Search Results - "Zarei, Bagher"

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  1. 1

    Community Detection in Social Networks Using a Local Approach Based on Node Ranking by Sheykhzadeh, Jafar, Zarei, Bagher, Soleimanian Gharehchopogh, Farhad

    Published in IEEE access (2024)
    “…Community detection is crucial for analyzing the structure of social networks and extracting hidden information from them. The goal is to find groups of nodes…”
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    Journal Article
  2. 2

    A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory by Zarei, Bagher, Meybodi, Mohammad Reza, Masoumi, Behrooz

    Published in New generation computing (01-04-2022)
    “…In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature…”
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    Journal Article
  3. 3

    Multiplex Community Detection in Social Networks Using a Chaos‐Based Hybrid Evolutionary Approach by Zarei, Bagher, Arasteh, Bahman, Asadi, Mehdi, Majidnezhad, Vahid, Afshord, Saeid Taghavi, Bouyer, Asgarali

    Published in Complexity (New York, N.Y.) (01-01-2024)
    “…Network analysis involves using graph theory to understand networks. This knowledge is valuable across various disciplines like marketing, management,…”
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    Journal Article
  4. 4

    Detecting community structure in complex networks using genetic algorithm based on object migrating automata by Zarei, Bagher, Meybodi, Mohammad Reza

    Published in Computational intelligence (01-05-2020)
    “…Community structure is an important topological feature of complex networks. Detecting community structure is a highly challenging problem in analyzing complex…”
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    Journal Article
  5. 5

    Improving learning ability of learning automata using chaos theory by Zarei, Bagher, Meybodi, Mohammad Reza

    Published in The Journal of supercomputing (2021)
    “…A learning automaton (LA) can be considered as an abstract system with a finite set of actions. LA operates by choosing an action from the set of its actions…”
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    Journal Article
  6. 6

    A Source-code Aware Method for Software Mutation Testing Using Artificial Bee Colony Algorithm by Arasteh, Bahman, Imanzadeh, Parisa, Arasteh, Keyvan, Gharehchopogh, Farhad Soleimanian, Zarei, Bagher

    Published in Journal of electronic testing (01-06-2022)
    “…The effectiveness of software test data relates to the number of found faults by the test data. Software mutation test is used to evaluate the effectiveness of…”
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    Journal Article
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