SNRI

(ICMLFDE - 26) | Hybrid Event
30th - 31st October 2026 | Geneva, Switzerland

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International Conference on Machine Learning for Fault Diagnosis in Engineering
(ICMLFDE - 26)

30th - 31st October 2026 Geneva, Switzerland (Hybrid Event)

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Call for Paper

The ICMLFDE 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 Data Science, 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:

  • Machine learning for fault diagnosis
  • Predictive analytics in engineering systems
  • Challenges in fault diagnosis applications
  • Real-time monitoring for fault detection
  • Statistical methods for fault diagnosis
  • Impact of IoT on fault diagnosis
  • Data fusion techniques for diagnosis
  • Visualization of fault diagnosis results
  • Machine learning algorithms for fault detection
  • Future trends in fault diagnosis technology
  • Integration of diverse data sources
  • Ethical considerations in fault diagnosis
  • Adaptive fault diagnosis strategies using AI
  • Collaborative approaches to fault diagnosis
  • Benchmarking fault diagnosis models
  • User experience in fault diagnosis applications
  • Data-driven decision making in diagnostics
  • Machine learning frameworks for fault diagnosis
  • Applications of deep learning in diagnostics
  • Case studies in fault diagnosis success
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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