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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | BITCOIN MARKET PRICE PREDICTION USING NEURAL NETWORKS |
| Authors Name: | Dr. A.RADHIKA , SUHANA PATHAN , SANA KAUSER , SANGOJU YOGENDRA , SHAIK AAMIR FAIZ |
| Download E-Certificate: | Download |
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IJNRD_190554
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| Published Paper Id: | IJNRD2304225 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Investors have recently become interested in cryptocurrency because of its inherent decentralization and transparency. In order to build efficient trading platforms, accurate value estimation is essential, given the cryptocurrencies' new features and volatility. The researchers propose a cutting-edge method for determining the value of Bitcoin (BTC), a well-known cryptocurrency, in order to accomplish this. The change point detection method is utilized to provide consistent prediction performance across previously unknown price ranges. Time-series data are split specifically so that segmentation-based normalization can be carried out separately. Price forecasting also makes use of on-chain data as an input variable. The various records that are contained in cryptocurrencies and saved on the blockchain are referred to as "on-chain data." Moreover, for on-chain variable assembles, this paper exhorts involving SAM-LSTM as the assumption model, which includes the thought part and a couple of LSTM modules. Self-consideration-based multiple long short-term memory is abbreviated as SAM-LSTM. Tests conducted with authentic BTC cost information and a variety of technique limits demonstrated that the proposed structure was effective in forecasting BTC prices. The highest individual values for the MAE, RMSE, MSE, and MAPE were 0.3462, 0.5035, 0.2536, and 1.3251, respectively. The outcomes are positive. |
| Keywords: | Bitcoin, machine learning, prediction methods, and change detection algorithms are all included. |
| Cite Article: | "BITCOIN MARKET PRICE PREDICTION USING NEURAL NETWORKS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.c201-c209, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304225.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304225 Registration ID: 190554 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: c201-c209 Country: Vijayawada, Andhra pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304225 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304225 |
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ISSN: 2456-4184
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