会议信息

ICIRCA 2027: International Conference on Inventive Research in Computing Applications

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截稿日期:
2027-04-15 还有 169 天
通知日期:
2027-05-12
会议日期:
2027-07-22
会议地点:
Coimbatore, Tamil Nadu, India
届数:
浏览: 15787   关注: 0   参加: 0
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ICIRCA
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会伴指数 (CP-I)

48.6 / 100
全站第 2,027 名 / 共 5,761 个会议 · 前 36%
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
59
社区关注 (10%)
10
资料公开度 (15%)
55

用到的输入: 有据可查的届次:8 · 过去 24 个月打开过本页的研究者:3 人

公开资料里还缺: 历年录用率 (+4.5) · 最佳论文记录 (+2.3)
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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 1.2 · 算于 2026-10-27

征稿

ICIRCA 2027 (International Conference on Inventive Research in Computing Applications) is an academic conference held in Coimbatore, Tamil Nadu, India on 2027-07-22. The paper submission deadline is 2027-04-15. Acceptance notifications are sent on 2027-05-12.

We are pleased to invite the submis sion of high-quality, original research papers for the 7th International Conference on Inventive Research in Computing Applications (ICIRCA 2027). We encourage contributions that provide theoretical insights, innovative applications and practical solutions. List of potential topics include, but not limited to: Track-1: Context-based modeling, adaptation and personalization Location-aware computing techniques and applications Situation recognition and situation-based decision-making User-aware computing, modeling and profiling Emotion detection and recognition Social network analysis and modeling Physical context sensing, manipulation and perception Human computer interaction with cognitive aware computing Multimodal awareness with contextual interpretation and reasoning Track-2: Lightweight and secure communication protocols for IoT devices Machine learning for malware detection and classification Deep learning and Explainable AI for network intrusion detection and decision making Awareness-based threat intelligence and analysis Context-sensitive authorization and authentication Privacy-enhancing techniques for data collection and analysis Risk-based access control techniques Federated learning and Differential Privacy Techniques Explainability and transparency of AI-driven security decisions
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