Deep Learning Neural Networks: Methods, Systems, and Applications

  in Special Issue   Posted on November 2, 2017

Information for the Special Issue

Submission Deadline: Sat 31 Mar 2018
Journal Impact Factor : 3.317
Journal Name : Neurocomputing
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/neurocomputing/call-for-papers/special-issue-on-deep-learning-neural-networks-methods-syste
Journal & Submission Website: https://www.journals.elsevier.com/neurocomputing

Special Issue Call for Papers:

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development.

The aim of this special issue of Neurocomputing is to showcase state-of-the-art work in the field of deep learning neural networks including their methods, systems, and applications. Original papers related are welcome. The list of possible topics includes, but is not limited to:

l New deep learning algorithms

l New neural network architectures for deep learning

l Hierarchical deep learning

l Multi-dimensional deep learning

l Deep learning of spatio-temporal data

l On-line deep learning neural networks

l Neuromorphic deep learning architectures

l Better combinations of existing algorithms and techniques for deep learning

l Combining policy learning, value learning, and model-based search

l Data-driven deep learning and control

l Optimization by deep neural networks

l Optimization and optimal decision in games by deep learning

l Mathematical analysis of deep learning (regarding convergence, optimality, stability, robustness, adaptability and so on)

l Applications of deep learning algorithms, architectures, and systems to robotics, control, data analysis, prediction and forecast, modeling and simulation, precision health, and other.

All high quality submitted papers related to the listed topics will be considered for publication in this special issue, provided they are recommended for publication after the review process. All manuscripts submission and review will be handled by Elsevier Editorial System http://ees.elsevier.com/neucom. Please, make sure to choose “SI:DLNN” as article type. All papers should be prepared according to NEUCOM Guide for Authors. Manuscripts should be no longer than 35 double-spaced pages without the title page, abstract, or references.

Important Dates:

Submission Deadline: 31 March 2018

First Review Decision: 15 July 2018

Revisions Due: 5 August 2018

Final Manuscript: 1 October 2018

Expected publication date: January 2019

Guest Editors:

Qinglai Wei,  Institute of Automation, Chinese Academy of Sciences, China

Nikola Kasabov,  KEDRI, Auckland University of Technology, New Zealand

Marios Polycarpou,  University of Cyprus, Cyprus

Zhigang Zeng,  Huazhong University of Science and Technology, China

 

 

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