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Nature-Inspired Computation in Data Mining and Machine Learning

  • Book
  • © 2020

Overview

Editors:
  1. Xin-She Yang

    1. School of Science and Technology, Middlesex University, London, UK

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  2. Xing-Shi He

    1. College of Science, Xi’an Polytechnic University, Xi’an, China

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    You can also search for this editor in PubMed   Google Scholar

  • Provides a timely review and summary of the latest developments in nature-inspired computation and its application in data mining and machine learning
  • Discusses key directions in topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, support vector machine, supervised learning, neural networks, logistic regression, feature selection and extraction, image processing and pattern recognition
  • Reviews both theoretical studies and applications, highlighting how nature-inspired computation combines with traditional techniques in data mining and machine learning to produce enhanced performance
  • Includes case studies from various applications and industries

Part of the book series: Studies in Computational Intelligence (SCI, volume 855)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xi
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  2. Adaptive Improved Flower Pollination Algorithm for Global Optimization

    • Douglas Rodrigues, Gustavo Henrique de Rosa, Leandro Aparecido Passos, João Paulo Papa
    Pages 1-21

  3. Algorithms for Optimization and Machine Learning over Cloud

    • Ratnik Gandhi, Mehul S Raval
    Pages 23-46

  4. Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities to Cyber Attacks

    • Mohamed Alloghani, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Thar Baker, Ahmed J. Aljaaf
    Pages 47-76

  5. Comparative Analysis of Different Classifiers on Crisis-Related Tweets: An Elaborate Study

    • Sukanya Manna, Haruto Nakai
    Pages 77-94

  6. An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm

    • Adis Alihodzic, Eva Tuba, Milan Tuba
    Pages 95-112

  7. Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services

    • Mohamed Alloghani, Thar Baker, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Ahmed J. Aljaaf
    Pages 113-136

  8. A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

    • Janmenjoy Nayak, Bighnaraj Naik, Danilo Pelusi, A. Vamsi Krishna
    Pages 137-159

  9. 3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures

    • Nabila Zrira, Mohamed Hannat, El Houssine Bouyakhf
    Pages 161-186

  10. Performance-Based Prediction of Chronic Kidney Disease Using Machine Learning for High-Risk Cardiovascular Disease Patients

    • Mohamed Alloghani, Dhiya Al-Jumeily, Abir Hussain, Panagiotis Liatsis, Ahmed J. Aljaaf
    Pages 187-206

  11. Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management

    • G. Remmiya Devi, M. Anand Kumar, K. P. Soman
    Pages 207-223

  12. Classification and Clustering Algorithms of Machine Learning with their Applications

    • Ravinder Ahuja, Aakarsha Chug, Shaurya Gupta, Pratyush Ahuja, Shruti Kohli
    Pages 225-248

  13. Hybrid Binary Particle Swarm Optimization and Flower Pollination Algorithm Based on Rough Set Approach for Feature Selection Problem

    • Mohamed A. Tawhid, Abdelmonem M. Ibrahim
    Pages 249-273
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Keywords

About this book

This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details.
 
Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.

Editors and Affiliations

  • School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  • College of Science, Xi’an Polytechnic University, Xi’an, China

    Xing-Shi He

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