM
Linguistic Indicators of Narcissistic Tendencies for Predicting Narcissistic Traits in Malaysians Through Social Media Content
No full textNarcissistic tendencies are increasingly observable in online interactions, particularly on social media platforms. X (formerly Twitter) has been identified as a popular platform among individuals exhibiting narcissistic traits. Predicting these traits based on language use remains an essential area of study. This research examines narcissistic tendencies among Malaysian social media users by analysing 2,129 posts on Twitter. Utilising natural language processing (NLP) methods and machine learning algorithms, we assess linguistic patterns, sentiment, and engagement metrics to identify indicators of narcissism. Four machine learning algorithms, Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, and Gradient Boosting, were assessed based on multiple performance metrics. Results indicate that SVM is the most effective model, achieving 80% accuracy with 10-fold cross-validation, demonstrating its reliability in predicting narcissistic traits. These findings contribute to computational psychology and social media analytics, offering insights into the psychological dimensions of digital self-presentation and the implications of narcissistic behaviours in online communities
Indonesian Language Sign Detection using Mediapipe with Long Short-Term Memory (LSTM) Algorithm
No full textPeople with disabilities mostly communicate using sign language, but the public still has little understanding of the Indonesian Sign Language System (ISLS). This causes obstacles in daily interactions. Advances in artificial intelligence technology, especially artificial neural networks, open opportunities in sign language recognition, but are still in the development stage. This study aims to build a ISLS sign language recognition model using the LSTM approach and MediaPipe Hands. The method of collecting hand keypoint data, 25 sequences per gesture, and 36 alphabetic and numeric gestures. The dataset is divided into three categories, namely 80% training, 10% validation, and 10% testing. The model developed to handle sequential data from hand gestures using the LSTM architecture. The results of the study can be shown model accuracy of 97.1%, average macro precision of 97%, recall of 96.6%, and F1-score of 96.4% and weighted average precision of 97.4%, recall of 97.1%, and F1-score of 97%. The results show that the combination of LSTM and MediaPipe can detect ISLS gestures with high accuracy. This can be used as a potential solution in automatic sign language translation, so that this model can improve the inclusiveness of communication for people with disabilities. Further research can be developed using a more accurate hand recognition framework, as well as improving data pre-processing, and exploring deep learning (DL) methods such as SSD, YOLO, or Faster-RCNN. In addition, pose and facial recognition can be added to improve accuracy in gesture recognition more comprehensively
Mapping Relational Database to Full-Text XML for Open Journal System Cross-Platform Article Distribution
No full textIn academic publications, the automation of full-text eXtensible Markup Language (XML) is increasingly essential, as generating full-text XML for article distribution is a complex and time-consuming process that requires metadata extraction from a relational database and transformation into hierarchical structures such as Journal Article Tag Suite (JATS). The lack of automation in this transformation process may cause inconsistencies and inaccuracies and may cause errors due to human error. The primary aim is to develop an automation system for transforming metadata from a relational database to full-text XML by reducing errors and speeding the process of generating full-text XML. This is crucial since the demand for automation has been increasing year by year. Furthermore, the motivation behind this research is the growing adoption of the Open Journal System (OJS), one of the popular platforms for managing scholarly journals. It supports a relational database to store the metadata and article information. Therefore, developing an automated system is essential for transforming this structured metadata to full-text XML. To address this issue, various techniques for mapping will be explored to enable the transformation of relational database structures into full-text XML formats. The proposed method involves metadata extraction, mapping logic, and various validation mechanisms to ensure the XML is structured and the accuracy of it. The preliminary result indicates that the metadata has been successfully mapped from a relational database to XML. However, the JATS-specific tagging has not yet been implemented and will be addressed in future work. This research is significant to the publication community, as it brings convenience by reducing some manual work and ensuring metadata standardization
The Role of Generative AI in e-Commerce Recommender Systems: Methods, Trends and Insights
No full textRecommender systems have existed for decades, shaping how people consume digital content, receive information, and engage in day-to-day activities, among others. Undoubtably, recommender systems also play a crucial role in e-commerce applications as well, with industry players like Amazon, AliBaba, eBay using recommender systems within their ecosystems to give suitable and value-driven insights. However, recommender systems face some main concerns such as data sparsity, cold-start problems and so on. As a result, research is currently ongoing to solve these issues and provide high-quality recommendations to consumers. This review aims to identify prevailing gaps surrounding these issues by analysing existing research on generative Artificial Intelligence (AI) recommender systems within an e-commerce context. It explores the underlying framework of common generative AI techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, diffusion models and so on. VAEs and Transformers hold great potential within e-commerce as noted by most researchers due to their ease of training and qualitative generations. This review intends to enhance recommender systems better to improve the quality of life of digital users, providing better recommendations in e-commerce as well as maximizing the value of stakeholders. It also includes potential future work for researchers to advance existing knowledge in this sector
Research Article Recommender Systems: A Comprehensive Review of Models, Approaches and Evaluation Metrics
No full textWith the advent of the current digital era, individuals across the developed world are commonly equipped with devices that can access vast amounts of information at their fingertips. What was considered an impossible feat was realized through remarkable technological advancements. This positive transformation has had a profound impact on education, where traditional knowledge management, such as libraries, are no longer a primary determinant of a student’s academic success. Instead, it has been replaced by the internet as a medium for learning, practicing, and topic exploration. However, the sheer volume of the ever-increasing information available online can easily overwhelm a user, particularly when conducting detailed research on a specific topic. Therefore, the need for a reliable research article recommender system cannot be understated, helping students and researchers to navigate the expansive knowledge space better and achieve their learning and research objectives. This review paper aims to study the most common types of recommendation system techniques in research articles recommender systems (RS). A total of ten related works and relevant evaluation metrics written by other researchers will be studied and accessed rigorously using comparative analysis, granting further insights into the current work similar or related to the domain of this paper. Finally, this paper will identify and elaborate their current trends and gaps in the discussion section
Loan Default Prediction Using Machine Learning Algorithms
No full textFinancial institutions constantly face at the risk of default by borrowers which can result in significant financial losses. It is essential to develop an appropriate predictive model for loan default to reduce these risks and minimise financial losses. The objective of this study is to identify the most suitable machine learning model to predict loan default by comparing four models which are Random Forest, Decision Tree, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Additionally, it also examines the key features influencing loan default prediction. The dataset used in this study is sourced from Kaggle and it consists of 148,670 rows with 34 features. As class imbalance is common in the model prediction, Synthetic Minority Over-sampling Technique (SMOTE) is applied during model training to enhance predictive performance. Model performance is evaluated using five significant assessment metrics: accuracy, precision, F1-score, recall, and the area under the receiver operating characteristic curve (ROC AUC). The outcomes indicate that LightGBM performs the best among the other models with the highest accuracy (0.9764), in addition to precision (0.9747) and recall (0.9503) scores. Feature importance analysis is conducted by using permutation importance. It identifies interest, credit type, interest rate spread, and upfront charges as the four most significant features of loan default. These findings provide useful information for financial institutions aiding risk assessment and decision-making to mitigate potential losses
Multi-objective and Multi-disciplinary Optimization of Vertical Axis Wind Turbine Blades
No full textThe demand for renewable energy is increasing, leading to more research on Vertical Axis Wind Turbines (VAWTs) because they can be used in cities and rural areas. This review looks at the latest methods for improving the design of VAWT blades, to summarize the advancements in the multi-objective optimization and to highlight the interdisciplinary nature of the research, encompassing aerodynamics, materials science, and structural mechanics. It examines important factors like how air flows around the blades, their strength, and the materials used. The review also identifies gaps in current research and suggests future study directions. The goal is to enhance VAWT performance for better energy capture and use in various environments, especially where wind speeds are low. This research is crucial for advancing VAWT technology and making renewable energy more accessible and efficient. Aerodynamic performance remains a key focus, with computational fluid dynamic being the dominant method used for analysis. A few of the literature review findings are AI and machine learning are valuable tools for optimization but require validation. The structural and material innovations are advancing but need to be integrated with aerodynamic studies. Sustainable materials and manufacturing techniques are underexplored in the context of multi-objective optimization.
Manuscript received: 6 Jun 2025 | Revised: 20 Jul 2025 | Accepted: 11 Aug 2025 | Published: 30 Nov 202