Advanced Hybrid Information Processing Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21–22, 2019, Proceedings, Part II

Advanced Hybrid Information Processing Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21–22, 2019, Proceedings, Part II / edited by Guan Gui, Lin Yun.

This two-volume set LNICST 301 -302 constitutes the post-conference proceedings of the Third EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2019, held in Nanjing, China, in September 2019. The 101 papers presented were selected from 237 submissions and focus on hybrid...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Gui, Guan (Editor), Yun, Lin (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 302
Springer eBook Collection.
Subjects:
Computers.
Computer organization.
Computer simulation.
Artificial intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Description
Summary:This two-volume set LNICST 301 -302 constitutes the post-conference proceedings of the Third EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2019, held in Nanjing, China, in September 2019. The 101 papers presented were selected from 237 submissions and focus on hybrid big data processing. Since information processing has acted as an important research domain in science and technology today, it is now to develop deeper and wider use of hybrid information processing, especially information processing for big data. There are more remaining issues waiting for solving, such as classification and systemization of big data, objective tracking and behavior understanding in big multimedia data, encoding and compression of big data.
Physical Description:XIX, 476 p. 207 illus., 105 illus. in color. online resource.
ISBN:9783030364052
ISSN:1867-8211 ;
DOI:10.1007/978-3-030-36405-2

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