Malaysian Sign Language (MSL) is an important language that is used as the primary communication method for the deaf communities with the others. Currently, the MSL is poorly known by the Malaysians and the existing platform of learning the sign language is inefficient, not to mention the incomplete functionality of the existing mobile learning application in the market. Hence, the purpose of developing this application is aimed to increase the knowledge and recognition of the public towards the MSL and allows them to learn the sign language more effectively. One of the features in this application is sign detection, which could analyze the image captured by phone camera into sign meaning. The application also comprised various categories of the sign for efficient learning and quiz to test user knowledge against their learned sign language. Besides, there is a feedback module for the user to express their opinions and suggestions towards the application to the developer. This application is aimed to help the public to learn MSL efficiently by selecting the category of sign they wish to learn and test themselves by using the quiz module. Besides, the application could also detect the unknown sign by capturing the image and analyze it. The application helped to raise the recognition of MSL among the public and expose the public to the sign language knowledge. It had also become a small help in breaking the barrier of communication between the deaf communities and the public.

This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.

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Feature selection is a process to select the best feature among huge number of features in dataset, However, the problem in feature selection is to select a subset that give the better performs under some classifier. In producing better classification result, feature selection been applied in many of the classification works as part of preprocessing step; where only a subset of feature been used rather than the whole features from a particular dataset. This procedure not only can reduce the irrelevant features but in some cases able to increase classification performance due to finite sample size. In this study, Chi-Square (CH), Information Gain (IG) and Bat Algorithm (BA) are used to obtain the subset features on fourteen well-known dataset from various applications. To measure the performance of these selected features three benchmark classifier are used; k-Nearest Neighbor (kNN), Naïve Bayes (NB) and Decision Tree (DT). This paper then analyzes the performance of all classifiers with feature selection in term of accuracy, sensitivity, F-Measure and ROC. The objective of these study is to analyse the outperform feature selection techniques among conventional and heuristic techniques in various applications.