posted by organizer: kkfyuen || 290 views || tracked by 1 users: [display]

SI:HEST4BDAA 2018 : Big Data Research Journal Special Issue on Hybrid Evolutionary and Swarm Techniques for Big Data Analytics and Applications

FacebookTwitterLinkedInGoogle

Link: https://www.journals.elsevier.com/big-data-research/call-for-papers/special-issue-on-hybrid-evolutionary-and-swarm-techniques-fo
 
When Dec 30, 2017 - Jun 30, 2018
Where Online
Submission Deadline Nov 30, 2017
Notification Due Feb 25, 2018
Final Version Due Jun 20, 2018
Categories    big data   machine learning
 

Call For Papers

CALL FOR PAPERS
Big Data Research
Special Issue on Hybrid Evolutionary and Swarm Techniques for Big Data Analytics and Applications
https://www.journals.elsevier.com/big-data-research/call-for-papers/special-issue-on-hybrid-evolutionary-and-swarm-techniques-fo


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@suss.edu.sg , kevinkf.yuen@gmail.com )

Steven Sheng-Uei Guan, Research Institute of Big Data Analytics,
Xi’an Jiaotong-Liverpool University, China (email: Steven.Guan@xjtlu.edu.cn )

Richard Everson, Department of Computer Science,
Exeter University, United Kingdom (email: R.M.Everson@exeter.ac.uk )

Kit Yan Chan, Department of Electrical and Computer Engineering,
Curtin University, Australia (email: kit.chan@curtin.edu.au)

Vasile Palade, Faculty of Engineering and Computing
Coventry University, United Kingdom (email: vasile.palade@coventry.ac.uk )

Related Resources

ADAH 2017   Advanced Data Analytics in Health
ICPR 2018   24th International Conference on Pattern Recognition
ETHE Blearning 2017   Blended learning in higher education: research findings
ECCV 2018   European Conference on Computer Vision
NCUL 2018   Call For Book Chapters: Natural Computing for Unsupervised Learning Springer (USA)
NAACL HLT 2018   The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
MLDM 2018   14th International Conference on Machine Learning and Data Mining MLDM 2018
BigData Congress 2018   The 7th International Congress on Big Data
ICDM 2018   18th Industrial Conference on Data Mining ICDM 2018
DISP 2018   Special Issue on Data Intelligence in Security and Privacy, Journal of Information Security and Applications