Call for paper
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DMSA 2021
2021 International Conference on Data Mining and Statistical Applications （DMSA 2021）will be held in Chengdu on Oct 29-31, 2021.
DMSA 2021 will focus on the latest research fields of "Data Mining and Statistical Applications"
Topics of interest for submission include, but are not limited to:
| Data Mining | Statistical Applications |
1. Prediction model 2. Data segmentation 3. Link Analysis 4. Deviation Detection 5. Sequence statistics 6. Probability theory 7. Regression analysis 8. Category data 9. Decision Trees Theory 10. Neural Network 11. Rules Induction | 1. Basic mathematics courses 2. The theory of probability 3. Mathematical statistics 4. Operations research 5. Descriptive Statistics 6. Principle of sampling survey 7. Multivariate statistical analysis 8. Computer fundamentals 9. Apply random processes |
All papers, both invited and contributed, will be reviewed by two or three experts from the committees. After a careful reviewing process, all accepted papers of DMSA 2021 will be submitted for indexing by EI Compendex and Scopus.
Submission Methods
1.The submitted papers must not be under consideration elsewhere.
2.Please send the full paper(word+pdf) to SUBMISSION SYSTEM
3.Please submit the full paper, if presentation and publication are both needed.
4.Please submit the abstract only, if you just want to make presentations.
5.Templates Downlow：Templates
6.Should you have any questions, or you need any materials in English, please contact Shannen ( +86 17675667587）
Note:
1) Both Abstract and Full Paper are welcomed. The author can make an oral presentation after the Abstract is accepted and the payment is finished.
2) All submitted articles should report original, previously unpublished research results, experimental or theoretical. Articles submitted to the conference should meet these criteria and must not be under consideration for publication elsewhere. We firmly believe that ethical conduct is the most essential virtual of any academic. Hence any act of plagiarism is a totally unacceptable academic misconduct and cannot be tolerated.