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Halal Technocracy and Certification Governing: Social Media as Platform for Knowledge Dissemination
No full textIn pursuit of efficient governance, the Brunei Government adopted e-government systems in the early 2000s, later formalising its digital transformation through the Brunei Darussalam Digital Economy Blueprint 2020. This shift coincided with increased internet accessibility and the emergence of a smartphone-equipped generation that integrates digital media into everyday life. Against this backdrop, this study explores the role of social media in disseminating halal knowledge and governance, applying an Islamised Foucauldian governmentality theoretical framework. We analysed the typologies of halal information shared by Brunei’s Halal Food Control Division (HFCD) across social media platforms between 2011 and 2018, targeting diverse audience segments. Our findings reveal a nuanced spectrum: significant knowledge asymmetries emerged from the dissemination of impractical or irrelevant information, prompting critical reflection on the nature of “good” information. However, only limited evidence of halal knowledge co-creation and codifiable knowledge transfer was observed. We conclude that the effective deployment of social media for halal governance hinges on the distribution of contextually appropriate information, yet this remains constrained by the limited social and digital reflexivity of governing certification bodies.
Personalized Drug Recommendation System Using Wasserstein Auto-encoders and Adverse Drug Reaction Detection with Weighted Feed Forward Neural Network (WAES-ADR) in Healthcare
No full textIn recent years, the use of deep learning approaches in healthcare has yielded promising results in a variety of fields, most notably in the detection of adverse drug reactions (ADRs) and drug recommendations. This paper promises a breakthrough in this field by using Wasserstein autoencoders (WAEs) for personalized medicine recommendation and ADR detection. WAEs' capacity to manage complex data distributions and develop meaningful latent representations makes them ideal for modeling heterogeneous healthcare data. This study intends to improvise the precision and efficiency of drug recommendation systems while also improving patient safety by combining WAEs and early ADR detection strategies. Previous research has used social media data for pharmacovigilance, drug repositioning, and other machine learning algorithms to detect ADRs. However, our proposed methodology offers a novel perspective by combining Wasserstein autoencoders with ADR detection methods, outperforming existing approaches. Preliminary results show that the proposed methodology surpasses current methodologies, with much greater accuracy in ADR identification and medicine recommendation. In particular, the proposed model achieves an ADR detection accuracy of 96.04%, which is 15% higher than the most sophisticated techniques, with considerable improvements in precision, recall, and accuracy metrics. In conclusion, our study seeks to develop customized medicine in healthcare, perhaps leading to dramatically improved patient outcomes and safety
Exploring the Relationship Between Academic Achievement and ChatGPT Usage: A Survey of Higher Education Students in Malaysia
No full textStudents' academic achievement is caused by several factors which include cognitive capability, study habits, instructor effectiveness, family support, socio-economic status, and access to modern learning technologies like ChatGPT. Since its introduction, studies have suggested that ChatGPT has significantly impacted students’ academic performance. This study aims to explore whether students who perform well academically in higher education are more likely to use ChatGPT to enhance their studies and to gather their opinions on this learning tool. A survey was conducted with forty-one students with excellent academic performance. The results of this study show that most students with high academic achievement view ChatGPT as a valuable learning tool, with 82.9% states that it helps them understand complex topics and 73.2% find it useful for assignments. 51.2% use ChatGPT for academic purposes and 39% use it for both academic and non-academic tasks which indicate its broader utility. 80.5% of students indicate it is beneficial for their studies even though only 49.8% trust its accuracy. 73.4% of students acknowledge the risk of misuse such as cheating even though 53.7% still believe current protections are sufficient. Finally, students suggest improvements in ChatGPT such as the ability to provide more accurate responses and handle complex academic queries. In summary, the study suggests that students who perform well academically do use ChatGPT to improve their studies and appreciate its benefits. However, they also raise ethical concerns regarding its potential of misuse. For educators, the outcomes of this study will be of great benefit to them as the results highlight the need for them to allow students to use ChatGPT to improve their academic performance and at the same time tackling potential unethical issues such as misuse and cheating
Implementation of Conjugate Gradient Method for Estimating Inflation Rate in Malaysia
No full textOptimization methods are valuable for making decisions and identifying the most suitable alternative based on a given objective function. One of the mathematical optimization methods is Conjugate Gradient (CG) method which is commonly used to solve large-scale unconstrained optimization systems with less storage space. Recently, various optimization methods have been studied and used in economics estimating. However, just a few studies have predicted inflation rate using modified CG method. Random initial points are tested on New Three-Terms (NTT) which are modified Rivaie-Mustafa-Ismail-Leong (RMIL+) and Umar Mustapha Waziri (UMW) CG method with ten optimization test functions suggested by Andrei using MATLAB. NOI and CPU time obtained are compared by performance ratio of Dolan and Moré. NTT CG method stands out as the best performance. Data set of year 2010 until 2022 from Department of Statistics Malaysia (DOSM) is transformed into optimization problems to be solved. Estimated results of Least Square Conjugate Gradient (LSCG) are based on NTT CG and LS both for linear and quadratic models. Relative errors for LSCG, Least Square (LS) and Trendline Method are calculated. Linear LS is shown as the most suitable to estimator in inflation rate in Malaysia as it yields the least relative error compatible with the linear LSCG and Trendline Method that produce similar relative error in estimating inflation rate in Malaysia
Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, and Future Trends
No full textThe necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of each technique are listed and explained briefly. In addition, the actual application’s results are also presented in this paper. To evaluate the performance of the techniques, some of the important datasets that are used in evaluating recommender systems are also discussed along with measurement metrics to determine the effectiveness of technique. Example metrics used are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and so on. This paper synthesizes existing research to evaluate the advantages and limitations of diverse recommendation techniques, which aims to give suggestion on how to improve the design of movie recommendation systems so that the performance of the technique can be improved
Effects of Composition and Processing on the Properties of Sn-3Zn-4Bi and Sn-Ag-Cu Solder Alloys for Electronic Packaging
No full textThe mechanical properties of lead-free solder alloys are critical for ensuring the reliability of electronic packaging, with shear strength and hardness being particularly important as electronic devices become smaller and interconnection densities increase. Thermal fluctuations and external mechanical impacts further intensify shear stresses on solder joints, raising concerns about long-term performance. In this study, the shear stress behavior of Sn-Ag-Cu and Sn-3Zn-4Bi solder joints was examined under different reflow temperatures. Sn-Ag-Cu, a widely researched lead-free solder, demonstrated strong resistance to high stress levels, reinforcing its suitability for high-reliability applications; however, its relatively high melting temperature (~221 °C) limits its use in low-temperature reflow processes. By comparison, Sn-3Zn-4Bi solder, with a melting temperature only ~12 °C higher than eutectic SnPb solder, showed potential for low-temperature soldering, while also exhibiting higher microhardness values than Sn-Ag-Cu, suggesting improved structural robustness. Despite these advantages, concerns remain regarding its compatibility with copper substrates, where interfacial reactions may affect joint integrity. Overall, the results suggest that Sn-Ag-Cu is preferable for applications requiring high strength and thermal resistance, whereas Sn-3Zn-4Bi offers notable benefits for low-temperature processing, provided substrate interactions are properly managed.
Manuscript received: 15 Aug 2025 | Revised: 30 Sep 2025 | Accepted: 4 Oct 2025 | Published: 30 Nov 202
Numerical Integration Approach for Nonlinear Differential Equation in Growth Modelling
No full textThe nonlinear ordinary differential equation (ODE) is a common mathematical model for real-world problems. However, its analytical solution is hard to find and may not exist due to the nonlinear and complex structures. Thus, an approximate method is usually employed in mathematical modelling to obtain its solution. This study applies numerical integration techniques, namely Gaussian quadrature and Simpson’s rule methods, to solve nonlinear ODE, which is a hyperbolic growth model. We first discuss the ODE model and then substitute its exact solution model into the ODE model to obtain the model’s numerical solution using numerical integration approaches. Next, we aim to predict the solution of the nonlinear growth model by proposing two linear models and integrating them iteratively. We introduce a least square optimization problem and derive a set of first-order necessary conditions for estimating the model parameter optimally. A gradient descent method is employed to iterate and update the solution of the linear model. The numerical integration techniques are efficient, while the proposed method has proved to be an alternative approach to handling nonlinear ODEs, especially for a nonlinear growth model, since the optimal linear model solution satisfactorily approximates the growth model solution with a small mean square error value.
Manuscript received:3 Apr 2025 | Revised: 15 May 2025 | Accepted: 22 May 2025 | Published: 30 Jul 202
Fuzzy Frontiers in Rift Valley Fever Virus Control: Exploring the Dynamics of Transmission and Treatment
No full textRift Valley Fever (RVF) is a mosquito-borne zoonotic viral disease that poses significant health threats to both human and animal populations across Africa and parts of the Middle East. Traditional epidemiological models often assume precise parameter values, which may not accurately reflect the inherent uncertainty in real-world disease transmission. To address this, we propose a novel stochastic and fuzzy logic-based Susceptible-Infected-Susceptible (SIS) model to analyze the spread of RVF under uncertain conditions. The model incorporates fuzziness in transmission and recovery rates using fuzzy set theory. Equilibrium points are analytically derived, and stability analysis is performed to explore the long-term dynamics of the disease. We compute and compare the fuzzy expectation of infected individuals with the classical expectation to assess the effect of parameter uncertainty. The basic reproduction number is calculated for both strictly increasing and strictly decreasing transmission functions, and their impacts on transcritical and backward bifurcations are thoroughly investigated. Furthermore, we incorporate optimal control strategies, including vaccination and vector control, within the fuzzy framework and evaluate how uncertainty influences their effectiveness. Numerical simulations validate the analytical results and illustrate the temporal progression of the disease. Our findings emphasize that integrating fuzzy logic with stochastic modeling provides a more realistic and robust approach to understanding and controlling RVF than conventional deterministic models, offering valuable insights for public health intervention planning under uncertainty.
Manuscript received:8 Apr 2025 | Revised: 2 Jun 2025 | Accepted: 19 Jun 2025 | Published: 30 Jul 202
Identifying Potentially Illicit Money Laundering and Terrorism Financing Transactions Through Machine Learning Techniques : DOI: https://doi.org/10.33093/ijomfa.2025.6.2.9
No full textFinancial institutions worldwide face significant challenges in detecting and preventing illicit financial activities, such as money laundering and terrorism financing. Traditional rule-based methods often generate high false positive rates, increasing manual verification efforts and higher operational costs. This research explores machine learning techniques to enhance the detection of suspicious transactions. Several algorithms, including K-Nearest Neighbors, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, and Naïve Bayes, are applied and evaluated using a dataset from a financial institution. After a comprehensive performance assessment, the Random Forest model is the most effective, exhibiting the highest accuracy of 0.9333 in identifying suspicious transactions while minimising false positives. These findings highlight the potential of integrating machine learning into financial crime prevention protocols. It serves as a guide for practitioners to predict suspicious transactions in financial institutions based on previous patterns of transactions. It also helps financial institutions to reduce compliance costs, which are typically higher than those of standard rule-based systems. However, this work presents a suspicious transaction prediction paradigm from prior behaviour with no transparency in features and with high accuracy and zero false positives, as with the Financial Action Task Force (FATF) promotion of new Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) initiatives
The Impact of Social Media on Consumer Satisfaction: The Role of Usage Frequency, Brand Image, Brand Identification, and Content Quality: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.10
No full textIn recent years, consumers have widely utilised social media to be particularly involved with interested brands via online platforms, share their opinions, and easily explore brand-related information. Hence, this study examined the impacts of social media, from the perspectives of usage frequency, brand image, brand identity, and brand content, on consumer satisfaction in China based on social identity theory and attachment theory. The research applied a quantitative method via an online questionnaire, and 302 valid responses were received. After the data was collected and a research model was built, multiple regression analysis was utilised to analyse the data and prove the hypothesis. As such, the findings showed that brand content quality significantly positively affected consumer satisfaction, while usage frequency, brand image, and brand identity had insignificant relationships to consumer satisfaction. The study's results contributed to the existing related literature by further analysing the impacts of usage frequency on consumer satisfaction and aiming to provide marketers with feasible strategies to help improve consumer satisfaction