Nature-Inspired Computation in Data Mining and Machine Learning

✍ Scribed by Xin-She Yang, Xing-Shi He

• Publisher: Springer International Publishing
• Year: 2020
• Tongue: English
• Leaves: 282
• Series: Studies in Computational Intelligence 855
• Edition: 1st ed. 2020
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xi
Adaptive Improved Flower Pollination Algorithm for Global Optimization (Douglas Rodrigues, Gustavo Henrique de Rosa, Leandro Aparecido Passos, João Paulo Papa)....Pages 1-21
Algorithms for Optimization and Machine Learning over Cloud (Ratnik Gandhi, Mehul S Raval)....Pages 23-46
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
Comparative Analysis of Different Classifiers on Crisis-Related Tweets: An Elaborate Study (Sukanya Manna, Haruto Nakai)....Pages 77-94
An Improved Extreme Learning Machine Tuning by Flower Pollination Algorithm (Adis Alihodzic, Eva Tuba, Milan Tuba)....Pages 95-112
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
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
3D Object Categorization in Cluttered Scene Using Deep Belief Network Architectures (Nabila Zrira, Mohamed Hannat, El Houssine Bouyakhf)....Pages 161-186
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
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
Classification and Clustering Algorithms of Machine Learning with their Applications (Ravinder Ahuja, Aakarsha Chug, Shaurya Gupta, Pratyush Ahuja, Shruti Kohli)....Pages 225-248
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

✦ Subjects

Engineering; Computational Intelligence; Data Mining and Knowledge Discovery