{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T09:32:21Z","timestamp":1769419941151,"version":"3.49.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Bond University Limited"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper extends a series of deep learning models developed on US equity data to the Australian market. The model architectures are retrained, without structural modification, and tested on Australian data comparable with the original US data. Relative to the original US-based results, the retrained models are statistically less accurate at predicting next day returns. The models were also modified in the standard train\/validate manner on the Australian data, and these models yielded significantly better predictive results on the holdout data. It was determined that the best-performing models were a CNN and LSTM, attaining highly significant Z-scores of 6.154 and 8.789, respectively. Due to the relative structural similarity across all models, the improvement is ascribed to regional influences within the respective training data sets. Such unique regional differences are consistent with views in the literature stating that deep learning models in computational finance that are developed and trained on a single market will always contain market-specific bias. Given this finding, future research into the development of deep learning models trained on global markets is recommended.<\/jats:p>","DOI":"10.1007\/s00521-022-07805-1","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T03:39:36Z","timestamp":1664249976000},"page":"1483-1492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adapting deep learning models between regional markets"],"prefix":"10.1007","volume":"35","author":[{"given":"Isaac","family":"Tonkin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1666-5501","authenticated-orcid":false,"given":"Adrian","family":"Gepp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoff","family":"Harris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bruce","family":"Vanstone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,27]]},"reference":[{"issue":"7587","key":"7805_CR1","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver D et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484\u2013489. https:\/\/doi.org\/10.1038\/nature16961","journal-title":"Nature"},{"key":"7805_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.btre.2019.e00321","author":"A Buetti-Dinh","year":"2019","unstructured":"Buetti-Dinh A et al (2019) Deep neural networks outperform human expert\u2019s capacity in characterizing bioleaching bacterial biofilm composition. Biotechnol Rep. https:\/\/doi.org\/10.1016\/j.btre.2019.e00321","journal-title":"Biotechnol Rep"},{"key":"7805_CR3","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"issue":"3","key":"7805_CR4","doi-asserted-by":"publisher","first-page":"3995","DOI":"10.1007\/s11042-021-11670-w","volume":"81","author":"A Kumar","year":"2022","unstructured":"Kumar A et al (2022) Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction. Multimed Tools Appl 81(3):3995\u20134013. https:\/\/doi.org\/10.1007\/s11042-021-11670-w","journal-title":"Multimed Tools Appl"},{"key":"7805_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-09410-2","author":"X Wang","year":"2021","unstructured":"Wang X, Gong C, Khishe M, Mohammadi M, Rashid T (2021) Pulmonary diffuse airspace opacities diagnosis from chest x-ray images using deep convolutional neural networks fine-tuned by whale optimizer. Wireless Pers Commun. https:\/\/doi.org\/10.1007\/s11277-021-09410-2","journal-title":"Wireless Pers Commun"},{"key":"7805_CR6","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2021.1970237","author":"K Shrestha","year":"2021","unstructured":"Shrestha K et al (2021) A novel solution of an elastic net regularisation for dementia knowledge discovery using deep learning. J Exp Theor Artif Intell. https:\/\/doi.org\/10.1080\/0952813X.2021.1970237","journal-title":"J Exp Theor Artif Intell"},{"issue":"5","key":"7805_CR7","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1111\/mice.12263","volume":"32","author":"Y-J Cha","year":"2017","unstructured":"Cha Y-J, Choi W, B\u00fcy\u00fck\u00f6zt\u00fcrk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32(5):361\u2013378. https:\/\/doi.org\/10.1111\/mice.12263","journal-title":"Comput Aided Civ Infrastruct Eng"},{"issue":"2","key":"7805_CR8","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.eng.2018.11.030","volume":"5","author":"BF Spencer Jr","year":"2019","unstructured":"Spencer BF Jr, Hoskere V, Narazaki Y (2019) Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering 5(2):199\u2013222. https:\/\/doi.org\/10.1016\/j.eng.2018.11.030","journal-title":"Engineering"},{"key":"7805_CR9","doi-asserted-by":"publisher","unstructured":"Szegedy C, et al (2014) Going deeper with convolutions. https:\/\/doi.org\/10.48550\/arxiv.1409.4842","DOI":"10.48550\/arxiv.1409.4842"},{"key":"7805_CR10","unstructured":"Bloomberg LP (2021) Bloomberg world exchange market capitalization. Bloomberg Database"},{"key":"7805_CR11","doi-asserted-by":"publisher","unstructured":"Vaswani A, et al (2017) Attention is all you need. https:\/\/doi.org\/10.48550\/arxiv.1706.03762","DOI":"10.48550\/arxiv.1706.03762"},{"key":"7805_CR12","first-page":"2672","volume-title":"Advances in neural information processing systems","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I et al (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger K (eds) Advances in neural information processing systems, vol 27, pp  2672\u20132680"},{"key":"7805_CR13","unstructured":"Vanstone B, Finnie G (2006) Combining technical analysis and neural networks in the Australian stockmarket. del Pobil AP (ed.), Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006, 125\u2013130"},{"key":"7805_CR14","unstructured":"Li H, Ng WWY, Lee JWT, Sun B, Yeung DS (2008) Quantitative study on candlestick pattern for Shenzhen Stock Market. IEEE (ed.), 2008 IEEE International Conference on Systems, Man and Cybernetics, Singapore, 54\u201359"},{"issue":"1","key":"7805_CR15","first-page":"41","volume":"11","author":"B Vanstone","year":"2010","unstructured":"Vanstone B, Finnie G, Hahn T (2010) Stockmarket trading using fundamental variables and neural networks. Aust J Intell Inf Process Syst 11(1):41\u201347","journal-title":"Aust J Intell Inf Process Syst"},{"key":"7805_CR16","doi-asserted-by":"crossref","unstructured":"Gabrielsson P, Johansson U (2015) High-frequency equity index futures trading using recurrent reinforcement learning with candlesticks. IEEE (ed.), 2015 IEEE Symposium Series on Computational Intelligence, pp 734\u2013741","DOI":"10.1109\/SSCI.2015.111"},{"key":"7805_CR17","doi-asserted-by":"publisher","first-page":"15249","DOI":"10.1007\/s00521-020-04877-9","volume":"32","author":"S Ghoshal","year":"2020","unstructured":"Ghoshal S, Roberts S (2020) Thresholded ConvNet ensembles: neural networks for technical forecasting. Neural Comput Appl 32:15249\u201315262","journal-title":"Neural Comput Appl"},{"issue":"2","key":"7805_CR18","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ejor.2016.10.031","volume":"259","author":"C Krauss","year":"2017","unstructured":"Krauss C, Do X, Huck N (2017) Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500. Eur J Oper Res 259(2):689\u2013702. https:\/\/doi.org\/10.1016\/j.ejor.2016.10.031","journal-title":"Eur J Oper Res"},{"issue":"2","key":"7805_CR19","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654\u2013669. https:\/\/doi.org\/10.1016\/j.ejor.2017.11.054","journal-title":"Eur J Oper Res"},{"key":"7805_CR20","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 L, Trajanov D (2020) Evaluation of sentiment analysis in finance: from lexicons to transformers. IEEE Access 8:131662\u2013131682. https:\/\/doi.org\/10.1109\/ACCESS.2020.3009626","journal-title":"IEEE Access"},{"key":"7805_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2022.127158","author":"V D\u2019Amato","year":"2022","unstructured":"D\u2019Amato V, Levantesi S, Piscopo G (2022) Deep learning in predicting cryptocurrency volatility. Phys A. https:\/\/doi.org\/10.1016\/j.physa.2022.127158","journal-title":"Phys A"},{"key":"7805_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116629","author":"Y Li","year":"2022","unstructured":"Li Y, Fu K, Zhao Y, Yang C (2022) How to make machine select stocks like fund managers? use scoring and screening model. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2022.116629","journal-title":"Expert Syst Appl"},{"issue":"1","key":"7805_CR23","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.jfineco.2021.06.002","volume":"144","author":"K Obaid","year":"2022","unstructured":"Obaid K, Pukthuanthong K (2022) A picture is worth a thousand words: measuring investor sentiment by combining machine learning and photos from news. J Financ Econ 144(1):273\u2013297. https:\/\/doi.org\/10.1016\/j.jfineco.2021.06.002","journal-title":"J Financ Econ"},{"issue":"2","key":"7805_CR24","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s41060-021-00279-9","volume":"13","author":"Y Li","year":"2022","unstructured":"Li Y, Pan Y (2022) A novel ensemble deep learning model for stock prediction based on stock prices and news. Int J Data Sci Anal 13(2):139\u2013149. https:\/\/doi.org\/10.1007\/s41060-021-00279-9","journal-title":"Int J Data Sci Anal"},{"key":"7805_CR25","volume-title":"Deep learning with Python","author":"F Chollet","year":"2017","unstructured":"Chollet F (2017) Deep learning with Python. Manning Publications, Shelter Island"},{"key":"7805_CR26","doi-asserted-by":"crossref","unstructured":"Gudelek M, Boluk S, Ozbayoglu A (2018) A deep learning based stock trading model with 2-d cnn trend detection, Vol. 2018-January, 1\u20138","DOI":"10.1109\/SSCI.2017.8285188"},{"key":"7805_CR27","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.eswa.2019.03.029","volume":"129","author":"E Hoseinzade","year":"2019","unstructured":"Hoseinzade E, Haratizadeh S (2019) Cnnpred: Cnn-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273\u2013285. https:\/\/doi.org\/10.1016\/j.eswa.2019.03.029","journal-title":"Expert Syst Appl"},{"key":"7805_CR28","doi-asserted-by":"publisher","unstructured":"Jearanaitanakij K, Passaya B (2019) Predicting short trend of stocks by using convolutional neural network and candlestick patterns. Proceedings of 2019 4th International conference on information technology: encompassing intelligent technology and innovation towards the new era of human life, InCIT 2019, pp 159\u2013162. https:\/\/doi.org\/10.1109\/INCIT.2019.8912115","DOI":"10.1109\/INCIT.2019.8912115"},{"key":"7805_CR29","doi-asserted-by":"publisher","first-page":"91894","DOI":"10.1109\/ACCESS.2020.2994282","volume":"8","author":"S Birogul","year":"2020","unstructured":"Birogul S, Temur G, Kose U (2020) Yolo object recognition algorithm and buy-sell decision model over 2d candlestick charts. IEEE Access 8:91894\u201391915. https:\/\/doi.org\/10.1109\/ACCESS.2020.2994282","journal-title":"IEEE Access"},{"key":"7805_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-020-00187-0","author":"J-H Chen","year":"2020","unstructured":"Chen J-H, Tsai Y-C (2020) Encoding candlesticks as images for pattern classification using convolutional neural networks. Financ Innov. https:\/\/doi.org\/10.1186\/s40854-020-00187-0","journal-title":"Financ Innov"},{"issue":"1","key":"7805_CR31","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1037\/0033-295X.98.1.74","volume":"98","author":"G Hinton","year":"1991","unstructured":"Hinton G, Shallice T (1991) Lesioning an attractor network: investigations of acquired dyslexia. Psychol Rev 98(1):74\u201395. https:\/\/doi.org\/10.1037\/0033-295X.98.1.74","journal-title":"Psychol Rev"},{"issue":"2","key":"7805_CR32","doi-asserted-by":"publisher","first-page":"215","DOI":"10.3758\/BF03204624","volume":"26","author":"F Bremner","year":"1994","unstructured":"Bremner F, Gotts S, Denham D (1994) Hinton diagrams: viewing connection strengths in neural networks. Behav Res Methods Instrum Comput 26(2):215\u2013218. https:\/\/doi.org\/10.3758\/BF03204624","journal-title":"Behav Res Methods Instrum Comput"},{"key":"7805_CR33","unstructured":"Shrikumar A, Greenside P, Kundaje A (2017) Learning important features through propagating activation differences. In: 34th International conference on machine learning, ICML 2017, vol 7, pp 4844\u20134866"},{"key":"7805_CR34","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/978-3-030-33778-0_24","volume":"11828 LNAI","author":"C de S\u00e1","year":"2019","unstructured":"de S\u00e1 C (2019) Variance-based feature importance in neural networks. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 11828 LNAI:306\u2013315. https:\/\/doi.org\/10.1007\/978-3-030-33778-0_24","journal-title":"Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)"},{"issue":"8","key":"7805_CR35","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"7805_CR36","doi-asserted-by":"publisher","unstructured":"Cho K, van Merrienboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder\u2013decoder approaches. https:\/\/doi.org\/10.48550\/arxiv.1409.1259","DOI":"10.48550\/arxiv.1409.1259"},{"key":"7805_CR37","doi-asserted-by":"publisher","unstructured":"Nelson D, Pereira A, De Oliveira R (2017) Stock market\u2019s price movement prediction with LSTM neural networks. In: Proceedings of the international joint conference on neural networks, pp 1419\u20131426. https:\/\/doi.org\/10.1109\/IJCNN.2017.7966019","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"7805_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-4498-9_4","author":"K Matsumoto","year":"2020","unstructured":"Matsumoto K, Makimoto N (2020) Time series prediction with lstm networks and its application to equity investment. Adv Stud Financ Technol Cryptocurr Mark. https:\/\/doi.org\/10.1007\/978-981-15-4498-9_4","journal-title":"Adv Stud Financ Technol Cryptocurr Mark"},{"key":"7805_CR39","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0212320","author":"T Kim","year":"2019","unstructured":"Kim T, Kim H (2019) Forecasting stock prices with a feature fusion lstm-cnn model using different representations of the same data. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0212320","journal-title":"PLoS ONE"},{"key":"7805_CR40","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1007\/978-3-319-70096-0_21","volume":"10635 LNCS","author":"S Liu","year":"2017","unstructured":"Liu S, Zhang C, Ma J (2017) Cnn-lstm neural network model for quantitative strategy analysis in stock markets. Lect Notes Comput Sci 10635 LNCS:198\u2013206. https:\/\/doi.org\/10.1007\/978-3-319-70096-0_21","journal-title":"Lect Notes Comput Sci"},{"key":"7805_CR41","volume-title":"Japanese candlestick charting techniques: a contemporary guide to the ancient investment techniques of the far east","author":"S Nison","year":"1991","unstructured":"Nison S (1991) Japanese candlestick charting techniques: a contemporary guide to the ancient investment techniques of the far east. Institute of Finance, New York. ISBN: 0139316507"},{"issue":"3\u20134","key":"7805_CR42","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1080\/135048698334637","volume":"5","author":"G Caginalp","year":"1998","unstructured":"Caginalp G, Laurent H (1998) The predictive power of price patterns. Appl Math Financ 5(3\u20134):181\u2013205","journal-title":"Appl Math Financ"},{"key":"7805_CR43","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.physa.2016.03.081","volume":"457","author":"S Chen","year":"2016","unstructured":"Chen S, Bao S, Zhou Y (2016) The predictive power of Japanese candlestick charting in Chinese stock market. Phys A 457:148\u2013165","journal-title":"Phys A"},{"key":"7805_CR44","doi-asserted-by":"crossref","unstructured":"Jasemi M, Kimiagari A, Memariani A (2011) A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese candlestick. Exp Syst Appl. pp 3884\u20133890","DOI":"10.1016\/j.eswa.2010.09.049"},{"key":"7805_CR45","doi-asserted-by":"crossref","unstructured":"Hu G, et al (2018) Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions. IEEE (ed.), ICASSP, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 2706\u20132710","DOI":"10.1109\/ICASSP.2018.8462215"},{"issue":"4","key":"7805_CR46","first-page":"63","volume":"28","author":"T Strader","year":"2017","unstructured":"Strader T, Rozycki J, Root T, Huang Y-HJ (2017) Machine learning stock market prediction studies: review and research directions. J Int Technol Inf Manag 28(4):63\u201383","journal-title":"J Int Technol Inf Manag"},{"issue":"1","key":"7805_CR47","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1111\/acfi.12140","volume":"57","author":"B Vanstone","year":"2017","unstructured":"Vanstone B, Hahn T (2017) Australian momentum: performance, capacity and the GFC effect. Account Financ 57(1):261\u2013287. https:\/\/doi.org\/10.1111\/acfi.12140","journal-title":"Account Financ"},{"key":"7805_CR48","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/978-3-319-92058-0_53","volume":"10868 LNAI","author":"B Vanstone","year":"2018","unstructured":"Vanstone B, Gepp A, Harris G (2018) The effect of sentiment on stock price prediction. Lect Notes Comput Sci 10868 LNAI:551\u2013559. https:\/\/doi.org\/10.1007\/978-3-319-92058-0_53","journal-title":"Lect Notes Comput Sci"},{"key":"7805_CR49","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo J (2019) Bidirectional lstm with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325\u2013338. https:\/\/doi.org\/10.1016\/j.neucom.2019.01.078","journal-title":"Neurocomputing"},{"key":"7805_CR50","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press, Cambridge"},{"issue":"584 PART B","key":"7805_CR51","doi-asserted-by":"publisher","first-page":"2145","DOI":"10.1256\/003590002320603584","volume":"128","author":"S Mason","year":"2002","unstructured":"Mason S, Graham N (2002) Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation. Q J R Meteorol Soc 128(584 PART B):2145\u20132166. https:\/\/doi.org\/10.1256\/003590002320603584","journal-title":"Q J R Meteorol Soc"},{"key":"7805_CR52","first-page":"294","volume-title":"Advances in neural information processing systems","author":"C Rasmussen","year":"2001","unstructured":"Rasmussen C, Ghahramani Z (2001) Occam\u2019s Razor. In: Leen T, Dietterich T, Tresp V (eds) Advances in neural information processing systems, vol 13.  MIT Press, Cambridge, MA, USA, pp 294\u2013300"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07805-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07805-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07805-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T17:14:45Z","timestamp":1673284485000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07805-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,27]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["7805"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07805-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,27]]},"assertion":[{"value":"17 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2022","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 conflict of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}