{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:02:46Z","timestamp":1773824566366,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"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":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10044-025-01448-3","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T23:25:15Z","timestamp":1742685915000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel fusion approach with a robust ParallelNet model for diabetic retinopathy diagnosis"],"prefix":"10.1007","volume":"28","author":[{"given":"Haroon","family":"Mahmood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saad","family":"Ather","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aamir","family":"Wali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arshad","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tayyaba Gul","family":"Malik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wardah","family":"Kafeel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"1448_CR1","doi-asserted-by":"publisher","DOI":"10.56021\/9781421440002","volume-title":"The eye book: a complete guide to eye disorders and health","author":"GH Cassel","year":"2021","unstructured":"Cassel GH (2021) The eye book: a complete guide to eye disorders and health. JHU Press"},{"key":"1448_CR2","doi-asserted-by":"publisher","first-page":"113172","DOI":"10.1109\/ACCESS.2022.3217216","volume":"10","author":"H Mustafa","year":"2022","unstructured":"Mustafa H, Ali SF, Bilal M, Hanif MS (2022) Multi-stream deep neural network for diabetic retinopathy severity classification under a boosting framework. IEEE Access 10:113172\u2013113183","journal-title":"IEEE Access"},{"key":"1448_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104370","volume":"80","author":"G Yue","year":"2023","unstructured":"Yue G et al (2023) Attention-driven cascaded network for diabetic retinopathy grading from fundus images. Biomed Signal Process Control 80:104370","journal-title":"Biomed Signal Process Control"},{"key":"1448_CR4","unstructured":"IEEE. Diabetic retinopathy screening using a two-stage deep convolutional neural network trained on an extremely un-balanced dataset"},{"key":"1448_CR5","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.amsu.2022.103901","volume":"79","author":"S Azeem","year":"2022","unstructured":"Azeem S, Khan U, Liaquat A (2022) The increasing rate of diabetes in Pakistan: a silent killer. Ann Med Surg 79:37. https:\/\/doi.org\/10.1016\/j.amsu.2022.103901","journal-title":"Ann Med Surg"},{"issue":"12","key":"1448_CR6","doi-asserted-by":"publisher","first-page":"4364","DOI":"10.4103\/ijo.IJO_126_22","volume":"70","author":"AH Jokhio","year":"2022","unstructured":"Jokhio AH, Talpur KI, Shujaat S, Talpur BR, Memon S (2022) Prevalence of diabetic retinopathy in rural Pakistan: a population based cross-sectional study. Indian J Ophthalmol 70(12):4364\u20134369","journal-title":"Indian J Ophthalmol"},{"key":"1448_CR7","unstructured":"Desiani A, Suprihatin B, Husein FR, Wahyudi Y et al (2022) A novelty patching of circular random and ordered techniques on retinal image to improve cnn u-net performance. Eng Lett 30(4)"},{"key":"1448_CR8","doi-asserted-by":"crossref","unstructured":"Mehendale N, Vora K, Thakker D, Mehta D (2023) A deep learning based approach to segment exudates in retinal fundus images using recurrent residual u-net. Authorea Preprints","DOI":"10.36227\/techrxiv.21196657.v1"},{"key":"1448_CR9","doi-asserted-by":"publisher","first-page":"111985","DOI":"10.1109\/ACCESS.2021.3102176","volume":"9","author":"C Chen","year":"2021","unstructured":"Chen C, Chuah JH, Ali R, Wang Y (2021) Retinal vessel segmentation using deep learning: a review. IEEE Access 9:111985\u2013112004","journal-title":"IEEE Access"},{"issue":"17","key":"1448_CR10","doi-asserted-by":"publisher","first-page":"52253","DOI":"10.1007\/s11042-023-17462-8","volume":"83","author":"P Saranya","year":"2023","unstructured":"Saranya P, Umamaheswari K (2023) Detection of exudates from retinal images for non-proliferative diabetic retinopathy detection using deep learning model. Multimed Tools Appl 83(17):52253\u201373","journal-title":"Multimed Tools Appl"},{"key":"1448_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103810","volume":"77","author":"L Fang","year":"2022","unstructured":"Fang L, Qiao H (2022) Diabetic retinopathy classification using a novel dag network based on multi-feature of fundus images. Biomed Signal Process Control 77:103810","journal-title":"Biomed Signal Process Control"},{"issue":"28","key":"1448_CR12","doi-asserted-by":"publisher","first-page":"70861","DOI":"10.1007\/s11042-024-18407-5","volume":"83","author":"S Ather","year":"2024","unstructured":"Ather S, Wali A, Malik TG, Fahd KM, Fatima S (2024) A novel vessel extraction technique for a three-way classification of diabetic retinopathy using cascaded classifier. Multimed Tools Appl 83(28):70861\u201381","journal-title":"Multimed Tools Appl"},{"key":"1448_CR13","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.media.2017.04.012","volume":"39","author":"G Quellec","year":"2017","unstructured":"Quellec G, Charri\u00e8re K, Boudi Y, Cochener B, Lamard M (2017) Deep image mining for diabetic retinopathy screening. Med Image Anal 39:178\u2013193. https:\/\/doi.org\/10.1016\/j.media.2017.04.012","journal-title":"Med Image Anal"},{"key":"1448_CR14","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","volume":"1","author":"MD Abr\u00e0moff","year":"2018","unstructured":"Abr\u00e0moff MD, Lavin PT, Birch M, Shah JR, Folk JC (2018) Pivotal trial of an autonomous ai-based diagnostic system for detection of diabetic retinopathy in primary care. NPJ Digit Med 1:39. https:\/\/doi.org\/10.1038\/s41746-018-0040-6","journal-title":"NPJ Digit Med"},{"key":"1448_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122742","volume":"241","author":"Q Van Do","year":"2024","unstructured":"Van Do Q et al (2024) Segmentation of hard exudate lesions in color fundus image using two-stage cnn-based methods. Expert Syst Appl 241:122742","journal-title":"Expert Syst Appl"},{"key":"1448_CR16","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.aej.2023.10.040","volume":"83","author":"M Alharbi","year":"2023","unstructured":"Alharbi M, Gupta D (2023) Segmentation of diabetic retinopathy images using deep feature fused residual with u-net. Alex Eng J 83:307\u2013325","journal-title":"Alex Eng J"},{"key":"1448_CR17","doi-asserted-by":"crossref","unstructured":"Padmasini N, Krithika G, Lithiga P, Akshaya S (2023) Automatic detection and segmentation of retinal manifestations due to diabetic retinopathy. In: 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), Ch. IEEE, pp 1\u20136","DOI":"10.1109\/IConSCEPT57958.2023.10170621"},{"issue":"1","key":"1448_CR18","doi-asserted-by":"publisher","first-page":"2824","DOI":"10.1038\/s41598-023-29916-y","volume":"13","author":"M Monemian","year":"2023","unstructured":"Monemian M, Rabbani H (2023) Exudate identification in retinal fundus images using precise textural verifications. Sci Rep 13(1):2824","journal-title":"Sci Rep"},{"issue":"21","key":"1448_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7138","volume":"34","author":"J Kaur","year":"2022","unstructured":"Kaur J, Kaur P (2022) Uniconv: an enhanced u-net based inceptionv3 convolutional model for dr semantic segmentation in retinal fundus images. Concurr Comput Pract Exp 34(21):e7138","journal-title":"Concurr Comput Pract Exp"},{"key":"1448_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.925901","volume":"10","author":"MAA Albadr","year":"2022","unstructured":"Albadr MAA, Ayob M, Tiun S, Al-Dhief FT, Hasan MK (2022) Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection. Front Public Health 10:925901","journal-title":"Front Public Health"},{"issue":"6","key":"1448_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/ima.23213","volume":"34","author":"AQ Khan","year":"2024","unstructured":"Khan AQ, Sun G, Khalid M, Farrash M, Bilal A (2024) Multi-deep learning approach with transfer learning for 7-stages diabetic retinopathy classification. Int J Imaging Syst Technol 34(6):e23213","journal-title":"Int J Imaging Syst Technol"},{"issue":"6","key":"1448_CR22","doi-asserted-by":"publisher","first-page":"17233","DOI":"10.1007\/s11042-023-16262-4","volume":"83","author":"Z Jiang","year":"2023","unstructured":"Jiang Z, Zaheer W, Wali A, Gilani S (2023) Visual sentiment analysis using data-augmented deep transfer learning techniques. Multimed Tools Appl 83(6):17233\u201349","journal-title":"Multimed Tools Appl"},{"key":"1448_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.104187","volume":"141","author":"A Wali","year":"2023","unstructured":"Wali A, Naseer A, Tamoor M, Gilani S (2023) Recent progress in digital image restoration techniques: a review. Digit Signal Process 141:104187","journal-title":"Digit Signal Process"},{"issue":"27","key":"1448_CR24","doi-asserted-by":"publisher","first-page":"69797","DOI":"10.1007\/s11042-024-18309-6","volume":"83","author":"A Mary","year":"2024","unstructured":"Mary A, Kavitha P (2024) Diabetic retinopathy disease detection using Shapley additive ensembled densenet-121 resnet-50 model. Multimed Tools Appl 83(27):69797\u2013824","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"1448_CR25","doi-asserted-by":"publisher","first-page":"14259","DOI":"10.1007\/s11042-023-16049-7","volume":"83","author":"K Ohri","year":"2024","unstructured":"Ohri K, Kumar M (2024) Supervised fine-tuned approach for automated detection of diabetic retinopathy. Multimed Tools Appl 83(5):14259\u201314280","journal-title":"Multimed Tools Appl"},{"key":"1448_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108099","volume":"171","author":"A Bilal","year":"2024","unstructured":"Bilal A, Liu X, Shafiq M, Ahmed Z, Long H (2024) Nimeq-sacnet: a novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data. Comput Biol Med 171:108099","journal-title":"Comput Biol Med"},{"key":"1448_CR27","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.039672","author":"A Bilal","year":"2024","unstructured":"Bilal A et al (2024) Deepsvdnet: a deep learning-based approach for detecting and classifying vision-threatening diabetic retinopathy in retinal fundus images. Comput Syst Sci Eng. https:\/\/doi.org\/10.32604\/csse.2023.039672","journal-title":"Comput Syst Sci Eng"},{"issue":"1","key":"1448_CR28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0295951","volume":"19","author":"A Bilal","year":"2024","unstructured":"Bilal A et al (2024) Improved support vector machine based on cnn-svd for vision-threatening diabetic retinopathy detection and classification. Plos one 19(1):e0295951","journal-title":"Plos one"},{"issue":"27","key":"1448_CR29","doi-asserted-by":"publisher","first-page":"70089","DOI":"10.1007\/s11042-024-18403-9","volume":"83","author":"J Thomas","year":"2024","unstructured":"Thomas J, Jerome SA (2024) Diabetic retinopathy detection using ensembled transfer learning based thrice cnn with svm classifier. Multimed Tools Appl 83(27):70089\u2013115","journal-title":"Multimed Tools Appl"},{"key":"1448_CR30","doi-asserted-by":"crossref","unstructured":"Priya SSS (2023) Detection and Classification of Diabetic Retinopathy Using Pretrained Deep Neural Networks. In: 2023 International Conference on Innovations in Engineering and Technology (ICIET), Ch. Springer, pp 1\u20137","DOI":"10.1109\/ICIET57285.2023.10220715"},{"issue":"1","key":"1448_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJSIR.309940","volume":"13","author":"LK Singh","year":"2022","unstructured":"Singh LK, Garg H, Khanna M et al (2022) Histogram of oriented gradients (hog)-based artificial neural network (ann) classifier for glaucoma detection. Int J Swarm Intell Res (IJSIR) 13(1):1\u201332","journal-title":"Int J Swarm Intell Res (IJSIR)"},{"issue":"32","key":"1448_CR32","doi-asserted-by":"publisher","first-page":"77873","DOI":"10.1007\/s11042-024-18624-y","volume":"83","author":"LK Singh","year":"2024","unstructured":"Singh LK, Khanna M, Singh R (2024) Feature subset selection through nature inspired computing for efficient glaucoma classification from fundus images. Multimed Tools Appl 83(32):77873\u2013944","journal-title":"Multimed Tools Appl"},{"key":"1448_CR33","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1007\/s00354-024-00255-4","volume":"42","author":"LK Singh","year":"2024","unstructured":"Singh LK, Khanna M, Monga H, Pandey G et al (2024) Nature-inspired algorithms-based optimal features selection strategy for COVID-19 detection using medical images. New Gener Comput 42:761\u2013824","journal-title":"New Gener Comput"},{"issue":"3","key":"1448_CR34","doi-asserted-by":"publisher","first-page":"2651","DOI":"10.1007\/s40747-021-00318-9","volume":"9","author":"G Kalyani","year":"2023","unstructured":"Kalyani G, Janakiramaiah B, Karuna A, Prasad LN (2023) Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst 9(3):2651\u20132664","journal-title":"Complex Intell Syst"},{"key":"1448_CR35","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.aej.2023.10.040","volume":"83","author":"M Alharbi","year":"2023","unstructured":"Alharbi M, Gupta D (2023) Segmentation of diabetic retinopathy images using deep feature fused residual with u-net. Alex Eng J 83:307\u2013325","journal-title":"Alex Eng J"},{"issue":"1","key":"1448_CR36","doi-asserted-by":"publisher","first-page":"6174","DOI":"10.1038\/s41598-022-09675-y","volume":"12","author":"A Galdran","year":"2022","unstructured":"Galdran A et al (2022) State-of-the-art retinal vessel segmentation with minimalistic models. Sci Rep 12(1):6174","journal-title":"Sci Rep"},{"issue":"9","key":"1448_CR37","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.3390\/math10091536","volume":"10","author":"M Arsalan","year":"2022","unstructured":"Arsalan M, Haider A, Koo JH, Park KR (2022) Segmenting retinal vessels using a shallow segmentation network to aid ophthalmic analysis. Mathematics 10(9):1536","journal-title":"Mathematics"},{"key":"1448_CR38","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.821565","volume":"9","author":"J Xu","year":"2022","unstructured":"Xu J et al (2022) A few-shot learning-based retinal vessel segmentation method for assisting in the central serous chorioretinopathy laser surgery. Front Med 9:821565","journal-title":"Front Med"},{"key":"1448_CR39","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Ch. IEEE, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"3","key":"1448_CR40","doi-asserted-by":"publisher","first-page":"54","DOI":"10.5755\/j02.eie.30594","volume":"28","author":"K Aurangzeb","year":"2022","unstructured":"Aurangzeb K, Haider SI, Alhussein M (2022) Retinal vessel segmentation based on the anam-net model. Elektronr Elektrotech 28(3):54\u201364","journal-title":"Elektronr Elektrotech"},{"key":"1448_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108669","volume":"128","author":"C Beeche","year":"2022","unstructured":"Beeche C et al (2022) Super u-net: a modularized generalizable architecture. Pattern Recognit 128:108669","journal-title":"Pattern Recognit"},{"key":"1448_CR42","doi-asserted-by":"publisher","first-page":"105069","DOI":"10.1109\/ACCESS.2023.3317348","volume":"11","author":"K Aurangzeb","year":"2023","unstructured":"Aurangzeb K, Alharthi RS, Haider SI, Alhussein M (2023) Systematic development of ai-enabled diagnostic systems for glaucoma and diabetic retinopathy. IEEE Access 11:105069\u2013105081","journal-title":"IEEE Access"},{"key":"1448_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120987","volume":"234","author":"Y Fu","year":"2023","unstructured":"Fu Y, Zhang G, Lu X, Wu H, Zhang D (2023) Rmca u-net: hard exudates segmentation for retinal fundus images. Expert Syst Appl 234:120987","journal-title":"Expert Syst Appl"},{"key":"1448_CR44","doi-asserted-by":"crossref","unstructured":"Ali MYS, Abdel-Nasser M, Jabreel M, Valls A, Baget M (2022) Exu-Eye: Retinal Exudates Segmentation based on Multi-Scale Modules and Gated Skip Connection. 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), Ch. IEEE, 1\u20135","DOI":"10.1109\/IMPACT55510.2022.10029297"},{"key":"1448_CR45","doi-asserted-by":"crossref","unstructured":"Vora K, Mehta D, Thakker D, Mehendale N (2022) A deep learning based approach to segment exudates in retinal fundus images using recurrent residual u-net","DOI":"10.36227\/techrxiv.21196657.v2"},{"key":"1448_CR46","doi-asserted-by":"crossref","unstructured":"Ameri N, Shoeibi N, Abrishami M (2022) Segmentation of hard exudates in retina fundus images using BCDU-Net. In: 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), Ch. IEEE, pp 123\u2013128","DOI":"10.1109\/ICCKE57176.2022.9960101"},{"key":"1448_CR47","doi-asserted-by":"crossref","unstructured":"Wali A, Ahmad M, Naseer A, Tamoor M, Gilani S (2023) Stynmedgan: Medical images augmentation using a new gan model for improved diagnosis of diseases. J Intell Fuzzy Syst (Preprint), pp 1\u201318","DOI":"10.3233\/JIFS-223996"},{"issue":"5","key":"1448_CR48","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s00138-023-01429-8","volume":"34","author":"HM Hamza","year":"2023","unstructured":"Hamza HM, Wali A (2023) Pakistan sign language recognition: leveraging deep learning models with limited dataset. Mach Vis Appl 34(5):71","journal-title":"Mach Vis Appl"},{"issue":"1","key":"1448_CR49","first-page":"1","volume":"1","author":"A Fawaz","year":"2021","unstructured":"Fawaz A, Ali MB, Adan M, Mujtaba M, Wali A (2021) A deep learning framework for efficient high-fidelity speech synthesis: Styletts. iKSP J Comput Sci Eng 1(1):1\u201310","journal-title":"iKSP J Comput Sci Eng"},{"key":"1448_CR50","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.biosystems.2018.11.007","volume":"175","author":"A Wali","year":"2019","unstructured":"Wali A, Saeed M (2019) m-calp-yet another way of generating handwritten data through evolution for pattern recognition. Biosystems 175:24\u201329","journal-title":"Biosystems"},{"key":"1448_CR51","first-page":"77","volume":"24","author":"A Wali","year":"2018","unstructured":"Wali A, Saeed M (2018) Biologically inspired cellular automata learning and prediction model for handwritten pattern recognition. Biol Inspir Cognit Archit 24:77\u201386","journal-title":"Biol Inspir Cognit Archit"},{"key":"1448_CR52","doi-asserted-by":"publisher","first-page":"71426","DOI":"10.1109\/ACCESS.2023.3294566","volume":"11","author":"Y Xu","year":"2023","unstructured":"Xu Y, Wali A (2023) Handwritten pattern recognition using birds-flocking inspired data augmentation technique. IEEE Access 11:71426\u201334","journal-title":"IEEE Access"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01448-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01448-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01448-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:38:53Z","timestamp":1751474333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01448-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,21]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1448"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01448-3","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,21]]},"assertion":[{"value":"9 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 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 declare that they have no relevant financial or non-financial interests to disclose. There is no personal relationship that could influence the work reported in this paper. The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This statement is to certify that the author list is correct. The Authors also confirm that this research has not been published previously and that it is not under consideration for publication elsewhere. On behalf of all Co-Authors, the Corresponding Author shall bear full responsibility for the submission. There is no Conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"66"}}