International Conference on Sensor Networks and Machine Learning
(ICSNML - 26)

8th - 9th October 2026 Abu Dhabi, UAE (Hybrid Event)

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Call for Paper

The ICSNML 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 Machine Learning, 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:

  • Sensor data fusion techniques
  • Machine learning for sensor networks
  • Real-time data processing methods
  • Anomaly detection in sensor data
  • Energy-efficient sensor network designs
  • Scalability challenges in sensor networks
  • Machine learning algorithms for IoT
  • Applications of sensor networks
  • Data privacy in sensor networks
  • Distributed machine learning approaches
  • Sensor network security issues
  • Edge computing in sensor networks
  • Predictive maintenance using sensors
  • Sensor network optimization strategies
  • Data visualization techniques for sensors
  • Machine learning model interpretability
  • Collaborative sensor network systems
  • Wireless communication protocols for sensors
  • Smart city applications of sensors
  • Future trends in sensor networks
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.

Sponsored & Indexed by

Advancing Research Stability

SNRI maintains uninterrupted academic processes in the current global situation. Participants can engage and publish through online and blended conference formats.

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