Conference Information

ICICF 2027: International Conference on Intelligent Computing Frontiers

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Submission Date:
2026-09-30
Notification Date:
2026-12-01
Conference Date:
2027-01-21
Location:
Bengaluru, India
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Conference Partner Index (CP-I)

43.6 / 100
Ranked #3,482 of 5,695 conferences · Top 62%

#431 of 741 in Artificial Intelligence & Machine Learning

Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
8
Public record completeness (15%)
35

Inputs used: Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-11

Call For Papers

ICICF 2027 (International Conference on Intelligent Computing Frontiers) is an academic conference held in Bengaluru, India on 2027-01-21. The paper submission deadline is 2026-09-30. Acceptance notifications are sent on 2026-12-01.

Frontiers in Cognitive and Autonomous Systems Neuro symbolic AI and cognitive architectures Autonomous agents and self adaptive systems Human AI collaboration and ethical AI Intelligent decision making and reinforcement learning Emotion aware and context aware computing Information Theory and Intelligent Communication Systems Information theory and entropy based learning Coding theory and error correcting codes Data compression and information representation Information theoretic machine learning Reliable data transmission in IoT and communication networks Signal processing for intelligent communication systems Next Generation AI for Data, Vision, and Language Multimodal learning across text, vision, and speech Generative AI and foundation models AI for computer vision and video analytics Natural language understanding and conversational agents Bias, fairness, and interpretability in large scale AI models
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