Title: Optimisation algorithm-based recurrent neural network for big data classification

Authors: Md Mobin Akhtar; Danish Ahamad; Shabi AlamHameed

Addresses: Department of Basic Science, Riyadh Elm University, Riyadh, Kingdom of Saudi Arabia ' Collage of Science and Arts, Sajir, Shaqra University, Kingdom of Saudi Arabia ' Department of Computer Science, College of Science and Humanities, Huraymla, Shaqra University, Kingdom of Saudi Arabia

Abstract: This paper introduces a technique for big data classification using an optimisation algorithm. Here, the classification of big data is performed in a Hadoop MapReduce framework, wherein the map and reduce functions are based on the proposed dragonfly rider optimisation algorithm (DROA), which is designed by integrating the dragonfly algorithm (DA) and rider optimisation algorithm (ROA). The mapper uses the proposed optimisation as a mapper function for selecting the optimal features from the input big-data, for which the fitness function is based on Renyi entropy. Then, the selected features are subjected to the reducer phase, where the classification of the big data is performed using the DROA-based recurrent neural network (RNN), in which the RNN is trained by the proposed DROA. The result proves that the proposed method acquired a maximal accuracy of 0.996, the sensitivity of 0.995, and specificity of 0.995, respectively.

Keywords: big data classification; optimisation; MapReduce; recurrent neural network; RNN; Renyi entropy.

DOI: 10.1504/IJIIDS.2021.114527

International Journal of Intelligent Information and Database Systems, 2021 Vol.14 No.2, pp.153 - 176

Received: 30 Sep 2019
Accepted: 11 Apr 2020

Published online: 02 Mar 2021 *

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