Flex Conference (Physical / Digital)

International Conference on Machine Learning and Big Data in IT Service Management - (ICMLBDITSM-27)

17th - 18th March 2027 , Bodrum - Turkey

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Call For Papers

The (ICMLBDITSM-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 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:

  • Machine learning in IT service management
  • Big data analytics for service optimization
  • Predictive analytics in IT service delivery
  • Machine learning for incident management
  • Big data applications in customer support
  • Service quality improvement using ML
  • Data-driven decision making in IT services
  • Machine learning for service performance analysis
  • Big data in IT service automation
  • Real-time analytics for service management
  • Machine learning for user experience enhancement
  • Big data visualization for service insights
  • Data governance in IT service management
  • Machine learning for service level agreements
  • Big data in IT operations management
  • Machine learning for change management
  • Big data applications in IT support
  • Machine learning for capacity planning
  • Future trends in IT service management
  • Big data-driven IT service transformation

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.