Investigating the extent of Information Technology (IT) usage in Malaysian Batik industry / Yap May Lin, Yap Bee Wah and Jasber Kaur Gian Singh

In this new information technology era, business enterprises in Malaysia should capitalize on the use of information technology (IT) to gain the competitive advantage and bring their businesses to the global forefront. The recent advancement of information technology enables a community network portal to provide faster and more efficient communication, dissemination of information and business transactionsfor organizations. This paper discusses the extent of IT usage in the Malaysian Batik Industry and then proposes a community net portal (CNP) reference model for the Malaysian batik industry

Fatality prediction model for motorcycle accidents in Malaysia

This paper involves building a fatality predictive model for motorcycle accidents data in Malaysia. The number of registered motorcycles in Malaysia has increased four-fold compared to the last 20 years. Thus, the motorcycle accidents rate and fatality rates among riders and pillion in Malaysia has also increased dramatically. However, results show that when taken into account the numbers of fatalities per 10,000 registered motorcycles, the fatality rate shows a decreasing trend starting from 1996 onwards. The motorcycle accident data for the period of 1996 to 2010 was analyzed using Smeed’s Law and regression method. The results show that regression method approach gives better estimates of fatality rate than Smeed’s equation

Determinants for Healthy Lifestyle of Patients with Familial Hypercholesterolaemia

Lifestyle modification is a pivotal intervention for Familial Hypercholesterolaemia (FH). This study aims to describe the lifestyles (physical activity and healthy diet) and their associations with sociodemography, illness characteristics, psychological elements, family support and level of barrier. 100 participants were given Pro forma questionnaires to assess sociodemography and illness characteristics. The lifestyles, psychological elements, family support and level of barrier were assessed using the Theory of Planned Behaviour questionnaire. The determinants of healthy lifestyles include the status of receiving treatment, level of barrier and intention for behavioural change. The findings may inform the strategy for lifestyle modification of FH patients.Keywords: Familial Hypercholesterolaemia; lifestyle; physical activity; healthy diet.eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/ebpj.v5i14.233

Investigating the extent of information technology usage in Malaysian batik industry / Yap May Lin, Yap Bee Wah and Jasber Kaur Gian Singh

In this new information technology era, business enterprises in Malaysia should capitalize on the use of information technology (IT) to gain competitive advantage and bring their businesses to the global forefront. This paper discusses the extent of IT usage in the Malaysian Batik Industry. A survey study involving eleven batik companies in the Klang Valley was carried out. Information was also gathered via interviews with four related organization. Survey results indicated that communication via telephone is still the major mode of communication with suppliers, producers and customers Payment by cheque is still the most favored mode of payment and very few batik companies provide online payment facilities. Only four batik companies have set up their own websites. The services they wish to add to their websites are shopping cart, online payment and immediate credit card validation services. The main benefits they gain from their websites are faster and more efficient communication with customers while the two main problems they face are funding and lack of expertise in their company to maintain the website. Results of this study also show that improved sales, profitability, competitive position and recognition of market brand are significantly correlated with the batik company commitment to using IT in their business

Psychological Influence towards Health Consumers Intention to use A Malaysia National Web based Health Information Service

Drawing upon Health Belief Model, the study investigated the psychological predictors that determine the usage intention of the Malaysian web-based health information service, MyHEALTH Portal. The results of the measurement model show the evidences of outcome expectations and internal cues as the predictors to the portal usage, while external cues was found to be insignificant. The findings would help the Malaysia Ministry of Health in identifying significant psychological factors that influence the portal usage. This would allow them to re-strategize the portal’s marketing and promotional works effectively thus to be maximally used by the public while achieving its long-term goal

A Lightweight Data Preprocessing Strategy with Fast Contradiction Analysis for Incremental Classifier Learning

A prime objective in constructing data streaming mining models is to achieve good accuracy, fast learning, and robustness to noise. Although many techniques have been proposed in the past, efforts to improve the accuracy of classification models have been somewhat disparate. These techniques include, but are not limited to, feature selection, dimensionality reduction, and the removal of noise from training data. One limitation common to all of these techniques is the assumption that the full training dataset must be applied. Although this has been effective for traditional batch training, it may not be practical for incremental classifier learning, also known as data stream mining, where only a single pass of the data stream is seen at a time. Because data streams can amount to infinity and the so-called big data phenomenon, the data preprocessing time must be kept to a minimum. This paper introduces a new data preprocessing strategy suitable for the progressive purging of noisy data from the training dataset without the need to process the whole dataset at one time. This strategy is shown via a computer simulation to provide the significant benefit of allowing for the dynamic removal of bad records from the incremental classifier learning process

Analysis of panel count data model for Malaysian road accidents / Prof. Madya Dr Mohd Alias Lazim, Prof. Madya Dr Yap Bee Wah and Wan Fairos Wan Yaacob

The most common probability models for modeling count data are those based on the traditional Poisson and Negative Binomial assumption usually developed on individual basis. On the other hand, this research focused on modeling procedure using panel data analysis approach. The fixed-effects Poisson and Negative Binomial (FENB), random effects negative binomial (RENB) model and the cross-sectional negative binomial (NB) model were examined in order to take into account for heterogeneity in the accident data on a panel of 14 states in Malaysia covering the period of 1996 to 2007. We examined various factors associated with road accidents occurrence. It is hypothesized that the factors considered to affect road accidents are the monthly registered vehicle within the state, the amount of rainfall, the number of rainy day, time trend and the monthly seasonal effect. Various model specifications were estimated including the pooled Poisson, Fixed and Random Effects Poisson as well as Fixed and Random Effects Negative Binomial model. The results showed that road accident occurrences are positively associated with the increase in the number of registered vehicle, increase in the amount of rain and time of the occurrence. The effect of seasonality also indicates that accident occurrence is higher in the month of October, November and December. The models developed confirmed the factors identified that have effect on the number of road accidents in Malaysia. The specification comparisons also indicate the benefits gained from using the NB model with spatial and temporal effects. The RENB model was found to be more superior when incorporated temporal and cross sectional variations which offers advantages in model flexibility

Discovering potential blood-based cytokine biomarkers for Alzheimer’s disease using Firth Logistic Regression

Background: Alzheimer’s disease (AD) is a neurodegenerative disorder where patients suffer from memory loss, cognitive impairment and progressive disability. Individual blood biomarkers have not been successful in defining the disease pathology, progression and diagnosis of AD. There is a need to identify multiplex panels of blood biomarkers for early diagnosis of AD with high sensitivity and specificity. This study focused on identification of cytokine biomarkers. The maximum likelihood estimates of the ordinary logistic regression model cannot be obtained when there is complete separation and the alternative is Firth logistic regression which uses a penalised Maximum Likelihood in parameter estimation.  Methods: This paper reports a Firth logistic regression application in finding potential blood-based cytokine biomarkers for Alzheimer’s disease in a matched case control study. We used a principle component analysis to discriminate the correlated, completely separated covariates.  Results: The Firth logistic regression results showed that nine individual biomarkers IL-1β, IL-6, IL-12, IFN-γ, IL-10, IL-13, IP-10, MCP-1 and MIP-1α had a significant relationshipwith elevated risk for AD as compared to the healthy control (HC). Principal component analysis with varimax rotation for the nine biomarkers revealed four factors (total variance explained=85.5%). The main principal component biomarkers were IL-1β, IL-6, IL-13 and MCP-1 (total variance explained=62.3%). Firth’s logistic regression model with the first principal component had accuracy of 78.2% with sensitivity and specificity of 71.8% and 75% respectively.  Conclusion: Firth’s logistic regression is a useful technique in identification of significant biomarkers when there is an issue of data separation.&nbsp

Haze alarm visual map (HazeViz): an intelligent haze forecaster

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results

Predicting automobile insurance fraud using classical and machine learning models

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases