Gait and body movement are a window to human brain which make these activities unique for each person. These activities are used to diagnose some disorders related to parts of brain which causes have not been known such as Autism Disorders (AD). The traditional diagnostic methods of AD are time-consuming and highly dependent on clinician’s judgment which is based on behaviour assessment. This approach leads to subjective interpretations that differ from doctor to another and affect by strengths and weaknesses of patient. Therefore this paper aims to diagnosis of AD based on gait and body movement analysis. At first, Kinect v2 uses to create a 3D dataset, which includes three dimensional joints positions, joints trajectories video, skeleton movement video captured by Kinect v2, and color videos captured by Samsung Note 9 camera. This paper also aims to classify children with autism from normal children by proposed system based on four stages: Augmentation of the database by using seven transformations to deal with small number of autism cases; Extracting features that we think play an important role in classification; Reducing data dimensions using Principal Component Analysis; using Rough Set to classify dataset. Results show that classification is 92% after augmentation.

Image understanding is considered important among researchers. In this paper, a new technique is proposed to classify a detected face into two classes as a smile or nonsmile category. First, the system detects and segments only the face. Then, it converts the image from RGB to Gray-Scale and enhances the image via an equalization technique. Where the contribution of this research is depending only on the lower half of the face since most of the smiling information can be perceived from the mouth and its perimeter. Then, a convolutional neural network (CNN) is applied to generate two output nodes. Public GENKI-4K database is used for the experiments, which contains 4000 challenged face images. The results demonstrate that the accuracy with 4-Fold cross-validation is 91%. This approach achieves a promising performance as compared with the state-of-the-art techniques in both accuracy and processing time.

The literary text must contain the elements of imagination and surprise, and these elements are provided by shifts in metaphor, linguistic structures, phonetic aspects, and letters through which the language contains shifts that push and shake the recipient and its role in supplying a scene in which the scenes are crowded, prompting the recipient to reveal and be affected by it, that the Al-Hudhali poetic text revealed the ability and ability of Al-Hudhali, which he enjoys. The poetic artistically and aesthetically showed the poet Al-Hudhili as a producer that presents scenes and events in a sequential, sequential manner, and its ability lies in moving the feelings of the recipient and attracting attention by charging poetic scenes with situations based on a sequence in narrating events and making images, which enables us to see and see people, places and time in a state of moving beyond the constraints of stability and survival. within reach of the recipient.Its importance in the poetic text lies in its ability to move the scenes and intensify the significance and make them in a dramatic sequential sequence of growing events, making the recipient a listener, beholder, and witness to it.

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