Login
Register
Reset Password
Publish & Distribute
Publishing Solutions
Distribution Solutions
Subjects
Architecture and Design
Arts
Business and Economics
Chemistry
Classical and Ancient Near Eastern Studies
Computer Sciences
Cultural Studies
Engineering
General Interest
Geosciences
History
Industrial Chemistry
Jewish Studies
Law
Library and Information Science, Book Studies
Life Sciences
Linguistics and Semiotics
Literary Studies
Materials Sciences
Mathematics
Medicine
Music
Pharmacy
Philosophy
Physics
Social Sciences
Sports and Recreation
Theology and Religion
Publications
Journals
Books
Proceedings
Publishers
Blog
Contact
Search
EUR
USD
GBP
English
English
Deutsch
Polski
Español
Français
Italiano
Cart
Home
Journals
Applied Mathematics and Nonlinear Sciences
Volume 9 (2024): Issue 1 (January 2024)
Open Access
A Computerized Information Security Communication Method Based on Deep Learning Theory
Yingyun Kang
Yingyun Kang
| Aug 05, 2024
Applied Mathematics and Nonlinear Sciences
Volume 9 (2024): Issue 1 (January 2024)
About this article
Previous Article
Next Article
Abstract
References
Authors
Articles in this Issue
Preview
PDF
Cite
Share
Published Online:
Aug 05, 2024
Page range:
-
Received:
Apr 05, 2024
Accepted:
Jul 04, 2024
DOI:
https://doi.org/10.2478/amns-2024-2249
Keywords
LSTM
,
GAN
,
CNN
,
High frequency residual features
,
Covert communication
© 2024 Yingyun Kang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Yingyun Kang
Fair Friend Institute of Intelligent Manufacturing, Hangzhou Vocational & Technical College
Hangzhou, China