Flex Conference (Physical / Digital)

International Conference on Machine Learning and Statistical Computing - (ICMLSC-27)

11th - 12th June 2027 , Istanbul - Turkey

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Call For Papers

The (ICMLSC-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 Statistics, Data Science 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:

  • Machine learning techniques in statistics
  • Statistical computing for big data analysis
  • Applications of machine learning in research
  • Statistical methods for model evaluation
  • Machine learning for predictive modeling
  • Data visualization techniques in machine learning
  • Statistical challenges in machine learning
  • Machine learning for time series forecasting
  • Robustness in machine learning models
  • Applications of machine learning in finance
  • Statistical methods for feature selection
  • Machine learning in social sciences research
  • Deep learning and statistical methods
  • Ethics in machine learning applications
  • Machine learning for healthcare analytics
  • Statistical computing for high-dimensional data
  • Future trends in machine learning and statistics
  • Integrating machine learning with statistical theory
  • Statistical methods for ensemble learning
  • Machine learning for causal inference

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.