(ICBDAMLF - 26) | Hybrid Event
11th - 12th June 2026 | Monrovia, Liberia

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

11th - 12th June 2026 Monrovia, Liberia (Hybrid Event)

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Call for Paper

The ICBDAMLF is committed to addressing global challenges through impactful research and sustainable solutions. It brings together researchers dedicated to advancing knowledge for societal benefit. Focusing on Big Data,Machine Learning,Information Technology, the conference promotes research aligned with global development goals and long-term sustainability. 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
Review & Publication

All submissions will be reviewed for their contribution to global impact and research quality. Accepted papers will be presented and considered for publication in reputed platforms.

Registration

Join participants from around the world by completing your registration and becoming part of a global research community.

Publication

Accepted papers will gain international exposure through conference presentations and publication opportunities.

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