IJCNN 2025: Call for Tutorial Proposals and Workshop Proposals!

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Call for Tutorial &
Workshop Proposals

Submission Deadline: 15 December 2024
15 November 2024: Special Session & Competition Proposals
15 December 2024: Tutorial & Workshop Proposals
15 January 2025: Regular Paper Submission
17 March 2025: Paper Acceptance Notification
1 May 2025: Final Camera Ready Paper
IJCNN is the premier international conference in the area of neural networks theory, analysis and applications. Whether you are presenting your latest work, learning from experts, or networking with peers, IJCNN 2025 promises to be an unforgettable experience that will inspire and propel the AI and neural network community forward. We look forward to welcoming you in Rome!

IJCNN 2025 will feature pre-conference tutorials, covering fundamental and advanced topics in AI and neural networks.

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Topics of Interest
Papers for IJCNN 2025 will be reviewed by experts in the fields and ranked based on the criteria of originality, significance, quality, and clarity. Prospective authors are invited to submit complete papers of no more than eight (8) pages in IEEE two-column conference proceedings format. Authors should submit their papers in PDF through the online submission system, which will be available at https://2025.ijcnn.org/.
Topics of interest include but are not limited to:
  • AI for Critical Infrastructures
  • AI for Education
  • AI for Neural Engineering
  • Brain-machine Interfaces
  • Cognitive Models
  • Collective & Ensemble Intelligence
  • Computational Neuroscience
  • Dynamic Neural Networks
  • Efficient and Tiny Neural Networks
  • Ethics and Regulation in AI
  • Generative AI Models
  • Graph Neural Networks
  • Interpretable and Explainable AI
  • Large Language Models
  • Large-Scale Neural Networks
  • Mixture of Experts
  • Modular Neural Networks
  • Neural Engineering
  • Neural Network Applications
  • Neural Networks for Sciences
  • Neuromorphic Systems
  • Neurosymbolic AI
  • Perceptual Neural Networks
  • Quantum Neural Networks
  • Reinforcement Learning
  • Representation & Reasoning
  • Reservoir & Echo-State Networks
  • Spiking Neural Networks
  • Theory of Neural Networks
  • Transformer Networks
  • Trustworthy and Reliable AI
  • Unsupervised Learning
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Conference Sponsor

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