Optimisation algorithm-based recurrent neural network for big data classification. (2nd March 2021)

• Record Type: Journal Article
• Authors: Akhtar, Md Mobin; Ahamad, Danish; AlamHameed, Shabi
• Is Part Of: International journal of intelligent information and database systems. Volume 14:Number 2(2021)
• Page Start: 153
• Page End: 176
• Publication Date: 2021-03-02
• Subjects: big data classification -- optimisation -- MapReduce -- recurrent neural network -- RNN -- Renyi entropy; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.