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Nature Inspired Computing for Data Science

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Cover of 'Nature Inspired Computing for Data Science'

Table of Contents

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    Book Overview
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    Chapter 1 An Efficient Classification of Tuberous Sclerosis Disease Using Nature Inspired PSO and ACO Based Optimized Neural Network
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    Chapter 2 Mid-Term Home Health Care Planning Problem with Flexible Departing Way for Caregivers
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    Chapter 3 Performance Analysis of NASNet on Unconstrained Ear Recognition
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    Chapter 4 Optimization of Performance Parameter for Vehicular Ad-hoc NETwork (VANET) Using Swarm Intelligence
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    Chapter 5 Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data
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    Chapter 6 Application of Genetic Algorithms for Unit Commitment and Economic Dispatch Problems in Microgrids
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    Chapter 7 Application of Genetic Algorithms for Designing Micro-Hydro Power Plants in Rural Isolated Areas—A Case Study in San Miguelito, Honduras
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    Chapter 8 Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making
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    Chapter 9 Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multi-objective Particle Swarm Optimization
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    Chapter 10 Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study
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    Chapter 11 An Evolutionary Algorithm Based Hybrid Parallel Framework for Asia Foreign Exchange Rate Prediction
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Title
Nature Inspired Computing for Data Science
Published by
Springer International Publishing, March 2020
DOI 10.1007/978-3-030-33820-6
ISBNs
978-3-03-033819-0, 978-3-03-033820-6
Editors

Rout, Minakhi, Rout, Jitendra Kumar, Das, Himansu

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 14%
Student > Bachelor 1 14%
Other 1 14%
Student > Master 1 14%
Unknown 3 43%
Readers by discipline Count As %
Computer Science 3 43%
Engineering 1 14%
Unknown 3 43%