A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption

• Main Authors: Chiroma, H., Abdullahi, U.A., Hashem, I.A.T., Saadi, Y., Al-Dabbagh, R.D., Ahmad, M.M., Dada, G.E., Danjuma, S., Maitama, J.Z., Abubakar, A., Abdulhamid, S.â��M.
• Format: Article
• Published: Springer Verlag 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069435045&doi=10.1007%2f978-3-319-69889-2_1&partnerID=40&md5=e381282f0ac52300283784893075ad37; http://eprints.utp.edu.my/23498/

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore...