Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CISP-BMEI51763.2020.9263638 | 2020 13TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2020) |
Keywords | DocType | Citations |
Recurrent Neural Network, Hashing Methods, Collision Probabilities, Intelligent Paradigms, Message Digest | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raed Abu Zitar | 1 | 87 | 10.95 |
| Mirna Nachouki | 2 | 0 | 0.34 |
| Hanan Hussain | 3 | 0 | 0.34 |
| Farid Alzboun | 4 | 0 | 0.34 |