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Advances on Computational Intelligence in Energy

Overview of attention for book
Cover of 'Advances on Computational Intelligence in Energy'

Table of Contents

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    Book Overview
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    Chapter 1 A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption
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    Chapter 2 Artificial Bee Colony for Minimizing the Energy Consumption in Mobile Ad Hoc Network
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    Chapter 3 A Novel Chicken Swarm Neural Network Model for Crude Oil Price Prediction
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    Chapter 4 Forecasting OPEC Electricity Generation Based on Elman Network Trained by Cuckoo Search Algorithm
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    Chapter 5 Variable Neighborhood Search-Based Symbiotic Organisms Search Algorithm for Energy-Efficient Scheduling of Virtual Machine in Cloud Data Center
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    Chapter 6 Energy Savings in Heterogeneous Networks with Self-Organizing Backhauling
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    Chapter 7 Integrated Resource Allocation Model for Cloud and Fog Computing: Toward Energy-Efficient Infrastructure as a Service (IaaS)
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    Chapter 8 Energy-Efficient Bias-Based User Association for Heterogeneous Networks in LTE-Advanced
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    Chapter 9 Energy Efficiency of Backhauling Options for Future Heterogeneous Networks
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    Chapter 10 Analysis of Energy Consumption Model in Cloud Computing Environments
Attention for Chapter 6: Energy Savings in Heterogeneous Networks with Self-Organizing Backhauling
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mendeley
3 Mendeley
Summary
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Chapter title
Energy Savings in Heterogeneous Networks with Self-Organizing Backhauling
Chapter number 6
Book title
Advances on Computational Intelligence in Energy
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-319-69889-2_6
Book ISBNs
978-3-31-969888-5, 978-3-31-969889-2
Authors

Nasir Faruk, Abdulkarim A. Oloyede, Abubakar Abdulkarim, Lukman A. Olawoyin, Yinusa A. Adediran

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 33%
Professor 1 33%
Lecturer > Senior Lecturer 1 33%
Readers by discipline Count As %
Unspecified 1 33%
Economics, Econometrics and Finance 1 33%
Unknown 1 33%

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