Intelligent and Innovative Data Science Methodologies to Support Real-World Applications

This special session is organised and supported by the University of Anbar

Session Committee:

  • Dr Ahmed J. Aljaaf (Centre of Computer – University of Anbar, Iraq)

  • Dr Mohamed Alloghani (Khawarizmi International College, UAE)

  • Dr Khattab Alheeti (College of Computer and IT – University of Anbar, Iraq) Dr Mohamed Mahyoub (Liverpool John Moores University, UK)

  • Dr Omar Khaldoon Abdulrahman (University of Anbar, Iraq)

  • Dr Salah L. Zubaidi (College of Engineering – University of Wasit, Iraq)

  • Dr Atheer Bassel (Centre of Computer – University of Anbar, Iraq)


Advances of data collection means and methods lead to a massive amount of multi-structured data that can be used to derive better information as a breakthrough toward more intelligent real-world applications. Using such data in a proper scientific way, via intelligent and data science solutions, can help to derive new solutions in Disease Prediction and Medical Imaging, develop Predictive Models for different Engineering Applications, Handling Complex Models for Cloud, IoT and Smart Traffic Applications, and helps to identify global trends on Social Media Networks. This special session provides an outstanding international forum for sharing knowledge and insights into intelligent and innovative research methodologies and results, in the light of intelligent data driven real-world applications.


This special session invites authors to submit high-quality research papers on the topics which include (but are not limited to) the following:

  • Data Science Methodologies.

  • Data analytics,Visualization and Decision Making.

  • Innovative approaches in Classification and Prediction using Machine Learning.

  • Computational Intelligence in Data-driven Applications.

  • Missing Data Analysis and Imputation using Data Science solutions and Machine Leaning.

  • Big Data Analytics using Data Science solutions and Machine Leaning.

  • Data science and machine learning in blockchain technology and cyber security.

  • Data Science to support Health and Medicine.

  • Data Science for Feature Learning and Extraction.

  • Data Science for Cloud Computing and Internet of Things (IoT).

  • Data Science and machine learning for Environmental and Civil Engineering.

  • Next-Generation Enterprise Data Science and Intelligent Methods.


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.

Submission implies the willingness of at least one of the authors to register and present their papers.