Special issue: Advances in Deep Learning: Recent Trends, Challenges, and Applications (ADLRTCA)

Overview:

The concept of deep learning has been utilized in plenty of research areas and it can be considered as a fast-growing research field that has a tremendous impact on a plethora of daily life applications. In fact, deep learning has triggered a revolution in computation and engineering. This revolution started from the successful application of deep neural networks to artificial intelligence and was quickly spread to other fields such as electronic engineering, industrial engineering, cognitive science, and many others.

However, in spite of the considerable success of deep learning in various tasks, there are still significant challenges and existing tools and presented models cannot efficiently cover the requirements of researchers and developers who are working in this field. In fact, although deep learning has made considerable improvements, it also yielded significant side effects, such as complexity. Moreover, deep learning is particularly data-hungry and cannot be useful while a large number of training data is not available.

In this regard, we believe that today’s scientific and technological landscape looks appealing and positive for deep learning based researches. To this end, the goal of this special issue is to examine the latest theoretical and practical applications of deep learning to various fields to face the existing challenges with a particular focus on emerging applications for Engineering science, Computer Engineering, Artificial Intelligence, Electronic Engineering, Industrial Engineering, etc. Therefore, we aim to gather researchers with extensive expertise in deep learning to discuss their innovative works as well as their perspectives on future directions. Both original and review articles that are related to this rapidly growing interdisciplinary field are invited.

 

 

Potential topics:

Potential topics include but are not limited to the following:

  • Deep learning algorithms and architectures
  • Applications of deep learning in computer engineering
  • Applications of deep learning in electronic engineering
  • Applications of deep learning in industrial engineering
  • Applications of deep learning in computing and processing
  • Applications in any other field using deep learning methods

 

 

Deadline for manuscript submissions:

Important Dates

  • Paper Submission Due: February  28, 2021
  • Notification of Review Results: April 30, 2021
  • Revised Manuscript Due: May 30, 2021
  • Final Decision: July 30, 2021

 

Guest Editors:

Hossein Sadr, Department of  Computer Engineering, Rasht Branch, Islamic Azad University, Iran (sadr@qiau.ac.ir).

Eslam Nazemi, Faculty of Computer Science and Engineering Shahid Beheshti University Tehran, Iran (nazemi@sbu.ac.ir).

Gustavo Olague, Department of  Computer Science, CICESE Research Center, San Diego, United States (olague@cicese.mx).

Mehrgan Mahdavi, College of Engineering & Science, Victoria University, Sydney Campus, NSW 2000, Sydney, Australia (mehregan.Mahdavi@vu.edu.au).

Mohammad Reza Yamaghani, Department of  Computer Engineering. Lahijan Branch, Islamic Azad University, Iran (o_yamaghani@Liau.ac.ir ).

Alireza Nikravan, Department of  Computer Engineering. Karaj Branch, Islamic Azad University, Iran (nikravan@kiau.ac.ir).

 

 

 

Please submit a full-length paper through the Journal of Applied Research on Industrial Engineering online submission system and indicate it is to this special issue. Papers should be formatted according to the “Instructions for Authors” on the journal website.

 

To submit your manuscript to this special issue  you need to" Select Manuscript Type" as "SI: ADLRTCA".