Flex Conference (Physical / Digital)

International Conference on Big Data-driven Machine Learning for IT Optimization - (ICBDMLITO-26)

31st - 1st November 2026 , Guatemala City - Guatemala

Registration Options

Access Flexible Participation Categories

Call For Papers

The (ICBDMLITO-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-driven optimization techniques
  • Machine learning for operational efficiency
  • Data analytics for IT optimization
  • Big data applications in performance tuning
  • Machine learning for resource allocation
  • Big data insights for decision making
  • Optimization strategies using big data
  • Machine learning for cost reduction
  • Big data in supply chain optimization
  • Real-time analytics for IT performance
  • Big data visualization for optimization
  • Machine learning for predictive analytics
  • Big data in customer experience enhancement
  • Optimization of IT infrastructure with ML
  • Big data applications in marketing strategies
  • Machine learning for process improvement
  • Big data-driven business model innovation
  • Data quality for optimization processes
  • Machine learning for competitive advantage
  • Future directions in big data optimization

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.