American Journal of Neural Networks and Applications

Special Issue

Electrical and Computer Science, Applications of Neural Networks and Deep Learning in Engineering

  • Submission Deadline: 20 December 2019
  • Status: Submission Closed
  • Lead Guest Editor: Ali Yekkehkhany
About This Special Issue
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
Lead Guest Editor
  • Ali Yekkehkhany

    University of Illinois, Urbana–Champaign, United States

Guest Editors
  • Mehdi Jafarnia

    University of Southern California, Los Angeles, United States

  • Negin Musavi

    University of Illinois at Urbana-Champaign, Urbana-Champaign, United States

  • Ebrahim Arian

    University of Illinois at Urbana-Champaign, Urbana-Champaign, United States

  • Payam Dibaeinia

    University of Illinois at Urbana-Champaign, Urbana-Champaign, United States

  • Alireza Moradzadeh

    University of Illinois at Urbana-Champaign, Urbana-Champaign, United States

  • Bobo Ju

    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China

  • Fan Xinxin

    Department of Information communication, State Grid Tongling Electric Power Supply Company, Tong Ling, China

  • Mohana Mohana

    Department of Computer Science and Engineering, Saranathan College of Engineering, Trichirappalli, India