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Title: Progress on Artificial Neural Networksfor Big Data Analytics: A Survey
Authors: Chiroma, Haruna
Abdullahi, Usman Ali
Abdulhamid, Shafi’i Muhammad
Keywords: Big data analytics
artificial neural networks
evolutionary neural network
convolutionalneural network
Issue Date: 3-Apr-2018
Publisher: IEEE Access
Series/Report no.: 7: 2018;
Abstract: Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has broughtthe world into the era of ‘‘big data.’’ Artificial neural networks (ANNs) are known for their effectiveness andefficiency for small datasets, and this era of big data has posed a challenge to the big data analytics usingANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics andis still ongoing, although it is in it is early stages. The purpose of this paper is to summarize recent progress,challenges, and opportunities for future research. This paper presents a concise view of the state of the art,challenges, and future research opportunities regarding the applications of the ANN in big data analyticsand reveals that progress has been made in this area. Our review points out the limitations of the previousapproaches, the challenges in the ANN approaches in terms of their applications in big data analytics, andseveral ANN architecture that have not yet been explored in big data analytics and opportunities for futureresearch. We believe that this paper can serve as a yardstick for future progress on the applications of theANN in big data analytics as well as a starting point for new researchers with an interest in the explorationof the ANN in big data analytics.
ISSN: 2169-3536
Appears in Collections:Cyber Security Science

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