2017 - Big Data Research: Special Issue on Hybrid Evolutionary and Swarm Techniques for Big Data Analytics and Applications

Big Data Research
Special Issue on Hybrid Evolutionary and Swarm Techniques for Big Data Analytics and Applications
https://www.journals.elsevier.com/big-data-researc...
IMPORTANT DATES:
• Submission Deadline: 30 Nov 2017
• Author Notification: 25 Feb 2018
• Revised Manuscript Due: 25 April 2018
• Notification of Acceptance: 30 May 2018
• Final Manuscript Due: 20 June 2018
• Tentative Publication Date: Sep 2018
SUBMISSION GUIDELINES:
1. http://www.journals.elsevier.com/big-data-research...
2. choose SI:HEST4BDAA
TOPIC SUMMARY:
Rapid growth of data has led to the urgent need to develop effective and efficient big data analytics techniques for industries and academia to discover information or knowledge from big data. Big data analytics concerns modern statistical and machine learning techniques to analyze huge amounts of data. Challenging issues in Big Data Analytics particularly include the high dimensionality of data and multiple objectives of the problems under study, in addition to the conventional 5Vs, i.e., large scale of data (Volume), multiple sources of data (Variety), rapid growth of data (Velocity), quality of data (Veracity), and usefulness of data (Value).
With powerful search capabilities for optimization, Evolutionary and Swarm Algorithms (ESA) have the potential to address the above challenges in the big data analytics today. Combined ESA with other conventional and recent statistical and machine learning techniques, development of hybrid ESA techniques for Big Data Analytics is a fast-growing and promising multidisciplinary research area. Hybrid ESA can be developed, with the foundations of ESA such as Genetic Algorithms, Differential Evolution, Particle Swarms, Ant Colony, Memetic Computing, Bacterial Foraging, Artificial Bees, and their hybrids, along with other general machine learning methods, for clustering, classification, regression, case-based reasoning, decision making methods, modelling.
This special issue aims to bring together academia and industry experts to report on the recent developments on hybrid evolutionary and swarm techniques for solving specific challenges of big data analytics from various industries. Relevant areas of interests include (but are not limited to) the following:
Hybrid analytics techniques with ESA for Big Data Analytics (BDA):
 Clustering with ESA for Big Data Analytics
 Regression with ESA for Big Data Analytics
 Classification with ESA for Big Data Analytics
 Association learning with ESA for Big Data Analytics
 Reinforcement learning with ESA for Big Data Analytics
 Fuzzy systems with ESA for Big Data Analytics
 Decision and recommendation algorithms with ESA for Big Data Analytics
 Knowledge based systems with ESA for Big Data Analytics
 Neural network algorithms with ESA for Big Data Analytics, etc
Big data analytics applications using hybrid ESA techniques in:
 Industrial systems
 Energy research
 Social network analysis
 Operations research and decision sciences
 Financial and economic analysis
 Internet computing
 Image processing
 Bioinformatics and computational biology
 Medicine and healthcare
 Environment and urban design, etc
GUEST EDITORS:
Kevin Kam Fung Yuen, School of Business,
Singapore University of Social Sciences, Singapore; (email: kfyuen-AT-suss.edu.sg , kevinkf.yuen-AT-gmail.com )
Steven Sheng-Uei Guan, Research Institute of Big Data Analytics,
Xi’an Jiaotong-Liverpool University, China (email: Steven.Guan-AT-xjtlu.edu.cn )
Richard Everson, Department of Computer Science,
Exeter University, United Kingdom (email: R.M.Everson-AT-exeter.ac.uk )
Kit Yan Chan, Department of Electrical and Computer Engineering,
Curtin University, Australia (email: kit.chan-AT-curtin.edu.au)
Vasile Palade, Faculty of Engineering and Computing
Coventry University, United Kingdom (email: vasile.palade-AT-coventry.ac.uk )