Deep learning-based bitcoin price prediction using long short-term memory networks (Record no. 22820)
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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 | 20250509110310.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250509b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26142 |
| Author | Sajithabanu, S. |
| 245 ## - TITLE STATEMENT | |
| Title | Deep learning-based bitcoin price prediction using long short-term memory networks |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.10(2), May-Aug |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Ghaziabad |
| Name of publisher, distributor, etc. | MAT Journals |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 1-8p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Cryptocurrencies, notably Bitcoin, have surged in popularity and importance, attracting widespread attention due to their volatile nature and potential for significant financial gains. Predicting Bitcoin prices accurately has emerged as a challenging yet indispensable task for investors, traders, and policymakers seeking to navigate the complexities of the cryptocurrency market. Traditional forecasting methods often struggle to capture the intricate temporal patterns inherent in this domain, prompting the exploration of advanced techniques such as deep learning. This paper presents a comprehensive study on employing Long Short-Term Memory (LSTM) networks for Bitcoin price prediction, a variant of Recurrent Neural Networks (RNNs). Our research encompasses the entire process, from data collection to model evaluation, focusing on addressing the unique challenges posed by cryptocurrency market data. We begin by discussing the methodology involved in data acquisition, preprocessing, and feature engineering, which are crucial steps for ensuring the quality and relevance of input data. |
| 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) | 26143 |
| Co-Author | Mahmootha, A. Asrin |
| 773 0# - HOST ITEM ENTRY | |
| Title | Journal of computer science engineering and software testing |
| International Standard Book Number | 2581-6969 |
| Place, publisher, and date of publication | Ghaziabad MAT Journals |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://matjournals.net/engineering/index.php/JOCSES/article/view/447 |
| 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 |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 09/05/2025 | 2025-0798 | 09/05/2025 | 09/05/2025 | Articles Abstract Database |