Ensemble Clustering (EC) methods became more popular in recent years. In this methods, some primary clustering Algorithms are considered to be as inputs and a single cluster is generated to achieve the best results combined with each other. In this paper, we considered three hierarchical methods, which are single-Link, average-Link, and complete-Link as the primary clustering and the results were combined with each other. This combination was done based on correlation matrix. The basic Algorithms were combined as binary and triplicate and the results were evaluated as well. the IMDB film dataset were clustered based on existing features. CH, Silhouette and Dunn Index criteria were used to evaluate the results. These criteria evaluate the clustering quality by calculating intra-cluster and inter-cluster distances. CH index had the highest value when all three basic clusters are combined. Our method shows that EC can achieve better results and present clusters with higher robustness and accuracy.

Discovering communities in time-varying social networks is one of the highly challenging area of research and researchers are welcome to propose new models for this domain. The issue is more problematic when overlapping structure of communities is going to be considered. In this research, we present a new online and incremental community detection Algorithm called Link-clustering which uses Link-based clustering paradigm intertwined with a novel representative-based Algorithm to handle these issues. The Algorithm works in both weighted and binary networks and intrinsically allows for overlapping communities. Comparison with the state of art evolutionary Algorithms and Link-based clustering shows the accuracy of this method in detecting communities over times and motivates the extended research in Link-based clustering paradigm for dynamic overlapping community detection purpose.

Predicting collaboration between two authors, using their research interests, is one of the important issues that could improve the group researches. One type of social networks is the co-authorship network that is one of the most widely used data sets for studying. As a part of recent improvements of research, far much attention is devoted to the computational analysis of these social networks. The dynamics of these networks makes them challenging to study. Link prediction is one of the main problems in social networks analysis. If we represent a social network with a graph, Link prediction means predicting edges that will be created between nodes in the future. The output of Link prediction Algorithms is using in the various areas such as recommender systems. Also, collaboration prediction between two authors using their research interests is one of the issues that improve group researches. There are few studies on Link prediction that use content published by nodes for predicting collaboration between them. In this study, a new Link prediction Algorithm is developed based on the people interests. By extracting fields that authors have worked on them via analyzing papers published by them, this Algorithm predicts their communication in future. The results of tests on SID dataset as co-author dataset show that developed Algorithm outperforms all the structure-based Link prediction Algorithms. Finally, the reasons of Algorithm’s efficiency are analyzed and presented.

In this article the genetic Algorithm is employed to optimize scissor-Link foldable structures. The Advantage of using GA lies in the fact that the discrete spaces can be optimized without any Complexity. Here displacement method is used for analysis with uniplet elements.      

In the current study, a one-step numerical Algorithm is presented to solve strongly non-linear Full fractional duffing equations. A new fractional-order operational matrix of integration via  quasi-hat functions (QHFs) is introduced. Utilizing the operational matrices of QHFs, the main problem will be transformed into  a number of univariate polynomial equations. Absolute errors of the results in approximations and convergence analysis are addressed. Ultimately, five examples are provided to illustrate the capabilities of this Algorithm. The numerical results are illustrated in some Tables and Figures, for different values of the parameters $\alpha~ and~ \beta$.