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DSA' 2026: International Conference on Data Science and Applications

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截稿日期:
2026-08-16
通知日期:
2026-10-24
会议日期:
2026-11-27
会议地点:
Zurich, Switzerland
届数:
浏览: 18331   关注: 2   参加: 1

会伴指数 (CP-I)

49.2 / 100
全站第 1,785 名 / 共 5,687 个会议 · 前 32%

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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%)
23
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：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 2026-09-28

征稿

DSA' 2026 (International Conference on Data Science and Applications) is an academic conference held in Zurich, Switzerland on 2026-11-27. The paper submission deadline is 2026-08-16. Acceptance notifications are sent on 2026-10-24.

Scope 7th International Conference on Data Science and Applications (DSA 2026) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Science and Applications. 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 following areas, but are not limited to. Topics of interest · Big Data Algorithms · Big Data Applications · Big Data Fundamentals · Big Data Management and Frameworks · Big Data Search · Big Data security · Bioinformatics · Biometrics · Consistent Data Model · Data and Knowledge Representation · Data Streams Mining, Graph Mining, Spatial Data Mining · Databases · Deep Learning · Exploring Data Analysis · Financial Modeling · Forecasting, Classification, Clustering · Image Analysis · Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge · Infrastructures for Big Data · Integrating Constraints and Knowledge in the KDD Process · Integration of Data Warehousing · Interactive Data Exploration / Visualization and Discovery · Knowledge Discovery Framework and Process, Including Pre- and Post-Processing · Languages and Interfaces for Data Mining · Learning in knowledge-intensive systems · Learning Problems · Machine Learning Applications · Mining from Low-Quality Information Sources · Mining Trends, Opportunities and Risks · OLAP and Data Mining · Parallel and Distributed Data Mining Algorithms · Pre-Processing Techniques, Visualization · Security and Information Hiding in Data Mining · Social Networks, Educational Data Mining · Statistical Techniques for Generation a Robust · Text video, Multimedia Data Mining, Web Mining
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