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Recurrent Neural Networks for Signature Generation

  • Ajman University
  • Public Sector of Jordan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

A new technique for producing hash values for text documents is introduced in this report. The method uses Recurrent Neural Networks (RNN). RNNs are functionally and temporally dependent on the input vectors of the neural networks (RNN). RNN 's capacity to integrate current values of inputs with previous values that manipulate the associations and the semanticists of the document constitutes a competitive framework for discovering internal interpretations of document details in a special way. In contrast to conventional approaches, two forms of RNNs are evaluated. Current approaches have been adequately examined and the effects of this study reveal the applicability of this artificial intelligence model to construct hash values for plain text. RNNs are very lightweight , portable and parallel in nature and their abilities are used as a potential professional document hashing technology is presented in this article.

Original languageEnglish
Title of host publicationProceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020
EditorsQiang Zheng, Xiaopeng Zheng, Xiangfu Zhao, Weiqing Yan, Nan Zhang, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1093-1097
Number of pages5
ISBN (Electronic)9780738105451
DOIs
StatePublished - 17 Oct 2020
Event13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020 - Virtual, Chengdu, China
Duration: 17 Oct 202019 Oct 2020

Publication series

NameProceedings - 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020

Conference

Conference13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020
Country/TerritoryChina
CityVirtual, Chengdu
Period17/10/2019/10/20

Keywords

  • Collision Probabilities
  • Hashing Methods
  • Intelligent Paradigms
  • Message Digest
  • Recurrent Neural Network

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