{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T23:17:13Z","timestamp":1773875833758,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"21-22","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"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":["Soft Comput"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s00500-025-10901-8","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T10:39:27Z","timestamp":1759142367000},"page":"5803-5829","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["StockAI 3.0: Ensemble fusion paradigms using novel gating mechanism in long short-term memory architectures for forecasting sentiment-based stock trends"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8768-8593","authenticated-orcid":false,"given":"Yuvraj","family":"Sharma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5631-0550","authenticated-orcid":false,"given":"Siddharth","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7069-1519","authenticated-orcid":false,"given":"Neha","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Ekta","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Rajesh","family":"Singh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2957-3825","authenticated-orcid":false,"given":"Narendra N.","family":"Khanna","sequence":"additional","affiliation":[]},{"given":"Mustafa","family":"Al-Maini","sequence":"additional","affiliation":[]},{"given":"Vijay","family":"Rathore","sequence":"additional","affiliation":[]},{"given":"Puneet","family":"Ahluwalia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1908-2080","authenticated-orcid":false,"given":"Vandana","family":"Kumari","sequence":"additional","affiliation":[]},{"given":"Subbaram","family":"Naidu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2870-3771","authenticated-orcid":false,"given":"Luca","family":"Saba","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6499-396X","authenticated-orcid":false,"given":"Jasjit S.","family":"Suri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"10901_CR1","unstructured":"Agarwal A, Vats S, Agarwal R, Ratra A, Sharma V, Gopal L (2023) Sentiment Analysis in Stock Price Prediction: A Comparative Study of Algorithms, in 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1403\u20131407. [Online]. Available: https:\/\/ieeexplore.ieee.org\/abstract\/document\/10112565 Accessed: Feb. 17, 2025."},{"key":"10901_CR2","doi-asserted-by":"publisher","unstructured":"Beck M, et al. (2024) xLSTM: Extended Long Short-Term Memory, arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2405.04517.","DOI":"10.48550\/ARXIV.2405.04517"},{"issue":"2\/3","key":"10901_CR3","doi-asserted-by":"publisher","DOI":"10.5121\/mlaij.2023.10301","volume":"10","author":"S Bhatt","year":"2023","unstructured":"Bhatt S, Ghazanfar M, Amirhosseini M (2023b) Sentiment-driven cryptocurrency price prediction: a machine learning approach utilizing historical data and social media sentiment analysis. Machine Learning and Appl: an Int J (MLAIJ) 10(2\/3):2\/3. https:\/\/doi.org\/10.5121\/mlaij.2023.10301","journal-title":"Machine Learning and Appl: an Int J (MLAIJ)"},{"key":"10901_CR4","doi-asserted-by":"crossref","unstructured":"Bhatt S, Ghazanfar M, Amirhosseini M (2023) Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment, in Computer Science & Information Technology (CS & IT), Sydney, Australia: AIRCC Publishing Corporation, pp. 1\u201311. [Online]. Available: https:\/\/aircconline.com\/csit\/abstract\/v13n10\/csit131001.html Accessed: Feb. 17, 2025.","DOI":"10.5121\/csit.2023.131001"},{"issue":"3","key":"10901_CR5","doi-asserted-by":"publisher","first-page":"392","DOI":"10.2741\/4725","volume":"24","author":"M Biswas","year":"2019","unstructured":"Biswas M et al (2019) State-of-the-art review on deep learning in medical imaging. Front Biosci (Landmark Ed) 24(3):392\u2013426. https:\/\/doi.org\/10.2741\/4725","journal-title":"Front Biosci (Landmark Ed)"},{"key":"10901_CR6","unstructured":"Business News Today: Read Latest Business news, India Business News Live, Share Market & Economy News | The Economic Times. [Online]. Available: https:\/\/economictimes.indiatimes.com\/?from=mdr Accessed: Apr. 29, 2025."},{"issue":"3","key":"10901_CR7","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.ijforecast.2023.07.002","volume":"40","author":"G Campisi","year":"2024","unstructured":"Campisi G, Muzzioli S, De Baets B (2024) A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices. Int J Forecast 40(3):869\u2013880. https:\/\/doi.org\/10.1016\/j.ijforecast.2023.07.002","journal-title":"Int J Forecast"},{"key":"10901_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2019.112395","volume":"365","author":"Z Chen","year":"2020","unstructured":"Chen Z, Li C, Sun W (2020) Bitcoin price prediction using machine learning: an approach to sample dimension engineering. J Comput Appl Math 365:112395. https:\/\/doi.org\/10.1016\/j.cam.2019.112395","journal-title":"J Comput Appl Math"},{"key":"10901_CR67","doi-asserted-by":"publisher","unstructured":"Cheng Z et al. (2020) Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units, arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2002.11338.","DOI":"10.48550\/ARXIV.2002.11338"},{"issue":"2","key":"10901_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11020250","volume":"11","author":"MK Daradkeh","year":"2022","unstructured":"Daradkeh MK (2022) A hybrid data analytics framework with sentiment convergence and multi-feature fusion for stock trend prediction. Electronics 11(2):250. https:\/\/doi.org\/10.3390\/electronics11020250","journal-title":"Electronics"},{"key":"10901_CR10","doi-asserted-by":"publisher","unstructured":"Department of Finance, Henry W. Bloch School of Management, University of Missouri,Kansas City, USA and Z. Ahmadirad, \u201cThe Effects of Bitcoin ETFs on Traditional Markets: A Focus on Liquidity, Volatility, and Investor Behavior,\u201d rr, Jul. 2024, https:\/\/doi.org\/10.52845\/currentopinion.v4i3.317.","DOI":"10.52845\/currentopinion.v4i3.317"},{"issue":"3","key":"10901_CR11","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/tnnls.2016.2522401","volume":"28","author":"Y Deng","year":"2017","unstructured":"Deng Y, Bao F, Kong Y, Ren Z, Dai Q (2017) Deep direct reinforcement learning for financial signal representation and trading. IEEE Trans Neural Netw Learn Syst 28(3):653\u2013664. https:\/\/doi.org\/10.1109\/tnnls.2016.2522401","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"10901_CR12","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17148\/IJARCCE.2019.8107","volume":"8","author":"Y Deshmukh","year":"2019","unstructured":"Deshmukh Y, Saratkar D, Tiwari Y (2019) Stock market prediction using machine learning. Int J Advanced Res in Comput Commun Engineer 8(1):31\u201335. https:\/\/doi.org\/10.17148\/IJARCCE.2019.8107","journal-title":"Int J Advanced Res in Comput Commun Engineer"},{"issue":"11","key":"10901_CR13","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.3390\/diagnostics13111954","volume":"13","author":"AK Dubey","year":"2023","unstructured":"Dubey AK et al (2023) Ensemble deep learning derived from transfer learning for classification of COVID-19 patients on hybrid deep-learning-based lung segmentation: a data augmentation and balancing framework. Diagnostics (Basel) 13(11):1954. https:\/\/doi.org\/10.3390\/diagnostics13111954","journal-title":"Diagnostics (Basel)"},{"issue":"7","key":"10901_CR14","doi-asserted-by":"publisher","first-page":"2879","DOI":"10.1109\/TNNLS.2020.3046629","volume":"33","author":"G Dudek","year":"2022","unstructured":"Dudek G, Pelka P, Smyl S (2022) A hybrid residual dilated LSTM and exponential smoothing model for midterm electric load forecasting. IEEE Trans Neural Netw Learning Syst 33(7):2879\u20132891. https:\/\/doi.org\/10.1109\/TNNLS.2020.3046629","journal-title":"IEEE Trans Neural Netw Learning Syst"},{"issue":"1","key":"10901_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3758\/BF03203630","volume":"28","author":"E Erdfelder","year":"1996","unstructured":"Erdfelder E, Faul F, Buchner A (1996) GPOWER: a general power analysis program. Behav Res Methods Instrum Comput 28(1):1\u201311. https:\/\/doi.org\/10.3758\/BF03203630","journal-title":"Behav Res Methods Instrum Comput"},{"issue":"10","key":"10901_CR16","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451\u20132471. https:\/\/doi.org\/10.1162\/089976600300015015","journal-title":"Neural Comput"},{"issue":"4","key":"10901_CR17","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.62527\/joiv.7.4.1640","volume":"7","author":"AEK Gunawan","year":"2023","unstructured":"Gunawan AEK, Wibowo A (2023) Stock price movement classification using ensembled model of long short-term memory (LSTM) and random forest (RF). JOIV\u202f: Int J Inform Visualization 7(4):2255. https:\/\/doi.org\/10.62527\/joiv.7.4.1640","journal-title":"JOIV : Int J Inform Visualization"},{"key":"10901_CR18","doi-asserted-by":"publisher","unstructured":"Haarnoja T, Zhou A, Abbeel P, Levine S (2018) Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1801.01290.","DOI":"10.48550\/ARXIV.1801.01290"},{"key":"10901_CR19","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 (2019) Literature review: machine learning techniques applied to financial market prediction. Expert Syst Appl 124:226\u2013251. https:\/\/doi.org\/10.1016\/j.eswa.2019.01.012","journal-title":"Expert Syst Appl"},{"key":"10901_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106018","volume":"150","author":"AM Johri","year":"2022","unstructured":"Johri AM et al (2022) Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. Comput Biol Med 150:106018. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106018","journal-title":"Comput Biol Med"},{"issue":"22","key":"10901_CR21","doi-asserted-by":"publisher","first-page":"6844","DOI":"10.3390\/jcm11226844","volume":"11","author":"NN Khanna","year":"2022","unstructured":"Khanna NN et al (2022) Cardiovascular\/stroke risk stratification in diabetic foot infection patients using deep learning-based artificial intelligence: an investigative study. JCM 11(22):6844. https:\/\/doi.org\/10.3390\/jcm11226844","journal-title":"JCM"},{"issue":"2","key":"10901_CR22","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s00296-021-05062-4","volume":"42","author":"G Konstantonis","year":"2022","unstructured":"Konstantonis G et al (2022) Cardiovascular disease detection using machine learning and carotid\/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatol Int 42(2):215\u2013239. https:\/\/doi.org\/10.1007\/s00296-021-05062-4","journal-title":"Rheumatol Int"},{"issue":"2","key":"10901_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/telecom3020019","volume":"3","author":"P Koukaras","year":"2022","unstructured":"Koukaras P, Nousi C, Tjortjis C (2022) Stock market prediction using microblogging sentiment analysis and machine learning. Telecom 3(2):2. https:\/\/doi.org\/10.3390\/telecom3020019","journal-title":"Telecom"},{"key":"10901_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2021.126068","volume":"577","author":"P-C Kuang","year":"2021","unstructured":"Kuang P-C (2021) Measuring information flow among international stock markets: an approach of entropy-based networks on multi time-scales. Physica A Stat Mech Appl 577:126068. https:\/\/doi.org\/10.1016\/j.physa.2021.126068","journal-title":"Physica A Stat Mech Appl"},{"key":"10901_CR25","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/j.proeng.2014.02.109","volume":"70","author":"A Lacasta","year":"2014","unstructured":"Lacasta A, Morales-Hern\u00e1ndez M, Brufau P, Garc\u00eda-Navarro P (2014) Simulation of PID control applied to irrigation channels. Procedia Eng 70:978\u2013987. https:\/\/doi.org\/10.1016\/j.proeng.2014.02.109","journal-title":"Procedia Eng"},{"key":"10901_CR26","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.neunet.2021.08.030","volume":"144","author":"F Landi","year":"2021","unstructured":"Landi F, Baraldi L, Cornia M, Cucchiara R (2021) Working memory connections for LSTM. Neural Netw 144:334\u2013341. https:\/\/doi.org\/10.1016\/j.neunet.2021.08.030","journal-title":"Neural Netw"},{"key":"10901_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112872","volume":"140","author":"K Lei","year":"2020","unstructured":"Lei K, Zhang B, Li Y, Yang M, Shen Y (2020) Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Syst Appl 140:112872. https:\/\/doi.org\/10.1016\/j.eswa.2019.112872","journal-title":"Expert Syst Appl"},{"key":"10901_CR28","doi-asserted-by":"publisher","unstructured":"Lu Y, Salem FM (2017) Simplified gating in long short-term memory (LSTM) recurrent neural networks, in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA: IEEE. pp. 1601\u20131604. https:\/\/doi.org\/10.1109\/MWSCAS.2017.8053244.","DOI":"10.1109\/MWSCAS.2017.8053244"},{"key":"10901_CR29","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1016\/j.procs.2023.01.086","volume":"218","author":"J Maqbool","year":"2023","unstructured":"Maqbool J, Aggarwal P, Kaur R, Mittal A, Ganaie IA (2023) Stock prediction by integrating sentiment scores of financial news and MLP-regressor: a machine learning approach. Procedia Comput Sci 218:1067\u20131078. https:\/\/doi.org\/10.1016\/j.procs.2023.01.086","journal-title":"Procedia Comput Sci"},{"key":"10901_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.126196","volume":"267","author":"M Merikhipour","year":"2025","unstructured":"Merikhipour M, Khanmohammadidoustani S, Abbasi M (2025) Transportation mode detection through spatial attention-based transductive long short-term memory and off-policy feature selection. Expert Syst Appl 267:126196. https:\/\/doi.org\/10.1016\/j.eswa.2024.126196","journal-title":"Expert Syst Appl"},{"issue":"7540","key":"10901_CR31","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533. https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"issue":"1","key":"10901_CR32","doi-asserted-by":"publisher","DOI":"10.24017\/science.2020.1.3","volume":"5","author":"RM Nabi","year":"2020","unstructured":"Nabi RM, Saeed SAM, Harron H (2020) A novel approach for stock price prediction using gradient boosting machine with feature engineering (GBM-wFE). Kurdistan J Appl Res 5(1):1. https:\/\/doi.org\/10.24017\/science.2020.1.3","journal-title":"Kurdistan J Appl Res"},{"key":"10901_CR33","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, Mosavi SSA (2020) Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access 8:150199\u2013150212. https:\/\/doi.org\/10.1109\/ACCESS.2020.3015966","journal-title":"IEEE Access"},{"key":"10901_CR34","unstructured":"N. S. E. India, \u201cNSE - National Stock Exchange of India Ltd,\u201d NSE India. [Online]. Available: https:\/\/www.nseindia.com Accessed: Apr. 29, 2025."},{"key":"10901_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.irfa.2024.103329","volume":"94","author":"BO Odusami","year":"2024","unstructured":"Odusami BO, Akinsomi O (2024) Diversifying and hedging REIT portfolios with cryptocurrencies: evidence from global and regional REIT indices. Int Rev Financ Anal 94:103329. https:\/\/doi.org\/10.1016\/j.irfa.2024.103329","journal-title":"Int Rev Financ Anal"},{"issue":"1","key":"10901_CR36","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.eswa.2014.07.040","volume":"42","author":"J Patel","year":"2015","unstructured":"Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst Appl 42(1):259\u2013268. https:\/\/doi.org\/10.1016\/j.eswa.2014.07.040","journal-title":"Expert Syst Appl"},{"issue":"1","key":"10901_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12010166","volume":"12","author":"S Paul","year":"2022","unstructured":"Paul S et al (2022) Bias investigation in artificial intelligence systems for early detection of Parkinson\u2019s disease: a narrative review. Diagnostics 12(1):166. https:\/\/doi.org\/10.3390\/diagnostics12010166","journal-title":"Diagnostics"},{"key":"10901_CR38","doi-asserted-by":"publisher","unstructured":"Qu H, Zhang Z A Time Series Data Augmentation Method based on SMOTE,\u201d in 2024 36th Chinese Control and Decision Conference (CCDC), May 2024, pp. 5336\u20135341. https:\/\/doi.org\/10.1109\/CCDC62350.2024.10587816.","DOI":"10.1109\/CCDC62350.2024.10587816"},{"issue":"5","key":"10901_CR39","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1007\/s10554-020-02124-9","volume":"37","author":"L Saba","year":"2021","unstructured":"Saba L et al (2021) Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease\/stroke risk assessment system. Int J Cardiovasc Imaging 37(5):1511\u20131528. https:\/\/doi.org\/10.1007\/s10554-020-02124-9","journal-title":"Int J Cardiovasc Imaging"},{"key":"10901_CR40","doi-asserted-by":"publisher","unstructured":"Salton GD, Kelleher JD (2018) Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists, 2018, arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1810.04437.","DOI":"10.48550\/ARXIV.1810.04437"},{"key":"10901_CR41","doi-asserted-by":"publisher","unstructured":"Salem FM (2022) Gated RNN: The Long Short-Term Memory (LSTM) RNN. Recurrent Neural Networks. Springer International Publishing, Cham, pp 71\u201382. https:\/\/doi.org\/10.1007\/978-3-030-89929-5_4","DOI":"10.1007\/978-3-030-89929-5_4"},{"issue":"11","key":"10901_CR42","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11112109","volume":"11","author":"SS Sanagala","year":"2021","unstructured":"Sanagala SS et al (2021) Ten fast transfer learning models for carotid ultrasound plaque tissue characterization in augmentation framework embedded with heatmaps for stroke risk stratification. Diagnostics 11(11):2109. https:\/\/doi.org\/10.3390\/diagnostics11112109","journal-title":"Diagnostics"},{"issue":"16","key":"10901_CR43","doi-asserted-by":"publisher","first-page":"23945","DOI":"10.1007\/s11042-022-14216-w","volume":"82","author":"M Sharaf","year":"2023","unstructured":"Sharaf M, Hemdan EE-D, El-Sayed A, El-Bahnasawy NA (2023) An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis. Multimed Tools Appl 82(16):23945\u201323977. https:\/\/doi.org\/10.1007\/s11042-022-14216-w","journal-title":"Multimed Tools Appl"},{"key":"10901_CR44","doi-asserted-by":"publisher","unstructured":"She J, Gong S, Yang S, Yang H, Lu S (2022) Xigmoid: An Approach to Improve the Gating Mechanism of RNN, in 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy: IEEE, pp. 1\u201310. https:\/\/doi.org\/10.1109\/IJCNN55064.2022.9892346.","DOI":"10.1109\/IJCNN55064.2022.9892346"},{"issue":"11","key":"10901_CR45","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc8110143","volume":"8","author":"O Shobayo","year":"2024","unstructured":"Shobayo O, Adeyemi-Longe S, Popoola O, Ogunleye B (2024) Innovative sentiment analysis and prediction of stock price using FinBERT, GPT-4 and logistic regression: a data-driven approach. Big Data Cogn Comput 8(11):11. https:\/\/doi.org\/10.3390\/bdcc8110143","journal-title":"Big Data Cogn Comput"},{"key":"10901_CR46","doi-asserted-by":"publisher","first-page":"117643","DOI":"10.1109\/ACCESS.2023.3325705","volume":"11","author":"N Sinha","year":"2023","unstructured":"Sinha N, Kumar MAG, Joshi AM, Cenkeramaddi LR (2023) DASmcc: data augmented SMOTE multi-class classifier for prediction of cardiovascular diseases using time series features. IEEE Access 11:117643\u2013117655. https:\/\/doi.org\/10.1109\/ACCESS.2023.3325705","journal-title":"IEEE Access"},{"key":"10901_CR47","unstructured":"Silver D, Lever G, Heess N, Degris T, Wierstra D, Riedmiller M (2014) Deterministic Policy Gradient Algorithms, in Proceedings of the 31st International Conference on Machine Learning, PMLR, pp. 387\u2013395. [Online]. Available: https:\/\/proceedings.mlr.press\/v32\/silver14.html Accessed: May 01, 2025."},{"key":"10901_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103958","volume":"125","author":"SS Skandha","year":"2020","unstructured":"Skandha SS (2020) 3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular\/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic\u2122 2.0. Comput Biol Med 125:103958. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103958","journal-title":"Comput Biol Med"},{"key":"10901_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105131","volume":"141","author":"SS Skandha","year":"2022","unstructured":"Skandha SS et al (2022) A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework. Comput Biol Med 141:105131. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105131","journal-title":"Comput Biol Med"},{"issue":"6","key":"10901_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12061482","volume":"12","author":"J Suri","year":"2022","unstructured":"Suri J et al (2022a) COVLIAS 2.0-cXAI: cloud-based explainable deep learning system for COVID-19 lesion localization in computed tomography scans. Diagnostics 12(6):1482. https:\/\/doi.org\/10.3390\/diagnostics12061482","journal-title":"Diagnostics"},{"issue":"3","key":"10901_CR51","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12030722","volume":"12","author":"JS Suri","year":"2022","unstructured":"Suri JS et al (2022b) A powerful paradigm for cardiovascular risk stratification using multiclass, multi-label, and ensemble-based machine learning paradigms: a narrative review. Diagnostics 12(3):722. https:\/\/doi.org\/10.3390\/diagnostics12030722","journal-title":"Diagnostics"},{"issue":"7","key":"10901_CR52","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.3390\/diagnostics12071543","volume":"12","author":"JS Suri","year":"2022","unstructured":"Suri JS et al (2022) Deep learning paradigm for cardiovascular disease\/stroke risk stratification in Parkinson\u2019s disease affected by COVID-19: a narrative review. Diagnostics 12(7):1543. https:\/\/doi.org\/10.3390\/diagnostics12071543","journal-title":"Diagnostics"},{"key":"10901_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116523","volume":"195","author":"M Taghian","year":"2022","unstructured":"Taghian M, Asadi A, Safabakhsh R (2022) Learning financial asset-specific trading rules via deep reinforcement learning. Expert Syst Appl 195:116523. https:\/\/doi.org\/10.1016\/j.eswa.2022.116523","journal-title":"Expert Syst Appl"},{"key":"10901_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105639","volume":"147","author":"JS Teji","year":"2022","unstructured":"Teji JS, Jain S, Gupta SK, Suri JS (2022) NeoAI 1.0: machine learning-based paradigm for prediction of neonatal and infant risk of death. Comput Biol Med 147:105639. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105639","journal-title":"Comput Biol Med"},{"key":"10901_CR55","unstructured":"Vaswani A et al. (2017) Attention is All you Need, in Advances in Neural Information Processing Systems, Curran Associates, Inc., [Online]. Available: https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html Accessed: Apr. 17, 2025."},{"issue":"1","key":"10901_CR56","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1109\/TIV.2023.3282996","volume":"9","author":"S Wang","year":"2024","unstructured":"Wang S, Qu Z, Gao L (2024) Multi-spatial pyramid feature and optimizing focal loss function for object detection. IEEE Trans Intell Veh 9(1):1054\u20131065. https:\/\/doi.org\/10.1109\/TIV.2023.3282996","journal-title":"IEEE Trans Intell Veh"},{"key":"10901_CR57","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neucom.2021.02.046","volume":"441","author":"PB Weerakody","year":"2021","unstructured":"Weerakody PB, Wong KW, Wang G, Ela W (2021) A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing 441:161\u2013178. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.046","journal-title":"Neurocomputing"},{"issue":"3","key":"10901_CR58","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s10258-023-00246-1","volume":"23","author":"X Wei","year":"2024","unstructured":"Wei X, Tian Y, Li N, Peng H (2024) Evaluating ensemble learning techniques for stock index trend prediction: a case of China. Port Econ J 23(3):505\u2013530. https:\/\/doi.org\/10.1007\/s10258-023-00246-1","journal-title":"Port Econ J"},{"issue":"3","key":"10901_CR59","doi-asserted-by":"publisher","DOI":"10.3390\/s25030976","volume":"25","author":"J Yang","year":"2025","unstructured":"Yang J, Li P, Cui Y, Han X, Zhou M (2025) Multi-sensor temporal fusion transformer for stock performance prediction: an adaptive Sharpe ratio approach. Sensors 25(3):976. https:\/\/doi.org\/10.3390\/s25030976","journal-title":"Sensors"},{"issue":"5","key":"10901_CR60","doi-asserted-by":"publisher","DOI":"10.3390\/metabo14050258","volume":"14","author":"S Yu","year":"2024","unstructured":"Yu S et al (2024) Prediction of myocardial infarction using a combined generative adversarial network model and feature-enhanced loss function. Metabolites 14(5):5. https:\/\/doi.org\/10.3390\/metabo14050258","journal-title":"Metabolites"},{"issue":"8","key":"10901_CR61","doi-asserted-by":"publisher","first-page":"800","DOI":"10.3390\/math9080800","volume":"9","author":"X Zhang","year":"2021","unstructured":"Zhang X, Liu S, Zheng X (2021) Stock price movement prediction based on a deep factorization machine and the attention mechanism. Mathematics 9(8):800. https:\/\/doi.org\/10.3390\/math9080800","journal-title":"Mathematics"},{"issue":"1","key":"10901_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3611311","volume":"18","author":"Q Zhang","year":"2024","unstructured":"Zhang Q, Zhang Y, Yao X, Li S, Zhang C, Liu P (2024) A dynamic attributes-driven graph attention network modeling on behavioral finance for stock prediction. ACM Trans Knowl Discov Data 18(1):1\u201329. https:\/\/doi.org\/10.1145\/3611311","journal-title":"ACM Trans Knowl Discov Data"},{"key":"10901_CR63","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3060449","author":"H Zhang","year":"2021","unstructured":"Zhang H, Sun A, Jing W, Zhen L, Zhou JT, Goh RSM (2021a) Natural Language Video Localization: A Revisit in Span-based Question Answering Framework IEEE Trans Mach. Intell Pattern Anal. https:\/\/doi.org\/10.1109\/TPAMI.2021.3060449","journal-title":"Intell Pattern Anal"},{"key":"10901_CR64","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1653","volume":"9","author":"C Zhao","year":"2023","unstructured":"Zhao C, Li F, Peng Z, Zhou X, Zhuge Y (2023) A structured multi-head attention prediction method based on heterogeneous financial data. PeerJ Comput Sci 9:e1653. https:\/\/doi.org\/10.7717\/peerj-cs.1653","journal-title":"PeerJ Comput Sci"},{"issue":"1","key":"10901_CR65","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-019-0138-0","volume":"5","author":"X Zhong","year":"2019","unstructured":"Zhong X, Enke D (2019) Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financ Innov 5(1):24. https:\/\/doi.org\/10.1186\/s40854-019-0138-0","journal-title":"Financ Innov"},{"key":"10901_CR66","doi-asserted-by":"publisher","unstructured":"Zhang Q, Liu J, Tian D, Yue H (2021) Application of Stacking ensemble learning in option implied volatility, in 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), Nov. 2021, pp. 623\u2013627. https:\/\/doi.org\/10.1109\/ICAICE54393.2021.00123.","DOI":"10.1109\/ICAICE54393.2021.00123"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10901-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10901-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10901-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T10:26:48Z","timestamp":1761906408000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10901-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":67,"journal-issue":{"issue":"21-22","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10901"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10901-8","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"1 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 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":"The authors have no conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}