Aim: The study is aimed to investigate the ways that implementing intelligent educational systems can inform MOBILE LEARNING. Method: The research method was quasi-experimental. The statistical population is composed of male students of the third-grade secondary school of Smart in the city of Grammar of the academic year 97-96. According to Morgan schedule، in this study، 102 students of third grade high school from intelligent schools were selected using multistage cluster sampling and were randomly assigned into experimental and control groups. Several lessons from the English language book were selected as the content of the training، and before the training، they were done on both groups of pre-tests. The control group was trained on an intelligent LEARNING system as usual. But the LEARNING group was experimenting with intelligent LEARNING systems through MOBILE devices (tablets and MOBILEs). After training، the post-test was performed on both groups. Analyzee: Data were analyzed using SPSSv. 18 software in two levels of descriptive and inferential statistics (independent t-test). Results: The results of the study showed that the English language test grades of the experimental group، which were trained using intelligent educational systems in the field of MOBILE LEARNING، were more than the English language test grades that were taught using intelligent educational systems. So، we can say that the implementation of intelligent educational systems in the context of MOBILE LEARNING has a positive effect on education.

Smartphones are now well integrated with advanced capabilities and technologies such as the Internet. Today, due to the facilities and capabilities and the widespread use of smart MOBILE devices, MOBILE security has become a vital issue worldwide. Smartphones are not properly protected compared to computers and computer networks, and users do not consider security updates. Recently, MOBILE devices and networks have been targeted by one of the most dangerous cyber threats known as botnets. MOBILE Bantent An enhanced example of Boutons has the ability to perform malicious operations such as denial of service attacks, data theft, eavesdropping, and more. Bunters use three communication protocols: HTTP, SMS and Bluetooth to communicate with each other,So when users are not connected to the Internet, botnets are able to communicate with each other. In this study, to identify MOBILE batonet from 14 Android baton families, including 1932 samples of Android MOBILE devices applications and 4304 samples of safe and secure Android MOBILE devices applications have been used. Application permissions were extracted for reverse engineering to automatically classify and detect types of botnets, then based on these permissions, each application was converted to an equivalent image using the proposed method. Labeled images were then used to train convolutional neural networks. The results of evaluation and comparison of this method with classical methods including backup vector machine and decision tree showed that the proposed method is able to achieve higher efficiency in detecting different types of botnets and separating it from healthy programs