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ICAIEE 2026: International Conference on Artificial Intelligence for Ecology and Environment

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Día de Entrega:
2026-09-04 Faltan 9 días
Fecha de Notificación:
2026-09-11
Fecha de conferencia:
2026-09-18
Ubicación:
Qufu, China
Vistas: 34   Seguidores: 0   Asistentes: 0

Solicitud de Artículos

ICAIEE 2026 (International Conference on Artificial Intelligence for Ecology and Environment) is an academic conference held in Qufu, China on 2026-09-18. The paper submission deadline is 2026-09-04. Acceptance notifications are sent on 2026-09-11.

The 2026 International Conference on Artificial Intelligence for Ecology and Environment (ICAIEE 2026) will be held in Qufu, Shandong, China from September 18 to 20, 2026. The conference focuses on how artificial intelligence can empower ecological and environmental research and support sustainable development. ICAIEE 2026 will feature keynote speeches, thematic forums, poster presentations, and editorial roundtable discussions. It aims to build an interdisciplinary academic platform for researchers working in ecology, AI, geography, resource management, environmental big data, biodiversity conservation, and ecological risk prediction. Original papers are invited in areas such as remote sensing ecology, AI-enabled environmental monitoring, pollution ecology, ecosystem modeling, biodiversity protection, intelligent soil ecology, climate change, forest ecosystem management, and sustainable land management. All papers will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers will bepublished in the ACM Conference Proceedings, and submitted to EI Compendex, Scopus for indexing. The topics of interest for submission include, but are not limited to: Track 1: Computational Intelligence in Ecology and Environment Computational Intelligence for Ecology and Environment Evolutionary Computation Methods Swarm Intelligence Algorithms Environmental System Optimization Heuristic Optimization Methods Adaptive Environmental Systems Hybrid Intelligent Models Learning-Driven Optimization Complex Environmental System Modeling   Track 2: Environmental Data-Driven Modeling and Analysis Environmental Data Modeling Environmental Data Analytics Spatiotemporal Environmental Data Environmental Predictive Analytics Feature Learning Methods Data Mining Techniques Intelligent Data Analysis Track 3: Ecological and Climate System Modeling Ecosystem Modeling Climate Change Analysis Biodiversity Research Resource and Environmental Modeling Environmental Prediction Methods System Simulation Techniques Sustainable Development Modeling Carbon Cycle and Environmental Systems Ecological Data Analysis   Track 4: Machine Learning for Earth Sciences Satellite Image Processing Neural Networks Hybrid Models Earth System Science Causal Inference Complex Climate Risk Management Remote Sensing Image Processing
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