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International Conference on Statistical Learning and Machine Learning Integration - (ICSLMLI-26)

29th - 30th October 2026 , Seville - Spain

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Call For Papers

The (ICSLMLI-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:

  • Integration of statistical learning and machine learning
  • Applications of machine learning in statistics
  • Statistical methods for predictive modeling
  • Bayesian statistics and machine learning synergy
  • Statistical learning techniques for big data
  • Feature selection methods in statistical learning
  • Statistical validation of machine learning models
  • Deep learning applications in statistical analysis
  • Statistical approaches to model interpretability
  • Ensemble methods in statistical learning
  • Statistical methods for time series forecasting
  • Applications of neural networks in statistics
  • Statistical learning in bioinformatics
  • Causal inference in machine learning contexts
  • Statistical frameworks for unsupervised learning
  • Statistical software for machine learning applications
  • Challenges in integrating statistics and machine learning
  • Statistical methods for anomaly detection
  • Ethics in statistical machine learning applications
  • Future directions in statistical learning 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.

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