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Flex Conference (Physical / Digital)

International Conference on Statistical Learning and Artificial Intelligence Applications - (ICSL-AIA-26)

5th - 6th October 2026 , Abu Dhabi - UAE

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Call For Papers

The (ICSL-AIA-26) 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 Statistics 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:

  • Statistical learning techniques and applications
  • Artificial intelligence in statistical analysis
  • Machine learning methods for data insights
  • Statistical challenges in AI applications
  • Applications of statistical learning in healthcare
  • Predictive modeling using statistical learning
  • Ethics in statistical learning research
  • Statistical software for AI applications
  • Data visualization in statistical learning
  • Statistical methods for deep learning
  • Meta-analysis of statistical learning studies
  • Future trends in statistical learning
  • Collaborative approaches in AI research
  • Statistical learning in social sciences
  • Statistical power in AI studies
  • Applications of statistical learning in finance
  • Statistical methods for natural language processing
  • Data-driven decision making with AI
  • Statistical learning in environmental studies
  • Applications of AI in educational research

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.