Neural Networks Special Issue: Neural Network Learning in Big Data

Big data is much more than storage of and access to data. Analytics plays an important role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms. Most machine learning algorithms encounter theoretical challenges in scaling up to big data. Plus there are challenges of high dimensionality, velocity and variety for all types of machine learning algorithms. The neural network field has historically focused on algorithms that learn in an online, incremental mode without requiring in-memory access to huge amounts of data. The brain is arguably the best and most elegant big data processor and is the inspiration for neural network learning methods. Neural network type of learning is not only ideal for streaming data (as in the Industrial Internet or the Internet of Things), but could also be used for stored big data. For stored big data, neural network algorithms can learn from all of the data instead of from samples of the data. And the same is true for streaming data where not all of the data is actually stored. In general, online, incremental learning algorithms are less vulnerable to size of the data. Neural network algorithms, in particular, can take advantage of massively parallel (brain-like) computations, which use very simple processors, that other machine learning technologies cannot. Specialized neuromorphic hardware, originally meant for large-scale brain simulations, is becoming available to implement these algorithms in a massively parallel fashion. Neural network algorithms, therefore, can deliver very fast and efficient real-time learning through the use of hardware and this could be particularly useful for streaming data in the Industrial Internet. Neural network technologies thus can become significant components of big data analytics platforms and this special issue will begin that journey with big data.

For this special issue of Neural Networks, we invite papers that address many of the challenges of learning from big data. In particular, we are interested in papers on efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of online learning to solve real-world big data problems (e.g. health care, transportation, and electric power and energy management).

RECOMMENDED TOPICS:

Topics of interest include, but are not limited to:

1. Autonomous, online, incremental learning – theory, algorithms and applications in big data
2. High dimensional data, feature selection, feature transformation – theory, algorithms and applications for big data
3. Scalable neural network algorithms for big data
4. Neural network learning algorithms for high-velocity streaming data
5. Deep neural network learning
6. Neuromorphic hardware for scalable neural network learning
7. Big data analytics using neural networks in healthcare/medical applications
8. Big data analytics using neural networks in electric power and energy systems
9. Big data analytics using neural networks in large sensor networks
10. Big data and neural network learning in computational biology and bioinformatics

SUBMISSION PROCEDURE:

Prospective authors should visit http://ees.elsevier.com/neunet/ for information on paper submission. During the submission process, there will be steps to designate the submission to this special issue. However, please indicate on the first page of the manuscript that the manuscript is intended for the Special Issue: Neural Network Learning in Big Data. Manuscripts will be peer reviewed according to Neural Networks guidelines.

Manuscript submission due: December 15, 2014
First review completed: March 1, 2015
Revised manuscript due: April 1, 2015
Second review completed, final decisions to authors: April 15, 2015
Final manuscript due: April 30, 2015

GUEST EDITORS:

• Asim Roy, Arizona State University, USA (asim.roy@asu.edu) (lead guest editor)
• Kumar Venayagamoorthy, Clemson University, USA (gkumar@ieee.org)
• Nikola Kasabov, Auckland University of Technology, New Zealand (nkasabov@aut.ac.nz)
• Irwin King, Chinese University of Hong Kong, China (irwinking@gmail.com)

Call for papers: IJCNN 2015

The 2015 International Joint Conference on Neural Networks will be held at the Killarney Convention Centre in Killarney, Ireland, July 12–16, 2015. The conference is organized jointly by the International Neural Network Society and the IEEE Computational Intelligence Society, and is the premiere international meeting for researchers and other professionals in neural networks and related areas.

The range of topics covered include, but is not limited to the following. (See http://ijcnn.org/2015 for a more detailed list of topics).

• Neural network theory & models
• Pattern recognition
• Computational neuroscience 
• Machine learning
• Cognitive models 
• Collective intelligence
• Brain-machine interfaces 
• Hybrid systems
• Embodied robotics 
• Self-aware systems
• Evolutionary neural systems 
• Data mining
• Neurodynamics 
• Sensor networks
• Neuroinformatics 
• Agent-based systems
• Neuroengineering 
• Computational biology
• Hardware, memristors 
• Bioinformatics
• Neural network applications 
• Artificial life
• Machine vision 
• Connectomics
• Big data 
• Deep learning

The conference will feature:

• Contributed technical talks and posters of latest research from around the world.
• Plenary lecturers by world-famous researchers in neural networks and related fields.
• Special sessions covering topics of active current interest.
• Pre-conference tutorials and post-conference workshops with presentations by experts.
• Challenging competitions on applying neural networks to hard computational problems.

Important Dates

Special session & competition proposals submission: November 10, 2014
Tutorial and workshop proposal submission: December 15, 2014
Paper submission: January 15, 2015
Paper decision notification: March 15, 2015
Camera-ready submission: April 15, 2015

Organizing Committee

General Chair
De-Shuang Huang, Tongji University, China
dshuang@tongji.edu.cn 

Program Chair
Yoonsuck Choe, Texas A&M University, USA
choe@tamu.edu
 
Technical Program Co-Chair
Haibo He, University of Rhode Island, USA
he@ele.uri.edu
 
Technical Program Co-Chair
Asim Roy, Arizona State University, USA
Asim.Roy@asu.edu
 
Plenary Chair
Carlo Morabito, Mediterranea Univ., Italy
 
Special Sessions Chair
Mike Gashler, University of Arkansas, USA
 
Tutorials Chair
Martin McGinnity, University of Ulster, UK
 
Workshop Chair
Pierre-Yves Oudeyer, Ensta Paris Tech, France
 
Poster Session Chair
Xiao-Hua (Helen) Yu, Cal Polytech Univ., USA
 
Competition Chair
Abir Hussain, Liverpool John Moores U., UK
 
Panels Chair
Juyang (John) Weng, Michigan State U., USA
 
Awards Chair
Plamen Angelov, Lancaster University, UK
 
Web Reviews Chair
Tomasz Cholewo, Lexmark Int’l Inc., US
 
Sponsors & Exhibits Chair
Hanning Zhou, Zhigu Technology, China
 
Publications Chair
Amir Hussain, University of Stirling, UK
 
International Liaison
Nikola Kasabov, Auckland U. of Tech., New Zealand
 
European Liaison
Peter Erdi, Kalamazoo College, USA
 
Publicity Co-Chair
Yun Raymond Fu, Northeastern Univ., USA
 
Publicity Co-Chair
Bill Howell, Natural Resources Canada, Canada
 
Local Arrangements Chair
Kang Li, Queens Univ., Belfast, UK
 
Webmaster
Jaerock Kwon, Kettering University, USA

Deadline extended for IJCNN 2013

The deadline for submitting papers to the International Joint Conference on Neural Networks (IJCNN) 2013 has been extended to 1 March 2013. Proposals for tutorials, workshops and panel sessions will also be accepted until this date. This conference will be held in Dallas, Texas, 4-9 August 2013.

Call for papers: IJCNN 2013

The deadline for submitting papers to the IEEE International Joint Conference on Neural Networks (IJCNN) 2013 is February 1, 2013. This conference will be held in Dallas, Texas, August 4-9, 2013.

Final call for papers: INNS-WC 2012

The final deadline for submitting papers to the International Neural Network Society Winter Conference (INNS-WC) has been extended to 15 June 2012. This conference will be held in Bangkok, Thailand, October 3-5, 2012.