Flex Conference (Physical / Digital)

International Conference on Mathematical Modeling in Artificial Intelligence - (ICMMAI-27)

27th - 28th February 2027 , La Paz - Bolivia

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Call For Papers

The (ICMMAI-27) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Mathematical Modeling are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Mathematical modeling in artificial intelligence
  • Applications of neural networks in modeling
  • Machine learning algorithms for modeling
  • Mathematical models of intelligent systems
  • Optimization techniques in AI modeling
  • Data-driven approaches in AI applications
  • Mathematical modeling of cognitive processes
  • Applications of AI in healthcare modeling
  • Mathematical models of robotic systems
  • AI-enhanced decision-making models
  • Mathematical modeling of natural language processing
  • Applications of AI in climate modeling
  • Mathematical models of machine learning systems
  • AI in predictive analytics and modeling
  • Mathematical modeling of computer vision systems
  • Applications of AI in financial modeling
  • Mathematical models of social media dynamics
  • AI-driven optimization in logistics modeling
  • Mathematical modeling of autonomous systems
  • Ethical considerations in AI modeling

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.