Special Session 1

Employing Machine Learning and Deep Learning for a Sustainable Future

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

Tishk International University, Erbil-Iraq

 

Session Chair:

• Prof. Kasim Mousa Al-Aubidy, Tishk International University, Erbil, Iraq.

• Prof. Nabil Derbel, Sfax University, Tunisia.

 

Session Co-Chairs:

• Prof. Adnan Al-Anbuky, Auckland University of Technology, New Zealand.

• Prof. Laszlo Koczy, Budapest University of Technology and Economics, Hungary.

• Prof. Faouzi Derbel, Leipzig University of Applied Sciences, Germany.

• Prof. Olfa Kanoun, Chemnitz University of Technology, Germany.

• Prof. Mohamed A. Deriche, Ajman University, UAE.

• Prof. Mohammed Baniyounis, Philadelphia University, Jordan.

• Prof. Ahmed Said Nouri, ENIS, Tunisia.

• Prof. Abdul Wahid Al-Saif, KFUPM, Saudi Arabia.

• Dr. Carlo Trigona, University of Catania, Italy.

• Dr. Ezideen Hasso, Tishk International University, Erbil, Iraq.

• Dr. Mohammed Salih, The University of Leeds, UK.

 

Synopsis:

Machine learning and deep learning are two key branches of artificial intelligence. In recent years, they have emerged as powerful technologies for managing complex systems and are increasingly forming the backbone of future sustainable solutions. Through the use of these technologies, models that can learn extremely complex patterns and relationships may be created. These models are capable of learning highly complex patterns and relationships including system behaviour and performance analysis, voices and image recognition, and enhancing command, control, monitoring, and alarming systems.

 

Topics:

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, revolutionising 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 The track welcomes the submission of unpublished research papers covering topics related to the use of deep learning in AI-based systems, which include, but are not limited to: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.

• Smart energies and smart cities.

• Recommendation and decision-making systems.

• Healthcare and biomedical systems.

• IoT and large-scale data analysis.