会议信息

HNNDL 2027: International Conference on Human-Computer Interaction, Neural Networks and Deep Learning

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截稿日期:
2026-11-03 还有 28 天
通知日期:
2026-12-08
会议日期:
2027-01-22
会议地点:
Qingdao, China
届数:
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学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
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会议传承 (20%)
30
社区关注 (10%)
12
资料公开度 (15%)
55

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置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-06

征稿

HNNDL 2027 (International Conference on Human-Computer Interaction, Neural Networks and Deep Learning) is an academic conference held in Qingdao, China on 2027-01-22. The paper submission deadline is 2026-11-03. Acceptance notifications are sent on 2026-12-08.

2027 2nd International Conference on Human-Computer Interaction, Neural Networks and Deep Learning provides a premier forum for the presentation of new advances and research results in the fields of Human-Computer Interaction, Neural Networks and Deep Learning. The conference will bring together leading researchers, social workers and scientists in the domain of interest from around the world. The topics of interest for submission include, but are not limited to: ◕ Human–Computer Interaction Intelligent User Interfaces; Human–AI Collaboration; Affective Computing; Virtual and Augmented Reality; Brain–Computer Interfaces; Adaptive Interaction Systems; Eye-Tracking Interaction; Gesture and Speech Interaction; Usability and Accessibility; Multimodal Interaction; Human Behavior Analysis; Cognitive Modeling; Intelligent Agents; Personalized Interaction; Embodied Interaction ◕ Neural Networks Neural Network Architectures; Convolutional Neural Networks; Recurrent Neural Networks; Spiking Neural Networks; Graph Neural Networks; Attention Mechanisms; Neural Network Optimization; Transfer Learning; Neural Information Processing; Bio-inspired Neural Models; Reinforcement Learning; Self-Organizing Networks; Federated Neural Learning; Explainable Neural Models; Neural Computation for HCI ◕ Deep Learning Deep Representation Learning; Generative Models; Multimodal Deep Learning; Self-Supervised Learning; Large Language Models; Deep Reinforcement Learning; Few-shot and Zero-shot Learning; Explainable Deep Learning; Edge and Cloud Deep Learning; Trustworthy AI; Deep Learning for HCI; Vision–Language Models; Emotion Recognition; Adaptive Deep Systems; Deep Learning for Robotics
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