Kernel Metadata Declaration — on demand

What the DOI identifies

CRYPTOCURRENCY PRICE ANALYSIS WITH ARTIFICIAL INTELLIGENCE

10.46243/jst.2023.v8.i04.pp18-24 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2023-10-04

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 4 · pp. 18–24

Principal agents

• Mrs. P.Lakshmi Satya Satya (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 14 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and merchant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that influence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilize useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This project provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model",
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            "unstructured": "An exploration On Bitcoin worth Prediction mistreatment Machine Learning Algorithms, Lekkala Sreekanth Reddy, Dr. P. Sriramya, 2020"
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            "unstructured": "Bitcoin worth prediction mistreatment LSTM and"
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        {
            "key": "ref3",
            "doi": "10.1109/icsc45622.2019.8938251",
            "unstructured": "Fold Cross validation, Sakshi Tandon, Shreya Tripathi, Pragya Saraswat, Chetna Dabas, 2019"
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            "doi": "10.1109/macs48846.2019.9024772",
            "unstructured": "Bitcoin worth prediction mistreatment Deep Learning algorithmic program, Muhammad Rizwan, Dr. Sanam Narejo, Dr. Moazzam Javed, 2019"
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            "key": "ref5",
            "doi": "10.1109/icoei.2019.8862585",
            "unstructured": "CryptoCurrency worth prediction mistreatment call Tree and Regression techniques, Karunya Rathan, Somarouthu Venkat Sai, Tubati Sai Manikanta, 2019"
        },
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            "key": "ref6",
            "unstructured": "Cryptocurrency worth Analysis With AI, Wang Yiying, Zang Yeze, 2019"
        },
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            "key": "ref7",
            "doi": "10.1109/icisc47916.2020.9171147",
            "unstructured": "Performance analysis of Machine Learning Algorithms for Bitcoin worth Prediction, Kavitha H, Uttam Kumar Sinha, SurbhiS Jain, 2020"
        },
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            "key": "ref8",
            "unstructured": "Greaves, A., & Au, B. (2015). Using the bitcoin transaction graph to predict the price of bitcoin"
        },
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            "key": "ref9",
            "doi": "10.1016/j.tele.2016.05.005",
            "unstructured": "Hayes, A. S. (2017). Cryptocurrency value formation: An empirical study leading to a cost of production modelfor valuing bitcoin. Telematics and Informatics, 34(7), 1308-1321"
        },
        {
            "key": "ref10",
            "doi": "10.1109/allerton.2014.7028484",
            "unstructured": "Shah, D., & Zhang, K. (2014, September). Bayesian regression and Bitcoin. In Communication, Control, and Computing (Allerton), 2014 52nd Annual Allerton Conference on (pp. 409-414). IEEE"
        },
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            "unstructured": "Amjad, M. J. & Shah, D. (2017). “Trading Bitcoin and Online Time Series Prediction”. Proceedings of the Time Series Workshop at NIPS 2016, (pp. PMLR 55:1-15)"
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            "doi": "10.5120/ijca2015906952",
            "unstructured": "Billah, M. & Waheed, S. (2015). “Predicting Closing Stock Price using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS): The Case of the Dhaka Stock Exchange”,. International Journal of Computer Applications (0975-8887), Volume 129-No.11"
        },
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            "unstructured": "Box, J. & Reinsel. (1994). Time Series Analysis, Forecasting and Control (3 ed.). Englewood Clifs, NJ: Prentice Hall. [13] MESSIDOR, Methods for Evaluating Segmentation and Indexing technique Dedicated to Retinal Ophthalmology, http,// messidor.crihan.fr/index-en.php, 2004"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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