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Perceptions of Language Teachers and Parents on Writing Module and Online Learning Platforms in Evolving Students' Writing Skills
No full textTechnology resources play an imperative role in the education system in this digital era. The Ministry of Education of Malaysia (MOE), through Shift 7 from the Malaysian Education Blueprint, elucidated the need to apply Information and Communication Technology (ICT) to improve the quality of the learning system. This present study explores the perceptions of language teachers and parents concerning the usefulness of incorporating writing modules, online learning platforms and mobile learning to develop upper secondary students' writing skills at Malaysian National Secondary Schools. It was a qualitative research design in which the data were collected through semi-structured interviews with the participants. The findings revealed that language teachers and parents considered writing modules, online learning platforms, and mobile learning environments to be vital resources for developing students' writing skills. The language teachers and parents viewed the approachability of technology resources, their interactive features, and their capability to provide immediate feedback for students in developing their writing skills. Generally, this study emphasised the usefulness of writing modules, online learning platforms and mobile learning as additional resources. This study also indicated the implication of considering technologies from both language teachers and parents to develop students' writing skills. In addition, this study emphasised that future researchers could use appropriate instructional models to create writing modules and proper behaviour theories to design online writing activities. This will motivate the students to practise writing skills via technology resources
The Effects of User-Generated and Influencer-Generated Content on Beauty Product Purchases: Navigating Scepticism in Malaysia
No full textThis study investigates the influence of User-Generated Content (UGC) and Influencer-Generated Content (IGC) on consumer purchase decisions for beauty products in Malaysia, with particular focus on the moderating role of scepticism. As social media platforms become a crucial space for product discovery and consumer decision-making, trustworthiness, expertise, and attractiveness emerge as critical factors shaping consumer behaviour. This research examines how these content attributes impact purchase intentions and explores how scepticism moderates the effectiveness of UGC and IGC. A survey was conducted with 361 respondents who are active social media users, utilizing a five-point Likert scale to measure perceived trustworthiness, expertise, and attractiveness of both UGC and IGC. The findings reveal that trustworthiness is the most significant predictor of purchase decisions, with scepticism weakening the effectiveness of IGC but having little impact on UGC, which naturally maintains consumer trust due to its authentic, peer-driven nature. Expertise, while important in both UGC and IGC contexts, has a stronger effect in influencer marketing, particularly for high-involvement products. In contrast, attractiveness was found to be less influential in driving purchase decisions, especially in the context of IGC. This study contributes to the understanding of digital marketing strategies, highlighting the need for marketers to focus on fostering trust, transparency, and expertise to engage sceptical consumers effectively. These insights offer valuable implications for beauty brands seeking to optimize their content strategies on social media platforms
Machine Learning Approaches for Detecting Vine Diseases: A Comparative Analysis
No full textThis study investigates the classification of vine leaf diseases using convolutional neural networks (CNNs), focusing on three major diseases: powdery mildew, caused by fungus Uncinula necator, Red Blotches associated with pathogens such as Phomopsis viticola, Grapevine Leafroll Disease and leafroll associated Grape -linked virus (GLRaV). Accurate diagnosis of these high-risk diseases is critical to vine health and yields. We evaluated the performance of three CNN algorithms—MobileNetV2, ResNet50, and VGG16 —by comparing their training and validation accuracies, as well as loss over ten seasons. MobileNetV2 emerged as the most robust model, exhibiting high accuracy and low loss, indicating strong generalizability. ResNet50 showed a steady increase in accuracy, but with high variability, indicating that probabilities with complex models or extended training requirements VGG16 showed notable improvements in training accuracy but encountered difficulties it involves consistency during validation, which means overfitting. Although MobileNetV2 proved to be the most efficient for this task, our analysis suggests that replicating ResNet50 and VGG16 can improve their performance. Future research will explore longer training times, larger data sets, and other methods to further improve the generalizability and robustness of this model This work highlights the ability of CNN to detect vine leaves emphasize early diseases and provide a strategy for sustainable viticultural practices
Improving Code Effectiveness Through Refactoring: A Case Study
No full textSoftware refactoring is a crucial practice in modern software development methodologies, such as Agile and DevOps, as it enables teams to iteratively improve and evolve their codebases while minimizing the risk of introducing bugs or regressions. It fosters a culture of continuous improvement and code hygiene, ultimately leading to more robust, maintainable, and scalable software systems. However, research examining the impact of refactoring on code effectiveness is scarce. This study, therefore, seeks to investigate the impact of refactoring methods on the code’s effectiveness. The study was carried out in four distinct phases: refactoring methods selection, case study selection, software metric selection for evaluating the effectiveness of the code, and refactoring methods implementation. The five most prevalent refactoring methods (Extract Subclass, Extract Class, Introduce Parameter Object, Extract Method, and Move Method) were chosen and implemented in the jHotDraw case study. The refactoring methods were implemented 86 times across five experiments in the jHotDraw case study. The results indicate that Extract Subclass, Extract Class, and Introduce Parameter Object have a significant positive impact on code effectiveness, while Extract Method and Move Method do not affect code effectiveness. Practitioners and software designers can utilize this knowledge to make informed assessments regarding refactoring methods and produce software systems that are more reliable and effective
Societies’ Funds Management System Using Blockchain
No full textLack of transparency of the funds and lack of immutability of the funds’ records are large problems today, especially in the societies’ funds. Leveraging the Ethereum Blockchain, the system ensures complete transparency and security in recording and accessing financial transactions for any society. Advanced encryption and blockchain consensus protocols guarantee data privacy and resilience against fraud or tampering. The information entered in the system helps the treasurer to effectively manage funds and accurately and transparently pass appropriate financial transactions. This project meets the challenges of transparency and immutability in society’s fund records, providing an assurance system and promoting growth for the community's financial integrity. According to the iterative development model, the major components are user login, user management, payment status monitor, and funds history. A solution for a live transparent platform for their team members to have access to their financial data to build trust and get them involved. The system organizes the administrative tasks of bookkeeping, enabling the treasurer to be financially secure while maintaining trackable transactions. The key module of this system includes user login, user management, payment status tracking, and funds history. In the end, this project solves the stated problems by providing a safe path for growth and financial transparency
Optimised Heterogeneous Handover in Mobile IPV6 Using Enhanced Predictive Fast Proxy with Media Independent Handover (MIH) Support
No full textIn wireless networks, handover performance is essential for enabling real-time traffic applications. Long handover delays make it impossible for a Mobile Node (MN) to send and receive data packets, which is very undesirable for real-time applications like video conferencing and VoIP. Therefore, in order to guarantee better handover performance, decreasing handover duration is crucial. The Internet Engineering Task Force (IETF) has standardized Fast Proxy Mobile IPv6 (FPMIPv6) as an enhancement to the novel Proxy Mobile IPv6 (PMIPv6) to attain better handover performance. FPMIPv6 functions in two modes: predictive and reactive, using a link-layer triggering mechanism. The predictive mode uses link-layer triggers to improve FPMIPv6's handover performance. However, FPMIPv6 experiences packet loss, signalling overhead and unable to manage heterogeneous handovers effectively because it needs a unified Layer 2 triggering mechanism which could result in handover accomplishment either early or late. Consequently, this research, provide an incorporation between FPMPV6 with MIH by expanding its current standard services. In addition, a new predictive handover architecture that generate timely link triggers using information from adjacent network was implemented, enabling crucial handover operations to finished prior to the present link deteriorating. And used piggyback technique to reduce signalling overhead. Performance analysis using simulations indicates the pro-FPMIPv6-MIH achieved improved handover performance, particularly in decreasing handover delay, packet loss and signalling overhead
Review on Secure and Efficient IoT-based Healthcare System with the Integration of Machine Learning and Firewalls
No full textThe integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review has focused on how the integration of IoT with Machine Learning (ML) and stringent security measures tackle the challenging situation of data privacy and cyber threats in healthcare. Current methodologies point toward how essential advanced sensors, cloud computing, and wireless technologies for IoT-based healthcare systems necessary to secure patient data. patient record kept in files and now forward to the cloud database system so that in any case of emergency it could access and keep safe from cyber-attacks, and no one can breach the security of data only authorized user can access. To achieve this security, concern firewalls, encryption technologies are used. These protection systems are applied to block unauthorized access, protect data communication channels, and make private patient information confidential always. IoT-based, ML-enabled systems perform way better in real-time monitoring, predictive analysis, and personalized treatment in contrast with conventional healthcare strategies. This discussion delineates the need for implementation of firewalls and encryption techniques for data security and patient privacy. This critical review underlines that while IoT truly has enormous potential to change healthcare, it will require continuous innovation and rigorous security protocols to help maximize these benefits
SmartRecruit: A Fuzzy Rule-Based Expert System for Candidate Screening and Ranking
No full textHuman Resource (HR) management plays a pivotal role in organizational success, with recruitment being one of its most critical functions. In recent years, the integration of Artificial Intelligence (AI) into HR processes has gained significant attention, particularly in automating recruitment to enhance efficiency and reduce biases. While AI-driven systems have demonstrated advanced capabilities, many lack adaptability across diverse job roles and often fail to provide transparency in decision-making. This research addresses these limitations by proposing a novel fuzzy A Fuzzy Rule-Based Expert System for Candidate Screening and Ranking (SmartRecruit). The system evaluates candidates based on key parameters such as skills, educational qualifications (e.g., CGPA), and work experience, offering an efficient, unbiased and transparent approach to hiring.
Manuscript received: 9 Apr 2025 | Revised: 12 Jun 2025 | Accepted: 30 Jun 2025 | Published: 30 Nov 202
A Narrative Review of Data Mesh Architecture Principles and Implementation Outcomes
No full textCentralised data architectures often create operational bottlenecks that limit organisational agility. Data Mesh offers a distributed alternative through domain ownership and federated governance. This narrative review synthesises 52 sources published between 2001 and 2024, examining the evolution from traditional data architectures to Data Mesh implementations across financial services, healthcare, e-commerce, and technology sectors. The review traces the progression from centralised data warehouses through distributed computing frameworks to Data Mesh's emergence, identifying four foundational principles domain-oriented decentralisation, data as a product, self-serve infrastructure, and federated governance. Analysis of recent implementation studies reveals mixed outcomes. Successful adoptions demonstrate improved domain autonomy and reduced central bottlenecks. However, multiple case reports significant coordination complexity and extended implementation timelines, with transformations requiring substantial investments in platform engineering. Consistent challenges emerge, including skill gaps in domain teams transitioning to data ownership, policy conflicts in federated governance structures, infrastructure investments that exceed traditional architectures, and cultural resistance to distributed accountability. Implementation success correlates with existing DevOps maturity, sustained executive sponsorship, phased adoption approaches, and robust metadata management capabilities. The review identifies critical research gaps in standardised success metrics, quantitative failure analysis, privacy-preserving techniques for federated environments, and long-term sustainability assessment. Based on the analysed cases, Data Mesh appears most suitable for large enterprises with diverse data domains and established platform engineering capabilities. Smaller organisations may find centralised approaches more appropriate given the complexity and resource requirements of distributed architectures. This synthesis provides practitioners with evidence-based insights while highlighting priorities for future research.
Manuscript received: 9 Jun 2025 | Revised: 24 Jul 2025 | Accepted: 30 Jul 2025 | Published: 30 Nov 202
Intrusive and Non-Intrusive Techniques for Blood Sugar Measurement: A Practical Review
No full textMeasurement of sugar levels in blood is the main means of diagnosing for diabetes and other complications of blood sugar levels. The established principle of the methodology for this is the extraction of blood from the subject and submission of the blood sample to chemical tests that determine the presence of substances, such as glucose, that indicate blood sugar levels. This principle is inherently intrusive; R&D into methods with this principle has the goal of improving convenience and minimizing amount of sampling needed, while maintaining reliable accuracy. There is also R&D into developing non-intrusive methods that estimate blood sugar levels without blood sampling, with the aim of producing results that can be comparable with intrusive methods. The common goal of either approach is making blood sugar measurement more convenient for as many people as possible. At this time of writing, non-intrusive methods have yet to replace the gold standard. A breakthrough in this matter can facilitate the implementation of machine learning in interpreting blood sugar levels.
Manuscript received: 7 Aug 2025 | Revised: 11 Sep 2025 | Accepted: 16 Sep 2025 | Published: 30 Nov 202