An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods
Determining the sign of a number is not as simple as addition and multiplication. When using the traditional notation of a number in binary form with two's complement code, it allows you to store the sign of the number and process it. However, when representing a number in modular form, or in a...
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2024
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ir-123456789-293562024-12-11T08:29:15Z An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods Shiriaev, E. M. Ширяев, Е. М. Lutsenko, V. V. Луценко, В. В. Babenko, M. G. Бабенко, М. Г. Homomorphic encryption Sign function Residue number system (RNS) Neural networks Determining the sign of a number is not as simple as addition and multiplication. When using the traditional notation of a number in binary form with two's complement code, it allows you to store the sign of the number and process it. However, when representing a number in modular form, or in any other forms, for example, in the form of a homomorphic cipher, where the operation of determining the sign cannot be performed explicitly. In such cases, it is necessary to resort to various computationally complex methods. In this work, we are conducting research on the possibility of using neural networks to calculate an approximate function of the sign of a number, this will reduce the computational costs of traditional approaches to determining the sign. 2024-12-11T08:28:17Z 2024-12-11T08:28:17Z 2024 Статья Shiriaev, E., Lutsenko, V., Babenko, M. An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods // Lecture Notes in Networks and Systems. - 2025. - 1207 LNNS. - pp. 247-255. - DOI: 10.1007/978-3-031-77229-0_25 https://dspace.ncfu.ru/handle/123456789/29356 en Lecture Notes in Networks and Systems application/pdf Springer Science and Business Media Deutschland GmbH |
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Homomorphic encryption Sign function Residue number system (RNS) Neural networks |
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Homomorphic encryption Sign function Residue number system (RNS) Neural networks Shiriaev, E. M. Ширяев, Е. М. Lutsenko, V. V. Луценко, В. В. Babenko, M. G. Бабенко, М. Г. An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods |
| description |
Determining the sign of a number is not as simple as addition and multiplication. When using the traditional notation of a number in binary form with two's complement code, it allows you to store the sign of the number and process it. However, when representing a number in modular form, or in any other forms, for example, in the form of a homomorphic cipher, where the operation of determining the sign cannot be performed explicitly. In such cases, it is necessary to resort to various computationally complex methods. In this work, we are conducting research on the possibility of using neural networks to calculate an approximate function of the sign of a number, this will reduce the computational costs of traditional approaches to determining the sign. |
| format |
Статья |
| author |
Shiriaev, E. M. Ширяев, Е. М. Lutsenko, V. V. Луценко, В. В. Babenko, M. G. Бабенко, М. Г. |
| author_facet |
Shiriaev, E. M. Ширяев, Е. М. Lutsenko, V. V. Луценко, В. В. Babenko, M. G. Бабенко, М. Г. |
| author_sort |
Shiriaev, E. M. |
| title |
An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods |
| title_short |
An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods |
| title_full |
An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods |
| title_fullStr |
An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods |
| title_full_unstemmed |
An Approximate Algorithm for Determining the Sign Function of a Number Using Neural Network Methods |
| title_sort |
approximate algorithm for determining the sign function of a number using neural network methods |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2024 |
| url |
https://dspace.ncfu.ru/handle/123456789/29356 |
| work_keys_str_mv |
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