Multimedia University

The purpose of this study is to analyze the convergence dynamics in renewable energy innovation in 25 OECD countries during the period 1990-2022. The research makes a unique contribution to the literature by examining renewable energy innovation in two different dimensions: the number of patents per capita (RPPC) and economic intensity (RPINT) indicators. Stochastic, beta, and sigma convergence tests were applied in a comprehensive approach. The results show a clear trend toward convergence among OECD countries in renewable energy innovation, particularly in terms of innovation per capita. Beta convergence analyses show that this process between countries is influenced by structural factors such as renewable energy consumption, ecological footprint, real GDP per capita, globalization, and financial development. On the other hand, it has been found that countries starting from low levels have recorded faster growth rates in innovation, supported by knowledge and technology transfer. Policy recommendations emphasize increasing public-private partnerships, research and development (R&D) investments, supporting multinational partnerships, and developing country-specific strategies.

The rapid growth of the Internet and the increasing reliance on digital systems have significantly expanded the global digital footprint, creating new challenges for cybersecurity. Artificial Intelligence (AI) technologies, particularly Machine Learning (ML) and Deep Learning (DL), have become central to addressing these challenges by enabling the automation of complex and data-intensive tasks across antivirus solutions, intrusion prevention systems, threat intelligence platforms, and email security tools. While these technologies provide high levels of accuracy in detecting anomalies, malware, and other forms of malicious activity, they are often criticized for operating as “black-box” systems. The lack of interpretability in their decision-making processes limits the ability of cybersecurity professionals to fully understand, validate, and trust the outcomes of AI-driven models, thereby restricting their practical adoption in high-stakes environments. To mitigate these limitations, Explainable Artificial Intelligence (XAI) has emerged as a promising paradigm that aims to make AI outputs transparent, interpretable, and actionable. By providing human-understandable explanations of automated decisions, XAI can bridge the gap between technical performance and practitioner usability, enabling analysts to make informed decisions, improve incident response, and strengthen organizational resilience against both known and emerging threats. This paper reviews recent state-of-the-art developments in XAI for cybersecurity, with a particular emphasis on anomaly detection a critical area for identifying insider threats, zero-day exploits, and atypical system behavior. The review follows a structured literature analysis of peer-reviewed studies published between 2018 and 2025, identified through systematic searches in major academic databases including IEEE Xplore, Scopus, Web of Science, and ACM Digital Library. After applying predefined inclusion and exclusion criteria focused on XAI applications in cybersecurity, 53 relevant studies were analysed to synthesize methodological trends, application domains, and evaluation practices. Drawing on these findings, the paper consolidates fragmented research contributions, identifies current gaps, and provides recommendations for advancing the design and adoption of explainable, trustworthy AI systems in cybersecurity. The analysis further highlights a critical deployment challenge: the integration of explainability mechanisms often introduces trade-offs between predictive accuracy, computational efficiency, and real-time scalability factors that are essential in operational cybersecurity environments.

This study addresses the critical challenge of fetal distress identification, with either maternal or neonatal death coming first in this case. Manual CTG interpretation, being subjective, leads to disadvantages, and a high degree of inter-observer variability may result in misdiagnosis or late clinical intervention with detrimental consequences. Thus, our research fills the gap wherein there has been very little comprehensive and rigorous comparative analysis of a large number of machine learning models. We systematically studied a wide and diverse range of classifiers in our comparison, including tree-based ensembles such as XGBoost, Random Forest, CatBoost, and LightGBM, but also Gradient Boosting, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Naive Bayes, and Convolutional Neural Networks (CNN). These models were trained and tested on a very large and open-source CTG dataset to validate their predictive power. Our findings reveal that the XGBoost model demonstrated superior performance with an impressive accuracy of 99.04%, while CatBoost, LightGBM, and Random Forest, which had intense predictive powers well above traditional diagnostic means. The above-mentioned accuracy-driven models have found their aptitude in capturing highly complicated nonlinear patterns occurring in CTG data and therefore hold a promising prospect to be developed and applied toward a large-scale and automated diagnostic aid. The successful implementation of these novel techniques would have huge potential for improving the quality of prenatal care and clinical decision-making in resource-poor areas, where expert supervision may be widely scarce.

This research explores how international students in Malaysian higher education develop digital literacy, the barriers that impede such development, and strategies that may enhance institutional support. To understand their experiences using digital tools within an academic context, semistructured interviews were conducted with international students in a qualitative design. The results showed that confidence develops through repeated use and structured support, and barriers include complexities of software applications, integration challenges across different platforms, and limited chances for hands-on training. Participants called for targeted workshops, peer mentorship, improved resource access, and early integration into the curriculum. This paper offers practical suggestions useful to universities seeking to foster international students' adjustment to digitally mediated learning environments.

The COVID-19 pandemic has significantly disrupted Malaysia's tourism industry, as well as highlighted many vulnerabilities in its resilience and governance. While government interventions were mounted, undetermined challenges continued to be faced. While post-pandemic recovery has been one of the most commonly analyzed issues, the study of the operational and strategic obstacles faced by Malaysian tourism practitioners remains very limited. Using a summative content analysis of tourism reports and academic literature, supplemented by in-depth interviews, this study uncovers the strategic post-pandemic challenges in Malaysia's tourism and hospitality industry from the industry's perspective. Our findings offer practical insights for policymakers and industry stakeholders in the development of effective policies, operational strategies, and travel packages that strengthen the 'Malaysia Truly Asia' image, enhance resilience, and ensure a sustained competitiveness again in this region.

This study explores the enhancement of tribological properties in epoxy composites reinforced with pineapple leaf fibre (PALF) ( Ananas comosus ) using optimization and machine learning techniques. The rationale behind this research is to develop sustainable, high-performance materials for industrial applications by utilizing bio-based fibers, which offer environmental benefits. Response Surface Methodology (RSM) with Central Composite Design (CCD) was used, involving 24 runs. Key factors included fiber weight percentage (0 wt% pure epoxy and 30 wt% PALF/epoxy), applied load (10 N and 30 N), sliding velocity (0.7 mm/s and 2 mm/s), and sliding distance (500 mm and 1500 mm). X-ray diffraction (XRD) analysis showed that pure epoxy exhibited an amorphous structure, while the 30 wt% PALF/epoxy composite displayed a peak shift to 23.9°, indicating increased crystallinity. Analysis of Variance (ANOVA) revealed that increasing fiber content improved tribological properties. The composite with 30 wt% PALF at optimized condition showed an 94 % reduction in wear rate and a 78 % decrease in the coefficient of friction (COF), with R² values of 0.9135 for wear rate and 0.9203 for COF. Three machine learning models—linear regression, gradient boosting, and Gaussian Process (GP) regression—were employed for predictions. The GP model outperformed the others, achieving R² values above 0.98. SHAP analysis identified fiber weight percentage as the most influential factor on wear rate. This study demonstrates that experimental, statistical, and machine learning methods optimize tribological properties of bio-composites for industrial applications. • Pineapple leaf fiber (PALF) reinforced epoxy composites made by hand lay-up and cured for fabrication. • X-ray Diffraction (XRD) showed a shift in the main peak to 23.9° for 30 wt.% PALF/epoxy sample. • The experiment used Response Surface Methodology (RSM) with Central Composite Design (CCD), considering fiber ratio. • Increasing the fiber content to 30 wt.% led to a 94% reduction in wear rate and a 78% decrease in friction coefficient. • Gaussian Process (GP) regression showed high predictive accuracy with a coefficient of determination (R²) > 0.98.

This study develops and evaluates a comprehensive deep-learning framework for early detection of Autism Spectrum Disorder (ASD) through facial image analysis. Five state-of-the-art convolutional neural network (CNN) architectures, VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, were systematically assessed using a balanced dataset of 5,000 images (2,500 ASD, 2,500 non-ASD). Transfer learning and data augmentation enhanced model generalization. VGG19 achieved the highest overall accuracy (77.89%) and F1-score (0.7962), ResNet50 attained the best precision (82.53%), and InceptionV3 produced the highest recall (99.67%), indicating strong screening potential. The findings confirm that deep CNNs can capture subtle facial morphological cues linked to ASD, supporting their feasibility as non-invasive diagnostic tools. This work provides a benchmark for future multimodal, explainable, and clinically validated AI systems for autism detection.