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A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption

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 explor...

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Bibliographic Details
Main Authors: Haruna, Chiroma, Abdullahi, Usman Ali, Targio Hashem, Ibrahim Abaker, Saadi, Younes, Al-Dabbagh, Rawaa Dawoud, Ahmad, Muhammad Murtala, Emmanuel Dada, Gbenga, Danjuma, Sani, Maitama, Jaafar Zubairu, Abubakar, Adamu, Abdulhamid, Shafi’i Muhammad
Format: Book Chapter
Language:English
English
Published: Springer Verlag 2019
Subjects:
Q350 Information theory
Online Access:http://irep.iium.edu.my/74314/1/Advances%2Bon%2BComputational%2BIntelligence%2Bi.pdf
http://irep.iium.edu.my/74314/7/73214_A%20Theoretical%20Framework%20for%20Big%20Data%20Analytics_Scopus.pdf
http://irep.iium.edu.my/74314/
https://link.springer.com/chapter/10.1007/978-3-319-69889-2_1
https://doi.org/10.1007/978-3-319-69889-2_1
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Internet

http://irep.iium.edu.my/74314/1/Advances%2Bon%2BComputational%2BIntelligence%2Bi.pdf
http://irep.iium.edu.my/74314/7/73214_A%20Theoretical%20Framework%20for%20Big%20Data%20Analytics_Scopus.pdf
http://irep.iium.edu.my/74314/
https://link.springer.com/chapter/10.1007/978-3-319-69889-2_1
https://doi.org/10.1007/978-3-319-69889-2_1

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