Conference Information

ICOAI 2026: International Conference on Artificial Intelligence

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ICOAI
Submission Date:
2026-07-20 Due in 8 days Extended
Notification Date:
2026-08-20
Conference Date:
2026-09-25
Location:
Paris, France
Years:
13
Viewed: 6300   Tracked: 0   Attend: 0

Call For Papers

ICOAI 2026 (International Conference on Artificial Intelligence) is an academic conference held in Paris, France on 2026-09-25. The paper submission deadline is 2026-07-20 (extended). Acceptance notifications are sent on 2026-08-20.

Topics of interest for submission include, but are not limited to: Track 1: Foundations of AI and Computational Intelligence - Artificial Intelligence in Scheduling and Optimization - Cognitive Systems and Applications - Collective Intelligence and Game Theory - Differential Evolution and Evolutionary Design - Knowledge Engineering and Modeling - Search and Constraint Satisfaction Track 2: Machine Learning, Deep Learning, and Data Science - Deep Learning and Neural Networks - Machine Learning and Probabilistic Inference - Reinforcement Learning and Bayesian Networks - Large Language Models and Generative AI - Foundation Models - Data Mining and Big Data Analytics Track 3: Cognitive Systems, Human-AI Interaction, and Linguistics - Natural Language Processing - Speech and Dialogue Systems - Human–Computer Interaction and Multimodal Interaction - Brain-Machine Interfaces - Cognitive Computing - Intelligent Tutoring Systems Track 4: AI Hardware, Robotics, and Embodied Intelligence - Robotics and Automated Systems - Computer Vision and Perception - Sensor Fusion - Hardware Implementations for AI - Fuzzy Systems for Robotics - Modeling and Identification Track 5: AI Applications and Cross-Disciplinary Research - AI in Healthcare and Bioinformatics - AI in Education - Internet of Things (AIoT) - Intelligent Database Systems and Knowledge Management - AI in Scheduling and Optimization - Multimedia and Arts Track 6: Responsible AI, Ethics, and Trustworthy Systems - Explainable AI and Trustworthy AI - Fairness and Bias - Causality and AI Alignment - Privacy-Preserving Techniques - AI Regulations and Ethics - AI Safety
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