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International Conference on Statistical Learning and Stochastic Methods - (ICSL-SM-27)

18th - 19th May 2027 , Santa Coloma - Andorra

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Call For Papers

The (ICSL-SM-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 Probability Theory, 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:

  • Stochastic methods in machine learning
  • Statistical learning for dynamic systems
  • Stochastic modeling in data science
  • Applications of stochastic methods in finance
  • Statistical learning in time series forecasting
  • Stochastic methods for optimization problems
  • Statistical learning in computer vision
  • Stochastic modeling in healthcare systems
  • Applications of stochastic methods in engineering
  • Statistical learning for anomaly detection
  • Stochastic processes in artificial intelligence
  • Statistical learning in social sciences
  • Stochastic methods for risk management
  • Statistical learning in environmental modeling
  • Stochastic modeling in telecommunications
  • Applications of stochastic methods in logistics
  • Statistical learning for user behavior analysis
  • Stochastic methods in operations research
  • Statistical learning in energy systems
  • Stochastic modeling in supply chain optimization

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.

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