It is essential to get disabled people involved and connected to each other and to the rest of the society. Games can be used for this purpose as well as encouraging them to be active physically. However, many of the current interactive games interact with users through voice commands which could be a problem for the deaf/mute people. Microsoft Kinect opens a new aspect for the gaming industry. This hardware can interact with players through a 3D vision and sound detector. This means players can use their body movements as well as their voice commands to control the game environment. The aim of this paper is to develop a Kinect gesture-based game suitable for deaf/mute people. The Microsoft Kinect SDK for Windows is used to develop a game which recognize the gesture command and convert the sign to the text commands in the game (in this instance Microsoft Shape game). Therefore, the deaf/mute player can enjoy taking part in this interactive game. Conclusions are drawn on how researchers can adapt and develop the new game environment which is understandable and compatible with deaf/mute peoples' abilities.

Mammography is a significant screening test for early detection of breast cancer, which increases the patient’s chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.

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