Neural Information Processing : 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020, Proceedings. Part V / Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King (eds.).
2020
Q337.5 .N48 2020
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Title
Neural Information Processing : 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020, Proceedings. Part V / Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King (eds.).
ISBN
9783030638238 electronic book
3030638235 electronic book
9783030638221
3030638235 electronic book
9783030638221
Published
Cham : Springer, 2020.
Language
English
Description
1 online resource (xxix, 844 pages) : illustrations (some color).
Item Number
10.1007/978-3-030-63823-8 doi
Call Number
Q337.5 .N48 2020
Dewey Decimal Classification
006.4
Summary
The two-volume set CCIS 1332 and 1333 constitutes thoroughly refereed contributions presented at the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020.* For ICONIP 2020 a total of 378 papers was carefully reviewed and selected for publication out of 618 submissions. The 191 papers included in this volume set were organized in topical sections as follows: data mining; healthcare analytics-improving healthcare outcomes using big data analytics; human activity recognition; image processing and computer vision; natural language processing; recommender systems; the 13th international workshop on artificial intelligence and cybersecurity; computational intelligence; machine learning; neural network models; robotics and control; and time series analysis. * The conference was held virtually due to the COVID-19 pandemic.
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text file PDF
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Series
Communications in computer and information science ; 1333. 1865-0929
Available in Other Form
Print version: 9783030638245
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Table of Contents
Computational Intelligence
Machine Learning
Neural Network Models
Robotics and Control
Time Series Analysis.
Machine Learning
Neural Network Models
Robotics and Control
Time Series Analysis.