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IM4BigData 2016 : Special Issue on Computational Intelligence, Neural, and Nature-inspired Methods for Big Data Processing


When N/A
Where N/A
Submission Deadline May 27, 2016
Notification Due Aug 19, 2016
Final Version Due Oct 14, 2016
Categories    data mining   computational intelligence   journal special issue   big data

Call For Papers


Special Issue on Computational Intelligence, Neural,
and Nature-inspired Methods for Big Data Processing

a special issue of

Computational Intelligence and Neuroscience, Hindawi
SCI JCR IF=0.596 (Q4), Scimago SJR=2.9 (Q1)

Call for papers
Today’s complex cyber-physical and information systems are increasingly
characterized by the production of big data, a type of content known for its
challenging properties. Such data usually combines high dimensionality, large
volumes, heterogeneous nature, noisiness, and incompleteness with many other
properties that make it exceptionally hard to analyze. As an informal umbrella
term, big data is usually characterized by three or more Vs, volume, velocity,
variety, and value/variability/virtuality, or by the HACE theorem which
emphasizes the heterogeneous, decentralized, and autonomous nature of its
sources together with the complexity and evolving dispositions of its internal
relationships. An efficient processing (acquisition, transfer, and storage) and
analysis (mining, pattern recognition, visualization, classification, and
prediction) of such data is a key research challenge that requires
sophisticated, intelligent approaches.

It has been shown that it is difficult or even impossible to manage and analyze
big data using traditional algorithms and technologies. Computational
intelligence, neural, and nature-inspired models and methods, on the other hand,
have shown ability to successfully tackle a variety of hard real-world problems.
However, the complex nature of big data represents even for intelligent methods
a number of challenges and opens a variety of research questions.

This special issue focuses on recent development and applications of
intelligent, neural, and nature-inspired methods to big data processing. It aims
to bridge the gap between the big data and computational intelligence
communities and provides a common multidisciplinary platform for dissemination
of latest research in these fields.

Potential topics include, but are not limited to:

- Big data in neuroscience and neuroinformatics
- Neural modelling and computing for big data
- Deep learning, deep neural networks, neuroevolution, and big data
- Bayesian and probabilistic methods for big data
- Soft, fuzzy, and neuro-fuzzy systems for big data
- Supervised, unsupervised, and reinforcement learning for big data
- Swarm intelligence and evolutionary methods for big data
- Intelligent big graph representation, visualization, and mining
- Intelligent, neural, and nature-inspired pattern recognition in big data
- Intelligent, neural, and nature-inspired methods for big sensor networks and
streaming data
- Parallel, accelerated, and distributed intelligence for big data
- High performance computing for intelligent, neural, and nature-inspired

Papers submitted to this special issue for possible publication must be original
and must not be under consideration for publication in any other journal or
conference. Authors can submit their manuscripts via the Manuscript Tracking
System at

Important dates:
Manuscript Due Friday, 27 May 2016
First Round of Reviews Friday, 19 August 2016
Publication Date Friday, 14 October 2016

Guest editors:

Lead Guest Editor
Pavel Kromer, VSB-Technical University of Ostrava, Ostrava, Czech Republic

Guest Editors
Adel Alimi, University of Sfax, Sfax, Tunisia
Katarzyna Wegrzyn-Wolska, ESIGETEL, Villejuif, France
Vaclav Snasel, VSB-Technical University of Ostrava, Ostrava, Czech Republic

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