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International Conference on Feature Engineering in Engineering Datasets - (ICFEED-27)

8th - 9th May 2027 , Munich - Germany

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Call For Papers

The (ICFEED-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 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:

  • Feature engineering techniques for engineering datasets
  • Importance of feature selection in data science
  • Case studies of feature engineering in practice
  • Automated feature extraction methods
  • Challenges in high-dimensional feature spaces
  • Feature engineering for time series data
  • Applications of domain knowledge in feature design
  • Feature engineering for machine learning models
  • Visualization techniques for feature analysis
  • Impact of feature engineering on model performance
  • Future trends in feature engineering practices
  • User experience design for feature engineering tools
  • Ethical considerations in feature selection
  • Collaborative feature engineering approaches
  • Scalability issues in feature engineering
  • Integrating feature engineering with data pipelines
  • Data quality issues in feature engineering
  • Frameworks for evaluating feature importance
  • Real-time feature engineering for streaming data
  • Interdisciplinary approaches to feature engineering

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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