{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:43:33Z","timestamp":1785318213455,"version":"3.55.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s00521-023-09206-4","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T09:02:07Z","timestamp":1701507727000},"page":"3179-3196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["SwinDTI: swin transformer-based generalized fast estimation of diffusion tensor parameters from sparse data"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3807-4032","authenticated-orcid":false,"given":"Abhishek","family":"Tiwari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1369-5377","authenticated-orcid":false,"given":"Rajeev Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6159-7961","authenticated-orcid":false,"given":"Saurabh J.","family":"Shigwan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"9206_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117368","volume":"223","author":"L Zhong","year":"2020","unstructured":"Zhong L, Li T, Shu H, Huang C, Johnson JM, Schomer DF, Liu H-L, Feng Q, Yang W, Zhu H (2020) 2wm: tumor segmentation and tract statistics for assessing white matter integrity with applications to glioblastoma patients. Neuroimage 223:117368","journal-title":"Neuroimage"},{"key":"9206_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.117934","volume":"233","author":"F Zhang","year":"2021","unstructured":"Zhang F, Breger A, Cho KIK, Ning L, Westin C-F, O\u2019Donnell LJ, Pasternak O (2021) Deep learning based segmentation of brain tissue from diffusion MRI. Neuroimage 233:117934","journal-title":"Neuroimage"},{"issue":"5","key":"9206_CR3","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1097\/RMR.0000000000000062","volume":"24","author":"DB Douglas","year":"2015","unstructured":"Douglas DB, Iv M, Douglas PK, Ariana A, Vos SB, Bammer R, Zeineh M, Wintermark M (2015) Diffusion tensor imaging of TBI: potentials and challenges. Top Magn Reson Imaging TMRI 24(5):241","journal-title":"Top Magn Reson Imaging TMRI"},{"issue":"1","key":"9206_CR4","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/S0006-3495(94)80775-1","volume":"66","author":"PJ Basser","year":"1994","unstructured":"Basser PJ, Mattiello J, LeBihan D (1994) Mr diffusion tensor spectroscopy and imaging. Biophys J 66(1):259\u2013267","journal-title":"Biophys J"},{"issue":"4","key":"9206_CR5","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1002\/mrm.28544","volume":"85","author":"T Gong","year":"2021","unstructured":"Gong T, Tong Q, Li Z, He H, Zhang H, Zhong J (2021) Deep learning-based method for reducing residual motion effects in diffusion parameter estimation. Magn Reson Med 85(4):2278\u20132293","journal-title":"Magn Reson Med"},{"issue":"3","key":"9206_CR6","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1016\/j.neuroimage.2010.12.008","volume":"55","author":"G Douaud","year":"2011","unstructured":"Douaud G, Jbabdi S, Behrens TE, Menke RA, Gass A, Monsch AU, Rao A, Whitcher B, Kindlmann G, Matthews PM et al (2011) Dti measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in mci and mild alzheimer\u2019s disease. Neuroimage 55(3):880\u2013890","journal-title":"Neuroimage"},{"issue":"7","key":"9206_CR7","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.1002\/mp.13555","volume":"46","author":"Z Lin","year":"2019","unstructured":"Lin Z, Gong T, Wang K, Li Z, He H, Tong Q, Yu F, Zhong J (2019) Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network. Med Phys 46(7):3101\u20133116","journal-title":"Med Phys"},{"issue":"8","key":"9206_CR8","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.1109\/TMI.2022.3156868","volume":"41","author":"W Consagra","year":"2022","unstructured":"Consagra W, Venkataraman A, Zhang Z (2022) Optimized diffusion imaging for brain structural connectome analysis. IEEE Trans Med Imaging 41(8):2118\u20132129","journal-title":"IEEE Trans Med Imaging"},{"key":"9206_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118601","volume":"244","author":"JP de Almeida Martins","year":"2021","unstructured":"de Almeida Martins JP, Nilsson M, Lampinen B, While PT, Palombo M, Westin C-F, Szczepankiewicz F (2021) Neural networks for parameter estimation in microstructural MRI: application to a diffusion-relaxation model of white matter. NeuroImage 244:118601","journal-title":"NeuroImage"},{"key":"9206_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102330","volume":"130","author":"D Karimi","year":"2022","unstructured":"Karimi D, Gholipour A (2022) Diffusion tensor estimation with transformer neural networks. Artif Intell Med 130:102330","journal-title":"Artif Intell Med"},{"issue":"5","key":"9206_CR11","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/TMI.2016.2551324","volume":"35","author":"V Golkov","year":"2016","unstructured":"Golkov V, Dosovitskiy A, Sperl JI, Menzel MI, Czisch M, S\u00e4mann P, Brox T, Cremers D (2016) Q-space deep learning: twelve-fold shorter and model-free diffusion mri scans. IEEE Trans Med Imaging 35(5):1344\u20131351","journal-title":"IEEE Trans Med Imaging"},{"key":"9206_CR12","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.nicl.2013.07.006","volume":"3","author":"TM Nir","year":"2013","unstructured":"Nir TM, Jahanshad N, Villalon-Reina JE, Toga AW, Jack CR, Weiner MW, Thompson PM (2013) Effectiveness of regional DTI measures in distinguishing Alzheimer\u2019s disease, MCI, and normal aging. NeuroImage Clin 3:180\u2013195","journal-title":"NeuroImage Clin"},{"key":"9206_CR13","doi-asserted-by":"crossref","unstructured":"Gupta V, Ayache N, Pennec X (2013) Improving DTI resolution from a single clinical acquisition: a statistical approach using spatial prior. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22\u201326, 2013, Proceedings, Part III 16. Springer, Berlin, Heidelberg, pp 477\u2013484","DOI":"10.1007\/978-3-642-40760-4_60"},{"key":"9206_CR14","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.nicl.2016.11.023","volume":"13","author":"LJ O\u2019Donnell","year":"2017","unstructured":"O\u2019Donnell LJ, Suter Y, Rigolo L, Kahali P, Zhang F, Norton I, Albi A, Olubiyi O, Meola A, Essayed WI et al (2017) Automated white matter fiber tract identification in patients with brain tumors. NeuroImage Clin 13:138\u2013153","journal-title":"NeuroImage Clin"},{"key":"9206_CR15","doi-asserted-by":"publisher","first-page":"103483","DOI":"10.1016\/j.nicl.2023.103483","volume":"39","author":"S Aja-Fern\u00e1ndez","year":"2023","unstructured":"Aja-Fern\u00e1ndez S, Mart\u00edn-Mart\u00edn C, Planchuelo-G\u00f3mez \u00c1, Faiyaz A, Uddin MN, Schifitto G, Tiwari A, Shigwan SJ, Singh RK, Zheng T et al (2023) Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies. NeuroImage Clin 39:103483","journal-title":"NeuroImage Clin"},{"key":"9206_CR16","doi-asserted-by":"crossref","unstructured":"Tiwari A, Singh RK (2022) Performance, trust, or both? covid-19 diagnosis and prognosis using deep ensemble transfer learning on x-ray images. In: Proceedings of the thirteenth indian conference on computer vision, graphics and image processing. pp 1\u20139","DOI":"10.1145\/3571600.3571609"},{"issue":"4","key":"9206_CR17","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1002\/mrm.27568","volume":"81","author":"EK Gibbons","year":"2019","unstructured":"Gibbons EK, Hodgson KK, Chaudhari AS, Richards LG, Majersik JJ, Adluru G, DiBella EV (2019) Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning. Magn Reson Med 81(4):2399\u20132411","journal-title":"Magn Reson Med"},{"key":"9206_CR18","unstructured":"Leming M (2020) Application of deep learning to brain connectivity classification in large mri datasets. Doctoral dissertation, University of Cambridge"},{"key":"9206_CR19","doi-asserted-by":"publisher","first-page":"119033","DOI":"10.1016\/j.neuroimage.2022.119033","volume":"253","author":"Q Tian","year":"2021","unstructured":"Tian Q, Li Z, Fan Q, Polimeni J, Bilgi\u00e7 B, Salat DH, Huang SY (2021) Sdndti: self-supervised deep learning-based denoising for diffusion tensor MRI. NeuroImage 253:119033","journal-title":"NeuroImage"},{"key":"9206_CR20","doi-asserted-by":"crossref","unstructured":"Zhang F, Xue T, Cai WT, Rathi Y, Westin C-F, O\u2019Donnell LJ (2022) Tractoformer: a novel fiber-level whole brain tractography analysis framework using spectral embedding and vision transformers","DOI":"10.1007\/978-3-031-16431-6_19"},{"issue":"118","key":"9206_CR21","first-page":"830","volume":"249","author":"CMW Tax","year":"2021","unstructured":"Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Irfanoglu MO (2021) What\u2019s new and what\u2019s next in diffusion MRI preprocessing. NeuroImage 249(118):830","journal-title":"NeuroImage"},{"key":"9206_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118482","volume":"243","author":"D Karimi","year":"2021","unstructured":"Karimi D, Jaimes C, Machado-Rivas F, Vasung L, Khan S, Warfield SK, Gholipour A (2021) Deep learning-based parameter estimation in fetal diffusion-weighted MRI. Neuroimage 243:118482","journal-title":"Neuroimage"},{"issue":"4","key":"9206_CR23","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1002\/mp.13400","volume":"46","author":"E Aliotta","year":"2019","unstructured":"Aliotta E, Nourzadeh H, Sanders J, Muller D, Ennis DB (2019) Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks. Med Phys 46(4):1581\u20131591","journal-title":"Med Phys"},{"issue":"1","key":"9206_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.jmr.2006.06.020","volume":"182","author":"CG Koay","year":"2006","unstructured":"Koay CG, Chang L-C, Carew JD, Pierpaoli C, Basser PJ (2006) A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J Magn Reson 182(1):115\u2013125","journal-title":"J Magn Reson"},{"key":"9206_CR25","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.mri.2019.07.012","volume":"62","author":"V Nath","year":"2019","unstructured":"Nath V, Schilling KG, Parvathaneni P, Hansen CB, Hainline AE, Huo Y, Blaber JA, Lyu I, Janve V, Gao Y et al (2019) Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magn Reson Imaging 62:220\u2013227","journal-title":"Magn Reson Imaging"},{"key":"9206_CR26","doi-asserted-by":"crossref","unstructured":"Koppers S, Haarburger C, Edgar JC, Merhof D (2017) Reliable estimation of the number of compartments in diffusion mri. In: Proceedings des Workshops vom 12. bis 14. M\u00e4rz 2017 in Heidelberg. Springer Berlin Heidelberg, pp 203\u2013208","DOI":"10.1007\/978-3-662-54345-0_46"},{"key":"9206_CR27","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-319-47157-0_7","volume":"10019","author":"S Koppers","year":"2016","unstructured":"Koppers S, Merhof D (2016) Direct estimation of fiber orientations using deep learning in diffusion imaging. Mach Learn Med Imaging 10019:53\u201360","journal-title":"Mach Learn Med Imaging"},{"key":"9206_CR28","doi-asserted-by":"crossref","unstructured":"Koppers S, Friedrichs M, Merhof D (2017) Reconstruction of diffusion anisotropies using 3d deep convolutional neural networks in diffusion imaging. In: Modeling, analysis, and visualization of anisotropy. Springer International Publishing, pp 393\u2013404","DOI":"10.1007\/978-3-319-61358-1_17"},{"key":"9206_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117017","volume":"219","author":"Q Tian","year":"2020","unstructured":"Tian Q, Bilgic B, Fan Q, Liao C, Ngamsombat C, Hu Y, Witzel T, Setsompop K, Polimeni JR, Huang SY (2020) Deepdti: high-fidelity six-direction diffusion tensor imaging using deep learning. NeuroImage 219:117017","journal-title":"NeuroImage"},{"issue":"6","key":"9206_CR30","doi-asserted-by":"publisher","first-page":"3334","DOI":"10.1002\/mrm.28937","volume":"86","author":"H Li","year":"2021","unstructured":"Li H, Liang Z, Zhang C, Liu R, Li J, Zhang W, Liang D, Shen B, Zhang X, Ge Y et al (2021) Superdti: ultrafast DTI and fiber tractography with deep learning. Magn Reson Med 86(6):3334\u20133347","journal-title":"Magn Reson Med"},{"key":"9206_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141., Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"9206_CR32","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp  10\u00a0012\u201310\u00a0022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"9206_CR33","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In : Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17\u201321, 2016, Proceedings, Part II 19. Springer, pp 424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"9206_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118543","volume":"244","author":"JS Elam","year":"2021","unstructured":"Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JL, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen J-M et al (2021) The human connectome project: a retrospective. NeuroImage 244:118543","journal-title":"NeuroImage"},{"issue":"6","key":"9206_CR35","doi-asserted-by":"publisher","first-page":"2714","DOI":"10.1109\/JBHI.2022.3159031","volume":"26","author":"Y Liang","year":"2022","unstructured":"Liang Y, Xu G (2022) Multi-level functional connectivity fusion classification framework for brain disease diagnosis. IEEE J Biomed Health Inf 26(6):2714\u20132725","journal-title":"IEEE J Biomed Health Inf"},{"key":"9206_CR36","doi-asserted-by":"crossref","unstructured":"Yang Y-Q, Wang P-S, Liu Y (2021) Interpolation-aware padding for 3d sparse convolutional neural networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7467\u20137475","DOI":"10.1109\/ICCV48922.2021.00737"},{"key":"9206_CR37","unstructured":"Ramachandran P, Parmar N, Vaswani A, Bello I, Levskaya A, Shlens J (2019) Stand-alone self-attention in vision models. Adv Neural Inf Process Syst 32"},{"key":"9206_CR38","doi-asserted-by":"crossref","unstructured":"Hu H, Zhang Z, Xie Z, Lin S (2019) Local relation networks for image recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3464\u20133473","DOI":"10.1109\/ICCV.2019.00356"},{"key":"9206_CR39","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3389\/fninf.2014.00008","volume":"8","author":"E Garyfallidis","year":"2014","unstructured":"Garyfallidis E, Brett M, Amirbekian B, Rokem A, Van Der Walt S, Descoteaux M, Nimmo-Smith I, Contributors D (2014) Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform 8:8","journal-title":"Front Neuroinform"},{"issue":"6","key":"9206_CR40","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1002\/mrm.20279","volume":"52","author":"DS Tuch","year":"2004","unstructured":"Tuch DS (2004) Q-ball imaging. Magn Reson Med Off J Int Soc Magn Reson Med 52(6):1358\u20131372","journal-title":"Magn Reson Med Off J Int Soc Magn Reson Med"},{"key":"9206_CR41","doi-asserted-by":"publisher","first-page":"1972","DOI":"10.1109\/TMI.2016.2528820","volume":"35","author":"M Baust","year":"2016","unstructured":"Baust M, Weinmann A, Wieczorek M, Lasser T, Storath M, Navab N (2016) Combined tensor fitting and tv regularization in diffusion tensor imaging based on a Riemannian manifold approach. IEEE Trans Med Imaging 35:1972\u20131989","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"9206_CR42","first-page":"534","volume":"13","author":"D Le Bihan","year":"2001","unstructured":"Le Bihan D, Mangin J-F, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging Off J Int Soc Magn Reson Med 13(4):534\u2013546","journal-title":"J Magn Reson Imaging Off J Int Soc Magn Reson Med"},{"issue":"9","key":"9206_CR43","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TMI.2010.2048121","volume":"29","author":"JG Malcolm","year":"2010","unstructured":"Malcolm JG, Shenton ME, Rathi Y (2010) Filtered multitensor tractography. IEEE Trans Med Imaging 29(9):1664\u20131675","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"9206_CR44","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1080\/02699052.2018.1493225","volume":"32","author":"SM Jurick","year":"2018","unstructured":"Jurick SM, Hoffman SN, Sorg S, Keller AV, Evangelista ND, DeFord NE, Sanderson-Cimino M, Bangen KJ, Delano-Wood L, Deoni S et al (2018) Pilot investigation of a novel white matter imaging technique in veterans with and without history of mild traumatic brain injury. Brain Injury 32(10):1255\u20131264","journal-title":"Brain Injury"},{"issue":"5","key":"9206_CR45","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1038\/npp.2013.322","volume":"39","author":"N Fani","year":"2014","unstructured":"Fani N, King TZ, Reiser E, Binder EB, Jovanovic T, Bradley B, Ressler KJ (2014) Fkbp5 genotype and structural integrity of the posterior cingulum. Neuropsychopharmacology 39(5):1206\u20131213","journal-title":"Neuropsychopharmacology"},{"issue":"7","key":"9206_CR46","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TCSVT.2013.2240918","volume":"23","author":"C Yeo","year":"2013","unstructured":"Yeo C, Tan HL, Tan YH (2013) On rate distortion optimization using SSIM. IEEE Trans Circ Syst Video Technol 23(7):1170\u20131181","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"9206_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2020.102168","volume":"26","author":"PAL Laguna","year":"2020","unstructured":"Laguna PAL, Combes AJ, Streffer J, Einstein S, Timmers M, Williams SC, Dell\u2019Acqua F (2020) Reproducibility, reliability and variability of fa and md in the older healthy population: a test-retest multiparametric analysis. NeuroImage Clin 26:102168","journal-title":"NeuroImage Clin"},{"issue":"2","key":"9206_CR48","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/j.neuroimage.2007.12.035","volume":"40","author":"S Mori","year":"2008","unstructured":"Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K, Hua K, Faria AV, Mahmood A, Woods R et al (2008) Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40(2):570\u2013582","journal-title":"Neuroimage"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09206-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09206-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09206-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:14:08Z","timestamp":1705994048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09206-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":48,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["9206"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09206-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"26 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2023","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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}