1.

Record Nr.

UNINA9910427696303321

Titolo

Neural Information Processing : 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part V / / edited by Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020

ISBN

3-030-63823-5

Edizione

[1st ed. 2020.]

Descrizione fisica

1 online resource (XXIX, 844 p. 344 illus., 225 illus. in color.)

Collana

Communications in Computer and Information Science, , 1865-0937 ; ; 1333

Disciplina

006.32

Soggetti

Pattern recognition systems

Application software

Artificial intelligence

Computer engineering

Computer networks

Computers

Automated Pattern Recognition

Computer and Information Systems Applications

Artificial Intelligence

Computer Engineering and Networks

Computing Milieux

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Computational Intelligence -- Machine Learning -- Neural Network Models -- Robotics and Control -- Time Series Analysis.

Sommario/riassunto

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.