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Nature-Inspired Computation in Data Mining and Machine Learning

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Cover of 'Nature-Inspired Computation in Data Mining and Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Adaptive Improved Flower Pollination Algorithm for Global Optimization
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    Chapter 2 Algorithms for Optimization and Machine Learning over Cloud
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    Chapter 3 Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks
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    Chapter 4 Comparative Analysis of Different Classifiers on Crisis-Related Tweets: An Elaborate Study
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    Chapter 5 An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm
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    Chapter 6 Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services
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    Chapter 7 A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks
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    Chapter 8 3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures
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    Chapter 9 Performance-Based Prediction of Chronic Kidney Disease Using Machine Learning for High-Risk Cardiovascular Disease Patients
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    Chapter 10 Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management
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    Chapter 11 Classification and Clustering Algorithms of Machine Learning with their Applications
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    Chapter 12 Hybrid Binary Particle Swarm Optimization and Flower Pollination Algorithm Based on Rough Set Approach for Feature Selection Problem
Attention for Chapter 3: Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks
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Citations

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Readers on

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63 Mendeley
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Chapter title
Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks
Chapter number 3
Book title
Nature-Inspired Computation in Data Mining and Machine Learning
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-28553-1_3
Book ISBNs
978-3-03-028552-4, 978-3-03-028553-1
Authors

Mohamed Alloghani, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Thar Baker, Ahmed J. Aljaaf, Alloghani, Mohamed, Al-Jumeily, Dhiya, Hussain, Abir, Mustafina, Jamila, Baker, Thar, Aljaaf, Ahmed J.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 8%
Student > Bachelor 5 8%
Researcher 4 6%
Lecturer 4 6%
Professor 3 5%
Other 9 14%
Unknown 33 52%
Readers by discipline Count As %
Computer Science 11 17%
Engineering 8 13%
Arts and Humanities 3 5%
Agricultural and Biological Sciences 1 2%
Business, Management and Accounting 1 2%
Other 5 8%
Unknown 34 54%

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