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<p class="MsoNormal">Apologies for cross posting.<o:p></o:p></p>
<p class="MsoNormal"><span style="color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal" style="line-height:16.8pt;background:#F3F3F3"><b><span style="font-size:12.5pt;font-family:"Helvetica","sans-serif";color:#666666">Special Issue on Neural Network Learning in Big Data<o:p></o:p></span></b></p>
<p class="MsoNormal" style="margin-left:15.0pt;line-height:13.5pt"><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><img width="220" height="160" id="Picture_x0020_1" src="cid:image001.jpg@01D00FCD.96B040B0" alt="Special Issue on Neural Network Learning in Big Data"></span><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<i><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Neural Networks</span></i><b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"> Special Issue: Neural Network Learning in Big Data</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">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.<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">For this special issue of
<i>Neural Networks</i>, 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).<o:p></o:p></span></p>
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<b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">RECOMMENDED TOPICS</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">:<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Topics of interest include, but are not limited to: <o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">1.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Autonomous, online, incremental learning – theory, algorithms and applications in big data<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">2.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">High dimensional data, feature selection, feature transformation – theory, algorithms and applications for big data<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">3.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Scalable neural network algorithms for big data<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">4.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Neural network learning algorithms for high-velocity streaming data<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">5.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Deep neural network learning<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">6.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Neuromorphic hardware for scalable neural network learning<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">7.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Big data analytics using neural networks in healthcare/medical applications<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">8.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Big data analytics using neural networks in electric power and energy systems<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">9.<span style="font:7.0pt "Times New Roman"">
</span></span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Big data analytics using neural networks in large sensor networks<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:3.75pt;margin-left:41.25pt;text-indent:-.25in;line-height:13.5pt;mso-list:l1 level1 lfo3">
<![if !supportLists]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333"><span style="mso-list:Ignore">10.</span></span><![endif]><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Big data and neural
network learning in computational biology and bioinformatics<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">SUBMISSION PROCEDURE</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">:<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Prospective authors should visit
<a href="http://ees.elsevier.com/neunet/" target="_blank"><span style="color:#00759B;text-decoration:none">http://ees.elsevier.com/neunet/</span></a> 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
<i>Special Issue: Neural Network Learning in Big Data</i>. Manuscripts will be peer reviewed according to
<i>Neural Networks</i> guidelines.<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Manuscript submission due: January 15, 2015<br>
First review completed: April 1, 2015<br>
Revised manuscript due: May 1, 2015<br>
Second review completed, final decisions to authors: May 15, 2015<br>
Final manuscript due: May 30, 2015<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">GUEST EDITORS</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">:<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Asim Roy,
</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Arizona State University, USA (<a href="mailto:asim.roy@asu.edu" target="_blank"><span style="color:#00759B;text-decoration:none">asim.roy@asu.edu</span></a>) (lead
guest editor)<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Kumar Venayagamoorthy</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">, Clemson University, USA (<a href="mailto:gvenaya@clemson.edu" target="_blank"><span style="color:#00759B;text-decoration:none">gkumar@ieee.org</span></a>)<o:p></o:p></span></p>
<p class="MsoNormal" style="mso-margin-top-alt:0in;margin-right:0in;margin-bottom:7.5pt;margin-left:15.0pt;line-height:18.0pt">
<b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Nikola Kasabov</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">, Auckland University of Technology, New Zealand (<a href="mailto:nkasabov@aut.ac.nz" target="_blank"><span style="color:#00759B;text-decoration:none">nkasabov@aut.ac.nz</span></a>)<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-left:15.0pt;line-height:18.0pt"><b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">Irwin King</span></b><span style="font-size:9.0pt;font-family:"Helvetica","sans-serif";color:#333333">, Chinese
University of Hong Kong, China (<a href="mailto:irwinking@gmail.com" target="_blank"><span style="color:#00759B;text-decoration:none">irwinking@gmail.com</span></a>)<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#1F497D"><o:p> </o:p></span></p>
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