Categories |
DATA MINING
MACHINE LEARNING
BIG DATA
NEURAL NETWORK
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About |
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The 21th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) is an annual international conference dedicated to emerging and challenging topics in intelligent data analysis, data mining and their associated learning systems and paradigms. The conference provides a unique opportunity and stimulating forum for presenting and discussing the latest theoretical advances and real-world applications in Computational Intelligence and Intelligent Data Analysis. |
Call for Papers |
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The International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) is an annual international conference dedicated to emerging and challenging topics in intelligent data analytics and associated learning systems and paradigms. After the hugely successful IDEAL 2018 in Madrid and in its 20th edition in Manchester with IDEAL 2020, it is going to Guimarães the birthplace of Portugal. Its main research themes or topics include, but not limited to:
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Summary |
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IDEAL 2020 : 21th International Conference on Intelligent Data Engineering and Automated Learning will take place in Guimarães, Portugal. It’s a 3 days event starting on Nov 04, 2020 (Wednesday) and will be winded up on Nov 06, 2020 (Friday). IDEAL 2020 falls under the following areas: DATA MINING, MACHINE LEARNING, BIG DATA, NEURAL NETWORK, etc. Submissions for this Conference can be made by Jun 05, 2020. Authors can expect the result of submission by Jul 10, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Jul 31, 2020 to the official website of the Conference. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the IDEAL 2020
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Credits and Sources |
| [1] IDEAL 2020 : 21th International Conference on Intelligent Data Engineering and Automated Learning |
