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International Conference on AI-driven Data Science for Environmental Monitoring - (ICIADSEM-26)

21st - 22nd September 2026 , Leipzig - Germany

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Call For Papers

The (ICIADSEM-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 Artificial Intelligence,Data Science,Machine Learning 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:

  • AI techniques for environmental data analysis
  • Data science applications in climate change
  • Predictive modeling for environmental monitoring
  • Machine learning for biodiversity conservation
  • AI in pollution tracking and management
  • Data-driven approaches to resource management
  • Remote sensing data analysis with AI
  • AI for sustainable agriculture practices
  • Environmental impact assessment using data science
  • AI-driven disaster response strategies
  • Climate modeling with machine learning
  • Data ethics in environmental monitoring
  • AI for renewable energy optimization
  • Smart sensors for environmental data collection
  • Machine learning for water quality assessment
  • AI applications in wildlife protection
  • Data visualization techniques for environmental data
  • AI in urban heat island mitigation
  • Data science for ecological forecasting
  • Future trends in environmental AI research

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.

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