会议信息

BIGML 2026: International conference on Big Data, Machine learning and Applications

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BIGML
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截稿日期:
2026-08-22
通知日期:
2026-10-24
会议日期:
2026-11-21
会议地点:
London, UK
届数:
浏览: 20382   关注: 2   参加: 0

会伴指数 (CP-I)

49.0 / 100
全站第 1,767 名 / 共 5,680 个会议 · 前 32%

数据挖掘与数据库 第 101 / 337 人工智能与机器学习 第 178 / 739

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%)
21
资料公开度 (15%)
55

用到的输入: 有据可查的届次：7 · 在会伴关注它的研究者：2 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) · 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 2026-09-12

征稿

BIGML 2026 (International conference on Big Data, Machine learning and Applications) is an academic conference held in London, UK on 2026-11-21. The paper submission deadline is 2026-08-22. Acceptance notifications are sent on 2026-10-24.

Scope & Topics 7th International conference on Big Data, Machine learning and Applications (BIGML 2025) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science and Information Technology. Topics of interest include, but are not limited to, the following: Big Data Big Data Techniques, models and algorithms Big Data Infrastructure and platform Big Data Search and Mining Big Data Security, Privacy and Trust Big Data Applications, Bioinformatics, Multimedia etc Big Data Tools and systems Big Data Mining Big Data Management Cloud and grid computing for Big Data Machine Learning and AI for Big Data Big Data Analytics and Social Media 5G and Networks for Big Data Machine Learning Machine Learning Applications Learning in knowledge-intensive systems Learning Methods and analysis Learning Problems Deep Learning
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