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International Conference on Computational Methods for Industrial Applications - (ICCMIA-26)

21st - 22nd September 2026 , Rawalpindi - Pakistan

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Call For Papers

The (ICCMIA-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 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:

  • Computational methods for industrial optimization
  • Simulation of manufacturing processes and systems
  • Data analytics in industrial applications
  • Computational modeling of supply chain dynamics
  • Machine learning in industrial automation
  • Optimization techniques for production systems
  • Computational methods for quality control
  • Simulation of logistics and transportation systems
  • Computational approaches to process engineering
  • Industry 4.0 and digital transformation
  • Computational modeling of energy systems
  • Predictive maintenance using data analytics
  • Computational methods for resource management
  • Simulation of industrial safety systems
  • AI applications in manufacturing efficiency
  • Computational techniques for product design
  • Integration of IoT in industrial applications
  • Computational methods for environmental sustainability
  • Simulation of complex industrial networks
  • Big data in industrial decision-making

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.