Understanding the flashover performance of the outdoor high voltage insulator has been in the interest of many researchers recently. Various studies have been performed to investigate the critical flashover voltage of outdoor high voltage insulators analytically and in the laboratory. However, laboratory experiments are expensive and time-consuming. On the other hand, mathematical models are based on certain assumptions which compromise on the accuracy of results. This paper presents an intelligent system based on Artificial Neural Networks (ANN) to predict the critical flashover voltage of High-Temperature Vulcanized (HTV) silicone rubber in polluted and humid conditions. Various types of learning algorithms are used, such as Gradient Descent (GD), Levenberg-Marquardt (LM), Conjugate Gradient (CG), Quasi-Newton (QN), Resilient Backpropagation (RBP), and Bayesian Regularization Backpropagation (BRBP) to train the ANN. The number of neurons in the hidden layers along with the learning rate was varied to understand the effect of these parameters on the performance of ANN. The proposed ANN was trained using experimental data obtained from extensive experimentation in the laboratory under controlled environmental conditions. The proposed model demonstrates promising results and can be used to monitor outdoor high voltage insulators. It was observed from obtained results that changing of the number of neurons, learning rates, and learning algorithms of ANN significantly change the performance of the proposed algorithm.

Autism is a complex neurobiological disorder that is prevalence worldwide. Most autistic children have weak communication and social skills. This re-searches aims to develop and test a mobile application, named MyVoice, which supports local autistic children. The proposed design and features are discussed, and a prototype is evaluated and tested by two therapists and an autistic child. Experimental results indicate positive feedback in terms of ease of use, aesthetic, and simplicity. Parents of the autistic child are satisfied with different features such as the alert notification. Results also indicate that autistic children need about one week to easily interact with MyVoice.

This research addresses the need for a mobile application to teach Arabic sign language to deaf children in the Arab world. Therefore, the team develops an educational android application to teach deaf children the Arabic sign language using gamification. Also, the research aims to investigate the impact of using a mobile application for Arabic sign language on children’s learning performance. To achieve its goals, the research entails two main stages. The first stage focuses on building the mobile application based on the literature review and searching the Google and Apple stores for similar applications. In the second stage, an experiment is conducted on 10 deaf children—the experimental group adopted the mobile application for the learning process, whereas the control group followed the traditional learning approach. A pre-test and post-test were conducted, and the results show that children who used the game-based mobile application performed better in their quiz.