Flex Conference (Physical / Digital)

International Conference on Big Data Analytics and Machine Learning Frameworks - (ICBDAMLF-26)

17th - 18th October 2026 , Miami - USA

Registration Options

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Call For Papers

The (ICBDAMLF-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 Big Data,Machine Learning,Information Technology 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:

  • Big data analytics frameworks comparison
  • Machine learning model development frameworks
  • Real-time analytics frameworks for big data
  • Big data visualization frameworks and tools
  • Machine learning for data analysis
  • Big data applications in analytics frameworks
  • Frameworks for scalable machine learning
  • Big data architecture for analytics solutions
  • Machine learning for data-driven insights
  • Big data analytics in cloud environments
  • Frameworks for big data processing
  • Machine learning for data integration
  • Big data governance frameworks
  • Frameworks for predictive analytics
  • Machine learning for data mining
  • Big data in business intelligence frameworks
  • Frameworks for data quality management
  • Machine learning for real-time analytics
  • Big data analytics for operational efficiency
  • Future trends in analytics frameworks

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.