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A multi hidden recurrent neural network with a modified grey wolf optimizer

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  • Tarik A Rashid
  • Dosti K Abbas
  • Yalin K Turel

Abstract

Identifying university students’ weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students’ outcomes. This proposed system would improve instruction by the faculty and enhance the students’ learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.

Suggested Citation

  • Tarik A Rashid & Dosti K Abbas & Yalin K Turel, 2019. "A multi hidden recurrent neural network with a modified grey wolf optimizer," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-23, March.
    • Handle: RePEc:plo:pone00:0213237
    DOI: 10.1371/journal.pone.0213237
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    Cited by:

    1. Hassan, Bryar A. & Rashid, Tarik A., 2020. "Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation," Applied Mathematics and Computation, Elsevier, vol. 370(C).

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