{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:29:23Z","timestamp":1781281763377,"version":"3.54.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002385","name":"Ministry of Higher Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002385","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00521-025-11457-2","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T12:30:13Z","timestamp":1751632213000},"page":"20315-20334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Edge-supervised convolutional neural network for histopathological classification of oral cancer images"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8998-8411","authenticated-orcid":false,"given":"Ahmed M.","family":"Gab Allah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed M. S.","family":"Gaballa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nada M.","family":"Elshennawy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amr","family":"Elkholy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"11457_CR1","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1177\/0022034520902128","volume":"99","author":"B Ilhan","year":"2020","unstructured":"Ilhan B, Lin K, Guneri P, Wilder-Smith P (2020) Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence. J Dent Res 99:241\u2013248. https:\/\/doi.org\/10.1177\/0022034520902128","journal-title":"J Dent Res"},{"key":"11457_CR2","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.oraloncology.2017.10.016","volume":"75","author":"JAA de Arruda","year":"2017","unstructured":"de Arruda JAA, de Oliveira Silva LV, de Oliveira CDNA, Schuch LF, Batista AC, Costa NL, Mesquita RA (2017) A multicenter study of malignant oral and maxillofacial lesions in children and adolescents. Oral oncol 75:39\u201345","journal-title":"Oral oncol"},{"key":"11457_CR3","doi-asserted-by":"publisher","DOI":"10.21608\/bmfj.2021.87517.1446","author":"O Bassyoni","year":"2021","unstructured":"Bassyoni O, Emara N, Rashad H (2021) The role of IMP3 and BCL2 in differentiating between irritated seborrheic keratosis, insitu and invasive squamous cell carcinomas of the skin. Benha Med J. https:\/\/doi.org\/10.21608\/bmfj.2021.87517.1446","journal-title":"Benha Med J"},{"key":"11457_CR4","doi-asserted-by":"publisher","DOI":"10.21608\/bmfj.2021.18949.1147","author":"S Madkour","year":"2021","unstructured":"Madkour S, Youssef S, Goda M, Abd Rabh R (2021) Significance of (IMP3) In Dysplasia- Adenocarcinoma Sequence in Barrett\u2019s Esophagus. (Immunohistochemical Study). Benha Med J. https:\/\/doi.org\/10.21608\/bmfj.2021.18949.1147","journal-title":"Benha Med J"},{"key":"11457_CR5","doi-asserted-by":"publisher","first-page":"38","DOI":"10.4103\/jpi.jpi_53_18","volume":"9","author":"HR Tizhoosh","year":"2018","unstructured":"Tizhoosh HR, Pantanowitz L (2018) Artificial Intelligence and Digital Pathology: challenges and Opportunities. J Pathol Inform 9:38. https:\/\/doi.org\/10.4103\/jpi.jpi_53_18","journal-title":"J Pathol Inform"},{"key":"11457_CR6","unstructured":"Howard AG, Zhu M, Chen B, et al (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"},{"key":"11457_CR7","first-page":"512","volume":"33","author":"B Neyshabur","year":"2020","unstructured":"Neyshabur B, Sedghi H, Zhang C (2020) What is being transferred in transfer learning? Adv Neural Inf Process Syst 33:512\u2013523","journal-title":"Adv Neural Inf Process Syst"},{"key":"11457_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang F, Qi Z, Duan K et al (2021) A Comprehensive Survey on Transfer Learning. Proc IEEE 109:43\u201376. https:\/\/doi.org\/10.1109\/JPROC.2020.3004555","journal-title":"Proc IEEE"},{"key":"11457_CR9","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.3390\/biom13071090","volume":"13","author":"R Mohan","year":"2023","unstructured":"Mohan R, Rama A, Raja RK et al (2023) OralNet: fused optimal deep features framework for oral squamous cell carcinoma detection. Biomolecules 13:1090. https:\/\/doi.org\/10.3390\/biom13071090","journal-title":"Biomolecules"},{"key":"11457_CR10","doi-asserted-by":"publisher","first-page":"918","DOI":"10.3390\/diagnostics13050918","volume":"13","author":"B Ananthakrishnan","year":"2023","unstructured":"Ananthakrishnan B, Shaik A, Kumar S et al (2023) Automated detection and classification of oral squamous cell carcinoma using deep neural networks. Diagnostics 13:918. https:\/\/doi.org\/10.3390\/diagnostics13050918","journal-title":"Diagnostics"},{"key":"11457_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/6364102","volume":"2022","author":"MA Deif","year":"2022","unstructured":"Deif MA, Attar H, Amer A et al (2022) Diagnosis of oral squamous cell carcinoma using deep neural networks and binary particle swarm optimization on histopathological images: an AIoMT approach. Comput Intell Neurosci 2022:1\u201313. https:\/\/doi.org\/10.1155\/2022\/6364102","journal-title":"Comput Intell Neurosci"},{"key":"11457_CR12","doi-asserted-by":"crossref","unstructured":"Hou L, Samaras D, Kurc TM, et al (2016) Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2424\u20132433","DOI":"10.1109\/CVPR.2016.266"},{"key":"11457_CR13","doi-asserted-by":"publisher","first-page":"2343","DOI":"10.3390\/diagnostics11122343","volume":"11","author":"AM Gab Allah","year":"2021","unstructured":"Gab Allah AM, Sarhan AMAM, Elshennawy NMNM et al (2021) Classification of brain MRI tumor images based on deep learning PGGAN augmentation. Diagnostics 11:2343. https:\/\/doi.org\/10.3390\/diagnostics11122343","journal-title":"Diagnostics"},{"key":"11457_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jjimei.2020.100004","volume":"1","author":"A Aggarwal","year":"2021","unstructured":"Aggarwal A, Mittal M, Battineni G (2021) Generative adversarial network: an overview of theory and applications. Int J Inf Manag Data Insights 1:100004. https:\/\/doi.org\/10.1016\/j.jjimei.2020.100004","journal-title":"Int J Inf Manag Data Insights"},{"key":"11457_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101816","volume":"67","author":"Y Xue","year":"2021","unstructured":"Xue Y, Ye J, Zhou Q et al (2021) Selective synthetic augmentation with HistoGAN for improved histopathology image classification. Med Image Anal 67:101816. https:\/\/doi.org\/10.1016\/j.media.2020.101816","journal-title":"Med Image Anal"},{"key":"11457_CR16","doi-asserted-by":"crossref","unstructured":"Titoriya AK, Singh MP (2022) Few-Shot Learning on Histopathology Image Classification. In: 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, pp 251\u2013256","DOI":"10.1109\/CSCI58124.2022.00048"},{"key":"11457_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582688","volume":"55","author":"Y Song","year":"2023","unstructured":"Song Y, Wang T, Cai P et al (2023) A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities. ACM Comput Surv 55:1\u201340. https:\/\/doi.org\/10.1145\/3582688","journal-title":"ACM Comput Surv"},{"key":"11457_CR18","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.micron.2011.09.016","volume":"43","author":"MMR Krishnan","year":"2012","unstructured":"Krishnan MMR, Venkatraghavan V, Acharya UR et al (2012) Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. Micron 43:352\u2013364. https:\/\/doi.org\/10.1016\/j.micron.2011.09.016","journal-title":"Micron"},{"key":"11457_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/cnr2.1293","volume":"3","author":"TY Rahman","year":"2020","unstructured":"Rahman TY, Mahanta LB, Choudhury H et al (2020) Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques. Cancer Rep 3:e1293. https:\/\/doi.org\/10.1002\/cnr2.1293","journal-title":"Cancer Rep"},{"key":"11457_CR20","doi-asserted-by":"publisher","first-page":"11979","DOI":"10.1038\/s41598-017-12320-8","volume":"7","author":"M Aubreville","year":"2017","unstructured":"Aubreville M, Knipfer C, Oetter N et al (2017) Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Sci Rep 7:11979. https:\/\/doi.org\/10.1038\/s41598-017-12320-8","journal-title":"Sci Rep"},{"key":"11457_CR21","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1111\/jop.13135","volume":"50","author":"H Alkhadar","year":"2021","unstructured":"Alkhadar H, Macluskey M, White S et al (2021) Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma. J Oral Pathol Med 50:378\u2013384. https:\/\/doi.org\/10.1111\/jop.13135","journal-title":"J Oral Pathol Med"},{"key":"11457_CR22","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1111\/jop.13157","volume":"50","author":"A Alhazmi","year":"2021","unstructured":"Alhazmi A, Alhazmi Y, Makrami A et al (2021) Application of artificial intelligence and machine learning for prediction of oral cancer risk. J Oral Pathol Med 50:444\u2013450. https:\/\/doi.org\/10.1111\/jop.13157","journal-title":"J Oral Pathol Med"},{"key":"11457_CR23","doi-asserted-by":"publisher","first-page":"4546","DOI":"10.1016\/j.jksuci.2020.11.003","volume":"34","author":"S Panigrahi","year":"2022","unstructured":"Panigrahi S, Das J, Swarnkar T (2022) Capsule network based analysis of histopathological images of oral squamous cell carcinoma. J King Saud Univ - Comput Inf Sci 34:4546\u20134553. https:\/\/doi.org\/10.1016\/j.jksuci.2020.11.003","journal-title":"J King Saud Univ - Comput Inf Sci"},{"key":"11457_CR24","doi-asserted-by":"publisher","first-page":"3833","DOI":"10.3390\/s22103833","volume":"22","author":"A Rahman","year":"2022","unstructured":"Rahman A, Alqahtani A, Aldhafferi N et al (2022) Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning. Sensors 22:3833. https:\/\/doi.org\/10.3390\/s22103833","journal-title":"Sensors"},{"key":"11457_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10225326","author":"V Shavlokhova","year":"2021","unstructured":"Shavlokhova V, Sandhu S, Flechtenmacher C et al (2021) Deep learning on oral squamous cell carcinoma ex vivo fluorescent confocal microscopy data: a feasibility study. J Clin Med. https:\/\/doi.org\/10.3390\/jcm10225326","journal-title":"J Clin Med"},{"key":"11457_CR26","unstructured":"Palaskar R, Vyas R, Khedekar V, et al (2020) Transfer learning for oral cancer detection using microscopic images. arXiv Prepr arXiv201111610"},{"key":"11457_CR27","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.3390\/diagnostics13223461","volume":"13","author":"B Nagarajan","year":"2023","unstructured":"Nagarajan B, Chakravarthy S, Venkatesan VK et al (2023) A deep learning framework with an intermediate layer using the swarm intelligence optimizer for diagnosing oral squamous cell carcinoma. Diagnostics 13:3461. https:\/\/doi.org\/10.3390\/diagnostics13223461","journal-title":"Diagnostics"},{"key":"11457_CR28","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.imed.2023.01.004","volume":"3","author":"LM de Lima","year":"2023","unstructured":"de Lima LM, de Assis MCFR, Soares JP et al (2023) Importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma using deep neural networks. Intell Med 3:258\u2013266. https:\/\/doi.org\/10.1016\/j.imed.2023.01.004","journal-title":"Intell Med"},{"key":"11457_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122418","volume":"241","author":"BMS Maia","year":"2024","unstructured":"Maia BMS, Ribeiro de Assis MCF, de Lima LM et al (2024) Transformers, convolutional neural networks, and few-shot learning for classification of histopathological images of oral cancer. Expert Syst Appl 241:122418. https:\/\/doi.org\/10.1016\/j.eswa.2023.122418","journal-title":"Expert Syst Appl"},{"key":"11457_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104584","volume":"150","author":"Z Guo","year":"2024","unstructured":"Guo Z, Ao S, Ao B (2024) Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network. J Biomed Inform 150:104584. https:\/\/doi.org\/10.1016\/j.jbi.2024.104584","journal-title":"J Biomed Inform"},{"key":"11457_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107988","volume":"170","author":"Z Li","year":"2024","unstructured":"Li Z, Zhang N, Gong H et al (2024) SG-MIAN: Self-guided multiple information aggregation network for image-level weakly supervised skin lesion segmentation. Comput Biol Med 170:107988. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.107988","journal-title":"Comput Biol Med"},{"key":"11457_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.107845","volume":"107","author":"C Lv","year":"2025","unstructured":"Lv C, Li B, Wang X et al (2025) ECM-TransUNet: Edge-enhanced multi-scale attention and convolutional Mamba for medical image segmentation. Biomed Signal Process Control 107:107845. https:\/\/doi.org\/10.1016\/j.bspc.2025.107845","journal-title":"Biomed Signal Process Control"},{"key":"11457_CR33","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/s12652-019-01386-z","volume":"15","author":"V Bhateja","year":"2024","unstructured":"Bhateja V, Nigam M, Bhadauria AS et al (2024) Human visual system based optimized mathematical morphology approach for enhancement of brain MR images. J Ambient Intell Humaniz Comput 15:799\u2013807. https:\/\/doi.org\/10.1007\/s12652-019-01386-z","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"11457_CR34","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.1007\/s11831-024-10093-8","volume":"31","author":"M Amiriebrahimabadi","year":"2024","unstructured":"Amiriebrahimabadi M, Rouhi Z, Mansouri N (2024) A comprehensive survey of multi-level thresholding segmentation methods for image processing. Arch Comput Methods Eng 31:3647\u20133697. https:\/\/doi.org\/10.1007\/s11831-024-10093-8","journal-title":"Arch Comput Methods Eng"},{"key":"11457_CR35","doi-asserted-by":"crossref","unstructured":"Bin Samma AS, Abdul Salam R, Zawawi Talib A (2010) Enhancement of background subtraction approach for image segmentation. In: 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010). IEEE, pp 761\u2013764","DOI":"10.1109\/ISSPA.2010.5605418"},{"key":"11457_CR36","doi-asserted-by":"crossref","unstructured":"Knowlton R (2000) Clinical Applications of Image Registration. In: Handbook of Medical Imaging. Elsevier, pp 613\u2013621","DOI":"10.1016\/B978-012077790-7\/50044-8"},{"key":"11457_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-19070-6","author":"YR Haddadi","year":"2024","unstructured":"Haddadi YR, Mansouri B, Khodja FZI (2024) A novel medical image enhancement algorithm based on CLAHE and pelican optimization. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-19070-6","journal-title":"Multimed Tools Appl"},{"key":"11457_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108018","volume":"170","author":"K Faryna","year":"2024","unstructured":"Faryna K, van der Laak J, Litjens G (2024) Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology. Comput Biol Med 170:108018. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108018","journal-title":"Comput Biol Med"},{"key":"11457_CR39","unstructured":"Loshchilov I, Hutter F (2017) Fixing weight decay regularization in adam. arXiv Prepr arXiv171105101 5:"},{"key":"11457_CR40","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"11457_CR41","doi-asserted-by":"crossref","unstructured":"Xu W, Xu Y, Chang T, Tu Z (2021) Co-scale conv-attentional image transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 9981\u20139990","DOI":"10.1109\/ICCV48922.2021.00983"},{"key":"11457_CR42","doi-asserted-by":"crossref","unstructured":"Heo B, Yun S, Han D, et al (2021) Rethinking spatial dimensions of vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 11936\u201311945","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"11457_CR43","doi-asserted-by":"crossref","unstructured":"Radosavovic I, Kosaraju RP, Girshick R, et al (2020) Designing network design spaces. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 10428\u201310436","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"11457_CR44","unstructured":"Dosovitskiy A (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv Prepr arXiv201011929"},{"key":"11457_CR45","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"11457_CR46","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11457_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et al (2016) Rethinking the Inception Architecture for Computer Vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"11457_CR48","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv Prepr arXiv14091556"},{"key":"11457_CR49","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1111\/odi.13825","volume":"28","author":"F Jubair","year":"2022","unstructured":"Jubair F, Al-karadsheh O, Malamos D et al (2022) A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis 28:1123\u20131130. https:\/\/doi.org\/10.1111\/odi.13825","journal-title":"Oral Dis"},{"key":"11457_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-024-00507-y","author":"BS Deo","year":"2024","unstructured":"Deo BS, Pal M, Panigrahi PK, Pradhan A (2024) An ensemble deep learning model with empirical wavelet transform feature for oral cancer histopathological image classification. Int J Data Sci Anal. https:\/\/doi.org\/10.1007\/s41060-024-00507-y","journal-title":"Int J Data Sci Anal"},{"key":"11457_CR51","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1038\/modpathol.2011.50","volume":"24","author":"SAJHM Fleskens","year":"2011","unstructured":"Fleskens SAJHM, Bergshoeff VE, Voogd AC et al (2011) Interobserver variability of laryngeal mucosal premalignant lesions: a histopathological evaluation. Mod Pathol 24:892\u2013898. https:\/\/doi.org\/10.1038\/modpathol.2011.50","journal-title":"Mod Pathol"},{"key":"11457_CR52","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1111\/his.15400","volume":"86","author":"P Hankinson","year":"2025","unstructured":"Hankinson P, Clark M, Walsh H, Khurram SA (2025) A head-to-head comparison of four grading systems for oral epithelial dysplasia. Histopathology 86:933\u2013941. https:\/\/doi.org\/10.1111\/his.15400","journal-title":"Histopathology"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11457-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11457-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11457-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T01:02:48Z","timestamp":1757206968000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11457-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,4]]},"references-count":52,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["11457"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11457-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,4]]},"assertion":[{"value":"1 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}