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714 research outputs found
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Sacred trust and sustainable future: embedding tawhidic epistemology values in waqf enterprises
No full textThis study investigates the sustainability of waqf-based enterprises through the lens of tawhidic epistemology, positioning waqf not merely as a philanthropic mechanism but also as a divinely inspired economic model grounded in ubudiyyah (servitude to Allah), adl (justice), and intergenerational equity. The primary objective is to identify the underlying drivers that enable waqf enterprises to sustain their operations while fulfilling their Shari’ah-compliant fiduciary and social mission. Adopting a qualitative methodology, this research draws on in-depth semi-structured interviews with 15 waqf managers from various Malaysian states, selected for their leadership roles in managing active waqf entities across diverse institutional settings. Thematic analysis revealed five interdependent sustainability drivers: (1) value-based leadership rooted in taqwa (God-consciousness), (2) collaborative governance that balances Shari’ah and operational needs, (3) stakeholder trust and engagement, (4) adaptive innovation responsive to contemporary challenges, and (5) mission-centricity anchored in amanah (ethical stewardship). These findings contribute to both theoretical and practical discourse by proposing a spiritually anchored sustainability model for waqf enterprises
Instagram marketing and customer satisfaction in the fashion industry: a systematic review
No full textThis study systematically examines the impact of digital marketing strategies on customer satisfaction among Malaysian local fashion small and medium enterprises (SMEs), with particular attention paid to the brands SAOI, Astrid McStella, and Hanya. Utilizing a descriptive research approach and targeting young adult female customers in Malaysia, this study investigates the effects of digital marketing factors, such as social media marketing, on customer satisfaction within local fashion SME. The research framework is grounded in expectation-confirmation theory (ECT), which explains how satisfaction emerges when customers’ perceptions exceed their expectations, and the Technology Acceptance Model (TAM), which clarifies how perceived ease of use and perceived usefulness influence customers’ willingness to engage with digital marketing platforms. This systematic review highlights critical success factors, including user-friendliness, up-to-date content, content quality, and engagement activities on Instagram, which together drive positive satisfaction outcomes. The findings suggest that while Instagram marketing substantially improves satisfaction and brand loyalty, challenges such as limited resources, content restrictions, and inconsistent strategies constrain SMEs’ competitiveness of SMEs. This study contributes by integrating the ECT and TAM, offering a dual theoretical perspective on how Instagram marketing enhances satisfaction and long-term competitiveness in Malaysia’s fashion industry.
Drivers of Islamic financial literacy: the roles of financial behavior, digital literacy, and financial socialization
No full textThis study models the determinants of Islamic financial literacy (IFL) among university students in Malaysia, focusing on the roles of financial behavior, digital literacy, and financial socialization. Using a quantitative design, data were collected from 394 respondents using an online survey. The analysis employed ordinal logistic regression (OLR), and due to a violation of the proportional odds assumption, the generalized ordinal logistic model (GOLM) was applied to improve the model accuracy. The results indicate that financial behavior, digital literacy, and financial socialization are all statistically significant predictors of IFL at the 1% level. Among the demographic variables, age, year of study, field of study, and work experience were significantly associated with high IFL levels, whereas gender was statistically insignificant. Notably, financial socialization emerged as the strongest predictor, underscoring the influence of social and religious environments on financial knowledge acquisition. These findings suggest an urgent need for holistic educational interventions that integrate Shariah-compliant financial education with digital tools and social learning. This study’s insights offer valuable implications for universities and Islamic financial institutions seeking to improve IFL among youth
Intergenerational Cultural Transmission: A Phenomenological Inquiry into the Experiences of an Ilokano Filmmaker
No full textThis study explores Ilokano filmmaking within the broader context of Philippine regional cinema, referring to film practices outside the capital, including Ilocos, Visayas, and Mindanao. Regional films, particularly Ilokano works, reflect local heritage, traditions, and history in the face of modern challenges. Through a phenomenological inquiry into the lived experiences of Ilokano filmmaker Melver Ritz Gomez, the study sought to understand “What is filmmaking to an Ilokano filmmaker?” Data were gathered through in-depth interviews, transcribed, and analysed using the six-step thematic analysis of Lindlof and Taylor (2011). The findings revealed that Ilokano filmmaking is (a) collaborative work, (b) raw and authentic, (c) a medium for storytelling, (d) awareness and learning, (e) cultural preservation, (f) intra-cultural communication, and (g) appreciation of Ilokano culture. Collectively, these themes define filmmaking as a process of intergenerational cultural transmission that preserves and communicates Ilokano identity and values. This study emphasizes that filmmaking extends beyond entertainment and serves as a vital means of cultural expression and continuity. However, the single-participant design of this study limits its generalizability. Future research should include multiple participants or comparative regional analyses to deepen the understanding of regional cinema’s role in cultural preservation and identity formation
Hybrid Filtering for Personalized and Health-Conscious Recipe Recommendations in UniEats
No full textThis research paper introduces UniEats, a recipe recommendation website designed to help university students better organize their meals and adopt healthier eating habits. The system aims to address common challenges students face, including time constraints, limited cooking skills, and insufficient nutritional awareness, which often lead to unhealthy food choices. At the core of UniEats is its recommendation engine, which employs a hybrid filtering approach, combining content-based and collaborative filtering techniques to provide personalized recipe suggestions based on users' dietary preferences, rating history, and recipe attributes. UniEats offers a range of features, including recipe search, weekly meal planning, nutritional analysis through dashboards, and automatic grocery list generation. By enabling students to explore diverse culinary options, create balanced meal plans, and understand the nutritional content of their meals, UniEats empowers them to make informed dietary decisions. This research paper discusses the project's background, motivation, and objectives, emphasizing the importance of addressing students' dietary challenges. It also reviews existing recommendation systems and algorithms, justifying the choice of hybrid filtering for personalized meal suggestions. Additionally, the research paper details the system's design, implementation, and testing procedures, highlighting the development process. UniEats is a practical solution that leverages machine learning and data-driven methods to enhance students' culinary experiences, support skill development, and promote nutritional awareness. By tackling key challenges in meal planning and healthy eating, UniEats aims to improve the overall well-being of university students
Performance Benchmarking: Pre-trained Models and Custom Convolutional Neural Networks in Deep Learning
No full textRecent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial task for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These technologies' ability to accurately recognize traffic signs enables them to make informed decisions in real time, thereby elevating the standard for overall driving safety. The study uses a large, annotated dataset of images of traffic signs to train and assess the CNN model. We developed a model that can recognize a large number of traffic lights, even in challenging scenarios such as low light levels, adverse weather, or high traffic. CNN image processing enables the system to accurately recognize and categorize traffic signs. Real-time predictions made by the CNN model after training aid ADAS and autonomous vehicles in comprehending road conditions. Real-time recognition is essential for tasks like managing turns, stopping at red lights, and adhering to speed restrictions. The research also addresses real-world challenges to ensure the model performs effectively in light or weather changes. A thorough testing process validates the model's accuracy and reliability. Ultimately, this technology might significantly increase road safety by providing drivers with more precise information, improving ADAS and AV decision-making skills, and reducing the number of accidents caused by drivers misinterpreting traffic signals
Enhancing Financial Literacy: A Progressive Web Application Approach for Malaysian Youth
No full textFinancial management is a crucial skill that individuals of all age ranges should acquire and master. It offers a transparent view of our financial status, enabling us to comprehend where our expenses are directed and manage every facet of our finances. Studies have indicated that Malaysian youth lack understanding in financial management. Nowadays, with so many people using the internet, we have the opportunity to share this expertise with a larger audience. Providing easily accessible materials for learning about and managing personal finances is essential to comprehending people's individual financial circumstances. In light of this, the purpose of this article is to develop a useful, progressive web system for personal finance management that makes budgeting and cost tracking easier. This personal finance management system will be implemented using the Tailwind Cascading Style Sheets, Firebase, and React framework as development tools. React frameworks are used due to their ability to produce dynamic user interfaces. To sum up, this user-friendly interface mechanism enables the formulation of budgets and the tracking of expenses. It also consists of other features for data visualization, such as charts. This research has the potential to add some additional enhancements to its existing functionality. For instance, it could introduce a predictive budgeting function that uses historical user spending data to perform predictive analysis
Performance Evaluation on COVID-19 Prediction using Machine Learning Models
No full textThe COVID-19 pandemic has placed enormous strain on providing health care services internationally while reinforcing the argument for the need to strengthen forecasting techniques. Existing forecasting methods have drawbacks, especially in determining the long-term consequences of the pandemic and understanding its broad reach across various locations and populations. This project proposes an evaluation of machine learning (ML) models with the aim of improving predictions, particularly the accuracy in long-term forecasting, of subsequent trends of the COVID-19 pandemic. A systematic review highlights previous forecasting attempts as a reference for the approach. This project emphasizes extensive data collection, model formulation and testing to develop a strong prediction framework. The models considered for evaluation are Support Vector Regression (SVR), seasonal autoregressive integrated moving average (SARIMA), and artificial neural networks (ANN), which have overcome some of the deficiencies of epidemiological forecasting methods to date. The aim is to provide public health representatives with more rigorous forecasts, which could enhance planning and response measures and protect health and safety. Our findings show that the ANN model is superior, with high accuracy and comprehensive performance, confirming its broader use in various predictive applications. The Root Mean Square Error (RMSE) of prediction error was also relatively modest (R-square values were nearly 1)
Migraine Generative Artificial Intelligence based on Mobile Personalized Healthcare
No full textMigraine is a complicated genetic disorder characterized by episodes of moderate-to-severe headaches that are usually unilateral and are frequently accompanied by nausea and increased sensitivity to sound and light. A migraine attack induces intense pain, hindering an individual from engaging in daily activities and potentially persisting for hours or even days. By the growth of the Internet of Things, we have new opportunities to try to apply it to the medical field. To identify the origin of a migraine, specialists need access to a patient's medical history and a comprehensive understanding of migraine symptoms for effective treatment. Determining the true source of a migraine may take longer than expected. Nowadays, solving problems through the Internet has become very common in people's lives. Hence, the objective of this research is to create a mobile personalized healthcare mechanism that can assist migraine patients in promptly receiving optimal and precise treatment. Moreover, this research would establish a user-friendly interface that facilitates the presentation of compelling evidence regarding the repercussions of patient health issues. Additionally, machine learning training was designed to treat patients based on relevant demographic characteristics of the healthcare treatment, such as medical history and reports provided. Therefore, this paper can provide insights into the state of art in mobile based personalized healthcare system to recommend future paths, for integration and investigation to improve online migraine platforms for a wide range of migraine patients
A Fundamental Framework for Analysis of Rainfall Prediction Features Significance
No full textRainfall prediction efforts had been prevalent ever since the impact of climate change on occurrences of natural disasters globally. Implementation of machine and deep learning techniques on features that contribute to rainfall occurrences were conducted with aims of seeking greater prediction accuracy for rainfall occurrences with a lack of study for significance of features in rainfall occurrence prediction. This study presents a framework of rainfall prediction features' significance analysis in the case study of Peninsular Malaysia rainfall occurrences. Features investigated in this study consist of temperature, humidity and wind speed. The designed framework for the investigation includes phases of data collection, data preprocessing, integration of random forest (RF) for ensemble classification and feature importance (FI) for feature significance calculation and finally model evaluation based on the metrics of precision, recall, F1 score and receiver operating characteristic (ROC) curve. In the preliminary investigation, the prediction model demonstrated accuracy, precision, recall and F1-score of 80.65%, 80%, 81% and 0.80 respectively. Humidity was found to have highest significance to the model's predictive power as compared to temperature and wind speed. Rainfall occurrence correlation with lower temperature and higher humidity and vice versa was identified with further investigation of feature data distribution against rainfall occurrences