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Dengue virus had become the dominant mosquito-borne disease in Malaysia.  With no positive progress on the development of vaccine, other ways in dealing with the virus is to predict the next outbreak which is also the aim of this paper. This dengue model based on system dynamics approach gives valuable information to decision makers in determining the strategies for vector control. The array of factors involved such as temperature, rainfall and population density that significantly influence virus transmission, give opportunity for a system approach in providing answer to the complicated relationship which exist in dengue system. System dynamics dengue model is able to simulate reasonable and promising results, which can be used as basis for future researcher to model more accurate and detail dengue transmission control system.

Education institutions store large amounts of data regarding students, such as demographics, academic-related data, and student activities. These data were recorded and stored in many ways, including different filing systems and database formats. By having these data, education institutions have a better way to manage and understand their students. In addition, information related to their students can easily be accessed and extracted. As more data is recorded and stored, this could allow the educational institution to make more informed decisions and give educators good insight into the educational system. The research approach known as educational data mining (EDM) focuses on using data mining techniques to extract massive data from the educational context and transform it into knowledge that can improve educational systems and decisions. Clustering, an unsupervised learning technique, is one of the most powerful machine- learning tools for discovering patterns and unseen data. This work aims to provide insights into the data obtained from Oman Education Portal (OEP) related to the student’s performance by manipulating the k-means algorithm.

In the classification task, the presence of irrelevant features can significantly degrade the performance of classification algorithms, in terms of additional processing time, more complex models and the likelihood that the models have poor generalization power due to the over fitting problem. Practical applications of association rule mining often suffer from overwhelming number of rules that are generated, many of which are not interesting or not useful for the application in question. Removing rules comprised of irrelevant features can signifi cantly improve the overall performance. In this paper, we explore and compare the use of a feature selection measure to filter out unnecessary and irrelevant features/attributes prior to association rules generation. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data items. Empirical results confirm that by utilizing feature subset selection prior to association rule generation, a large number of rules with irrelevant features can be eliminated. More importantly, the results reveal that removing rules that hold irrelevant features improve the accuracy rate and capability to retain the rule coverage rate of structural associative association.

The stability of the economic system of a country very much depends on its banking industry. Data Envelopment Analysis (DEA) has been applied widely for measuring efficiency of banks. Limited studies, however, have employed the radial and non-radial DEA models to evaluate efficiency of banks without considering the ranking of the fully efficient banks since those banks have the same efficiency score. Considering the weakness of the radial and non-radial DEA, this paper aims to calculate the banks efficiency of nine commercial banks in Malaysia from 2004 to 2013 by adopting the two-stage of super efficiency slack-based measure (SE-SBM) model. This model can discriminate between the efficient banks and recalculate their efficiency scores. Then, the selected banks were able to be ranked according to their final efficiency scores. Moreover, comparative analyses of the efficiency of the banks and the year-wise efficiency of the selected banks were also conducted. The methodology consists of two stages.  In the first stage the SBM model is run to classify efficient and inefficient banks. In the second stage the super efficiency model is run to rank the efficient banks obtained from the first stage by calculating their super efficiency scores. Our empirical results show that: (1) the efficiency status of the banks fluctuated over the examined period, the high number of the efficient banks is achieved in the years 2006 and 2008, while the year 2012 has the lowest number of the efficient banks. (2) the ranking of the banks fluctuated in the studied period. (3) most of the banks are inefficient in terms of their average efficiency scores. This paper has two limitations. First, the paper did not integrate undesirable output, despite it deals with non-interest income. Second, performance evaluation of Malaysian commercial banks was only compared among the Malaysian banks.

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