The objective of the study is to propose several prototypes of digital game-based learning (DGBL) for the education of low functioning autism (LFA) children specifically in learning Al-Quran. Study on several models of the serious game has been conducted and the best model was selected to be applied in designing the prototypes. Experiment on fifteen LFA children, age within five to ten years old will be conducted in order to evaluate the effectiveness of the game in helping the process of learning Al-Quran. The evaluation will emphasize on the usability element on its user. The expectation of this study is that the game could engage the LFA children during the learning process and make them active in learning Al-Quran. Therefore with the creation of the game, it is hoped that it will provide the LFA children with opportunities to study Al-Quran like other normal Muslim children.

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The rising number of people with mental illness has become a major concern all over the world. Many efforts have been done to improve the process of detection and surveillance of people with mental illness and one of them is through analysing users' activities on social media platforms. Social media contains multi-content of data such as text, image and social interactions log. By mining all the data, it could help to determine the current mental state of social media users and detect those who are suffering from mental illness. However, most of the prior researches on mental illness in social media have mainly focused on single-based content rather than multi-based content. Due to a rapid growth of multi-content data, considering on single-based content only will ignore the complementary information that offered by other modalities. In consequence, single-based content does not provide an insight knowledge on the phenomenon of interest. This study aims to propose a design of multimodal fusion model, with the exploitation of all the multi-content data including text, image and social interaction data. This multimodal fusion model is expected to produce an accurate result for the prediction of mental illness among social media users.