会议信息

ASID 2024: International Conference on Anti-counterfeiting, Security, and Identification

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截稿日期:
2024-08-15 Extended
通知日期:
2024-09-10
会议日期:
2024-11-01
会议地点:
Xiamen, China
届数:
18
QUALIS: B4   浏览: 44230   关注: 21   参加: 19

会伴指数 (CP-I)

54.0 / 100
全站第 879 名 / 共 5,647 个会议 · 前 16%

安全与隐私 第 69 / 357

学术认可 (35%)
54
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入
会议传承 (20%)
79
社区关注 (10%)
40
资料公开度 (15%)
35

用到的输入: 收录等级:QUALIS B4 · 有据可查的届次:18 · 在会伴关注它的研究者:21 人 · 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) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后,可直接在这一页补上;分数每晚重算。如何提升这个分数

置信度 80% —— 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 2026-09-02

征稿

ASID 2024 (International Conference on Anti-counterfeiting, Security, and Identification) is a QUALIS B4 conference held in Xiamen, China on 2024-11-01. The paper submission deadline is 2024-08-15 (extended). Acceptance notifications are sent on 2024-09-10.

The 18th International Conference on Anti -counterfeiting, Security, and Identification (ASID'2024) will provide a unique forum for researchers, engineers and educators interested in live demonstrations describing novel work on systems, applications and services of the Internet reaching out into the real world of physical objects. The conference will be held on Nov.1 ~ 3, 2024 in Xiamen, China. The conference content will be submitted for inclusion into IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. (All papers of ASID’2007 ~ ASID’2023 had been indexd by EI Database). Topics include (but not limited to) l Machine Learning and Recognition Computing l AI in Manufacturing l Cryptographic Algorithm and Information Security l IC Design and System Integration l Network Analysis and Big Data l Intelligent Information Processing l Micro/Nano-sensor and BioMEMS l EMC Theory and RFID Techniques l Multi-core Processor and Reconfigurable Computing l Brain-like Computing and Brain-like Intelligent l Wearable Electronics and Health & Leisure l Biomedical Signal and Image Computing l Biological Electronics l Training and Application in Large Model
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