{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T20:56:37Z","timestamp":1778187397122,"version":"3.51.4"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19105-y","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T05:01:55Z","timestamp":1713157315000},"page":"1463-1488","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SIE: infrared and visible image fusion based on scene information embedding"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8101-1686","authenticated-orcid":false,"given":"Yingnan","family":"Geng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixuan","family":"Diao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"issue":"7","key":"19105_CR1","doi-asserted-by":"crossref","first-page":"7831","DOI":"10.1109\/TITS.2021.3073046","volume":"23","author":"Y Wang","year":"2022","unstructured":"Wang Y, Wei X, Tang X, Shen H, Zhang H (2022) Adaptive fusion cnn features for RGBT object tracking. IEEE Trans Intel Trans Syst 23(7):7831\u20137840","journal-title":"IEEE Trans Intel Trans Syst"},{"issue":"11","key":"19105_CR2","doi-asserted-by":"crossref","first-page":"22190","DOI":"10.1109\/TITS.2021.3130025","volume":"23","author":"D Dan","year":"2022","unstructured":"Dan D, Ying Y, Ge L (2022) Digital twin system of bridges group based on machine vision fusion monitoring of bridge traffic load. IEEE Trans Intel Trans Syst 23(11):22190\u201322205","journal-title":"IEEE Trans Intel Trans Syst"},{"issue":"4","key":"19105_CR3","doi-asserted-by":"crossref","first-page":"383","DOI":"10.3390\/electronics10040383","volume":"10","author":"M Gao","year":"2021","unstructured":"Gao M, Wang J, Chen Y et al (2021) An improved multi-exposure image fusion method for intelligent transportation system. Electronics 10(4):383","journal-title":"Electronics"},{"key":"19105_CR4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","volume":"44","author":"J Ma","year":"2019","unstructured":"Ma J, Yu W, Liang P, Li C, Jiang J (2019) FusionGAN: A generative adversarial network for infrared and visible image fusion. Inf Fusion 44:11\u201326","journal-title":"Inf Fusion"},{"key":"19105_CR5","doi-asserted-by":"crossref","unstructured":"Geng Y, Diao W, Zhao Y (2022) Infrared-RGB Image Registration for Power Thermal Fault Detection Based on Gradient Hash Matching. Proc. 22th IEEE Conf. Commun. Technol (ICCT), 1732\u20131735 November 2022","DOI":"10.1109\/ICCT56141.2022.10072971"},{"issue":"5","key":"19105_CR6","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2019","unstructured":"Li H, Wu X (2019) DenseFuse: A fusion approach to infrared and visible images. IEEE Trans Image Process 28(5):2614\u20132623","journal-title":"IEEE Trans Image Process"},{"key":"19105_CR7","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","volume":"29","author":"J Ma","year":"2020","unstructured":"Ma J, Xu H, Jiang J, Mei X, Zhang X (2020) Ddcgan: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans Image Process 29:4980\u20134995","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"19105_CR8","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2022","unstructured":"Xu H, Ma J, Jiang J, Guo X, Liang H (2022) U2Fusion: A unified unsupervised image fusion network. IEEE Trans Pattern Anal Mach Intell 44(1):502\u2013518","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"19105_CR9","doi-asserted-by":"crossref","unstructured":"Xu H, Wang X, Ma J (2021) DRF: Disentangled representation for visible and infrared image fusion. IEEE Trans Instrum Meas 70","DOI":"10.1109\/TIM.2021.3056645"},{"key":"19105_CR10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.inffus.2019.07.005","volume":"54","author":"J Ma","year":"2020","unstructured":"Ma J et al (2020) Infrared and visible image fusion via detail preserving adversarial learning. Inf Fusion 54:85\u201398","journal-title":"Inf Fusion"},{"key":"19105_CR11","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","volume":"45","author":"J Ma","year":"2019","unstructured":"Ma J, Ma Y, Li C (2019) Infrared and visible image fusion methods and applications: a survey. Inf Fusion 45:153\u2013178","journal-title":"Inf Fusion"},{"key":"19105_CR12","doi-asserted-by":"crossref","first-page":"4733","DOI":"10.1109\/TIP.2020.2975984","volume":"29","author":"H Li","year":"2020","unstructured":"Li H, Wu X, Kittler J (2020) MDLatLRR: A novel decomposition method for infrared and visible image fusion. IEEE Trans Image Process 29:4733\u20134746","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"19105_CR13","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.1109\/TIM.2020.2975405","volume":"69","author":"X Li","year":"2020","unstructured":"Li X, Guo X, Han P, Wang X, Li H, Luo T (2020) Laplacian redecomposition for multimodal medical image fusion. IEEE Trans Instrum Meas 69(9):6880\u20136890","journal-title":"IEEE Trans Instrum Meas"},{"key":"19105_CR14","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1109\/LSP.2020.2999788","volume":"27","author":"M Wang","year":"2020","unstructured":"Wang M, Shang X (2020) A fast image fusion with discrete cosine transform. IEEE Signal Process Lett 27:990\u2013994","journal-title":"IEEE Signal Process Lett"},{"key":"19105_CR15","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ins.2019.08.066","volume":"508","author":"J Chen","year":"2020","unstructured":"Chen J, Li X, Luo L, Mei X, Ma J (2020) Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Inf Sci 508:64\u201378","journal-title":"Inf Sci"},{"key":"19105_CR16","doi-asserted-by":"crossref","unstructured":"Wang Z, Cui Z, Zhu Y (2020) Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation. Comp Biology Medic 123","DOI":"10.1016\/j.compbiomed.2020.103823"},{"key":"19105_CR17","doi-asserted-by":"crossref","unstructured":"Li X, Zhou F, Tan H (2021) Joint image fusion and deniosing via three-layer decomposition and sparse representation. Know System 224","DOI":"10.1016\/j.knosys.2021.107087"},{"issue":"2","key":"19105_CR18","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/TMM.2019.2928516","volume":"22","author":"B Xiao","year":"2020","unstructured":"Xiao B, Ou G, Tang H, Bi X, Li W (2020) Multi-Focus image fusion by Hessian Matrix based decomposition. IEEE Trans Mult 22(2):285\u2013297","journal-title":"IEEE Trans Mult"},{"issue":"9","key":"19105_CR19","doi-asserted-by":"crossref","first-page":"11040","DOI":"10.1109\/TPAMI.2023.3268209","volume":"45","author":"H Li","year":"2023","unstructured":"Li H, Xu T et al (2023) Lrrnet: A novel representation learning guided fusion network for infrared and visible images. IEEE Trans Patt Anal Mach Intel 45(9):11040\u201311052","journal-title":"IEEE Trans Patt Anal Mach Intel"},{"key":"19105_CR20","doi-asserted-by":"crossref","unstructured":"Ren L, Pan Z, Cao J et al (2021) Infrared and visible image fusion based on edge-preserving guided filter and infrared feature decomposition. Signal Process 186","DOI":"10.1016\/j.sigpro.2021.108108"},{"key":"19105_CR21","doi-asserted-by":"crossref","unstructured":"Ma J, Zhou Y (2020) Infrared and visible image fusion via gradientlet filter. Vis Image Understand 197\u2013198","DOI":"10.1016\/j.cviu.2020.103016"},{"key":"19105_CR22","first-page":"296","volume":"320","author":"M Lou","year":"2019","unstructured":"Lou M, Liu Y, Yang F et al (2019) Image enhancement of palm veins based on adaptive fusion and gabor filter. Fuzz Syst Data Mining 320:296\u2013304","journal-title":"Fuzz Syst Data Mining"},{"issue":"4","key":"19105_CR23","first-page":"1177","volume":"7","author":"P Quesada-Barriuso","year":"2014","unstructured":"Quesada-Barriuso P, Argello F, Heras D (2014) Spectral-spatial classification of hyperspectral images using wavelets and extended morphological profiles. Selected Topics. Applied earth observations. Remote Sensing. 7(4):1177\u20131185","journal-title":"Remote Sensing."},{"issue":"7","key":"19105_CR24","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1109\/TIP.2013.2244222","volume":"22","author":"S Li","year":"2013","unstructured":"Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864\u20132875","journal-title":"IEEE Trans Image Process"},{"key":"19105_CR25","doi-asserted-by":"crossref","unstructured":"Wang G, Li W, Huang Y (2021) Medical image fusion based on hybrid three-layer decomposition model and nuclear norm. Comp Biology Medic 129","DOI":"10.1016\/j.compbiomed.2020.104179"},{"key":"19105_CR26","first-page":"1","volume":"71","author":"G Wang","year":"2022","unstructured":"Wang G, Li W, Gao X, Xiao B, Du J (2022) Functional and anatomical image fusion based on gradient enhanced decomposition model. IEEE Trans Instrum Meas 71:1\u201314","journal-title":"IEEE Trans Instrum Meas"},{"key":"19105_CR27","doi-asserted-by":"crossref","unstructured":"B R, Fadi A, R. S, M R, et al (2022) Intelligent multimodal medical image fusion with deep guided filtering. Multimed Syst 28(4):1449\u20131463","DOI":"10.1007\/s00530-020-00706-0"},{"key":"19105_CR28","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","volume":"54","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Liu Y, Sun P, Yan H, Zhao X, Zhang L (2020) IFCNN: A general image fusion framework based on convolutional neural network. Inf Fusion 54:99\u2013118","journal-title":"Inf Fusion"},{"key":"19105_CR29","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.imavis.2019.03.001","volume":"85","author":"HT Mustafa","year":"2019","unstructured":"Mustafa HT, Yang J, Zareapoor M (2019) Multi-scale convolutional neural network for multi-focus image fusion. Image Vis Comput 85:26\u201335","journal-title":"Image Vis Comput"},{"key":"19105_CR30","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2022.06.031","volume":"501","author":"D Yao","year":"2022","unstructured":"Yao D et al (2022) Multi-feature fusion: Graph neural network and cnn combining for hyperspectral image classification. Neurocomputing 501:246\u2013257","journal-title":"Neurocomputing"},{"issue":"5","key":"19105_CR31","first-page":"1","volume":"59","author":"L Shuo","year":"2020","unstructured":"Shuo L, Huan L, Zheng L et al (2020) Enhanced situation awareness through cnn-based deep multimodal image fusion. Optical Engine 59(5):1","journal-title":"Optical Engine"},{"key":"19105_CR32","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.patrec.2020.11.014","volume":"141","author":"L Heng","year":"2021","unstructured":"Heng L et al (2021) Multi-focus image fusion algorithm based on supervised learning for fully convolutional neural network. Pattern Recogn Lett 141:45\u201353","journal-title":"Pattern Recogn Lett"},{"key":"19105_CR33","doi-asserted-by":"crossref","unstructured":"Zhao Z, Bai H, et al (2023) CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion. 2023 IEEE\/CVF Conf. Comp Vis. Patt Recognition (CVPR), Vancouver, BC, Canada, 2023, 5906-5916 2023","DOI":"10.1109\/CVPR52729.2023.00572"},{"key":"19105_CR34","doi-asserted-by":"crossref","unstructured":"Zhao Z, Bai H, et al (2023) Equivariant multi-modality image fusion. arXiv:2305.11443","DOI":"10.1109\/CVPR52733.2024.02448"},{"key":"19105_CR35","doi-asserted-by":"crossref","unstructured":"Liu J, Liu Z, et al (2023) Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation. 2023 IEEE\/CVF Int Conf. Comp Vis (ICCV), Paris, France, 2023 , 8081-8090 2023","DOI":"10.1109\/ICCV51070.2023.00745"},{"key":"19105_CR36","doi-asserted-by":"crossref","unstructured":"Bhalla K et al (2022) A fuzzy convolutional neural network for enhancing multi-focus image fusion. Commun Image Represen 84","DOI":"10.1016\/j.jvcir.2022.103485"},{"issue":"7","key":"19105_CR37","doi-asserted-by":"crossref","first-page":"827","DOI":"10.3390\/e23070827","volume":"23","author":"B Wei","year":"2021","unstructured":"Wei B, Feng X, Wang K et al (2021) The multi-focus-image-fusion method based on convolutional neural network and sparse representation. Entropy 23(7):827","journal-title":"Entropy"},{"issue":"3","key":"19105_CR38","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TNNLS.2020.2980398","volume":"32","author":"R Dian","year":"2021","unstructured":"Dian R, Li S, Kang X (2021) Regularizing hyperspectral and multispectral image fusion by CNN denoiser. IEEE Trans Neural Net Learn Sys 32(3):1124\u20131135","journal-title":"IEEE Trans Neural Net Learn Sys"},{"key":"19105_CR39","doi-asserted-by":"crossref","unstructured":"Liu Z, Cao Y, Li Y et al (2019) Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network. Comp Methods Pro Biomed 187","DOI":"10.1016\/j.cmpb.2019.105019"},{"key":"19105_CR40","first-page":"1186","volume":"32","author":"Z Zhao","year":"2022","unstructured":"Zhao Z, Xu S, Zhang J et al (2022) Efficient and model-based infrared and visible image fusion via algorithm unrolling. Computer Sci 32:1186\u20131196","journal-title":"Computer Sci"},{"issue":"3","key":"19105_CR41","first-page":"1","volume":"16","author":"Y Liu","year":"2018","unstructured":"Liu Y, Chen X, Cheng J et al (2018) Infrared and visible image fusion with convolutional neural networks. Int J Wavelets Mult Inf Process 16(3):1\u201320","journal-title":"Int J Wavelets Mult Inf Process"},{"key":"19105_CR42","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/TIP.2021.3135485","volume":"31","author":"K Zeng","year":"2021","unstructured":"Zeng K, Wang Y, Mao J et al (2021) Deep stereo matching with hysteresis attention and supervised cost volume construction. IEEE Trans Image Process 31:812\u2013822","journal-title":"IEEE Trans Image Process"},{"key":"19105_CR43","first-page":"30392","volume":"2021","author":"X Tete","year":"2021","unstructured":"Tete X, Mannat S, Eric M et al (2021) Early Convolutions Help Transformers See Better. Conf Neural Inf Process Sys 2021:30392\u201330400","journal-title":"Conf Neural Inf Process Sys"},{"key":"19105_CR44","doi-asserted-by":"crossref","unstructured":"Jia X, Zhu C, Li M, Zhou W, T (2021) LLVIP: A Visible-infrared Paired Dataset for Low-light Vision. 2021 IEEE\/CVF Int Conf. Comp Vis Workshops (ICCVW), Montreal, BC, Canada, 2021. pp 3489\u20133497","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"19105_CR45","unstructured":"Alexander T, Maarten H (2014) Tno image fusion dataset."},{"key":"19105_CR46","doi-asserted-by":"crossref","unstructured":"Liu J, Fan X, Huang Z et al (2022) Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. Computer Vis Pattern Reco 5792\u20135801","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"19105_CR47","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83\u201384","author":"L Tang","year":"2022","unstructured":"Tang L, Yuan J, Ma J et al (2022) Piafusion: A progressive infrared and visible image fusion network based on illumination aware. Inf Fusion 83\u201384:79\u201392","journal-title":"Inf Fusion"},{"key":"19105_CR48","first-page":"12484","volume":"34","author":"H Xu","year":"2020","unstructured":"Xu H, Ma J, Le Z et al (2020) Fusiondn: a unified densely connected network for image fusion. AAAI Conf Artif Intel 34:12484\u201312491","journal-title":"AAAI Conf Artif Intel"},{"issue":"1","key":"19105_CR49","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/JSEN.2015.2478655","volume":"16","author":"D Bavirisetti","year":"2016","unstructured":"Bavirisetti D, Dhuli R (2016) Fusion of infrared and visible sensor images based on anisotropic diffusion and karhunen-loeve transform. IEEE Sensors J 16(1):203\u2013209","journal-title":"IEEE Sensors J"},{"key":"19105_CR50","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.infrared.2017.02.005","volume":"82","author":"J Ma","year":"2017","unstructured":"Ma J, Zhou Z, Wang B et al (2017) Infrared and visible image fusion based on visual saliency map and weighted least square optimization. Infrared Phy Technol 82:8\u201317","journal-title":"Infrared Phy Technol"},{"issue":"5","key":"19105_CR51","doi-asserted-by":"crossref","first-page":"479","DOI":"10.14429\/dsj.61.705","volume":"61","author":"V Naidu","year":"2011","unstructured":"Naidu V (2011) Image fusion technique using multi-resolution singular value decomposition. Defence Sci J 61(5):479\u2013484","journal-title":"Defence Sci J"},{"key":"19105_CR52","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.infrared.2016.01.009","volume":"76","author":"D Bavirisetti","year":"2016","unstructured":"Bavirisetti D, Dhuli R (2016) Two-scale image fusion of visible and infrared images using saliency detection. Infrared Phy Technol 76:52\u201364","journal-title":"Infrared Phy Technol"},{"key":"19105_CR53","doi-asserted-by":"crossref","unstructured":"Zhao Z, Xu S, Zhang C, et al (2020) DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion. 2020 Int Joint Conf. Artificial Intel. pp 970\u2013976","DOI":"10.24963\/ijcai.2020\/135"},{"key":"19105_CR54","doi-asserted-by":"crossref","unstructured":"Zhao Z, Bai H, Zhu Y, et al (2023) DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion. IEEE Int Conf. Computer Vis, abs\/2303.06840. pp 8048\u20138059","DOI":"10.1109\/ICCV51070.2023.00742"},{"issue":"5","key":"19105_CR55","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1364\/JOSAA.386410","volume":"37","author":"Y Chen","year":"2020","unstructured":"Chen Y, Shin H (2020) Multispectral image fusion based pedestrian detection using a multilayer fused deconvolutional single-shot detector. J Optical Soc America A 37(5):768\u2013779","journal-title":"J Optical Soc America A"},{"key":"19105_CR56","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.infrared.2015.07.026","volume":"72","author":"C Zhao","year":"2015","unstructured":"Zhao C, Guo Y, Wang Y (2015) A fast fusion scheme for infrared and visible light images in NSCT domain. Infrared Phy Technol 72:266\u2013275","journal-title":"Infrared Phy Technol"},{"issue":"5","key":"19105_CR57","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s11760-013-0556-9","volume":"9","author":"BK Shreyamsha Kumar","year":"2015","unstructured":"Shreyamsha Kumar BK (2015) Image fusion based on pixel significance using cross bilateral filter. Signal Image Video Process 9(5):1193\u20131204","journal-title":"Signal Image Video Process"},{"key":"19105_CR58","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.infrared.2017.01.012","volume":"81","author":"J Zhao","year":"2017","unstructured":"Zhao J, Cui G, Gong X et al (2017) Fusion of visible and infrared images using global entropy and gradient constrained regularization. Infrared Phy Technol 81:201\u2013209","journal-title":"Infrared Phy Technol"},{"issue":"12","key":"19105_CR59","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1016\/j.aeue.2015.09.004","volume":"69","author":"V Aslantas","year":"2015","unstructured":"Aslantas V, Bendes E (2015) A new image quality metric for image fusion: The sum of the correlations of differences. AEUE - Int J Electron Commun 69(12):1890\u20131896","journal-title":"AEUE - Int J Electron Commun"},{"issue":"1","key":"19105_CR60","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s40747-022-00792-9","volume":"9","author":"P Guo","year":"2022","unstructured":"Guo P, Xie G, Li R et al (2022) Multimodal medical image fusion with convolution sparse representation and mutual information correlation in nsst domain. Complex Intel Syst 9(1):317\u2013328","journal-title":"Complex Intel Syst"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19105-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19105-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19105-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T13:08:13Z","timestamp":1738069693000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19105-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,15]]},"references-count":60,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["19105"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19105-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,15]]},"assertion":[{"value":"6 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2024","order":4,"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":"Conflicts of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}