Special Session: Applied Mathematics in AI

A special issue in applied mathematics related to machine learning would likely include articles focusing on the mathematical foundations and different techniques used in various machine learning algorithms, as well as their impact into the real-world problems. This could include topics such as optimization, features extraction, features selection, statistical learning theory, deep learning, reinforcement learning, game theory, cypersecurity, and graphical representation….

The articles may also tackle challenges and limitations of current state of the arts related to machine learning and propose new approaches from mathematics to overcome them.

Our special session will be more interested in seeing how we can create new mathematical models that were inspired from unsolved conjectures and from Nature to tackle and solve challenges in Computer sciences.

 

Aims and Scope

 The recommended topics include, but are not limited to the following:

  • Data mining and knowledge discovery;
  • Data Management with AI
  • Feature Selection and Feature extraction
  • Data Fusion & Knowledge Discovery
  • Imbalanced Datasets
  • Feature Reduction techniques
  • Pattern recognition and prediction
  • Novel clustering techniques for datasets
  • Use of AI in Digital Image Processing
  • AI and machinelearning in signal processing
  • AI in mobile and modern wireless networks
  • Satellite communication and AI
  • Defense systems in the age of AI
  • AI for energy efficient systems
  • AI-enabled biomedical engineering
  • AI in health sciences
  • AI in Oil and Gas Sector
  • Regression and forecasting for medical and/or biomedical signals
  • AI for precision medicine
  • Recent advancements and Applications in AI
  • AI in decision making and policy making
  • AI towards Automation
  • Challenges and limitation in AI and future prospective of AI in general
  • Cybersecurity

 

Guest Editor

Samir Brahim Belhaouari

Hamad Bin Khalifa University, Qatar

 

Paper Submission for Special Sessions

Please send your manuscript files and any supporting documents by emailing This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

 

 

 

 

 

 

 

 

Contact us

Tel: (00)962-5-38 211 00 /1500

Fax: 962-5-3821120

Email: iacmc@zu.edu.jo 

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