Recurrent Neural Networks for Signature Generation
| Autor: | Farid Alzboun, Raed Abu Zitar, Mirna Nachouki, Hanan Hussain |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Artificial neural network Computer science business.industry Plain text 05 social sciences Hash function Cryptography 02 engineering and technology computer.file_format Construct (python library) Signature (logic) Recurrent neural network Digital signature 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer |
| Zdroj: | CISP-BMEI |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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