Price prognostication of currency with deep learning (Record no. 22737)
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| 000 -LEADER | |
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250428145512.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250428b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26020 |
| Author | Patil, Manisha |
| 245 ## - TITLE STATEMENT | |
| Title | Price prognostication of currency with deep learning |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.14(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3102-3105p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | n this modern era of technology, the more secured ways are needed to<br/>deal with financial investments or transactions. Cryptocurrency can be<br/>named as one of the solutions for this concern. Cryptocurrency is a<br/>digital payment system that doesn’t rely on banks to verify transactions.<br/>A digital payment system called cryptocurrency doesn’t rely on banks<br/>to validate transactions. Anyone can send and receive funds using this<br/>method. Payments made using cryptocurrencies only exist as digital<br/>records in an online database that detail specific transactions. This new<br/>sort of investment is providing vast areas for research to the<br/>researchers. By predicting its price this can be as more efficient asset<br/>for investment. Much research is going on in this area. This paper<br/>proposes two different recurrent neural network (RNN) algorithms to<br/>predict prices of cryptocurrency namely Bit coin and they are Long<br/>short-term memory (LSTM) and Gated Recurrent Unit (GRU). the<br/>measures being used in this paper to assess the accuracy of the used<br/>algorithms are mean squared error (MSE), Mean Absolute Percentage<br/>Error (MAPE), Root Mean Squared Error (RMSE), and Mean<br/>Absolute Error (MAE), are also used to assess different prediction<br/>algorithms. Comparisons are carried out on the basis of three datasets<br/>training, testing, and validation. The loss and evaluation functions are<br/>based on the mean squared error. The model performs better the lower<br/>the value. Based on findings the GRU model outperforms the LSTM<br/>algorithm in terms of accuracy and reliability in predicting<br/>cryptocurrency prices, but both algorithms produce excellent outcome. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26021 |
| Co-Author | Nandgave, Sunita |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/3_IJSC_Vol_14_Iss_1_Paper_3_3102_3105.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 28/04/2025 | 2025-0710 | 28/04/2025 | 28/04/2025 | Articles Abstract Database |