会议信息

BDCI 2027: International Conference on Big Data and Computational Intelligence

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截稿日期:
2027-01-30 还有 115 天
通知日期:
2027-02-25
会议日期:
2027-06-25
会议地点:
Seoul, South Korea
届数:
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会伴指数 (CP-I)

48.2 / 100
全站第 2,059 名 / 共 5,694 个会议 · 前 37%

数据挖掘与数据库 第 120 / 336 人工智能与机器学习 第 219 / 741

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%)
56
社区关注 (10%)
12
资料公开度 (15%)
55

用到的输入: 有据可查的届次：7 · 过去 24 个月打开过本页的研究者：4 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 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 2026-10-07

征稿

BDCI 2027 (International Conference on Big Data and Computational Intelligence) is an academic conference held in Seoul, South Korea on 2027-06-25. The paper submission deadline is 2027-01-30. Acceptance notifications are sent on 2027-02-25.

We invite submissions of original research papers on the topics related to big data and computational Intelligence for the upcoming BDCI conference. The conference aims to provide a platform for researchers, engineers, and practitioners from academia, industry, and government to exchange ideas, share experiences, and present their latest research findings in this field. Topics of interest for submission include, but are not limited to: 1. Big Data Analytics Data mining, processing, and visualization Big Data architectures and technologies (Hadoop, Spark, etc.) Scalable algorithms for Big Data Data privacy and security Real-time data processing and analytics 2. Computational Intelligence Machine learning and deep learning Neural networks, fuzzy systems, and genetic algorithms Evolutionary computation and swarm intelligence Reinforcement learning and optimization techniques Explainable AI (XAI) 3. Data-Driven Applications Healthcare and bioinformatics Smart cities and IoT Financial technology and risk management Autonomous systems and robotics Social media and sentiment analysis 4. Integration of Big Data and Computational Intelligence AI-driven Big Data solutions Hybrid models for Big Data analysis Data fusion and multi-source learning Cloud computing and edge computing for Big Data Ethical considerations and societal impacts of AI and Big Data
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