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DSA 2020 : International Conference on Data Science and Applications

London, United Kingdom
Event Date: Nov 28, 2020 - Nov 29, 2020
Submission Deadline: Oct 31, 2020
Notification of Acceptance: Nov 10, 2020
Camera Ready Version Due : Nov 16, 2020

About

International Conference on Data Science and Applications (DSA 2020) 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. 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 Data Science & Applications.
 

Call For Paper

Topics of Interest

Data mining foundations

  • Parallel and Distributed Data Mining Algorithms
  • Data Streams Mining, Graph Mining, Spatial Data Mining
  • Text video, Multimedia Data Mining, Web Mining
  • Pre-Processing Techniques, Visualization
  • Security and Information Hiding in Data Mining

Data mining Applications

  • Databases
  • Bioinformatics
  • Biometrics
  • Image Analysis
  • Financial Modeling
  • Forecasting, Classification, Clustering
  • Social Networks, Educational Data Mining

Knowledge Processing

  • Data and Knowledge Representation
  • Knowledge Discovery Framework and Process, Including Pre- and Post-Processing
  • Integration of Data Warehousing
  • OLAP and Data Mining,
  • Integrating Constraints and Knowledge in the KDD Process
  • Exploring Data Analysis
  • Inference of Causes, Prediction, Evaluating, Consolidating and Explaining
  • Discovered Knowledge
  • Statistical Techniques for Generation a Robust
  • Consistent Data Model
  • Interactive Data Exploration / Visualization and Discovery
  • Languages and Interfaces for Data Mining
  • Mining Trends, Opportunities and Risks
  • Mining from Low-Quality Information Sources

Data Mining & Machine learning Tasks

  • Machine Learning Applications
  • Learning in knowledge-intensive systems
  • Learning Methods andanalysis
  • Learning Problems
  • Deep Learning

Big Data

  • Big Data Algorithms
  • Big Data Fundamentals
  • Infrastructures for Big Data
  • Big Data Management and Frameworks
  • Big Data Search
  • Big Data security
  • Big Data Applications

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