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Empowering Malaysia's Islamic Social Finance System: Integrating Zakat Management Through Financial Technology
No full textState Islamic Religious Councils (SIRCs) play a pivotal role in managing and regulating Islamic affairs in Malaysia. According to the Second Schedule, Ninth Schedule of the Federal Constitution, SIRCs are constitutionally empowered to manage zakat collection and distribution. As the nation strives toward financial inclusion and economic sustainability, Islamic social finance (ISF) emerges as a critical pillar. The national digitalisation agenda facilitates these ambitions. SIRCs are responsible for ensuring the socio-economic development of the asnaf (eligible zakat recipients). However, there remains an inconsistency in zakat and waqf management across different states in Malaysia. Legally, zakat falls under state jurisdiction, as enshrined in the Constitution, resulting in non-uniform systems nationwide. Moreover, this study examines the potential of financial technology (FinTech) in zakat operations. Using doctrinal research methodology and secondary data from reputable sources, this study proposes strategic approaches to strengthen ISF systems through digital integration and advocates for a harmonised legal framework. The findings aim to contribute to the development of a more robust Islamic social finance system in Malaysia
How Words are Formed? A Case Study of Morphological Integration of Malay Words in Nyonya Cuisine
No full textThe Malay language plays a vital role in the morphological integration of Baba Malay in Nyonya cuisine, a signature dish of Baba Nyonya (traders who travelled to the Strait of Malacca in the 15th and 17th century from southern China, Fujian and intermarried with locals). The Malay language inspiration in Nyonya cuisine has yet to be investigated systematically. This qualitative study examines the morphological processes in the Malay language that influence the linguistic expansions of the Baba Malay. The study proved that integrating the Malay language helped to expand Nyonya cuisine nationwide. Baba Malay (in Melaka) is more influenced by standard Malay, particularly where the lexicon is concerned. The study grasps morphological integration in Nyonya cuisine, which results in cultural crossbreeding by the movement of the Baba Nyonya into the Malay language, geographical proximity, and political aspects. Th
Virtual Socialisation among Malaysian Animal Crossing Players During Movement Control Order
No full textDuring the COVID-19 lockdown of Movement Control Order (MCO), many Malaysians reportedly turned to video games for socialisation purposes. Drawing upon the sociocultural framework of media ecology, this study aims to explore in-depth how players engage in such virtual socialisation. Against the backdrop of the MCO, this study argues that Malaysians could perform socially meaningful actions through video games, just like they were in the real world, to address their social deprivation. The method employed is a qualitative textual analysis of paratexts published in a local gaming Facebook group, focusing on Nintendo’s Animal Crossing: New Horizons (2020). The findings indicated that Malaysian players sought companionships through reciprocal gaming interactions during the lockdown measures by playing as a single player, multiplayer, and community. In the gaming community, they built networks, interpersonal connections, and resources of mutual social support to enrich their gameplay. The findings highlighted the significance of virtual socialisation through the interactive medium – an effective approach to cushion the isolating effects of the MCO period. The study is relevant in showcasing the role played by virtual socialisation and gaming communities for the Sustainable Development Goals of SDG3 Good Health and Well-being and SDG16 of Peace, Justice, and Strong Institutions
Does Gender Matter? The Influence of Tolerance towards Homosexuality and Attitudes towards LGBTQ+ Advertising and Brands among Gen Z in Vietnam
No full textThe LGBTQ customer segment represents a market with substantial purchasing power and is increasingly gaining attention from businesses worldwide, accompanied by a growing presence of homosexual individuals in advertising campaigns. Advertisements featuring LGBTQ-related content have also become more prevalent across various media platforms in Vietnam. Generation Z (Gen Z) has emerged as a significant consumer group for brands targeting the two major cities, Ho Chi Minh City and Hanoi. This study investigates the impact of homosexuality tolerance on Gen Z's attitudes towards LGBTQ-themed advertisements and brands. Data were analysed using structural equation modelling (SEM) and independent samples t-tests to identify relationships among variables and to examine gender-based differences. The findings reveal that participants with higher tolerance levels exhibited more positive attitudes towards advertisements and brands. Furthermore, attitudes towards advertisements significantly influenced attitudes towards the brands. Notably, the study found no significant gender differences among Gen Z consumers, suggesting that this generation's evaluations and perceptions are primarily centred on core values and brand authenticity. These findings offer important implications for marketers and brands seeking to connect with Gen Z consumers in Vietnam while also contributing to the underexplored field of consumer behaviour research in the Vietnamese context
Hyperledger Fabric Blockchain for Securing the Edge Internet of Things: A Review
No full textLife has become more convenient, efficient, and productive in aspects like homes, healthcare, and other businesses’ due to applied IoT. Nonetheless, the proliferation of IoT has led to enormous data production, which has presented daunting tasks of providing sound protective security solutions. It is crucial to address the above challenges in order to protect the data assets in IoT systems. This work deals with the concern on how to extend Hyperledger Fabric to IoT, this being a very crucial aspect in allowing for secure techniques in the collection, storage and sharing of data. Hyperledger Fabric offers advanced capabilities of smart contract and offers authorized and conditional access control, and this feature alone is enough to fulfil the IoT security needs. Therefore, this paper introduces a new solution related to the existing security problems in permissioned blockchain architecture, which is based on a four-tier architecture integrated into the Hyperledger Fabric platform. As for architecture, our proposal divides it into four layers: the application layer, the blockchain platform layer, the cloud storage layer and the IoT device layer, to tackle the problems of security and efficiency of the whole process. To establish the proposed solution, a data literature review has been carried out to collate the analysis and apply the data from the different studies. This paper shows that the deployment of blockchain technology in IoT environments also optimises IoT systems in terms of security, efficiency, and capacity in terms of IoT applications. As more and more IoT solutions appear and evolve, the usage of block chain technology as a whole and the specific Hyperledger Fabric platform in particular opens the way to overcoming the rather fluid issues of this constantly developing sphere
Machine Learning Model for Assessing Human Well-being Using Brain Wave Activities
No full textThis study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength in distinguishing between optimal and suboptimal well-being states, with no misclassifications between these extremes. ROC curve analysis further confirmed excellent discriminative ability across all three classes, with AUC values ranging from 0.984 to 0.999. The study demonstrates the potential of machine learning in interpreting complex neurophysiological data for personalised health monitoring, potentially enabling real-time assessment and intervention strategies. While promising, the use of synthetic data necessitates further validation with real-world EEG recordings. This research contributes to the growing field of computational neuroscience and its applications in mental health and well-being assessment, potentially paving the way for more objective and personalised mental health interventions. Future directions include incorporating temporal dynamics, accounting for individual variability, and integrating multiple data sources for a more holistic approach to well-being assessment
Implementing Identity-based Signature Schemes for Secure Data Transfer in Cloud Computing Environments
No full textIn this paper, we present the implementation of the Cha-Cheon Identity-Based Signature (IBS) scheme to enhance secure data transfer in cloud computing environments. Cloud computing rely on traditional Public Key Infrastructure (PKI) systems, which is burdened by certificate management infrastructure. The primary focus of this research to simplify key and certificate management by leveraging identity-based elliptic curve cryptography (ECC) within the Cha-Cheon IBS framework. We show that the proposed IBS solution integrates seamlessly with Amazon Web Services (AWS), utilizing services like S3 for secure data storage and KMS for key management. By applying ECC, the Cha-Cheon scheme achieves efficient cryptographic operations with smaller key sizes, resulting in reduced computational overhead, faster key generation, signature creation, and verification times compared to RSA-based systems. We conducted extensive performance evaluations to compare the Cha-Cheon IBS scheme with traditional PKI-based systems. The results demonstrate that our implementation significantly outperforms RSA in terms of key generation, encryption, and signature verification times, especially under increased user loads and data sizes. Moreover, the security analysis confirms the robustness of the Cha-Cheon IBS against key compromise, offering strong resistance to unauthorized access and key revocation issues. The scheme also scales efficiently as the number of users increases, making it ideal for large-scale cloud infrastructures. This research highlights the potential of IBS as a viable alternative to PKI systems, providing a more streamlined and efficient approach to secure data transfers in cloud environments
Improved Accuracy for Heart Disease Diagnosis Using Machine Learning Techniques
No full textThis work primarily focuses on diagnosis of heart disease before explicit visit to the expert doctor. Machine learning based systems have been found useful in medical diagnosis applications because of their ability to learn human like expertise and to utilize acquired knowledge for diagnosis. This work is performs classification of heart disease utilizing subject’s vital parameters. Pathological laboratory results available after testing are not understood by common people and patients have to wait till they visit expert doctors for inference. In this paper, traditional methods like linear regression to various machine learning based systems including back propagation neural network, support vector machine(SVM) and k-nearest neighbor are developed for heart diseases classification. The proposed system transforms sensor inputs to stroke stage classification. With a view to ascertain the efficacy of proposed system, performances of all methods are compared on standard Cleveland database and with similar work. Simulation results show 100 percent correct diagnosis and henceforth robustness of SVM based approaches for test data given
Path To a Healthy Work-Life Balance: Mobile Application for Work and Personal Life Mastery
No full textDrawing insights from diverse organizational methods, this study endeavors to facilitate effective self-development and organization in the face of contemporary demands as solutions for integrating goal tracking, event coordination, and task management within a unified calendar framework in a mobile application. There are three primary objectives of this study: firstly, to explore essential functionalities crucial for addressing the multifaceted challenges of modern life; secondly, to design and develop a mobile application that seamlessly integrates these functionalities; and finally, to evaluate the usability of the application through rigorous testing and feedback mechanisms. Envisaged deliverables include a fully functional mobile application designed to operate on the Android platform. Guided by the principles of agile software development, this study emphasizes continuous improvement and responsiveness to user needs throughout the development process. By adopting an iterative approach, the study aims to ensure the highest quality outcome, thereby enhancing the user experience and maximizing the application's efficacy in promoting work-life balance. Through this comprehensive approach, this study seeks to contribute to the ongoing discourse on work-life balance and offer practical solutions to individuals grappling with the complexities of modern living. By bridging the gap between organizational tools and personal development strategies, this study aspires to empower users in their pursuit of a harmonious and fulfilling lifestyle with a mobile application
Detecting Black Hole Attack using Support Vector Machine with XGBoosting in Mobile Ad-Hoc Networks
No full textMobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One of the most used attacks that can affect MANET performance is the black hole attack. This attack leads to dropping the packets that means these packets will never arrive and it will decrease the delivery ratio for the packets. This attack is a real problem as the sender is not informed that the data has not reached the intended receiver. The main goal of this study is to propose a solution for detecting black hole attacks using Extreme Gradient Boosting (XGBoost) based on a Support Vector Machine (SVM), the system for detection seeks to examine network traffic and spot anomalies by examining node activities. Attacking nodes in black hole situations exhibit specific behavioural traits that set them apart from other nodes, the traffic under a black hole attack is created using an NS-2 simulator to test the effectiveness of this strategy, and the malicious node is then identified based on the classification of the traffic into malicious and non-malicious. The results of the proposed technique outperformed the existing machine learning techniques such as Neural Network (NN), SVM, k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), AdaBoost-SVM in terms of accuracy score as it achieved 98.67% as well as other classification performance measures (Precision, Recall, and F-measure)