Progress on Artificial Neural Networks for Big Data Analytics: A Survey

• Main Authors: Haruna Chiroma, Usman Ali Abdullahi, Shafi'i Muhammad Abdulhamid, Ala Abdulsalam Alarood, Lubna A. Gabralla, Nadim Rana, Liyana Shuib, Ibrahim Abaker Targio Hashem, Dada Emmanuel Gbenga, Adamu I. Abubakar, Akram M. Zeki, Tutut Herawan
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Big data analytics; artificial neural networks; evolutionary neural network; convolutional neural network; dataset
• Online Access: https://ieeexplore.ieee.org/document/8531611/

Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of “big data.” Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics and is 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 analytics and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, and several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of the ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of the ANN in big data analytics.