{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:31:15Z","timestamp":1783661475335,"version":"3.55.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"39","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"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\/s11042-024-19615-9","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T07:03:42Z","timestamp":1718780622000},"page":"86457-86478","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["SwinSight: a hierarchical vision transformer using shifted windows to leverage aerial image classification"],"prefix":"10.1007","volume":"83","author":[{"given":"Praveen Kumar","family":"Pradhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alloy","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amish","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Udayan","family":"Baruah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biswaraj","family":"Sen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Palash","family":"Ghosal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"19615_CR1","doi-asserted-by":"crossref","unstructured":"Chaganti SY, Nanda I, Pandi KR, Prudhvith TG, Kumar N (2020) Image classification using svm and cnn. In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), IEEE, pp 1\u20135","DOI":"10.1109\/ICCSEA49143.2020.9132851"},{"issue":"4","key":"19615_CR2","doi-asserted-by":"publisher","first-page":"511","DOI":"10.3390\/rs10040511","volume":"10","author":"A-J Gallego","year":"2018","unstructured":"Gallego A-J, Pertusa A, Gil P (2018) Automatic ship classification from optical aerial images with convolutional neural networks. Remote Sens 10(4):511","journal-title":"Remote Sens"},{"key":"19615_CR3","doi-asserted-by":"crossref","unstructured":"Hussain M, Bird JJ, Faria DR (2019) A study on cnn transfer learning for image classification. In: Advances in computational intelligence systems: contributions presented at the 18th UK workshop on computational intelligence, September 5-7, 2018, Nottingham, UK, Springer, pp 191\u2013202","DOI":"10.1007\/978-3-319-97982-3_16"},{"key":"19615_CR4","doi-asserted-by":"crossref","unstructured":"Jmour N, Zayen S, Abdelkrim A (2018) Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), IEEE, pp 397\u2013402","DOI":"10.1109\/ASET.2018.8379889"},{"key":"19615_CR5","doi-asserted-by":"crossref","unstructured":"Kyrkou C, Theocharides T (2019) Deep-learning-based aerial image classification for emergency response applications using unmanned aerial vehicles. In: CVPR Workshops, pp 517\u2013525","DOI":"10.1109\/CVPRW.2019.00077"},{"issue":"16","key":"19615_CR6","doi-asserted-by":"publisher","first-page":"8162","DOI":"10.3390\/app12168162","volume":"12","author":"L Mohammadpour","year":"2022","unstructured":"Mohammadpour L, Ling TC, Liew CS, Aryanfar A (2022) A survey of cnn-based network intrusion detection. Appl Sci 12(16):8162","journal-title":"Appl Sci"},{"issue":"3","key":"19615_CR7","first-page":"502","volume":"4","author":"HO Ikromovich","year":"2023","unstructured":"Ikromovich HO, Mamatkulovich BB (2023) Facial recognition using transfer learning in the deep cnn. Open Access Repository 4(3):502\u2013507","journal-title":"Open Access Repository"},{"issue":"1","key":"19615_CR8","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/s20010094","volume":"20","author":"H-T Nguyen","year":"2019","unstructured":"Nguyen H-T, Lee E-H, Lee S (2019) Study on the classification performance of underwater sonar image classification based on convolutional neural networks for detecting a submerged human body. Sensors 20(1):94","journal-title":"Sensors"},{"key":"19615_CR9","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/JSTARS.2020.3041859","volume":"14","author":"J Wang","year":"2020","unstructured":"Wang J, Zheng Y, Wang M, Shen Q, Huang J (2020) Object-scale adaptive convolutional neural networks for high-spatial resolution remote sensing image classification. IEEE J Selected Topic Appl Earth Observ Remote Sens 14:283\u2013299","journal-title":"IEEE J Selected Topic Appl Earth Observ Remote Sens"},{"issue":"16","key":"19615_CR10","doi-asserted-by":"publisher","first-page":"3188","DOI":"10.3390\/rs13163188","volume":"13","author":"H Takechi","year":"2021","unstructured":"Takechi H, Aragaki S, Irie M (2021) Differentiation of river sediments fractions in uav aerial images by convolution neural network. Remote Sens 13(16):3188","journal-title":"Remote Sens"},{"key":"19615_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12517-021-07791-z","volume":"14","author":"RSA Kareem","year":"2021","unstructured":"Kareem RSA, Ramanjineyulu AG, Rajan R, Setiawan R, Sharma DK, Gupta MK, Joshi H, Kumar A, Harikrishnan H, Sengan S (2021) Multilabel land cover aerial image classification using convolutional neural networks. Arab J Geosci 14:1\u201318","journal-title":"Arab J Geosci"},{"key":"19615_CR12","doi-asserted-by":"publisher","first-page":"102784","DOI":"10.1016\/j.adhoc.2022.102784","volume":"128","author":"M Sha","year":"2022","unstructured":"Sha M, Boukerche A (2022) Performance evaluation of cnn-based pedestrian detectors for autonomous vehicles. Ad Hoc Netw 128:102784","journal-title":"Ad Hoc Netw"},{"key":"19615_CR13","doi-asserted-by":"crossref","unstructured":"Yeruva AR, Choudhari P, Shrivastava A, Verma D, Shaw S, Rana A (2022) Covid-19 disease detection using chest x-ray images by means of cnn. In: 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), IEEE, pp 625\u2013631","DOI":"10.1109\/ICTACS56270.2022.9988148"},{"key":"19615_CR14","doi-asserted-by":"crossref","unstructured":"Lu Y, Tao X, Jiang F, Du J, Li G, Liu Y (2023) Image recognition of rice leaf diseases using atrous convolutional neural network and improved transfer learning algorithm. Multimed Tool Appl:1\u201319","DOI":"10.1007\/s11042-023-16047-9"},{"issue":"1","key":"19615_CR15","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s11042-022-13144-z","volume":"82","author":"PS Thakur","year":"2023","unstructured":"Thakur PS, Sheorey T, Ojha A (2023) Vgg-icnn: a lightweight cnn model for crop disease identification. Multimed Tool Appl 82(1):497\u2013520","journal-title":"Multimed Tool Appl"},{"key":"19615_CR16","doi-asserted-by":"crossref","unstructured":"Parashar J, Kushwah VS, Rai M (2023) Determination human behavior prediction supported by cognitive computing-based neural network. In: Soft Computing: Theories and Applications: Proceedings of SoCTA 2022, Springer, pp 431\u2013441","DOI":"10.1007\/978-981-19-9858-4_36"},{"issue":"2","key":"19615_CR17","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1109\/TCSVT.2022.3204753","volume":"33","author":"J Chen","year":"2022","unstructured":"Chen J, Liao X, Wang W, Qian Z, Qin Z, Wang Y (2022) Snis: a signal noise separation-based network for post-processed image forgery detection. IEEE Trans Circuits Syst Video Technol 33(2):935\u2013951","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"19615_CR18","doi-asserted-by":"crossref","unstructured":"Liao X, Wang Y, Wang T, Hu J, Wu X (2023) Famm: facial muscle motions for detecting compressed deepfake videos over social networks. IEEE Trans Circ Syst Video Technol","DOI":"10.1109\/TCSVT.2023.3278310"},{"key":"19615_CR19","doi-asserted-by":"crossref","unstructured":"Sameen MI, Pradhan B, Aziz OS (2018) Classification of very high resolution aerial photos using spectral-spatial convolutional neural networks. J Sens 2018","DOI":"10.1155\/2018\/7195432"},{"key":"19615_CR20","doi-asserted-by":"crossref","unstructured":"Tripathy S, Singh R (2022) Convolutional neural network: an overview and application in image classification. In: Proceedings of Third International Conference on Sustainable Computing: SUSCOM 2021, Springer, pp 145\u2013153","DOI":"10.1007\/978-981-16-4538-9_15"},{"issue":"1","key":"19615_CR21","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3390\/make4010002","volume":"4","author":"N Abou Baker","year":"2022","unstructured":"Abou Baker N, Zengeler N, Handmann U (2022) A transfer learning evaluation of deep neural networks for image classification. Mach Learn Knowled Extraction 4(1):22\u201341","journal-title":"Mach Learn Knowled Extraction"},{"issue":"5","key":"19615_CR22","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag 35(5):1285\u20131298","journal-title":"IEEE Trans Med Imag"},{"key":"19615_CR23","unstructured":"Qin Z, Han C, Wang Q, Nie X, Yin Y, Xiankai L (2023) Unified 3d segmenter as prototypical classifiers. In: Oh A, Neumann T, Globerson A, Saenko K, Hardt M, Levine S (eds) Advances in Neural Information Processing Systems, vol 36, pp 46419\u201346432. Curran Associates, Inc.. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/916cb4e1aeafaa0757953c9bacd17337-Paper-Conference.pdf"},{"key":"19615_CR24","doi-asserted-by":"publisher","unstructured":"Qin Z, Lu X, Nie X, Yin Y (2023) Video instance segmentation using graph matching transformer. In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp 995\u20131004. https:\/\/doi.org\/10.1109\/ICDMW60847.2023.00132","DOI":"10.1109\/ICDMW60847.2023.00132"},{"key":"19615_CR25","doi-asserted-by":"publisher","first-page":"6543","DOI":"10.1109\/TIP.2023.3328485","volume":"32","author":"Z Qin","year":"2023","unstructured":"Qin Z, Lu X, Liu D, Nie X, Yin Y, Shen J, Loui AC (2023) Reformulating graph kernels for self-supervised space-time correspondence learning. IEEE Trans Imag Process 32:6543\u20136557. https:\/\/doi.org\/10.1109\/TIP.2023.3328485","journal-title":"IEEE Trans Imag Process"},{"key":"19615_CR26","doi-asserted-by":"crossref","unstructured":"Wu P, Lu X, Shen J, Yin Y (2023) Clip fusion with bi-level optimization for human mesh reconstruction from monocular videos. In: Proceedings of the 31st ACM international conference on multimedia, pp 105\u2013115","DOI":"10.1145\/3581783.3611978"},{"key":"19615_CR27","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advan Neural Inform Process Syst 30"},{"key":"19615_CR28","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H (2021) Training data-efficient image transformers & distillation through attention. In: International conference on machine learning, PMLR, pp 10347\u201310357"},{"key":"19615_CR29","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, Fan D-P, Song K, Liang D, Lu T, Luo P, Shao L (2021) Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 568\u2013578","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"19615_CR30","doi-asserted-by":"crossref","unstructured":"Wang X, Yeshwanth C, Nie\u00dfner M (2021) Sceneformer: indoor scene generation with transformers. In: 2021 International Conference on 3D Vision (3DV), IEEE, pp 106\u2013115","DOI":"10.1109\/3DV53792.2021.00021"},{"key":"19615_CR31","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929"},{"key":"19615_CR32","doi-asserted-by":"crossref","unstructured":"Sun C, Shrivastava A, Singh S, Gupta A (2017) Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE international conference on computer vision, pp 843\u2013852","DOI":"10.1109\/ICCV.2017.97"},{"key":"19615_CR33","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"19615_CR34","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 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"19615_CR35","unstructured":"Sikkim Aerial Images dataset for Object Detection. Last accessed (27-04-2024). https:\/\/data.mendeley.com\/datasets\/vwznrr98b9\/1"},{"key":"19615_CR36","doi-asserted-by":"crossref","unstructured":"Liang Y, Monteiro ST, Saber ES (2016) Transfer learning for high resolution aerial image classification. In: 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pp 1\u20138","DOI":"10.1109\/AIPR.2016.8010600"},{"issue":"5","key":"19615_CR37","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1109\/LGRS.2016.2542358","volume":"13","author":"I \u0160evo","year":"2016","unstructured":"\u0160evo I, Avramovi\u0107 A (2016) Convolutional neural network based automatic object detection on aerial images. IEEE Geosci Remote Sens Lett 13(5):740\u2013744","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"19615_CR38","doi-asserted-by":"crossref","unstructured":"Iorga C, Neagoe V-E (2019) A deep cnn approach with transfer learning for image recognition. In: 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), IEEE, pp 1\u20136","DOI":"10.1109\/ECAI46879.2019.9042173"},{"key":"19615_CR39","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/s12524-020-01231-3","volume":"49","author":"MA Haq","year":"2021","unstructured":"Haq MA, Rahaman G, Baral P, Ghosh A (2021) Deep learning based supervised image classification using uav images for forest areas classification. J Indian Soc Remote Sens 49:601\u2013606","journal-title":"J Indian Soc Remote Sens"},{"key":"19615_CR40","unstructured":"Khose S, Tiwari A, Ghosh A (2021) Semi-supervised classification and segmentation on high resolution aerial images. arXiv:2105.08655"},{"key":"19615_CR41","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","volume":"141","author":"P Wang","year":"2021","unstructured":"Wang P, Fan E, Wang P (2021) Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recogn Lett 141:61\u201367","journal-title":"Pattern Recogn Lett"},{"key":"19615_CR42","doi-asserted-by":"publisher","first-page":"11905","DOI":"10.1007\/s11042-019-08376-5","volume":"79","author":"E-Y Huan","year":"2020","unstructured":"Huan E-Y, Wen G-H (2020) Transfer learning with deep convolutional neural network for constitution classification with face image. Multimed Tool Appl 79:11905\u201311919","journal-title":"Multimed Tool Appl"},{"key":"19615_CR43","doi-asserted-by":"crossref","unstructured":"Shaha M, Pawar M (2018) Transfer learning for image classification. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, pp 656\u2013660","DOI":"10.1109\/ICECA.2018.8474802"},{"key":"19615_CR44","doi-asserted-by":"crossref","unstructured":"Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 270\u2013279","DOI":"10.1145\/1869790.1869829"},{"key":"19615_CR45","unstructured":"Bradski G (2000) The OpenCV Library. Dr. Dobb\u2019s Journal of Software Tools"},{"key":"19615_CR46","doi-asserted-by":"publisher","unstructured":"Harris CR, Millman KJ, Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, Kerkwijk MH, Brett M, Haldane A, R\u00edo J, Wiebe M, Peterson P, G\u00e9rard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE (2020) Array programming with NumPy. Nature 585:357\u2013362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2","DOI":"10.1038\/s41586-020-2649-2"},{"key":"19615_CR47","doi-asserted-by":"publisher","unstructured":"Wightman R (2019) PyTorch Image Models GitHub. https:\/\/doi.org\/10.5281\/zenodo.4414861","DOI":"10.5281\/zenodo.4414861"},{"key":"19615_CR48","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"19615_CR49","doi-asserted-by":"crossref","unstructured":"Imambi S, Prakash KB, Kanagachidambaresan G (2021) Pytorch. Programming with TensorFlow: Solution Edge Comput Appl:87\u2013104","DOI":"10.1007\/978-3-030-57077-4_10"},{"key":"19615_CR50","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"19615_CR51","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advan Neural Inform Process Syst 25"},{"key":"19615_CR52","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv:1602.07360"},{"key":"19615_CR53","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"19615_CR54","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"19615_CR55","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"19615_CR56","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19615-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19615-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19615-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T13:16:19Z","timestamp":1732022179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19615-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":56,"journal-issue":{"issue":"39","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["19615"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19615-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,19]]},"assertion":[{"value":"15 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 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":"Conflict of Interest"}}]}}