Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: a case study on shoe product form features extraction-Reference-Cited by-同舟云学术

Rule-based back propagation neural networks for various precision rough set presented KANSEI knowledge prediction: a case study on shoe product form features extraction

Published:2016-11-17 Issue:3 Volume:28 Page:613-630
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Li Zairan,Shi Kai,Dey Nilanjan,Ashour Amira S.,Wang Dan,Balas Valentina E.,McCauley Pamela,Shi Fuqian^ORCID

Funder

Natural Science Foundation of Zhejiang Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-016-2707-8.pdf

Reference66 articles.

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3. Robson B, Boray S (2016) Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations. Comput Biol Med 73:71–93

4. Keshavamurthy BN, Khan AM, Toshniwal D (2013) Privacy preserving association rule mining over distributed databases using genetic algorithm. Neural Comput Appl 22:351–364

5. Khadse CB, Chaudhari MA, Borghate VB (2016) Conjugate gradient back-propagation based artificial neural network for real time power quality assessment. Int J Electr Power Energy Syst 82:197–206

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