Special Session 7

Deep Learning Techniques and Applications

This special session is organised and supported by  the following educational partner

Philadelphia University, Amman


Session Chair:

  • Prof. Kasim Mousa Al-Aubidy, Philadelphia University, Jordan
  • Prof. Adnan Al-Anbuky, Auckland University of Technology, New Zealand

Session Co-Chairs:

  • Prof. Faouzi Derbel, Leipzig University of Applied Sciences, Germany
  • Prof. Olfa Kanoun, Chemnitz University of Technology, Germany
  • Prof. Nabil Derbel, Sfax University, Tunisia
  • Prof. Mohamed A. Deriche, Ajman University, UAE
  • Prof. Ahmed Said Nouri, ENIS, Tunisia
  • Prof. Abdul Wahid Al-Saif, KFUPM, Saudi Arabia.


Deep learning is a type of machine learning that aims to train artificial neural networks with multiple hidden layers. Deep learning technology has become one of the most powerful technologies used in the field of artificial intelligence. Deep learning technology can be used to develop models that learn very complex patterns and relationships such as recognizing the behavior and performance of various engineering systems, recognizing sound and images, and using this in command, control and monitoring systems.


Deep learning uses artificial neural networks for learning and prediction from large amounts of data. Deep learning plays an important role in AI-based systems, revolutionizing the field and enabling significant advances in various applications. The track welcomes the submission of unpublished research papers covering topics related to the use of deep learning in AI-based systems, which include, but are not limited to:

  • Automatic learning and feature extraction from input data.
  • Pattern recognition and complex data classification.
  • Computer vision and image understanding.
  • Natural language processing.
  • Robotics and smart automation.
  • Recommendation and decision-making systems.
  • Healthcare and biomedical systems.
  • IoT and large-scale data analysis.

Paper Submission:

Prospective authors are invited to submit full-length papers (not exceeding 6 pages) conform to the IEEE format . All papers will be handled and processed electronically via the EDAS online submission system.