Special Session 4

From Data to Decisions: 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 Laslzlo 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 behavior 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, 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 The track welcomes the submission of unpublished research papers covering topics related to the use of deep learning in AI-based systems, which include, but are not limited to: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.
• Smart energies and smart cities.
• Recommendation and decision-making systems.
• Healthcare and biomedical systems.
• IoT and large-scale data analysis.

 Paper Submission