Search results

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.

This paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.

Taking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.

The results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.

Note that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.

The paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.

The purpose of this paper is to focus on the reliability assessment on the basis of automobile suspension fatigue life using wavelet decomposition method.

The discrete wavelet transform (DWT) of automobile coil spring signal is implemented as a response to different road surfaces. A reliability analysis is applied to determine the potential of the wavelet implementation in fatigue life analysis. The signals used in this study are highway and rural road.

On the basis of the implementation of wavelet decomposition method, low-level decomposition replicates the original signals in comparison with high-level decomposition. The fatigue life of low-level decomposition lies in the 2:1 and 1:2 correlation graph. The percentage difference for mean cycle to failure presents low values for low-level decomposition, with 44.31 per cent for highway and 44.20 per cent for rural road. The percentage of difference for high-level decomposition is high.

The determination of fatigue life analysis by using the DWT method is suitable for low-level decomposition. High-level decomposition is considered noise that cannot be eliminated and does not contribute to the failure of the structure.

This study aims to evaluate the knowledge structure of halal certification in food and beverages. The growth of the Muslim population and the expanding economic activities among the Muslim community have necessitated extensive and comprehensive halal certification, particularly in food and beverage products.

Through a science mapping approach, this study explores the knowledge structure based on past publications in food and beverages and its association with halal certification, logos and brands. Applying bibliographic coupling and co-word analysis, the present and future trends of halal certification in food and beverages are explored by presenting a network visualization to present the underlying themes and research streams.

The main finding from recent and emerging themes shows that the pivotal perspective is increasing consumers’ knowledge and awareness of halal-certified food products. Future trends stemming from the co-word analysis suggest the importance of exploring consumer halal food product determinants.

This study is relevant to stakeholders, scholars and practitioners, particularly in disseminating information and voice on the importance of halal-certified food products that are recognized as having better quality and contributing to environmental sustainability.

Despite the extant literature on purchasing behaviour, little is known about why and how consumers purchase and use pharmaceutical products in Malaysia. This paper aims to develop a holistic model that articulates the factors underlying pharmaceutical product purchase and use and their relationships.

Drawing upon the grounded theory approach, in-depth interviews and analysis on the basis of theoretical saturation were conducted. Enumerators, trained to perform interviews, the transcription and content analysis, sampled 120 Malaysian consumers in the first phase and 40 in the second. ATLAS.ti was used to assist the coding and analytical process.

The findings largely support extant literature on purchasing decisions. Particularly, the evaluation of product attributes was found to be an immediate determinant of purchase and use. The effect, in turn, was moderated by personal factors and facilitating conditions. The need for healthcare products and the knowledge about them were constructed as antecedents in the model. The study also highlights that consumers’ purchase and use of pharmaceutical products could be better understood and managed with a holistic view of the decision-making process.

This study proposes the conbe-hepro model to explicate the antecedents and determinants of consumers’ purchase and use of pharmaceutical products in the Malaysian context. It provides a theoretical grounding that enriches our knowledge of the changing behaviour of consumers in healthcare literature. It also yields practical insights to the government bodies and the healthcare providers in Malaysia and other developing countries.

The increasing global concerns about the environmental degradation and climate changes oblige architecture students to apply sustainable design approaches in their studio projects. Therefore, renewable energy raises the expectation of providing sustainable solutions for their architectural design proposals. This paper aims to investigate the learning of students in knowledge, awareness and applicability on sustainability during their first three years of the part 1 architecture programme. Surveys were conducted on 500 students from eight architecture schools from the local universities, two architecture schools from the polytechnic colleges and three architectural schools from the overseas universities. These survey results from 335 respondents confirmed that the learning on sustainability through self (51.6%), peer (48.6%) and design studio lecturers (37.0%). These results confirmed also that most respondents did rely on pre-design assessments to develop sustainable design strategies in their final architectural design proposals. These results concluded that the perception of architecture students on learning sustainability is based mainly on other sources. These findings provide knowledge for educationists and practitioners towards the planning of architecture curriculum and the implementation of pedagogical approach in sustainability. This paper determines the most important source of learning on sustainability knowledge for students in the pedagogy at university level.

The Malaysian movement control order is a series of national lockdowns implemented by the Malaysian federal government to contain the spread of Coronavirus disease-2019 (COVID-19). The government has made it mandatory for all non-essential sectors to work remotely to mitigate the spread of COVID-19. The eating behavior of working adults may have altered following the changes in working norms. This study aimed to assess the eating behavior of remote working adults during the COVID-19 pandemic.

A total of 159 remote working adults were recruited through a convenience sampling approach. Bodyweight and body height were self-reported by the respondents. Body mass index was calculated according to the cut-off of World Health Organization standards. The eating behavior was evaluated using the eating behavior questionnaire.

Results revealed more than half of the remote working respondents did not practice meal skipping. Irrespective of the gender, more than 70% of the respondents consumed either breakfast, lunch or dinner every day. Most of the respondents consumed vegetables, bread/rice/noodles and meat/chicken at least two times per day. Regression analysis revealed meal skipping was associated with younger people, overweight status and median monthly household income.

Many sectors adopted remote working systems during the COVID-19 pandemic. It is postulated that working remotely will be a new normal even after this pandemic. Until now, to the best of the authors’ knowledge, little or no data are available on assessing the eating behavior of remote working adults during the COVID-19 pandemic. This study could provide an insight into the eating behavior of adults during remote working.

Studies showed that a proactive delivery system employing innovative artificial intelligence (AI) in urban services might perform better. This has become an important national policy for many countries. Thus, this study aims to explore the influence of AI in urban services in Malaysia.

Official documents such as Structure Plan and Government Transformation Programme Policy Document covering various levels of cities in Malaysia, articles related to urban studies mostly written by researchers regarding urban growth in Malaysia and the Urban Development Bulletin from the Federal Department of Town and Country Planning from 1957 to date were reviewed and analysed.

The findings show that the influence of AI in urban services has long existed and been carefully planned by local authorities since colonial times. The development of global digital technology influences the upgrading of AI in urban services in Malaysia. Also, the success of AI in these municipal services is influenced by the rate of information technology literacy among the urban population. These developments have led to the definition of a conceptual city.

This paper's findings and conclusion were based on reviewed literature but did not compromise the strength of this paper. Thus, as part of the implications for future research, mixed-methods research design has been suggested.

As part of the implications, this article intends to promote AI in urban services in Malaysia and other developing countries with similar urban services challenges.

This paper encourages AI applications in urban services because it enhances service delivery performance. This intends to key stakeholders to promote AI via policies across Malaysia's urban services as part of the study implications.

The surge in pharmaceutical and health supplement usage among consumers aims to enhance personal well-being. This growing opportunity for pharmaceutical brands has resulted in increased market share and intensified industry competition. Using the theory of planned behaviour (TPB), this study aims to identify the factors influencing Malaysians’ choices regarding pharmaceutical and health supplements. In addition, the variable of past behaviour was incorporated to account for consumer decisions based on prior experiences.

Using purposive sampling, 300 questionnaires were gathered and analysed via Statistical Package for the Social Sciences and structural equation modelling technique via Analysis of Moment Structures software to validate the reliability of each variables and the postulated relationships within the research framework.

Results revealed a pronounced impact of past behaviour on the intention to consume pharmaceutical and health supplements. The mediating role of perceived behavioural control in bridging past behaviour and consumption intention was also ascertained. Notably, the findings support the inclusion of past behaviour in the TPB as a pivotal determinant of intention.

The insights gleaned underscore the escalating trend of pharmaceutical consumption in Malaysia, providing strategies to enhance and maintain the competitive edge and market position of pharmaceutical brands.

The purpose of this study is to provide a comprehensive exploration of academic research on halal purchasing decisions and consumer behavior by integrating bibliometric and systematic review methodologies.

This study uses a multi-method approach, combining bibliometric and systematic review methodologies, to comprehensively analyze the domain of halal purchasing decisions and consumer behavior. A data set of 184 articles published between 2007 and 2024 was sourced from the Scopus database. The bibliometric analysis was conducted using Bibliometrix in R, facilitating performance analysis, science mapping and network analysis to explore key authors, affiliations, collaborations and thematic trends. Additionally, the systematic review examined the limitations and future research areas discussed in prior studies, providing the basis for formulating potential research questions to address identified gaps.

The study identifies significant contributions within the domain of halal purchasing decisions and consumer behavior, emphasizing the critical roles of religiosity, trust and halal certification as dominant themes. Bibliometric analysis reveals key authors, influential publications and collaborative networks, highlighting Malaysia as a central hub for research in this field. Additionally, the analysis underscores the intellectual structure and thematic evolution, identifying underexplored areas such as non-Muslim perspectives, emerging halal industries and geographic diversity. The systematic review complements these insights by addressing recurring methodological and theoretical limitations, offering targeted recommendations for future research.

This research uniquely combines bibliometric and systematic review methodologies to provide a comprehensive review of the halal consumer behavior literature, identifying limitations and gaps in prior studies and proposing actionable areas for future research.