• List of Articles Evolution

      • Open Access Article

        1 - COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network
        Hamideh Sadat Cheraghchi Ali Zakerolhossieni
        The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social Full Text
        The problem of community detection has a long tradition in data mining area and has many challenging facet, especially when it comes to community detection in time-varying context. While recent studies argue the usability of social science disciplines for modern social network analysis, we present a novel dynamic community detection algorithm called COGNISON inspired mainly by social theories. To be specific, we take inspiration from prototype theory and cognitive consistency theory to recognize the best community for each member by formulating community detection algorithm by human analogy disciplines. COGNISON is placed in representative based algorithm category and hints to further fortify the pure mathematical approach to community detection with stabilized social science disciplines. The proposed model is able to determine the proper number of communities by high accuracy in both weighted and binary networks. Comparison with the state of art algorithms proposed for dynamic community discovery in real datasets shows higher performance of this method in different measures of Accuracy, NMI, and Entropy for detecting communities over times. Finally our approach motivates the application of human inspired models in dynamic community detection context and suggest the fruitfulness of the connection of community detection field and social science theories to each other. Manuscript Document
      • Open Access Article

        2 - Handwritten Digits Recognition Using an Ensemble Technique Based on the Firefly Algorithm
        Azar Mahmoodzadeh Hamed Agahi Marzieh  Salehi
        This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminat Full Text
        This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminate between digits, and finally combining the classifiers to enhance the overall system performance. First, a pre-processing course is performed to prepare the images for the main steps. Then three structural and statistical characteristics are extracted which include several features, among which a multi-objective genetic algorithm selects those more effective ones in order to reduce the computational complexity of the classification step. For the base classification, a decision tree (DT), an artificial neural networks (ANN) and a k-nearest neighbor (KNN) models are employed. Finally, the outcomes of the classifiers are fed into a classifier ensemble system to make the final decision. This hybrid system assigns different weights for each class selected by each classifier. These voting weights are adjusted by a metaheuristic firefly algorithm which optimizes the accuracy of the overall system. The performance of the implemented approach on the standard HODA dataset is compared with the base classifiers and some state-of-the-art methods. Evaluation of the proposed technique demonstrates that the proposed hybrid system attains high performance indices including accuracy of 98.88% with only eleven features. Manuscript Document