Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing

✍ Scribed by Simon James Fong, Richard C. Millham

• Publisher: Springer Singapore;Springer
• Year: 2021
• Tongue: English
• Leaves: 228
• Series: Springer Tracts in Nature-Inspired Computing
• Edition: 1st ed.
• Category: Library

✦ Synopsis

This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.

✦ Table of Contents

Front Matter ....Pages i-ix
The Big Data Approach Using Bio-Inspired Algorithms: Data Imputation (Richard Millham, Israel Edem Agbehadji, Hongji Yang)....Pages 1-19
Parameter Tuning onto Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-Dimensional Bioinformatics Datasets (Richard Millham, Israel Edem Agbehadji, Hongji Yang)....Pages 21-42
Data Stream Mining in Fog Computing Environment with Feature Selection Using Ensemble of Swarm Search Algorithms (Simon Fong, Tengyue Li, Sabah Mohammed)....Pages 43-65
Pattern Mining Algorithms (Richard Millham, Israel Edem Agbehadji, Hongji Yang)....Pages 67-80
Extracting Association Rules: Meta-Heuristic and Closeness Preference Approach (Richard Millham, Israel Edem Agbehadji, Hongji Yang)....Pages 81-95
Lightweight Classifier-Based Outlier Detection Algorithms from Multivariate Data Stream (Simon Fong, Tengyue Li, Dong Han, Sabah Mohammed)....Pages 97-125
Comparison of Contemporary Meta-Heuristic Algorithms for Solving Economic Load Dispatch Problem (Simon Fong, Tengyue Li, Zhiyan Qu)....Pages 127-144
The Paradigm of Fog Computing with Bio-inspired Search Methods and the “5Vs” of Big Data (Richard Millham, Israel Edem Agbehadji, Samuel Ofori Frimpong)....Pages 145-167
Approach to Sentiment Analysis and Business Communication on Social Media (Israel Edem Agbehadji, Abosede Ijabadeniyi)....Pages 169-193
Data Visualization Techniques and Algorithms (Israel Edem Agbehadji, Hongji Yang)....Pages 195-205
Business Intelligence (Richard Millham, Israel Edem Agbehadji, Emmanuel Freeman)....Pages 207-218
Big Data Tools for Tasks (Richard Millham)....Pages 219-226

✦ Subjects

Engineering; Computational Intelligence; Algorithm Analysis and Problem Complexity; Big Data; Database Management; Information Systems Applications (incl.Internet)