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

• 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

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...