Link Prediction (LP) is one of the main research areas in Social Network Analysis (SNA). The problem of LP can help us understand the evolution mechanism of social networks, and it can be used in different applications such as recommendation systems, bioinformatics, and marketing. Social networks can be shown as a graph, and LP algorithms predict future connections by using previous network information. In this paper, a multi-wave cellular learning automaton (MWCLA) is introduced and used to solve the LP problem in social networks. The proposed model is a new CLA with a connected structure and a module of LAs in each cell where a cell module’s neighbors are its successors. In the MWCLA method for improving convergence speed and accuracy, multiple waves have been used parallelly in the network. By using multiple waves, different information of the network can be considered for predicting links in the social network. Here we show that the model converges upon a stable and compatible configuration. Then for the LP problem, it has been demonstrated that MWCLA produces much better results than other approaches compared to some state-of-the-art methods. Manuscript profile

Recently, fog computing has been introducedto solve the challenges of cloud computing regarding Internet objects. One of the challenges in the field of fog computing is the scheduling of tasks requested by Internet objects. In this study, a review of articles related to task scheduling in fog computing has been done. At first, the research questions and goals will be introduced, and then we will explain the process of finding and selecting the articles. A comprehensive analysis of the articles will be done. We have identified and listed 10 optimization metrics. Also, according to our study, in 79% of the studied articles, the mathematical model was used to express the problem. In 42% of the articles meta-heuristic algorithms proposed and 84% evaluated their algorithm by simulation. Finally, this paper presents the challenges and open issues of task scheduling in fog computing to the researchers. Manuscript profile