会议信息

BlockSys 2027: International Conference on Blockchain, Artificial Intelligence, and Trustworthy Systems

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截稿日期:
2027-01-31 还有 100 天
通知日期:
2027-03-15
会议日期:
2027-04-16
会议地点:
Guangzhou, China
届数:
CCF: C   浏览: 22915   关注: 7   参加: 3
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BlockSys
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会伴指数 (CP-I)

58.4 / 100
全站第 674 名 / 共 5,759 个会议 · 前 12%

安全与隐私 第 55 / 366 人工智能与机器学习 第 58 / 751

学术认可 (35%)
66
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
62
社区关注 (10%)
46
资料公开度 (15%)
55

用到的输入: 收录等级:CCF C · 有据可查的届次:9 · 在会伴关注它的研究者:7 人 · 过去 24 个月打开过本页的研究者:32 人

公开资料里还缺: 历年录用率 (+4.5) · 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 1.2 · 算于 2026-10-23

征稿

BlockSys 2027 (International Conference on Blockchain, Artificial Intelligence, and Trustworthy Systems) is a CCF C conference held in Guangzhou, China on 2027-04-16. The paper submission deadline is 2027-01-31. Acceptance notifications are sent on 2027-03-15.

The 9th International Conference on Blockchain, Artificial Intelligence, and Trustworthy Systems (BlockSys’2027) will be held in Guangzhou, China. This prestigious event brings together leading researchers, industry professionals, and academics from around the globe to explore the latest advancements and applications in blockchain technology, artificial intelligence (AI), and trustworthy systems. BlockSys’2027 aims to provide a comprehensive platform for discussing the integration and impact of these transformative technologies. The conference will feature a diverse range of topics, including blockchain protocols, AI algorithms, system security, privacy-preserving technologies, and trust management frameworks. Through keynote speeches, technical sessions, and panel discussions, participants will engage in in-depth exploration of how these technologies can drive innovation, enhance security, and address critical challenges in various domains. Attendees will have the opportunity to share their research findings, exchange ideas with peers, and explore new collaborations in a dynamic and forward-thinking environment. We invite you to join us in Guangzhou for BlockSys’2027 and contribute to shaping the future of technology and trustworthy systems. We welcome contributions addressing a broad range of topics within the following key themes: Blockchain Technology: Advances in blockchain protocols and architectures Smart contracts and decentralized applications Security, privacy, scalability, and interoperability of blockchain systems Blockchain integration with emerging technologies Blockchain in supply chain and logistics Blockchain for digital identity and authentication Artificial Intelligence: Advances in machine learning, deep learning, and AI algorithms AI ethics, fairness, and transparency Explainable AI and interpretability AI in healthcare, finance, and industry AI-driven data analysis and visualization Reinforcement learning and robotics AI applications in data analysis, decision-making, and automation Synergies between AI and blockchain technologies AI-driven innovations in trustworthy systems Trustworthy Systems: Techniques for enhancing system reliability and robustness Privacy-preserving methods and trust management Risk assessment and mitigation strategies Compliance and regulatory issues Frameworks for ensuring system trustworthiness Ethical and societal impacts of technology Policy and regulatory considerations Cross-disciplinary Research: Integration of blockchain and AI for improved security and reliability Innovative solutions at the nexus of blockchain, AI, and trustworthy systems Case studies showcasing successful applications and challenges
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相关期刊

CCF全称影响因子出版商ISSN
CNeurocomputing6.5Elsevier0925-2312
BlockchainELSP2959-1260
CKnowledge-Based Systems7.2Elsevier0950-7051
CFuture Generation Computer Systems5.9Elsevier0167-739X
CPattern Recognition Letters3.9Elsevier0167-8655
CIEEE Transactions on Industrial Informatics11.7IEEE1551-3203
CIEEE Internet of Things Journal8.9IEEE2327-4662
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
CExpert Systems with Applications7.5Elsevier0957-4174
CIEEE Transactions on Big Data5.7IEEE2332-7790

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