Flex Conference (Physical / Digital)

International Conference on Data-Driven Computational Techniques and Modeling - (ICDDCTM-27)

10th - 11th February 2027 , Dallas - USA

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Call For Papers

The (ICDDCTM-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 Computational 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:

  • Data-driven modeling techniques in engineering
  • Computational techniques for predictive analytics
  • Machine learning applications in modeling
  • Big data analytics in computational modeling
  • Data-driven optimization methods
  • Statistical modeling for data-driven insights
  • Simulation techniques for data-driven decisions
  • Modeling uncertainties in data-driven approaches
  • Applications of AI in computational techniques
  • Data-driven methodologies in scientific research
  • Integration of data science and modeling
  • Real-time data-driven computational techniques
  • Data-driven approaches to system design
  • Challenges in data-driven modeling
  • Innovations in computational modeling techniques
  • Data-driven decision-making frameworks
  • Collaborative data-driven modeling strategies
  • Data-driven simulations in various fields
  • Ethics in data-driven modeling practices
  • Future trends in data-driven computational techniques

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.