{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:25:45Z","timestamp":1742984745744,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031622687"},{"type":"electronic","value":"9783031622694"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-62269-4_15","type":"book-chapter","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T14:02:22Z","timestamp":1718892142000},"page":"214-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI Based Commercial Decisions: The Cryptocurrency Market Case"],"prefix":"10.1007","author":[{"given":"Sujata","family":"Joshi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohit","family":"Satya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Menachem","family":"Domb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2538290","author":"LH White","year":"2014","unstructured":"White, L.H.: The market for cryptocurrencies. SSRN Electron. J. (2014). https:\/\/doi.org\/10.2139\/ssrn.2538290","journal-title":"SSRN Electron. J."},{"issue":"11","key":"15_CR2","doi-asserted-by":"publisher","first-page":"513","DOI":"10.3390\/jrfm15110513","volume":"15","author":"H Gupta","year":"2022","unstructured":"Gupta, H., Chaudhary, R.: An empirical study of volatility in cryptocurrency market. J. Risk Financ. Manage. 15(11), 513 (2022). https:\/\/doi.org\/10.3390\/jrfm15110513","journal-title":"J. Risk Financ. Manage."},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"100785","DOI":"10.1016\/j.jbef.2022.100785","volume":"37","author":"J Almeida","year":"2023","unstructured":"Almeida, J., Gon\u00e7alves, T.C.: A systematic literature review of investor behavior in the cryptocurrency markets. J. Behav. Exp. Financ. 37, 100785 (2023). https:\/\/doi.org\/10.1016\/j.jbef.2022.100785","journal-title":"J. Behav. Exp. Financ."},{"key":"15_CR4","doi-asserted-by":"publisher","unstructured":"Gurupradeep, G., Harishvaran, M., Amsavalli, K.: Cryptocurrency price prediction using machine learning. IJARCCE 12 (2023). https:\/\/doi.org\/10.17148\/IJARCCE.2023.124140","DOI":"10.17148\/IJARCCE.2023.124140"},{"key":"15_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106682","volume":"186","author":"X Song","year":"2020","unstructured":"Song, X., et al.: Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. J. Petrol. Sci. Eng. 186, 106682 (2020). https:\/\/doi.org\/10.1016\/j.petrol.2019.106682","journal-title":"J. Petrol. Sci. Eng."},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","volume":"237","author":"L Zhou","year":"2017","unstructured":"Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350\u2013361 (2017). https:\/\/doi.org\/10.1016\/j.neucom.2017.01.026","journal-title":"Neurocomputing"},{"key":"15_CR7","doi-asserted-by":"publisher","unstructured":"Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3) (2021). https:\/\/doi.org\/10.1007\/s42979-021-00592-x","DOI":"10.1007\/s42979-021-00592-x"},{"issue":"4","key":"15_CR8","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1177\/02560909211059992","volume":"46","author":"A Prasad","year":"2021","unstructured":"Prasad, A., Seetharaman, A.: Importance of machine learning in making investment decision in stock market. Vikalpa J. Decis. Mak. 46(4), 209\u2013222 (2021). https:\/\/doi.org\/10.1177\/02560909211059992","journal-title":"Vikalpa J. Decis. Mak."},{"issue":"3","key":"15_CR9","doi-asserted-by":"publisher","first-page":"032007","DOI":"10.1088\/1757899X\/928\/3\/032007","volume":"928","author":"K Salman","year":"2020","unstructured":"Salman, K., Ibrahim, A.: Price prediction of different cryptocurrencies using technical trade indicators and machine learning. IOP Conf. Ser. Mater. Sci. Eng. 928(3), 032007 (2020). https:\/\/doi.org\/10.1088\/1757899X\/928\/3\/032007","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"Lindemann, B., M\u00fcller, T., Vietz, H., Jazdi, N., Weyrich, M.: A survey on long shortterm memory networks for time series prediction. In: 14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 15\u201317 July 2020, vol. 99, pp. 650\u2013655 (2021). https:\/\/doi.org\/10.1016\/j.procir.2021.03.088","DOI":"10.1016\/j.procir.2021.03.088"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/1265837","volume":"2022","author":"S Zhang","year":"2022","unstructured":"Zhang, S., Li, M., Yan, C.: The empirical analysis of bitcoin price prediction based on deep learning integration method. Comput. Intell. Neurosci. 2022, 1\u20139 (2022). https:\/\/doi.org\/10.1155\/2022\/1265837","journal-title":"Comput. Intell. Neurosci."},{"key":"15_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2023.100465","volume":"12","author":"B Amirshahi","year":"2023","unstructured":"Amirshahi, B., Lahmiri, S.: Hybrid deep learning and GARCHfamily models for forecasting volatility of cryptocurrencies. Mach. Learn. Appl. 12, 100465 (2023). https:\/\/doi.org\/10.1016\/j.mlwa.2023.100465","journal-title":"Mach. Learn. Appl."},{"key":"15_CR13","unstructured":"Palakurla, S.: Predictive analysis of cryptocurrency using machine learning with blockchain technology. Machine Learning, December 2020"},{"issue":"2","key":"15_CR14","doi-asserted-by":"publisher","first-page":"492","DOI":"10.3390\/su12020492","volume":"12","author":"R Cioffi","year":"2020","unstructured":"Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., De Felice, F.: Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustainability 12(2), 492 (2020). https:\/\/doi.org\/10.3390\/su12020492","journal-title":"Sustainability"},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Sebasti\u00e3o, H., Godinho, P.: Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financ. Innov. 7(1) (2021). https:\/\/doi.org\/10.1186\/s40854-020-00217-x","DOI":"10.1186\/s40854-020-00217-x"},{"key":"15_CR16","unstructured":"Bolt, W.: Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction (2017)"},{"key":"15_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108907","volume":"248","author":"S Park","year":"2022","unstructured":"Park, S., Yang, J.-S.: Interpretable deep learning LSTM model for intelligent economic decision-making. Knowl. Based Syst. 248, 108907 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.108907","journal-title":"Knowl. Based Syst."},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Jeyakumar, S., Hou, Z., Yugarajah, A., Palaniswami, M., Muthukkumarasamy, V.: Visualizing Blockchain Transaction Behavioural Pattern: A Graphbased Approach (2023). https:\/\/doi.org\/10.36227\/techrxiv.22329889.v1","DOI":"10.36227\/techrxiv.22329889.v1"},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":"Regev, Y., Vassdal, H., Halden, U., Catak, F.O., Cali, U.: Hybrid AIbased anomaly detection model using Phasor measurement unit data (2022). https:\/\/doi.org\/10.48550\/arXiv.2209.12665","DOI":"10.48550\/arXiv.2209.12665"},{"issue":"2","key":"15_CR20","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1093\/jfr\/fjaa010","volume":"6","author":"DA Zetzsche","year":"2020","unstructured":"Zetzsche, D.A., Arner, D.W., Buckley, R.P.: Decentralized finance. J. Financ. Regul. 6(2), 172\u2013203 (2020). https:\/\/doi.org\/10.1093\/jfr\/fjaa010","journal-title":"J. Financ. Regul."},{"issue":"1","key":"15_CR21","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3390\/jrfm16010051","volume":"16","author":"J Chen","year":"2023","unstructured":"Chen, J.: Analysis of bitcoin price prediction using machine learning. J. Risk Financ. Manage. 16(1), 51 (2023). https:\/\/doi.org\/10.3390\/jrfm16010051","journal-title":"J. Risk Financ. Manage."},{"issue":"2","key":"15_CR22","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3390\/fintech1020014","volume":"1","author":"D Liu","year":"2022","unstructured":"Liu, D., Wei, A.: Regulated LSTM artificial neural networks for option risks. FinTech 1(2), 180\u2013190 (2022). https:\/\/doi.org\/10.3390\/fintech1020014","journal-title":"FinTech"},{"key":"15_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jastp.2021.105624","volume":"218","author":"MW Liemohn","year":"2021","unstructured":"Liemohn, M.W., Shane, A.D., Azari, A.R., Petersen, A.K., Swiger, B.M., Mukhopadhyay, A.: RMSE is not enough: guidelines to robust datamodel comparisons for magnetospheric physics. J. Atmos. Sol. Terr. Phys. 218, 105624 (2021). https:\/\/doi.org\/10.1016\/j.jastp.2021.105624","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"15_CR24","doi-asserted-by":"publisher","unstructured":"Huang, X., et al.: LSTM based sentiment analysis for cryptocurrency prediction, March 2021. https:\/\/doi.org\/10.48550\/arxiv.2103.14804","DOI":"10.48550\/arxiv.2103.14804"},{"issue":"3","key":"15_CR25","doi-asserted-by":"publisher","first-page":"287","DOI":"10.3390\/electronics10030287","volume":"10","author":"IE Livieris","year":"2021","unstructured":"Livieris, I.E., Kiriakidou, N., Stavroyiannis, S., Pintelas, P.: An advanced CNN-LSTM model for cryptocurrency forecasting. Electronics 10(3), 287 (2021). https:\/\/doi.org\/10.3390\/electronics10030287","journal-title":"Electronics"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-62269-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T14:12:09Z","timestamp":1718892729000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-62269-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031622687","9783031622694"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-62269-4_15","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Science and Information Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/Computing","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}