{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:54:35Z","timestamp":1774353275694,"version":"3.50.1"},"reference-count":131,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00365-y","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T20:23:01Z","timestamp":1749846181000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Advanced AI and big data techniques in E-finance: a comprehensive survey"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4690-3371","authenticated-orcid":false,"given":"Rihab","family":"Najem","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0426-2860","authenticated-orcid":false,"given":"Ayoub","family":"Bahnasse","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0497-1595","authenticated-orcid":false,"given":"Meryem","family":"Fakhouri Amr","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2364-0804","authenticated-orcid":false,"given":"Mohamed","family":"Talea","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"365_CR1","unstructured":"Britannica, T. Editors of Encyclopaedia. finance. Encyclopedia Britannica. 2023. https:\/\/www.britannica.com\/money\/topic\/finance"},{"key":"365_CR2","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1016\/j.procs.2022.12.263","volume":"217","author":"A Telukdarie","year":"2023","unstructured":"Telukdarie A, Mungar A. The impact of digital financial technology on accelerating financial inclusion in developing economies. Proced Comput Sci. 2023;217:670\u20138. https:\/\/doi.org\/10.1016\/j.procs.2022.12.263.","journal-title":"Proced Comput Sci"},{"key":"365_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2022.113376","volume":"155","author":"F Campanella","year":"2023","unstructured":"Campanella F, Serino L, Battisti E, Giakoumelou A, Karasamani I. FinTech in the financial system: towards a capital-intensive and high competence human capital reality? J Bus Res. 2023;155: 113376. https:\/\/doi.org\/10.1016\/j.jbusres.2022.113376.","journal-title":"J Bus Res"},{"key":"365_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2021.102667","volume":"47","author":"SNM Daud","year":"2022","unstructured":"Daud SNM, Ahmad AH, Khalid A, Azman-Saini WNW. FinTech and financial stability: threat or opportunity? Financ Res Lett. 2022;47: 102667. https:\/\/doi.org\/10.1016\/j.frl.2021.102667.","journal-title":"Financ Res Lett"},{"key":"365_CR5","unstructured":"Paneru B, Paneru B. The nexus of AR\/VR, large language models, UI\/UX, and robotics technologies in enhancing learning and social interaction for children: a systematic review. arXiv preprint arXiv:2409.18162. 2024."},{"key":"365_CR6","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.eswa.2019.01.012","volume":"124","author":"BM Henrique","year":"2019","unstructured":"Henrique BM, Sobreiro VA, Kimura H. Literature review: machine learning techniques applied to financial market prediction. Expert Syst Appl. 2019;124:226\u201351. https:\/\/doi.org\/10.1016\/j.eswa.2019.01.012.","journal-title":"Expert Syst Appl"},{"key":"365_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2022.122262","volume":"188","author":"A Razzaq","year":"2023","unstructured":"Razzaq A, Yang X. Digital finance and green growth in China: appraising inclusive digital finance using web crawler technology and big data. Technol Forecast Soc Chang. 2023;188: 122262. https:\/\/doi.org\/10.1016\/j.techfore.2022.122262.","journal-title":"Technol Forecast Soc Chang"},{"issue":"8","key":"365_CR8","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella G, Hanna MG, Geneslaw L, Miraflor A, Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301\u20139.","journal-title":"Nat Med"},{"key":"365_CR9","first-page":"26","volume":"2","author":"K Lahiri","year":"2015","unstructured":"Lahiri K, D\u2019Souza J, Gahlowt P. Beneficial role of probiotic in acute childhood diarrhea. J Harmoniz Res Med Health Sci. 2015;2:26\u201330.","journal-title":"J Harmoniz Res Med Health Sci"},{"issue":"2","key":"365_CR10","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1111\/gcb.14872","volume":"26","author":"J Barlow","year":"2020","unstructured":"Barlow J, Berenguer E, Carmenta R, Fran\u00e7a F. Clarifying Amazonia\u2019s burning crisis. Glob Change Biol. 2020;26(2):319\u201321.","journal-title":"Glob Change Biol"},{"key":"365_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2019.119779","volume":"151","author":"M Palmi\u00e9","year":"2020","unstructured":"Palmi\u00e9 M, Wincent J, Parida V, Caglar U. The evolution of the financial technology ecosystem: an introduction and agenda for future research on disruptive innovations in ecosystems. Technol Forecast Soc Chang. 2020;151: 119779. https:\/\/doi.org\/10.1016\/j.techfore.2019.119779.","journal-title":"Technol Forecast Soc Chang"},{"issue":"2","key":"365_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.digbus.2022.100028","volume":"2","author":"MF Barroso","year":"2022","unstructured":"Barroso MF, Laborda J. Digital transformation and the emergence of the Fintech sector: systematic literature review. Digital Busin. 2022;2(2): 100028. https:\/\/doi.org\/10.1016\/j.digbus.2022.100028.","journal-title":"Digital Busin"},{"issue":"1","key":"365_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10614-021-10094-w","volume":"57","author":"P Gogas","year":"2021","unstructured":"Gogas P, Papadimitriou T. Machine learning in economics and finance. Comput Econ. 2021;57(1):1\u20134. https:\/\/doi.org\/10.1007\/s10614-021-10094-w.","journal-title":"Comput Econ"},{"key":"365_CR14","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.procs.2022.07.092","volume":"203","author":"R Najem","year":"2022","unstructured":"Najem R, Amr MF, Bahnasse A, Talea M. Artificial intelligence for digital finance, axes and techniques. Procedia Comput Sci. 2022;203:633\u20138. https:\/\/doi.org\/10.1016\/j.procs.2022.07.092.","journal-title":"Procedia Comput Sci"},{"key":"365_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbef.2021.100577","volume":"32","author":"JW Goodell","year":"2021","unstructured":"Goodell JW, Kumar S, Lim WM, Pattnaik D. Artificial intelligence and machine learning in finance: identifying foundations, themes, and research clusters from bibliometric analysis. J Behav Exp Financ. 2021;32: 100577. https:\/\/doi.org\/10.1016\/j.jbef.2021.100577.","journal-title":"J Behav Exp Financ"},{"key":"365_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cscee.2025.101128","volume":"31","author":"R Poudyal","year":"2025","unstructured":"Poudyal R, Paneru B, Paneru B, Giri T, Paneru B, Reynolds T, Poudyal KN, Dangi MB. Exploring cement production\u2019s role in GDP using explainable AI and sustainability analysis in nepal. Case Stud Chem Environ Eng. 2025;31: 101128.","journal-title":"Case Stud Chem Environ Eng"},{"key":"365_CR17","doi-asserted-by":"publisher","unstructured":"Salih A, Hagras H. Towards a type-2 fuzzy logic based system for decision support to minimize financial default in banking sector, 2018 10th Computer science and electronic engineering (CEEC), Colchester, UK, 2018, pp. 46\u201349, https:\/\/doi.org\/10.1109\/CEEC.2018.8674212.","DOI":"10.1109\/CEEC.2018.8674212"},{"issue":"3","key":"365_CR18","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.ausmj.2020.05.003","volume":"29","author":"E Mogaji","year":"2020","unstructured":"Mogaji E, Soetan T, Kieu TA. The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers. Australasian Marketing J. 2020;29(3):235\u201342. https:\/\/doi.org\/10.1016\/j.ausmj.2020.05.003.","journal-title":"Australasian Marketing J"},{"key":"365_CR19","doi-asserted-by":"publisher","unstructured":"Boujrad M, lamlili YE. A new Artificial Intelligence-Based strategy for digital marketing reinforcement, in\u00a0Lecture notes in networks and systems. 2021: pp. 689\u2013699. https:\/\/doi.org\/10.1007\/978-3-030-66840-2_52.","DOI":"10.1007\/978-3-030-66840-2_52"},{"key":"365_CR20","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.eneco.2019.05.006","volume":"81","author":"H Ghoddusi","year":"2019","unstructured":"Ghoddusi H, Creamer GG, Rafizadeh N. Machine learning in energy economics and finance: a review. Energy Econ. 2019;81:709\u201327. https:\/\/doi.org\/10.1016\/j.eneco.2019.05.006.","journal-title":"Energy Econ"},{"key":"365_CR21","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ijpe.2019.01.032","volume":"211","author":"Z You","year":"2019","unstructured":"You Z, Zhou L, Xie C, Wang G-J, Van Nguyen T. Forecasting SMEs\u2019 credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. Int J Prod Econ. 2019;211:22\u201333. https:\/\/doi.org\/10.1016\/j.ijpe.2019.01.032.","journal-title":"Int J Prod Econ"},{"issue":"5","key":"365_CR22","doi-asserted-by":"publisher","first-page":"716","DOI":"10.3846\/tede.2019.8740","volume":"25","author":"G Kou","year":"2019","unstructured":"Kou G, Chao X, Alsaadi FE, Herrera-Viedma E. Machine learning methods for systemic risk analysis in financial sectors. Technol Econ Dev Econ. 2019;25(5):716\u201342. https:\/\/doi.org\/10.3846\/tede.2019.8740.","journal-title":"Technol Econ Dev Econ"},{"key":"365_CR23","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.procs.2019.01.007","volume":"148","author":"I Sadgali","year":"2019","unstructured":"Sadgali I, Sael N, Benabbou F. Performance of machine learning techniques in the detection of financial frauds. Procedia Comput Sci. 2019;148:45\u201354. https:\/\/doi.org\/10.1016\/j.procs.2019.01.007.","journal-title":"Procedia Comput Sci"},{"issue":"3","key":"365_CR24","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1080\/0015198x.2019.1596678","volume":"75","author":"KC Rasekhschaffe","year":"2019","unstructured":"Rasekhschaffe KC, Jones RC. s\u201cMachine learning for stock selection.\u201d Financ Anal J. 2019;75(3):70\u201388. https:\/\/doi.org\/10.1080\/0015198x.2019.1596678.","journal-title":"Financ Anal J"},{"key":"365_CR25","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.jfds.2022.05.003","volume":"8","author":"D Broby","year":"2022","unstructured":"Broby D. The use of predictive analytics in finance. J Finan Data Sci. 2022;8:145\u201361. https:\/\/doi.org\/10.1016\/j.jfds.2022.05.003.","journal-title":"J Finan Data Sci"},{"key":"365_CR26","doi-asserted-by":"publisher","first-page":"110461","DOI":"10.1109\/access.2020.3000505","volume":"8","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Xiong F, Xie Y, Xuan F, Gu H. The impact of artificial intelligence and blockchain on the accounting profession. IEEE Access. 2020;8:110461\u201377. https:\/\/doi.org\/10.1109\/access.2020.3000505.","journal-title":"IEEE Access"},{"key":"365_CR27","doi-asserted-by":"publisher","first-page":"30898","DOI":"10.1109\/access.2021.3058133","volume":"9","author":"FGDC Ferreira","year":"2021","unstructured":"Ferreira FGDC, Gandomi AH, Cardoso RTN. Artificial intelligence applied to stock market trading: a review. IEEE Access. 2021;9:30898\u2013917. https:\/\/doi.org\/10.1109\/access.2021.3058133.","journal-title":"IEEE Access"},{"key":"365_CR28","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1007\/s10115-018-1315-6","volume":"61","author":"X Zhang","year":"2019","unstructured":"Zhang X, Li Y, Wang S, Fang B, Yu PS. Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data. Knowl Inf Syst. 2019;61:1071\u201390.","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"365_CR29","doi-asserted-by":"publisher","first-page":"2071","DOI":"10.1109\/jiot.2020.3041184","volume":"10","author":"G Muhammad","year":"2023","unstructured":"Muhammad G, Hossain MS, Garg S. Stacked autoencoder-based intrusion detection system to combat financial fraudulent. IEEE Internet Things J. 2023;10(3):2071\u20138. https:\/\/doi.org\/10.1109\/jiot.2020.3041184.","journal-title":"IEEE Internet Things J"},{"key":"365_CR30","first-page":"921","volume":"23","author":"S Rajab","year":"2019","unstructured":"Rajab S, Sharma V. An interpretable neuro-fuzzy approach to stock price forecasting. Soft Comput. 2019;23:921\u201336.","journal-title":"Soft Comput"},{"key":"365_CR31","doi-asserted-by":"publisher","first-page":"120321","DOI":"10.1109\/access.2020.3005808","volume":"8","author":"M Sivaram","year":"2020","unstructured":"Sivaram M, et al. An optimal least square support vector machine based earnings prediction of blockchain financial products. IEEE Access. 2020;8:120321\u201330. https:\/\/doi.org\/10.1109\/access.2020.3005808.","journal-title":"IEEE Access"},{"key":"365_CR32","doi-asserted-by":"publisher","first-page":"71326","DOI":"10.1109\/access.2020.2985763","volume":"8","author":"AH Bukhari","year":"2020","unstructured":"Bukhari AH, Raja MAZ, Sulaiman M, Islam S, Shoaib M, Kumam P. Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting. IEEE Access. 2020;8:71326\u201338. https:\/\/doi.org\/10.1109\/access.2020.2985763.","journal-title":"IEEE Access"},{"key":"365_CR33","doi-asserted-by":"publisher","first-page":"22672","DOI":"10.1109\/access.2020.2969293","volume":"8","author":"X Yuan","year":"2020","unstructured":"Yuan X, Yuan J, Jiang T, Ain QU. Integrated long-term stock selection models based on feature selection and machine learning algorithms for China stock market. IEEE Access. 2020;8:22672\u201385. https:\/\/doi.org\/10.1109\/access.2020.2969293.","journal-title":"IEEE Access"},{"key":"365_CR34","doi-asserted-by":"publisher","first-page":"111660","DOI":"10.1109\/access.2020.3002174","volume":"8","author":"S Kim","year":"2020","unstructured":"Kim S, Ku S, Chang W, Song JW. Predicting the direction of US stock prices using effective transfer entropy and machine learning techniques. IEEE Access. 2020;8:111660\u201382. https:\/\/doi.org\/10.1109\/access.2020.3002174.","journal-title":"IEEE Access"},{"key":"365_CR35","doi-asserted-by":"publisher","first-page":"28299","DOI":"10.1109\/access.2019.2901842","volume":"7","author":"M Wen","year":"2019","unstructured":"Wen M, Li P, Zhang L, Chen Y. Stock market trend prediction using high-order information of time series. IEEE Access. 2019;7:28299\u2013308. https:\/\/doi.org\/10.1109\/access.2019.2901842.","journal-title":"IEEE Access"},{"key":"365_CR36","doi-asserted-by":"publisher","first-page":"25579","DOI":"10.1109\/access.2020.2971354","volume":"8","author":"A Altaher","year":"2020","unstructured":"Altaher A, Malebary SJ. An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access. 2020;8:25579\u201387. https:\/\/doi.org\/10.1109\/access.2020.2971354.","journal-title":"IEEE Access"},{"key":"365_CR37","doi-asserted-by":"publisher","first-page":"201173","DOI":"10.1109\/access.2020.3033784","volume":"8","author":"TM Alam","year":"2020","unstructured":"Alam TM, et al. An investigation of credit card default prediction in the imbalanced datasets. IEEE Access. 2020;8:201173\u201398. https:\/\/doi.org\/10.1109\/access.2020.3033784.","journal-title":"IEEE Access"},{"key":"365_CR38","doi-asserted-by":"publisher","first-page":"150199","DOI":"10.1109\/access.2020.3015966","volume":"8","author":"M Nabipour","year":"2020","unstructured":"Nabipour M, Nayyeri P, Jabani H, Shahab S, Mosavi A. Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access. 2020;8:150199\u2013212. https:\/\/doi.org\/10.1109\/access.2020.3015966.","journal-title":"IEEE Access"},{"key":"365_CR39","doi-asserted-by":"publisher","first-page":"82804","DOI":"10.1109\/access.2020.2990659","volume":"8","author":"P Jay","year":"2020","unstructured":"Jay P, Kalariya V, Parmar P, Tanwar S, Kumar N, Alazab M. Stochastic neural networks for cryptocurrency price prediction. IEEE Access. 2020;8:82804\u201318. https:\/\/doi.org\/10.1109\/access.2020.2990659.","journal-title":"IEEE Access"},{"key":"365_CR40","doi-asserted-by":"publisher","first-page":"17287","DOI":"10.1109\/access.2019.2895252","volume":"7","author":"SM Idrees","year":"2019","unstructured":"Idrees SM, Alam MA, Agarwal P. A prediction approach for stock market volatility based on time series data. IEEE Access. 2019;7:17287\u201398. https:\/\/doi.org\/10.1109\/access.2019.2895252.","journal-title":"IEEE Access"},{"key":"365_CR41","doi-asserted-by":"publisher","first-page":"167260","DOI":"10.1109\/access.2019.2953542","volume":"7","author":"J Lee","year":"2019","unstructured":"Lee J, Kim R, Koh YH, Kang J. Global stock market prediction based on stock chart images using deep Q-Network. IEEE Access. 2019;7:167260\u201377. https:\/\/doi.org\/10.1109\/access.2019.2953542.","journal-title":"IEEE Access"},{"key":"365_CR42","doi-asserted-by":"publisher","first-page":"83105","DOI":"10.1109\/access.2021.3085085","volume":"9","author":"R Chandra","year":"2021","unstructured":"Chandra R, Goyal S, Gupta R. Evaluation of deep learning models for multi-step ahead time series prediction. IEEE Access. 2021;9:83105\u201323. https:\/\/doi.org\/10.1109\/access.2021.3085085.","journal-title":"IEEE Access"},{"key":"365_CR43","doi-asserted-by":"publisher","first-page":"34743","DOI":"10.1109\/access.2022.3163723","volume":"10","author":"E Koo","year":"2022","unstructured":"Koo E, Kim G. A hybrid prediction model integrating GARCH models with a distribution manipulation strategy based on LSTM networks for stock market volatility. IEEE Access. 2022;10:34743\u201354. https:\/\/doi.org\/10.1109\/access.2022.3163723.","journal-title":"IEEE Access"},{"key":"365_CR44","doi-asserted-by":"publisher","first-page":"56232","DOI":"10.1109\/access.2022.3177888","volume":"10","author":"G Kim","year":"2022","unstructured":"Kim G, Shin D-H, Choi JG, Lim S. A Deep Learning-Based cryptocurrency price prediction model that uses On-Chain data. IEEE Access. 2022;10:56232\u201348. https:\/\/doi.org\/10.1109\/access.2022.3177888.","journal-title":"IEEE Access"},{"key":"365_CR45","doi-asserted-by":"publisher","first-page":"34511","DOI":"10.1109\/access.2022.3163023","volume":"10","author":"N Patel","year":"2022","unstructured":"Patel N, et al. Fusion in cryptocurrency price prediction: a decade survey on recent advancements, architecture, and potential future directions. IEEE Access. 2022;10:34511\u201338. https:\/\/doi.org\/10.1109\/access.2022.3163023.","journal-title":"IEEE Access"},{"key":"365_CR46","doi-asserted-by":"publisher","first-page":"37848","DOI":"10.1109\/access.2022.3162858","volume":"10","author":"Z Shahbazi","year":"2022","unstructured":"Shahbazi Z, Byun Y-C. Machine learning-based analysis of cryptocurrency market financial risk management. IEEE Access. 2022;10:37848\u201356. https:\/\/doi.org\/10.1109\/access.2022.3162858.","journal-title":"IEEE Access"},{"key":"365_CR47","doi-asserted-by":"publisher","first-page":"28781","DOI":"10.1109\/access.2023.3258695","volume":"11","author":"J Choi","year":"2023","unstructured":"Choi J, Yoo S, Zhou X, Kim Y. Hybrid information mixing module for stock movement prediction. IEEE Access. 2023;11:28781\u201390. https:\/\/doi.org\/10.1109\/access.2023.3258695.","journal-title":"IEEE Access"},{"key":"365_CR48","doi-asserted-by":"publisher","first-page":"14322","DOI":"10.1109\/access.2023.3243232","volume":"11","author":"J Nasir","year":"2023","unstructured":"Nasir J, Aamir M, Haq ZU, Khan S, Amin MY, Naeem M. A new approach for forecasting crude oil prices based on stochastic and deterministic influences of LMD using ARIMA and LSTM models. IEEE Access. 2023;11:14322\u201339. https:\/\/doi.org\/10.1109\/access.2023.3243232.","journal-title":"IEEE Access"},{"key":"365_CR49","doi-asserted-by":"publisher","first-page":"28920","DOI":"10.1109\/access.2023.3259108","volume":"11","author":"B Jin","year":"2023","unstructured":"Jin B. A Mean-VAR based deep reinforcement learning framework for practical algorithmic trading. IEEE Access. 2023;11:28920\u201333. https:\/\/doi.org\/10.1109\/access.2023.3259108.","journal-title":"IEEE Access"},{"issue":"3","key":"365_CR50","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/jas.2020.1003132","volume":"7","author":"S Barra","year":"2020","unstructured":"Barra S, Carta S, Corriga A, Podda AS, Recupero DR. Deep learning and time series-to-image encoding for financial forecasting. IEEE\/CAA J Automat Sinica. 2020;7(3):683\u201392. https:\/\/doi.org\/10.1109\/jas.2020.1003132.","journal-title":"IEEE\/CAA J Automat Sinica"},{"key":"365_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04410-8","author":"S Kumar","year":"2022","unstructured":"Kumar S, Sharma D, Rao S, Lim WM, Mangla SK. Past, present, and future of sustainable finance: insights from big data analytics through machine learning of scholarly research. Ann Oper Res. 2022. https:\/\/doi.org\/10.1007\/s10479-021-04410-8.","journal-title":"Ann Oper Res"},{"key":"365_CR52","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00291-z","author":"M Hasan","year":"2020","unstructured":"Hasan M, Popp J, Ol\u00e1h J. Current landscape and influence of big data on finance. J Big Data. 2020. https:\/\/doi.org\/10.1186\/s40537-020-00291-z.","journal-title":"J Big Data."},{"key":"365_CR53","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10479-016-2342-x","volume":"268","author":"L Li","year":"2018","unstructured":"Li L, Chi T, Hao T, Yu T. Customer demand analysis of the electronic commerce supply chain using Big Data. Ann Oper Res. 2018;268:113\u201328.","journal-title":"Ann Oper Res"},{"issue":"2","key":"365_CR54","doi-asserted-by":"publisher","first-page":"147","DOI":"10.5937\/bankarstvo2202147h","volume":"51","author":"MH Sazu","year":"2022","unstructured":"Sazu MH, Jahan SA. How big data analytics is transforming the finance industry. Bankarstvo. 2022;51(2):147\u201372. https:\/\/doi.org\/10.5937\/bankarstvo2202147h.","journal-title":"Bankarstvo"},{"key":"365_CR55","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s41060-021-00278-w","volume":"12","author":"L Cao","year":"2021","unstructured":"Cao L, Yang Q, Yu PS. Data science and AI in FinTech: an overview. Int J Data Sci Anal. 2021;12:81\u201399. https:\/\/doi.org\/10.1007\/s41060-021-00278-w.","journal-title":"Int J Data Sci Anal"},{"key":"365_CR56","first-page":"16","volume":"8","author":"JENNIFER Huttunen","year":"2019","unstructured":"Huttunen JENNIFER, Jauhiainen JAANA, Lehti LAURA, Nylund ANNINA, Martikainen MINNA, Lehner OM. Big data, cloud computing and data science applications in finance and accounting. ACRN J Finance Risk Perspect. 2019;8:16\u201330.","journal-title":"ACRN J Finance Risk Perspect"},{"key":"365_CR57","doi-asserted-by":"crossref","unstructured":"Kumar R, Grover N, Singh R, Kathuria S, Kumar A, Bansal A. Imperative role of artificial intelligence and big data in finance and banking sector. In\u00a02023 International conference on sustainable computing and data communication systems (ICSCDS)\u00a0(pp. 523\u2013527). IEEE.","DOI":"10.1109\/ICSCDS56580.2023.10105062"},{"issue":"1","key":"365_CR58","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1080\/10864415.2018.1512283","volume":"23","author":"Y Tang","year":"2019","unstructured":"Tang Y, Xiong J, Luo Y, Zhang Y. How do the global stock markets influence one another? evidence from finance big data and granger causality directed network. Int J Electron Commer. 2019;23(1):85\u2013109. https:\/\/doi.org\/10.1080\/10864415.2018.1512283.","journal-title":"Int J Electron Commer"},{"key":"365_CR59","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jfds.2022.04.003","volume":"8","author":"D Nissim","year":"2022","unstructured":"Nissim D. Big data, accounting information, and valuation. J Finance Data Sci. 2022;8:69\u201385. https:\/\/doi.org\/10.1016\/j.jfds.2022.04.003.","journal-title":"J Finance Data Sci"},{"key":"365_CR60","doi-asserted-by":"publisher","first-page":"37100","DOI":"10.1109\/access.2019.2905301","volume":"7","author":"JR Saura","year":"2019","unstructured":"Saura JR, Herr\u00e1ez BR, Reyes-Menendez A. Comparing a traditional approach for financial brand communication analysis with a big data analytics technique. IEEE Access. 2019;7:37100\u20138. https:\/\/doi.org\/10.1109\/access.2019.2905301.","journal-title":"IEEE Access"},{"issue":"1","key":"365_CR61","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/tfuzz.2020.3012393","volume":"29","author":"J Wen","year":"2021","unstructured":"Wen J, Yang J, Jiang B, Song H, Wang H. Big data driven marine environment information forecasting: a time series prediction network. IEEE Trans Fuzzy Syst. 2021;29(1):4\u201318. https:\/\/doi.org\/10.1109\/tfuzz.2020.3012393.","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"365_CR62","doi-asserted-by":"publisher","first-page":"154035","DOI":"10.1109\/access.2019.2948949","volume":"7","author":"H Zhou","year":"2019","unstructured":"Zhou H, Sun G, Sha F, Liu J, Zhou X, Zhou J. A big data mining approach of PSO-Based BP neural network for financial risk management with IoT. IEEE Access. 2019;7:154035\u201343. https:\/\/doi.org\/10.1109\/access.2019.2948949.","journal-title":"IEEE Access"},{"key":"365_CR63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/24754269.2022.2146955","volume":"7","author":"W Zhou","year":"2023","unstructured":"Zhou W, Pang S, He Z. The study on systemic risk of rural finance based on macro\u2013micro big data and machine learning. Statist Theory Relat Fields. 2023;7:1\u201315.","journal-title":"Statist Theory Relat Fields."},{"issue":"2","key":"365_CR64","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1111\/eufm.12365","volume":"29","author":"DK Nguyen","year":"2022","unstructured":"Nguyen DK, Sermpinis G, Stasinakis C. Big data, artificial intelligence and machine learning: a transformative symbiosis in favour of financial technology. Eur Financ Manag. 2022;29(2):517\u201348. https:\/\/doi.org\/10.1111\/eufm.12365.","journal-title":"Eur Financ Manag"},{"key":"365_CR65","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhu Y. Construction and application of financial analysis model based on big data technology. In\u00a02023 4th international conference on education, knowledge and information management (ICEKIM 2023). 2023: pp. 1958\u20131963. Atlantis Press.","DOI":"10.2991\/978-94-6463-172-2_217"},{"key":"365_CR66","doi-asserted-by":"crossref","unstructured":"VenkateswaraRao M, Vellela S, Reddy V, Vullam N, Sk KB, Roja D. Credit investigation and comprehensive risk management system based big data analytics in commercial banking. In\u00a02023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)\u00a0(Vol. 1, pp. 2387\u20132391). IEEE. 2023.","DOI":"10.1109\/ICACCS57279.2023.10113084"},{"issue":"2","key":"365_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.jjimei.2022.100094","volume":"2","author":"V Singh","year":"2022","unstructured":"Singh V, Chen S-S, Singhania M, Nanavati B, Kar AK, Gupta A. How are reinforcement learning and deep learning algorithms used for big data-based decision making in financial industries\u2013a review and research agenda. Int J Informat Manage Data Insights. 2022;2(2): 100094. https:\/\/doi.org\/10.1016\/j.jjimei.2022.100094.","journal-title":"Int J Informat Manage Data Insights"},{"issue":"2","key":"365_CR68","first-page":"08","volume":"32","author":"J Gao","year":"2022","unstructured":"Gao J. Research on the corporate financial transformation with big data technologies. Int J Progress Sci Technol. 2022;32(2):08\u201312.","journal-title":"Int J Progress Sci Technol"},{"issue":"2","key":"365_CR69","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s10551-019-04203-x","volume":"160","author":"KNA Arthur","year":"2019","unstructured":"Arthur KNA, Owen R. A micro-ethnographic study of Big Data-Based innovation in the financial services sector: governance, ethics and organisational practices. J Bus Ethics. 2019;160(2):363\u201375. https:\/\/doi.org\/10.1007\/s10551-019-04203-x.","journal-title":"J Bus Ethics"},{"key":"365_CR70","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-020-00205-1","author":"A Gupta","year":"2020","unstructured":"Gupta A, Dengre V, Kheruwala HA, Shah M. Comprehensive review of text-mining applications in finance. Finan Innovat. 2020. https:\/\/doi.org\/10.1186\/s40854-020-00205-1.","journal-title":"Finan Innovat"},{"key":"365_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2023.104212","volume":"57","author":"J Shao","year":"2023","unstructured":"Shao J, Hong J, Wang X, Yan X. The relationship between social media sentiment and house prices in China: evidence from text mining and wavelet analysis. Financ Res Lett. 2023;57: 104212.","journal-title":"Financ Res Lett"},{"issue":"3","key":"365_CR72","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1111\/acfi.12453","volume":"59","author":"L Wei","year":"2019","unstructured":"Wei L, Li G, Zhu X, Li JP. Discovering bank risk factors from financial statements based on a new semi-supervised text mining algorithm. Account Finance. 2019;59(3):1519\u201352. https:\/\/doi.org\/10.1111\/acfi.12453.","journal-title":"Account Finance"},{"key":"365_CR73","doi-asserted-by":"publisher","unstructured":"Bach MP, Krsti\u0107 \u017d, Seljan S. Big data text mining in the financial sector. in\u00a0Routledge eBooks, 2019. pp. 80\u201396. https:\/\/doi.org\/10.4324\/9780429024061-6.","DOI":"10.4324\/9780429024061-6"},{"key":"365_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113851","volume":"162","author":"H Jung","year":"2020","unstructured":"Jung H, Lee BG. Research trends in text mining: semantic network and main path analysis of selected journals. Expert Syst Appl. 2020;162: 113851. https:\/\/doi.org\/10.1016\/j.eswa.2020.113851.","journal-title":"Expert Syst Appl"},{"issue":"12","key":"365_CR75","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.1080\/14697688.2020.1814008","volume":"20","author":"J Li","year":"2020","unstructured":"Li J, Li G, Zhu X, Yao Y. Identifying the influential factors of commodity futures prices through a new text mining approach. Quantitat Finance. 2020;20(12):1967\u201381. https:\/\/doi.org\/10.1080\/14697688.2020.1814008.","journal-title":"Quantitat Finance"},{"key":"365_CR76","doi-asserted-by":"crossref","unstructured":"Paneru B, Thapa B, Paneru B. Sentiment analysis of movie reviews: a flask application using CNN with RoBERTa Embeddings.\u00a0Syst Soft Comput. 2025: 200192.","DOI":"10.1016\/j.sasc.2025.200192"},{"key":"365_CR77","doi-asserted-by":"publisher","first-page":"101889","DOI":"10.1016\/j.ribaf.2023.101889","volume":"64","author":"YH Hoang","year":"2023","unstructured":"Hoang YH, Ngo VM, Vu NB. Central bank digital currency: a systematic literature review using text mining approach. Res Int Bus Finance. 2023;64:101889.","journal-title":"Res Int Bus Finance."},{"key":"365_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119509","volume":"217","author":"MN Ashtiani","year":"2023","unstructured":"Ashtiani MN, Raahemi B. News-based intelligent prediction of financial markets using text mining and machine learning: a systematic literature review. Expert Syst Appl. 2023;217: 119509. https:\/\/doi.org\/10.1016\/j.eswa.2023.119509.","journal-title":"Expert Syst Appl"},{"key":"365_CR79","doi-asserted-by":"publisher","first-page":"14211","DOI":"10.1109\/access.2023.3244065","volume":"11","author":"JA Garc\u00eda-D\u00edaz","year":"2023","unstructured":"Garc\u00eda-D\u00edaz JA, Garc\u00eda-S\u00e1nchez F, Valencia-Garc\u00eda R. Smart analysis of economics sentiment in Spanish based on linguistic features and transformers. IEEE Access. 2023;11:14211\u201324. https:\/\/doi.org\/10.1109\/access.2023.3244065.","journal-title":"IEEE Access"},{"key":"365_CR80","doi-asserted-by":"publisher","first-page":"49289","DOI":"10.1109\/access.2023.3275085","volume":"11","author":"H Xu","year":"2023","unstructured":"Xu H, Zhang Y, Xu Y-Q. Promoting financial market development-financial stock classification using graph convolutional neural networks. IEEE Access. 2023;11:49289\u201399. https:\/\/doi.org\/10.1109\/access.2023.3275085.","journal-title":"IEEE Access"},{"key":"365_CR81","doi-asserted-by":"publisher","first-page":"40269","DOI":"10.1109\/access.2020.2976725","volume":"8","author":"S Bouktif","year":"2020","unstructured":"Bouktif S, Fiaz A, Awad M. Augmented textual Features-Based stock market prediction. IEEE Access. 2020;8:40269\u201382. https:\/\/doi.org\/10.1109\/access.2020.2976725.","journal-title":"IEEE Access"},{"issue":"1","key":"365_CR82","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/s12559-021-09819-8","volume":"14","author":"D Valle-Cruz","year":"2021","unstructured":"Valle-Cruz D, Fernandez-Cortez V, L\u00f3pez-Chau A, Sandoval-Almazan R. Does Twitter affect stock market decisions? Financial sentiment analysis during pandemics: a comparative study of the H1N1 and the COVID-19 periods. Cogn Comput. 2021;14(1):372\u201387. https:\/\/doi.org\/10.1007\/s12559-021-09819-8.","journal-title":"Cogn Comput"},{"key":"365_CR83","unstructured":"Xie Y, Jiang H. Stock market forecasting based on text mining technology: A support vector machine method.\u00a0arXiv preprint arXiv:1909.12789. 2019."},{"issue":"4","key":"365_CR84","first-page":"799","volume":"14","author":"Z Azizi","year":"2021","unstructured":"Azizi Z, Abdolvand N, Asl HG, Harandi SR. The impact of persian news on stock returns through text mining techniques. Iranian J Manage Stud. 2021;14(4):799\u2013816.","journal-title":"Iranian J Manage Stud"},{"issue":"1","key":"365_CR85","doi-asserted-by":"publisher","first-page":"177","DOI":"10.3846\/jbem.2023.18805","volume":"24","author":"Z Jankov\u00e1","year":"2023","unstructured":"Jankov\u00e1 Z. Critical review of text mining and sentiment analysis for stock market prediction. J Bus Econ Manag. 2023;24(1):177\u201398. https:\/\/doi.org\/10.3846\/jbem.2023.18805.","journal-title":"J Bus Econ Manag"},{"key":"365_CR86","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-29447-1_15","author":"NTH Chau","year":"2023","unstructured":"Chau NTH, Van Kien L, Phong DT. Stock price movement prediction using text mining and sentiment analysis. Stud Computat Intell. 2023. https:\/\/doi.org\/10.1007\/978-3-031-29447-1_15.","journal-title":"Stud Computat Intell"},{"key":"365_CR87","doi-asserted-by":"publisher","unstructured":"Lashgari V. Assessing text mining and technical analyses on forecasting financial time series,\u201d\u00a0arXiv (Cornell University), 2023. https:\/\/doi.org\/10.48550\/arxiv.2304.14544.","DOI":"10.48550\/arxiv.2304.14544"},{"key":"365_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2023.100624","volume":"50","author":"E Senave","year":"2023","unstructured":"Senave E, Jans MJ, Srivastava RP. The application of text mining in accounting. Int J Account Inf Syst. 2023;50: 100624. https:\/\/doi.org\/10.1016\/j.accinf.2023.100624.","journal-title":"Int J Account Inf Syst"},{"key":"365_CR89","doi-asserted-by":"publisher","first-page":"131662","DOI":"10.1109\/access.2020.3009626","volume":"8","author":"K Mishev","year":"2020","unstructured":"Mishev K, Gjorgjevikj A, Vodenska I, Chitkushev LT, Trajanov D. Evaluation of sentiment analysis in finance: from lexicons to transformers. IEEE Access. 2020;8:131662\u201382. https:\/\/doi.org\/10.1109\/access.2020.3009626.","journal-title":"IEEE Access"},{"key":"365_CR90","doi-asserted-by":"crossref","unstructured":"Faccia A, Al Naqbi MY, Lootah SA. Integrated cloud financial accounting cycle: how artificial intelligence, blockchain, and XBRL will change the accounting, fiscal and auditing practices. In\u00a0Proceedings of the 2019 3rd International conference on cloud and big data computing. 2019: pp. 31\u201337.","DOI":"10.1145\/3358505.3358507"},{"issue":"1","key":"365_CR91","first-page":"1","volume":"4","author":"A Bodepudi","year":"2020","unstructured":"Bodepudi A, Reddy M. Cloud-based biometric authentication techniques for secure financial transactions: a review. Int J Informat Cybersecur. 2020;4(1):1\u201318.","journal-title":"Int J Informat Cybersecur"},{"key":"365_CR92","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.1016\/j.matpr.2021.11.121","volume":"51","author":"S Vinoth","year":"2022","unstructured":"Vinoth S, Vemula HL, Haralayya B, Mamgain P, Hasan MF, Naved M. Application of cloud computing in banking and e-commerce and related security threats. Mater Today Proceed. 2022;51:2172\u20135. https:\/\/doi.org\/10.1016\/j.matpr.2021.11.121.","journal-title":"Mater Today Proceed"},{"issue":"11","key":"365_CR93","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1631\/jzus.C1300109","volume":"14","author":"DY Xu","year":"2013","unstructured":"Xu DY, Yang SL, Liu RP. A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers. J Zhejiang Univer SCIENCE C. 2013;14(11):845\u201358.","journal-title":"J Zhejiang Univer SCIENCE C"},{"key":"365_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2022.102676","volume":"160","author":"D Ivanov","year":"2022","unstructured":"Ivanov D, Dolgui A, Sokolov B. Cloud supply chain: integrating industry 4.0 and digital platforms in the \u2018Supply Chain-as-a-Service.\u2019 Transport Res E-Log. 2022;160: 102676. https:\/\/doi.org\/10.1016\/j.tre.2022.102676.","journal-title":"Transport Res E-Log"},{"key":"365_CR95","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.cie.2019.01.056","volume":"129","author":"L Novais","year":"2019","unstructured":"Novais L, Mar\u00edn JMM, Ortiz A. A systematic literature review of cloud computing use in supply chain integration. Comput Ind Eng. 2019;129:296\u2013314. https:\/\/doi.org\/10.1016\/j.cie.2019.01.056.","journal-title":"Comput Ind Eng"},{"key":"365_CR96","doi-asserted-by":"crossref","unstructured":"Fahlevi M, Purnomo A. The integration of internet of things (IoT) and cloud computing in finance and accounting: systematic literature review. In\u00a02023 8th International Conference on Business and Industrial Research (ICBIR)\u00a0(pp. 525\u2013529). IEEE. 2023.","DOI":"10.1109\/ICBIR57571.2023.10147688"},{"key":"365_CR97","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-2650547\/v1","author":"G Pu","year":"2023","unstructured":"Pu G. Analysis and application of financial market investment strategy based on data mining and cloud computing service system. Res Square. 2023. https:\/\/doi.org\/10.21203\/rs.3.rs-2650547\/v1.","journal-title":"Res Square"},{"issue":"7","key":"365_CR98","doi-asserted-by":"publisher","first-page":"793","DOI":"10.21275\/sr22712093816","volume":"11","author":"J Gao","year":"2022","unstructured":"Gao J. Research on financial management informatization mode of SME under cloud computing. Int J Sci Res. 2022;11(7):793\u20136. https:\/\/doi.org\/10.21275\/sr22712093816.","journal-title":"Int J Sci Res"},{"key":"365_CR99","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.inffus.2022.10.006","volume":"91","author":"W-C Huang","year":"2023","unstructured":"Huang W-C, Chen C-T, Lee C, Kuo F-H, Huang S-H. Attentive gated graph sequence neural network-based time-series information fusion for financial trading. Informat Fusion. 2023;91:261\u201376. https:\/\/doi.org\/10.1016\/j.inffus.2022.10.006.","journal-title":"Informat Fusion"},{"key":"365_CR100","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.inffus.2020.08.019","volume":"65","author":"A Thakkar","year":"2021","unstructured":"Thakkar A, Chaudhari K. Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions. Informat Fusion. 2021;65:95\u2013107. https:\/\/doi.org\/10.1016\/j.inffus.2020.08.019.","journal-title":"Informat Fusion"},{"key":"365_CR101","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.inffus.2021.11.018","volume":"81","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Jiang C, Yue B, Wan J, Guizani M. Information fusion for edge intelligence: a survey. Informat Fusion. 2022;81:171\u201386. https:\/\/doi.org\/10.1016\/j.inffus.2021.11.018.","journal-title":"Informat Fusion"},{"key":"365_CR102","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.inffus.2022.10.025","volume":"91","author":"Y Ma","year":"2023","unstructured":"Ma Y, Mao R, Lin Q, Wu P, Wang Z. Multi-source aggregated classification for stock price movement prediction. Informat Fusion. 2023;91:515\u201328. https:\/\/doi.org\/10.1016\/j.inffus.2022.10.025.","journal-title":"Informat Fusion"},{"key":"365_CR103","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2020.11.006","volume":"69","author":"L Li","year":"2021","unstructured":"Li L, Zhu F, Sun H, Hu Y, Yang Y, Jin D. Multi-source information fusion and deep-learning-based characteristics measurement for exploring the effects of peer engagement on stock price synchronicity. Informat Fusion. 2021;69:1\u201321. https:\/\/doi.org\/10.1016\/j.inffus.2020.11.006.","journal-title":"Informat Fusion"},{"key":"365_CR104","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.inffus.2023.02.028","volume":"95","author":"L Zhu","year":"2023","unstructured":"Zhu L, Zhu Z, Zhang C, Xu Y, Kong X. Multimodal sentiment analysis based on fusion methods: a survey. Informat Fusion. 2023;95:306\u201325. https:\/\/doi.org\/10.1016\/j.inffus.2023.02.028.","journal-title":"Informat Fusion"},{"key":"365_CR105","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.inffus.2022.09.025","volume":"91","author":"A Gandhi","year":"2023","unstructured":"Gandhi A, Adhvaryu K, Poria S, Cambria E, Hussain A. Multimodal sentiment analysis: a systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Informat Fusion. 2023;91:424\u201344. https:\/\/doi.org\/10.1016\/j.inffus.2022.09.025.","journal-title":"Informat Fusion"},{"key":"365_CR106","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.inffus.2020.01.005","volume":"59","author":"S Ayesha","year":"2020","unstructured":"Ayesha S, Hanif MK, Talib R. Overview and comparative study of dimensionality reduction techniques for high dimensional data. Informat Fusion. 2020;59:44\u201358. https:\/\/doi.org\/10.1016\/j.inffus.2020.01.005.","journal-title":"Informat Fusion"},{"key":"365_CR107","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.inffus.2020.02.002","volume":"60","author":"Z Wang","year":"2020","unstructured":"Wang Z, Zhang J, Ji S, Meng C, Zheng Y. Predicting and ranking box office revenue of movies based on big data. Informat Fusion. 2020;60:25\u201340. https:\/\/doi.org\/10.1016\/j.inffus.2020.02.002.","journal-title":"Informat Fusion"},{"key":"365_CR108","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.inffus.2020.12.010","volume":"70","author":"J-H Jang","year":"2021","unstructured":"Jang J-H, Yoon J, Kim J-E, Gu J, Kim HY. DeepOption: a novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods. Informat Fusion. 2021;70:43\u201359. https:\/\/doi.org\/10.1016\/j.inffus.2020.12.010.","journal-title":"Informat Fusion"},{"key":"365_CR109","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.inffus.2021.10.004","volume":"79","author":"K Shaheed","year":"2022","unstructured":"Shaheed K, Mao A, Qureshi I, Kumar M, Hussain S, Zhang X. Recent advancements in finger vein recognition technology: methodology, challenges and opportunities. Informat Fusion. 2022;79:84\u2013109. https:\/\/doi.org\/10.1016\/j.inffus.2021.10.004.","journal-title":"Informat Fusion"},{"key":"365_CR110","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101819","volume":"97","author":"Z Chen","year":"2023","unstructured":"Chen Z, Ma M, Li T, Wang H, Li C. Long sequence time-series forecasting with deep learning: a survey. Informat Fusion. 2023;97: 101819. https:\/\/doi.org\/10.1016\/j.inffus.2023.101819.","journal-title":"Informat Fusion"},{"key":"365_CR111","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.inffus.2018.10.005","volume":"50","author":"A Diez-Olivan","year":"2019","unstructured":"Diez-Olivan A, Del Ser J, Galar D, Sierra B. Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Informat Fusion. 2019;50:92\u2013111. https:\/\/doi.org\/10.1016\/j.inffus.2018.10.005.","journal-title":"Informat Fusion"},{"key":"365_CR112","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.inffus.2022.10.008","volume":"91","author":"G Li","year":"2023","unstructured":"Li G, Jung J. Deep learning for anomaly detection in multivariate time series: approaches, applications, and challenges. Informat Fusion. 2023;91:93\u2013102. https:\/\/doi.org\/10.1016\/j.inffus.2022.10.008.","journal-title":"Informat Fusion"},{"key":"365_CR113","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101918","volume":"100","author":"F Giampaolo","year":"2023","unstructured":"Giampaolo F, et al. ENCODE\u2013ensemble neural combination for optimal dimensionality encoding in time-series forecasting. Informat Fusion. 2023;100: 101918. https:\/\/doi.org\/10.1016\/j.inffus.2023.101918.","journal-title":"Informat Fusion"},{"key":"365_CR114","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.inffus.2020.10.001","volume":"67","author":"L Erhan","year":"2021","unstructured":"Erhan L, et al. Smart anomaly detection in sensor systems: a multi-perspective review. Informat Fusion. 2021;67:64\u201379. https:\/\/doi.org\/10.1016\/j.inffus.2020.10.001.","journal-title":"Informat Fusion"},{"key":"365_CR115","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1016\/j.inffus.2022.10.032","volume":"91","author":"Z Qin","year":"2023","unstructured":"Qin Z, Zhao P, Zhuang T, Deng F, Ding Y, Chen D. A survey of identity recognition via data fusion and feature learning. Informat Fusion. 2023;91:694\u2013712. https:\/\/doi.org\/10.1016\/j.inffus.2022.10.032.","journal-title":"Informat Fusion"},{"key":"365_CR116","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.inffus.2018.12.002","volume":"51","author":"A Hafezalkotob","year":"2019","unstructured":"Hafezalkotob A, Hafezalkotob A, Liao H, Herrera F. An overview of MULTIMOORA for multi-criteria decision-making: theory, developments, applications, and challenges. Informat Fusion. 2019;51:145\u201377. https:\/\/doi.org\/10.1016\/j.inffus.2018.12.002.","journal-title":"Informat Fusion"},{"key":"365_CR117","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.inffus.2021.05.011","volume":"76","author":"JC-W Lin","year":"2021","unstructured":"Lin JC-W, Djenouri Y, Srivastava G. Efficient closed high-utility pattern fusion model in large-scale databases. Informat Fusion. 2021;76:122\u201332. https:\/\/doi.org\/10.1016\/j.inffus.2021.05.011.","journal-title":"Informat Fusion"},{"key":"365_CR118","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.inffus.2023.02.007","volume":"95","author":"Z Yuan","year":"2023","unstructured":"Yuan Z, Chen H, Luo C, Peng D. MFGAD: multi-fuzzy granules anomaly detection. Informat Fusion. 2023;95:17\u201325. https:\/\/doi.org\/10.1016\/j.inffus.2023.02.007.","journal-title":"Informat Fusion"},{"key":"365_CR119","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.inffus.2019.07.006","volume":"54","author":"J Sun","year":"2020","unstructured":"Sun J, Li H, Fujita H, Fu B, Ai W. Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting. Informat Fusion. 2020;54:128\u201344. https:\/\/doi.org\/10.1016\/j.inffus.2019.07.006.","journal-title":"Informat Fusion"},{"key":"365_CR120","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.inffus.2020.02.003","volume":"60","author":"Y Pan","year":"2020","unstructured":"Pan Y, Zhang L, Wu X, Skibniewski MJ. Multi-classifier information fusion in risk analysis. Informat Fusion. 2020;60:121\u201336. https:\/\/doi.org\/10.1016\/j.inffus.2020.02.003.","journal-title":"Informat Fusion"},{"key":"365_CR121","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.inffus.2021.11.015","volume":"81","author":"JM Ro\u017eanec","year":"2022","unstructured":"Ro\u017eanec JM, Fortuna B, Mladeni\u0107 D. Knowledge graph-based rich and confidentiality preserving explainable artificial intelligence (XAI). Informat Fusion. 2022;81:91\u2013102. https:\/\/doi.org\/10.1016\/j.inffus.2021.11.015.","journal-title":"Informat Fusion"},{"key":"365_CR122","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.inffus.2018.11.001","volume":"52","author":"S Kumar","year":"2019","unstructured":"Kumar S, Yadava M, Roy PP. Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Informat Fusion. 2019;52:41\u201352. https:\/\/doi.org\/10.1016\/j.inffus.2018.11.001.","journal-title":"Informat Fusion"},{"key":"365_CR123","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.inffus.2018.11.018","volume":"49","author":"SKS Modak","year":"2019","unstructured":"Modak SKS, Jha VK. Multibiometric fusion strategy and its applications: a review. Informat Fusion. 2019;49:174\u2013204. https:\/\/doi.org\/10.1016\/j.inffus.2018.11.018.","journal-title":"Informat Fusion"},{"key":"365_CR124","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.procs.2024.08.015","volume":"241","author":"R Najem","year":"2024","unstructured":"Najem R, Amr MF, Bahnasse A, Talea M. Toward an enhanced stock market forecasting with machine learning and deep learning models. Proced Comput Sci. 2024;241:97\u2013103. https:\/\/doi.org\/10.1016\/j.procs.2024.08.015.","journal-title":"Proced Comput Sci"},{"key":"365_CR125","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.inffus.2020.03.014","volume":"61","author":"HL Nguyen","year":"2020","unstructured":"Nguyen HL, Vu DT, Jung JJ. Knowledge graph fusion for smart systems: a survey. Informat Fusion. 2020;61:56\u201370. https:\/\/doi.org\/10.1016\/j.inffus.2020.03.014.","journal-title":"Informat Fusion"},{"issue":"21","key":"365_CR126","doi-asserted-by":"publisher","first-page":"4087","DOI":"10.1111\/bph.14819","volume":"176","author":"A Gogos","year":"2019","unstructured":"Gogos A, Langmead C, Sullivan JC, Lawrence AJ. The importance of sex differences in pharmacology research. Br J Pharmacol. 2019;176(21):4087.","journal-title":"Br J Pharmacol"},{"key":"365_CR127","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.inffus.2022.12.027","volume":"93","author":"P Zhang","year":"2023","unstructured":"Zhang P, Li T, Wang G, Wang D-X, Lai PL, Zhang F. A multi-source information fusion model for outlier detection. Informat Fusion. 2023;93:192\u2013208. https:\/\/doi.org\/10.1016\/j.inffus.2022.12.027.","journal-title":"Informat Fusion"},{"key":"365_CR128","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.inffus.2019.05.004","volume":"52","author":"BPL Lau","year":"2019","unstructured":"Lau BPL, et al. A survey of data fusion in smart city applications. Informat Fusion. 2019;52:357\u201374. https:\/\/doi.org\/10.1016\/j.inffus.2019.05.004.","journal-title":"Informat Fusion"},{"key":"365_CR129","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.inffus.2018.07.007","volume":"49","author":"F Rodrigues","year":"2019","unstructured":"Rodrigues F, Markou I, Pereira FC. Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach. Informat Fusion. 2019;49:120\u20139. https:\/\/doi.org\/10.1016\/j.inffus.2018.07.007.","journal-title":"Informat Fusion"},{"key":"365_CR130","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.inffus.2020.11.004","volume":"68","author":"P Zhang","year":"2021","unstructured":"Zhang P, et al. Multi-source information fusion based on rough set theory: a review. Informat Fusion. 2021;68:85\u2013117. https:\/\/doi.org\/10.1016\/j.inffus.2020.11.004.","journal-title":"Informat Fusion"},{"key":"365_CR131","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.procs.2023.12.193","volume":"231","author":"R Najem","year":"2024","unstructured":"Najem R, Amr MF, Bahnasse A, Talea M. Advancements in artificial intelligence and machine learning for stock market prediction: a comprehensive analysis of techniques and case studies. Procedia Comput Sci. 2024;231:198\u2013204. https:\/\/doi.org\/10.1016\/j.procs.2023.12.193.","journal-title":"Procedia Comput Sci"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00365-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00365-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00365-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T20:23:09Z","timestamp":1749846189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00365-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,13]]},"references-count":131,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["365"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00365-y","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,13]]},"assertion":[{"value":"4 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no Competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"102"}}