{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T06:30:20Z","timestamp":1750746620109,"version":"3.41.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"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\/s11042-024-19700-z","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T08:02:11Z","timestamp":1718870531000},"page":"15429-15452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing quality-based classification of perishable products: a convolutional neural network approach with statistical hyperparameter optimization"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1375-0532","authenticated-orcid":false,"given":"Ashish","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"19700_CR1","doi-asserted-by":"publisher","DOI":"10.5822\/978-1-61091-885-5","volume-title":"How to feed the world 2050","author":"FAO","year":"2009","unstructured":"FAO (2009) How to feed the world 2050. https:\/\/doi.org\/10.5822\/978-1-61091-885-5"},{"key":"19700_CR2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jclepro.2018.07.153","volume":"199","author":"BB Gardas","year":"2018","unstructured":"Gardas BB, Raut RD, Narkhede B (2018) Evaluating critical causal factors for post-harvest losses (PHL) in the fruit and vegetables supply chain in India using the DEMATEL approach. J Clean Prod 199:47\u201361. https:\/\/doi.org\/10.1016\/j.jclepro.2018.07.153","journal-title":"J Clean Prod"},{"issue":"3","key":"19700_CR3","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.iimb.2018.04.001","volume":"30","author":"G Naik","year":"2018","unstructured":"Naik G, Suresh DN (2018) Challenges of creating sustainable agri-retail supply chains. IIMB Manag Rev 30(3):270\u2013282. https:\/\/doi.org\/10.1016\/j.iimb.2018.04.001","journal-title":"IIMB Manag Rev"},{"issue":"August","key":"19700_CR4","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.scienta.2017.09.046","volume":"234","author":"MC Kyriacou","year":"2018","unstructured":"Kyriacou MC, Rouphael Y (2018) Towards a new definition of quality for fresh fruits and vegetables. Sci Hortic (Amsterdam) 234(August):463\u2013469. https:\/\/doi.org\/10.1016\/j.scienta.2017.09.046","journal-title":"Sci Hortic (Amsterdam)"},{"issue":"3","key":"19700_CR5","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.jksuci.2018.06.002","volume":"33","author":"A Bhargava","year":"2021","unstructured":"Bhargava A, Bansal A (2021) Fruits and vegetables quality evaluation using computer vision: A review. J King Saud Univ Comput Inf Sci 33(3):243\u2013257. https:\/\/doi.org\/10.1016\/j.jksuci.2018.06.002","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"5","key":"19700_CR6","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1080\/10408398.2011.600477","volume":"54","author":"JK Heising","year":"2014","unstructured":"Heising JK, Dekker M, Bartels PV, Van Boekel MAJS( (2014) Monitoring the Quality of Perishable Foods: Opportunities for Intelligent Packaging. Crit Rev Food Sci Nutr 54(5):645\u2013654. https:\/\/doi.org\/10.1080\/10408398.2011.600477","journal-title":"Crit Rev Food Sci Nutr"},{"key":"19700_CR7","doi-asserted-by":"publisher","unstructured":"A. Kumar, S. Tiwari, and S. Agrawal, \u201cConvolutional Neural Network Based Image Processing Model for Supply Chain Management,\u201d Lect. Notes Mech. Eng., no. March, pp. 113\u2013123, 2024, https:\/\/doi.org\/10.1007\/978-981-99-7445-0_11.","DOI":"10.1007\/978-981-99-7445-0_11"},{"issue":"3","key":"19700_CR8","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/S0925-5214(98)00090-8","volume":"15","author":"RE Paull","year":"1999","unstructured":"Paull RE (1999) Effect of temperature and relative humidity on fresh commodity quality. Postharvest Biol Technol 15(3):263\u2013277. https:\/\/doi.org\/10.1016\/S0925-5214(98)00090-8","journal-title":"Postharvest Biol Technol"},{"issue":"2","key":"19700_CR9","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/0092-8674(92)90093-R","volume":"70","author":"A Theologis","year":"1992","unstructured":"Theologis A (1992) One rotten apple spoils the whole bushel: The role of ethylene in fruit ripening. Cell 70(2):181\u2013184. https:\/\/doi.org\/10.1016\/0092-8674(92)90093-R","journal-title":"Cell"},{"issue":"December 2022","key":"19700_CR10","doi-asserted-by":"publisher","first-page":"108161","DOI":"10.1016\/j.compag.2023.108161","volume":"212","author":"A Kumar","year":"2023","unstructured":"Kumar A, Agrawal S (2023) Challenges and opportunities for agri-fresh food supply chain management in India. Comput Electron Agric 212(December 2022):108161. https:\/\/doi.org\/10.1016\/j.compag.2023.108161","journal-title":"Comput Electron Agric"},{"key":"19700_CR11","unstructured":"NHB, \u201cEducational Statistics at a Glance 2018,\u201d 2018. [Online]. Available: https:\/\/www.mhrd.gov.in\/sites\/upload_files\/mhrd\/files\/statistics-new\/ESAG-2018.pdf"},{"issue":"4","key":"19700_CR12","doi-asserted-by":"publisher","first-page":"83","DOI":"10.3390\/logistics5040083","volume":"5","author":"AZ Abideen","year":"2021","unstructured":"Abideen AZ, Sundram VPK, Pyeman J, Othman AK, Sorooshian S (2021) Food Supply Chain Transformation through Technology and Future Research Directions\u2014A Systematic Review. Logistics 5(4):83. https:\/\/doi.org\/10.3390\/logistics5040083","journal-title":"Logistics"},{"issue":"February","key":"19700_CR13","doi-asserted-by":"publisher","first-page":"100033","DOI":"10.1016\/j.jafr.2020.100033","volume":"2","author":"V Kakani","year":"2020","unstructured":"Kakani V, Nguyen VH, Kumar BP, Kim H, Pasupuleti VR (2020) A critical review on computer vision and artificial intelligence in food industry. J Agric Food Res 2(February):100033. https:\/\/doi.org\/10.1016\/j.jafr.2020.100033","journal-title":"J Agric Food Res"},{"issue":"2","key":"19700_CR14","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.inpa.2019.07.003","volume":"7","author":"MK Tripathi","year":"2020","unstructured":"Tripathi MK, Maktedar DD (2020) A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey. Inf Process Agric 7(2):183\u2013203. https:\/\/doi.org\/10.1016\/j.inpa.2019.07.003","journal-title":"Inf Process Agric"},{"issue":"June","key":"19700_CR15","doi-asserted-by":"publisher","first-page":"108304","DOI":"10.1016\/j.cie.2022.108304","volume":"169","author":"VS Yadav","year":"2022","unstructured":"Yadav VS, Singh AR, Raut RD, Mangla SK, Luthra S, Kumar A (2022) Exploring the application of Industry 4.0 technologies in the agricultural food supply chain: A systematic literature review. Comput Ind Eng 169(June):108304. https:\/\/doi.org\/10.1016\/j.cie.2022.108304","journal-title":"Comput Ind Eng"},{"issue":"March","key":"19700_CR16","doi-asserted-by":"publisher","first-page":"141910","DOI":"10.1016\/j.jclepro.2024.141910","volume":"450","author":"A Kumar","year":"2024","unstructured":"Kumar A, Agrawal S (2024) A quality-based sustainable supply chain architecture for perishable products using image processing in the era of industry 4.0. J Clean Prod 450(March):141910. https:\/\/doi.org\/10.1016\/j.jclepro.2024.141910","journal-title":"J Clean Prod"},{"issue":"11","key":"19700_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/pr8111431","volume":"8","author":"DI Onwude","year":"2020","unstructured":"Onwude DI, Chen G, Eke-Emezie N, Kabutey A, Khaled AY, Sturm B (2020) Recent advances in reducing food losses in the supply chain of fresh agricultural produce. Processes 8(11):1\u201331. https:\/\/doi.org\/10.3390\/pr8111431","journal-title":"Processes"},{"issue":"1","key":"19700_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01060-x","volume":"31","author":"G Haskins","year":"2020","unstructured":"Haskins G, Kruger U, Yan P (2020) Deep learning in medical image registration: a survey. Mach Vis Appl 31(1):1\u201318. https:\/\/doi.org\/10.1007\/s00138-020-01060-x","journal-title":"Mach Vis Appl"},{"issue":"3","key":"19700_CR19","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/s10044-021-00970-4","volume":"24","author":"AK Das","year":"2021","unstructured":"Das AK, Ghosh S, Thunder S, Dutta R, Agarwal S, Chakrabarti A (2021) Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Anal Appl 24(3):1111\u20131124. https:\/\/doi.org\/10.1007\/s10044-021-00970-4","journal-title":"Pattern Anal Appl"},{"issue":"6","key":"19700_CR20","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s001380050117","volume":"11","author":"AR Jim\u00e9nez","year":"2000","unstructured":"Jim\u00e9nez AR, Ceres R, Pons JL (2000) A vision system based on a laser range-finder applied to robotic fruit harvesting. Mach Vis Appl 11(6):321\u2013329. https:\/\/doi.org\/10.1007\/s001380050117","journal-title":"Mach Vis Appl"},{"issue":"5","key":"19700_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01081-6","volume":"31","author":"J Sun","year":"2020","unstructured":"Sun J, He X, Wu M, Wu X, Shen J, Lu B (2020) Detection of tomato organs based on convolutional neural network under the overlap and occlusion backgrounds. Mach Vis Appl 31(5):1\u201313. https:\/\/doi.org\/10.1007\/s00138-020-01081-6","journal-title":"Mach Vis Appl"},{"issue":"3","key":"19700_CR22","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s10044-019-00860-w","volume":"23","author":"S Hwang","year":"2020","unstructured":"Hwang S, Hong K, Son G, Byun H (2020) Learning CNN features from DE features for EEG-based emotion recognition. Pattern Anal Appl 23(3):1323\u20131335. https:\/\/doi.org\/10.1007\/s10044-019-00860-w","journal-title":"Pattern Anal Appl"},{"key":"19700_CR23","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/app10103443","volume":"10","author":"J Naranjo-Torres","year":"2020","unstructured":"Naranjo-Torres J, Mora M, Hern\u00e1ndez-Garc\u00eda R, Barrientos RJ, Fredes C, Valenzuela A (2020) A review of convolutional neural network applied to fruit image processing. Appl Sci 10:10. https:\/\/doi.org\/10.3390\/app10103443","journal-title":"Appl Sci"},{"key":"19700_CR24","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: A review. Neurocomputing 187:27\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.116","journal-title":"Neurocomputing"},{"issue":"6","key":"19700_CR25","doi-asserted-by":"publisher","first-page":"7611","DOI":"10.1007\/s11042-022-12150-5","volume":"81","author":"A Kazi","year":"2022","unstructured":"Kazi A, Panda SP (2022) Determining the freshness of fruits in the food industry by image classification using transfer learning. Multimed Tools Appl 81(6):7611\u20137624. https:\/\/doi.org\/10.1007\/s11042-022-12150-5","journal-title":"Multimed Tools Appl"},{"key":"19700_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-018-0284-8","volume-title":"\u201cDeep indicator for fine-grained classification of banana\u2019s ripening stages","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Lian J, Fan M, Zheng Y (2018) \u201cDeep indicator for fine-grained classification of banana\u2019s ripening stages"},{"issue":"3","key":"19700_CR27","doi-asserted-by":"publisher","first-page":"3613","DOI":"10.1007\/s11042-017-5243-3","volume":"78","author":"YD Zhang","year":"2019","unstructured":"Zhang YD et al (2019) Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimed Tools Appl 78(3):3613\u20133632. https:\/\/doi.org\/10.1007\/s11042-017-5243-3","journal-title":"Multimed Tools Appl"},{"key":"19700_CR28","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3390\/app9193971","volume":"9","author":"R Katarzyna","year":"2019","unstructured":"Katarzyna R, Pawe\u0142 M (2019) A vision-based method utilizing deep convolutional neural networks for fruit variety classification in uncertainty conditions of retail sales. Appl Sci 9:19. https:\/\/doi.org\/10.3390\/app9193971","journal-title":"Appl Sci"},{"key":"19700_CR29","doi-asserted-by":"crossref","unstructured":"S. Wang and Y. Chen, \u201cFruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique Content courtesy of Springer Nature , terms of use apply . Rights reserved. Content courtesy of Springer Nature, te,\u201d pp. 15117\u201315133, 2020","DOI":"10.1007\/s11042-018-6661-6"},{"issue":"December 2019","key":"19700_CR30","doi-asserted-by":"publisher","first-page":"109133","DOI":"10.1016\/j.scienta.2019.109133","volume":"263","author":"A Jahanbakhshi","year":"2020","unstructured":"Jahanbakhshi A, Momeny M, Mahmoudi M, Zhang YD (2020) Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks. Sci Hortic (Amsterdam) 263(December 2019):109133. https:\/\/doi.org\/10.1016\/j.scienta.2019.109133","journal-title":"Sci Hortic (Amsterdam)"},{"issue":"December 2019","key":"19700_CR31","doi-asserted-by":"publisher","first-page":"111204","DOI":"10.1016\/j.postharvbio.2020.111204","volume":"166","author":"M Momeny","year":"2020","unstructured":"Momeny M, Jahanbakhshi A, Jafarnezhad K, Zhang YD (2020) Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biol Technol 166(December 2019):111204. https:\/\/doi.org\/10.1016\/j.postharvbio.2020.111204","journal-title":"Postharvest Biol Technol"},{"key":"19700_CR32","doi-asserted-by":"publisher","unstructured":"N. Ismail and O. A. Malik, \u201cReal-time visual inspection system for grading fruits using computer vision and deep learning techniques,\u201d Inf. Process. Agric., no. xxxx, pp. 1\u201314, 2021, https:\/\/doi.org\/10.1016\/j.inpa.2021.01.005.","DOI":"10.1016\/j.inpa.2021.01.005"},{"key":"19700_CR33","doi-asserted-by":"publisher","first-page":"5248","DOI":"10.1016\/j.egyr.2021.08.028","volume":"7","author":"A Jahanbakhshi","year":"2021","unstructured":"Jahanbakhshi A, Momeny M, Mahmoudi M, Radeva P (2021) Waste management using an automatic sorting system for carrot fruit based on image processing technique and improved deep neural networks. Energy Rep 7:5248\u20135256. https:\/\/doi.org\/10.1016\/j.egyr.2021.08.028","journal-title":"Energy Rep"},{"issue":"July","key":"19700_CR34","doi-asserted-by":"publisher","first-page":"104071","DOI":"10.1016\/j.jfca.2021.104071","volume":"102","author":"J Choi","year":"2021","unstructured":"Choi J, Seo K, Cho J, Moon K (2021) Journal of Food Composition and Analysis Applying convolutional neural networks to assess the external quality of strawberries. J Food Compos Anal 102(July):104071. https:\/\/doi.org\/10.1016\/j.jfca.2021.104071","journal-title":"J Food Compos Anal"},{"key":"19700_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-96103-2","volume":"0123456789","author":"Y Li","year":"2021","unstructured":"Li Y, Feng X, Liu Y, Han X (2021) Apple quality identification and classification by image processing based on convolutional neural networks. Sci Rep 0123456789:1\u201315. https:\/\/doi.org\/10.1038\/s41598-021-96103-2","journal-title":"Sci Rep"},{"key":"19700_CR36","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3390\/app112210558","volume":"11","author":"NM Trieu","year":"2021","unstructured":"Trieu NM, Thinh NT (2021) Quality classification of dragon fruits based on external performance using a convolutional neural network. Appl Sci 11:22. https:\/\/doi.org\/10.3390\/app112210558","journal-title":"Appl Sci"},{"issue":"1","key":"19700_CR37","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s12530-020-09345-2","volume":"12","author":"AH Victoria","year":"2021","unstructured":"Victoria AH, Maragatham G (2021) Automatic tuning of hyperparameters using Bayesian optimization. Evol Syst 12(1):217\u2013223. https:\/\/doi.org\/10.1007\/s12530-020-09345-2","journal-title":"Evol Syst"},{"key":"19700_CR38","doi-asserted-by":"publisher","unstructured":"F. Albardi, H. M. Di. Kabir, M. M. I. Bhuiyan, P. M. Kebria, A. Khosravi, and S. Nahavandi, \u201cA Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification,\u201d Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., pp. 2767\u20132774, 2021, https:\/\/doi.org\/10.1109\/SMC52423.2021.9659161","DOI":"10.1109\/SMC52423.2021.9659161"},{"issue":"6","key":"19700_CR39","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"19700_CR40","unstructured":"J. S. Kaiming He, Xiangyu Zhang, Shaoqing Ren, \u201cDeep Residual Learning for Image Recognition\u201d."},{"key":"19700_CR41","unstructured":"K. Simonyan and \u2217 & Andrew Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d pp. 1\u201314, 2015."},{"key":"19700_CR42","doi-asserted-by":"publisher","unstructured":"S. Bhojanapalli, A. Chakrabarti, D. Glasner, D. Li, T. Unterthiner, and A. Veit, \u201cUnderstanding Robustness of Transformers for Image Classification,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp. 10211\u201310221, 2021, https:\/\/doi.org\/10.1109\/ICCV48922.2021.01007","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"19700_CR43","doi-asserted-by":"publisher","unstructured":"Z. Liu et al., \u201cSwin Transformer: Hierarchical Vision Transformer using Shifted Windows,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp. 9992\u201310002, 2021, https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"19700_CR44","unstructured":"A. Dosovitskiy et al., \u201can Image Is Worth 16X16 Words: Transformers for Image Recognition At Scale,\u201d ICLR 2021 - 9th Int. Conf. Learn. Represent., 2021"},{"key":"19700_CR45","unstructured":"J. C. Liang, Y. Cui, Q. Wang, T. Geng, W. Wang, and D. Liu, \u201cClusterFormer: Clustering As A Universal Visual Learner,\u201d no. NeurIPS, 2023"},{"key":"19700_CR46","doi-asserted-by":"publisher","unstructured":"H. Wu et al., \u201cCvT: Introducing Convolutions to Vision Transformers,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp. 22\u201331, 2021, https:\/\/doi.org\/10.1109\/ICCV48922.2021.00009","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"19700_CR47","doi-asserted-by":"publisher","unstructured":"B. Graham et al., \u201cLeViT: a Vision Transformer in ConvNet\u2019s Clothing for Faster Inference,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp. 12239\u201312249, 2021, https:\/\/doi.org\/10.1109\/ICCV48922.2021.01204.","DOI":"10.1109\/ICCV48922.2021.01204"},{"key":"19700_CR48","doi-asserted-by":"publisher","unstructured":"C. F. Chen, Q. Fan, and R. Panda, \u201cCrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp. 347\u2013356, 2021, https:\/\/doi.org\/10.1109\/ICCV48922.2021.00041.","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"19700_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17644-4","author":"L Gai","year":"2023","unstructured":"Gai L, Xing M, Chen W, Zhang Y, Qiao X (2023) Comparing CNN-based and transformer-based models for identifying lung cancer: which is more effective? Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-17644-4","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19700-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19700-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19700-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T10:40:24Z","timestamp":1747996824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19700-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,20]]},"references-count":49,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["19700"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19700-z","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,6,20]]},"assertion":[{"value":"2 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 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 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":"Competing interests"}}]}}