In recent years, the emergence of various social networks has led to the growth of social network users. However, activity in such networks depends on the level of trust that users have in each other. Therefore, trust is essential and important issue in these networks, especially when users interact with each other. In this article, we examine this issue and provide a method to evaluate it. It is not easy to measure the accuracy of trust for users who interact with social networks. Here, interactions are virtual. In this article, we have used the adaptive neuro-fuzzy inference system to evaluate trustworthiness by considering different personality attributes of users such as reliability, availability, interest, patience and adaptability. Using these features as input and based on the adaptive neuro-fuzzy inference system, we evaluated the trustworthiness of users in social network. The proposed adaptive neuro-fuzzy inference system is expandable because in this system, trust can be defined as a set of one or more personality attributes. Epinions social network dataset is also used to simulate and validate the proposed method. In the proposed method, the absolute mean value of error is less than 0.0095 and the value of F-score is more than 0.9884. Based on the obtained results and compared to the previous methods, the proposed adaptive neuro-fuzzy inference system shows an acceptable accuracy for evaluating the trustworthiness of users. تفاصيل المقالة

‎ The development of digital technologies has improved the transfer of data over the Internet in recent years. Image encryption is a technique to ensure security in information transfers. The current paper presents an evolutionary model on the basis of a hybridization of DNA biomolecule operators and the LS2 Map ‎chaos function for encryption of image. The model proposed here includes three stages. In the initial stage, the MSC (Magic Square Chaotic) algorithm and a secret key are utilized with the SHA-256 algorithm t‎o determine the initiating the LS2 Map function value, which is then employed to manipulate the pixels of the image. Then, DNA biomolecule operators and the chaos function are used for propagation. Additionally, the previous stages process is iterated with the starting population of the genetic algorithm in the third stage. Afterward, the optimization is carried out through genetic algorithm operators. The results indicate ‎that the introduced model is superior to other rivals. Furthermore, as for the high level of entropy obtained, the model exhibits strong resistance to common attacks.‎ تفاصيل المقالة