Children learn through play [1]. According to Crawford [2] educational game is the children's first contact with the computer. Therefore, it is important that the computer games for children are well designed and usable. Usability testing can also be adopted to assess learner's motivation towards learning. Assessing the usability with children requires special consideration. Children must be allowed to play freely and expressed their opinion while being observed. This study explores the measuring consideration for the usability of an educational game called Jelajah. Jelajah, was designed to teach pre-schools children to learn the Malay words and was developed based on a solid research on educational approaches. Five pre-school children were selected for this study. During the usability testing session, children are observed and their behaviors are recorded in an observation checklist. The session was video-taped for further observation. A post test was administered after the session to seek the children's satisfaction in using the game for learning. Data collected were quantitative and qualitative data. Data were later quantified to measure the effectiveness, efficiency and satisfaction towards the software. Based on the analysis, children showed interest and were highly motivated to learn when using educational game for learning.

Objective: This paper aims to measure the leaf area using image processing techniques that automate the grid counting method. Methods: For measurement of leaf area, firstly segmentation by Otsu method is required. Subsequently, denoising by median filter and followed by object recognition, boundary tracing and region filling techniques. The tool that is used in this study is Visual C++ 2010 Express using C++ and .Net languages. Findings: Three types of leaves have been tested and the results show that this new method could be used in determining the leaf area with a small relative error. Application/Improvement: Leaf area is an important data to agronomist in conducting their research on plant growth, plant photosynthesis, plant physiological behavior and any other research that requires leaf area data.

Ineffective protein feature representation poses problems in protein classification in hierarchical structures. Discrete wavelet transform (DWT) is a feature representation method which generates global and local features based on different wavelet families and decomposition levels. To represent protein sequences, the proper wavelet family and decomposition level must be selected. This paper proposed a hybrid optimization method using particle swarm optimization and the firefly algorithm (FAPSO) to choose the suitable wavelet family and decomposition level of wavelet transformation for protein feature representation. The suggested approach improved on the work of earlier researchers who, in most cases, manually selected the wavelet family and level of decomposition based solely on experience and not on data. The paper also applied the virtual class methods to overcome the error propagation problems in hierarchical classification. The effectiveness of the proposed method was tested on a G-Protein Coupled Receptor (GPCR) protein data set consisting of 5 classes at the family level, 38 classes at the subfamily level, and 87 classes at the sub-subfamily level. Based on the result obtained, the most selected wavelet family and decomposition level chosen to represent GPCR classes by FAPSO are Biorthogonal wavelets and decomposition level 1, respectively. The experimental results show that the representation of GPCR protein using the FAPSO algorithm with virtual classes can yield 97.9%, 86.9%, and 81.3% classification accuracy at the family, subfamily, and sub-subfamily levels, respectively. In conclusion, the result shows that the selection of optimized wavelet family and decomposition level by the FAPSO algorithm, and the virtual class method can be potentially used as the feature representation method and a hierarchical classification method for GPCR protein.

Recommender systems nowadays have been implemented in various fields. It helps its user to suggest the items based on user's preferences. Calculus is one of the tough subjects feared by most students. Thanks to the technology, many sources on the web can supply tutorials, working examples and solutions on the subject. However, there are too many of them. The students need to make selection, which one can fulfill their needs of specific calculus topics. The aim of this paper is to give a general overview and preliminary study on RS and review some similar education systems available.

Protein dataset contains high dimensional feature space. These features may encompass of noise and not relatively to protein function. Therefore, we need to select the appropriate features to improve the efficiency and performance of the classifier. Feature selection is an important step in any classification tasks. Filter methods are important in order to obtain only the relevant features to the class and to avoid redundancy. While wrapper methods are applied to get optimized features and better classification accuracy. This paper proposed a feature selection strategy for hierarchical classification of G-Protein-Coupled Receptors (GPCR) based on hybridization of correlation feature selection (CFS) filter and genetic algorithm (GA) wrapper methods. The optimum features were then classified using K-nearest neighbor algorithm. These methods are capable to reduce the features and achieved comparable classification accuracy at every hierarchy level. The results also shown that the integration between CFS and GA is capable of searching the optimum features for hierarchical protein classification.

Massive Multiplayer Online (MMO) game is one of the famous game genres among teenagers nowadays. MMO games allow gamers to interact and play with up to thousand players. Rainbow Six Siege (RSS) belongs to MMO type of game. However, due to many operators that are available in this game, the player needs to choose the right operator to counter the enemy operator. Therefore, based on the characteristic of the selected operator, this paper attempted to predict the outcomes of the game. In our prediction model, characteristics for these operators were extracted from 120 live stream replays. Three classification algorithms were utilized to predict the outcome of the game. Among these algorithms, IBK had obtained outstanding performance in the dataset. The accuracy of the model is 93.75%, applying 5-fold cross-validation test. The success rate reveals that our proposed model is suitable to predict the outcome of the game.