Apologies for cross posting. Note the DUE DATES. Special Issue on Neural Network Learning in Big Data [Special Issue on Neural Network Learning in Big Data] 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: January 15, 2015 First review completed: April 1, 2015 Revised manuscript due: May 1, 2015 Second review completed, final decisions to authors: May 15, 2015 Final manuscript due: May 30, 2015 GUEST EDITORS: Asim Roy, Arizona State University, USA ([email protected]<mailto:[email protected]>) (lead guest editor) Kumar Venayagamoorthy, Clemson University, USA ([email protected]<mailto:[email protected]>) Nikola Kasabov, Auckland University of Technology, New Zealand ([email protected]<mailto:[email protected]>) Irwin King, Chinese University of Hong Kong, China ([email protected]<mailto:[email protected]>)
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