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Electrical and Computer Science, Applications of Neural Networks and Deep Learning in Engineering
Submission Deadline: Sep. 30, 2019

This special issue currently is open for paper submission and guest editor application.

Join as Guest Editor Submit to Special Issue
Lead Guest Editor
University of Illinois, Urbana–Champaign, USA
Guest Editors
  • Mehdi Jafarnia
    University of Southern California
    Los Angeles, USA
  • Negin Musavi
    University of Illinois at Urbana-Champaign
    Urbana-Champaign, USA
  • Ebrahim Arian
    University of Illinois at Urbana-Champaign
    Urbana-Champaign, USA
  • Payam Dibaeinia
    University of Illinois at Urbana-Champaign
    Urbana-Champaign, USA
  • Alireza Moradzadeh
    University of Illinois at Urbana-Champaign
    Urbana-Champaign, USA
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=339). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

Special Issue Flyer (PDF)

Please download to know all details of the
Special Issue

Introduction
This special issue is on the theory of neural networks, deep learning, and reinforcement learning in addition to their applications in engineering, including but not limited to representation models, computational biology, automatic speech recognition, image recognition, visual art processing, drug discovery and toxicology, customer relationship management, recommendation systems, medical Image Analysis, mobile advertising, image restoration, financial fraud detection, bioinformatics, molecular dynamics, coarse-graining, natural language processing, etc. Although the main focus of this special issue is on neural network, deep learning, and reinforcement learning, it also includes other machine learning topics including but not limited to decision trees, support vector machines, Bayesian models, Genetic algorithms and their applications in supervised and semi-supervised learning, unsupervised learning, feature learning, anomaly detection, sparse dictionary learning, association rules, fairness in learning, prediction models, regression, online algorithms, ranking algorithms, recommendation systems, Bayesian inference, Bayesian prediction, hypothesis learning, risk minimization, regularization and stability, multi-class clustering, Bayesian SVM, evolutionary algorithms, and stochastic optimization. All researchers in the fields of electrical and computer engineering, computer science, mathematics, physics, civil engineering, biotechnology, mechanical engineering, chemistry, biology, medical, humanities, agriculture, horticulture, forestry, animation and multimedia are encouraged to submit a paper at this special issue as long as they take advantage of neural networks, deep learning, reinforcement learning, and any other machine learning tools in their papers.

Aims and Scope:

  1. Neural Networks
  2. Deep Learning
  3. Machine Learning
  4. Statistical Learning
  5. Artificial Intelligence
  6. Deep Neural Networks
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