{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:32:36Z","timestamp":1774524756500,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-03813-9","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:58:50Z","timestamp":1741600730000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimized XGBoost Hyper-Parameter Tuned Model with Krill Herd Algorithm (KHA) for Accurate Drinking Water Quality Prediction"],"prefix":"10.1007","volume":"6","author":[{"given":"Nikhil","family":"Malik","sequence":"first","affiliation":[]},{"given":"Arpna","family":"Kalonia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4325-9237","authenticated-orcid":false,"given":"Surjeet","family":"Dalal","sequence":"additional","affiliation":[]},{"given":"Dac-Nhuong","family":"Le","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"3813_CR1","doi-asserted-by":"publisher","first-page":"157218","DOI":"10.1109\/ACCESS.2020.3017743","volume":"8","author":"SI Abba","year":"2020","unstructured":"Abba SI, Linh NTT, Abdullahi J, Ali SIA, Pham QB, Abdulkadir RA, Costache R, Nam VT, Anh DT. Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration. IEEE Access. 2020;8:157218\u201337. https:\/\/doi.org\/10.1109\/ACCESS.2020.3017743.","journal-title":"IEEE Access"},{"issue":"2","key":"3813_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.ejrs.2016.12.002","volume":"21","author":"KW Abdelmalik","year":"2018","unstructured":"Abdelmalik KW. Role of statistical remote sensing for Inland water quality parameters prediction. Egypt J Remote Sens Sp Sci. 2018;21(2):193\u2013200. https:\/\/doi.org\/10.1016\/j.ejrs.2016.12.002.","journal-title":"Egypt J Remote Sens Sp Sci"},{"key":"3813_CR3","doi-asserted-by":"publisher","first-page":"109807","DOI":"10.1109\/ACCESS.2020.3001685","volume":"8","author":"S Abdullah","year":"2020","unstructured":"Abdullah S, Jaddi NS, Jaddi NS. Dual kidney-inspired algorithm for water quality prediction and cancer detection. IEEE Access. 2020;8:109807\u201320. https:\/\/doi.org\/10.1109\/ACCESS.2020.3001685.","journal-title":"IEEE Access"},{"issue":"3","key":"3813_CR4","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1016\/j.ejrs.2023.09.001","volume":"26","author":"W Ahmed","year":"2023","unstructured":"Ahmed W, Mohammed S, El-Shazly A, Morsy S. Tigris River water surface quality monitoring using remote sensing data and GIS techniques. Egypt J Remote Sens Sp Sci. 2023;26(3):816\u201325. https:\/\/doi.org\/10.1016\/j.ejrs.2023.09.001.","journal-title":"Egypt J Remote Sens Sp Sci"},{"key":"3813_CR5","doi-asserted-by":"publisher","first-page":"108527","DOI":"10.1109\/ACCESS.2021.3100490","volume":"9","author":"AO Al-Sulttani","year":"2021","unstructured":"Al-Sulttani AO, Al-Mukhtar M, Roomi AB, Farooque AA, Khedher KM, Yaseen ZM. Proposition of new ensemble data-intelligence models for surface water quality prediction. IEEE Access. 2021;9:108527\u201341. https:\/\/doi.org\/10.1109\/ACCESS.2021.3100490.","journal-title":"IEEE Access"},{"issue":"September","key":"3813_CR6","doi-asserted-by":"publisher","first-page":"119692","DOI":"10.1109\/ACCESS.2022.3221430","volume":"10","author":"B Aslam","year":"2022","unstructured":"Aslam B, Maqsoom A, Cheema AH, Ullah F, Alharbi A, Imran M. Water quality management using hybrid machine learning and data mining algorithms: an indexing approach. IEEE Access. 2022;10(September):119692\u2013705. https:\/\/doi.org\/10.1109\/ACCESS.2022.3221430.","journal-title":"IEEE Access"},{"key":"3813_CR7","doi-asserted-by":"publisher","first-page":"40372","DOI":"10.1109\/ACCESS.2021.3064029","volume":"9","author":"S Cao","year":"2021","unstructured":"Cao S, Zhou L, Zhang Z. Prediction of dissolved oxygen content in aquaculture based on clustering and improved ELM. IEEE Access. 2021;9:40372\u201387. https:\/\/doi.org\/10.1109\/ACCESS.2021.3064029.","journal-title":"IEEE Access"},{"key":"3813_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jssas.2023.08.004","author":"C Chawishborwornworng","year":"2023","unstructured":"Chawishborwornworng C, Luanwuthi S, Umpuch C, Puchongkawarin C. Bootstrap approach for quantifying the uncertainty in modeling of the water quality index using principal component analysis and artificial intelligence. J Saudi Soc Agric Sci. 2023. https:\/\/doi.org\/10.1016\/j.jssas.2023.08.004.","journal-title":"J Saudi Soc Agric Sci"},{"issue":"January","key":"3813_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2023.109882","volume":"146","author":"L Chen","year":"2023","unstructured":"Chen L, Wu T, Wang Z, Lin X, Cai Y. A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction. Ecol Ind. 2023;146(January): 109882. https:\/\/doi.org\/10.1016\/j.ecolind.2023.109882.","journal-title":"Ecol Ind"},{"key":"3813_CR10","doi-asserted-by":"publisher","first-page":"24638","DOI":"10.1109\/ACCESS.2022.3152818","volume":"10","author":"D Dheda","year":"2022","unstructured":"Dheda D, Cheng L, Abu-Mahfouz AM. Long short term memory water quality predictive model discrepancy mitigation through genetic algorithm optimisation and ensemble modeling. IEEE Access. 2022;10:24638\u201358. https:\/\/doi.org\/10.1109\/ACCESS.2022.3152818.","journal-title":"IEEE Access"},{"issue":"3","key":"3813_CR11","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/S1665-6423(14)71629-3","volume":"12","author":"YR Ding","year":"2014","unstructured":"Ding YR, Cai YJ, Sun PD, Chen B. The use of combined neural networks and genetic algorithms for prediction of river water quality. J Appl Res Technol. 2014;12(3):493\u20139. https:\/\/doi.org\/10.1016\/S1665-6423(14)71629-3.","journal-title":"J Appl Res Technol"},{"issue":"7","key":"3813_CR12","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.jssas.2020.08.001","volume":"19","author":"A El Bilali","year":"2020","unstructured":"El Bilali A, Taleb A. Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment. J Saudi Soc Agric Sci. 2020;19(7):439\u201351. https:\/\/doi.org\/10.1016\/j.jssas.2020.08.001.","journal-title":"J Saudi Soc Agric Sci"},{"key":"3813_CR13","doi-asserted-by":"publisher","first-page":"65730","DOI":"10.1109\/ACCESS.2021.3075849","volume":"9","author":"G Hassan","year":"2021","unstructured":"Hassan G, Goher ME, Shaheen ME, Taie SA. Hybrid predictive model for water quality monitoring based on sentinel-2A L1C data. IEEE Access. 2021;9:65730\u201349. https:\/\/doi.org\/10.1109\/ACCESS.2021.3075849.","journal-title":"IEEE Access"},{"issue":"8","key":"3813_CR14","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1016\/j.jksuci.2021.06.003","volume":"34","author":"MS Islam Khan","year":"2022","unstructured":"Islam Khan MS, Islam N, Uddin J, Islam S, Nasir MK. Water quality prediction and classification based on principal component regression and gradient boosting classifier approach. J King Saud Univ Comput Inform Sci. 2022;34(8):4773\u201381. https:\/\/doi.org\/10.1016\/j.jksuci.2021.06.003.","journal-title":"J King Saud Univ Comput Inform Sci"},{"key":"3813_CR15","doi-asserted-by":"publisher","unstructured":"JV, KK, PGM, CG, Subramaniam PR, Rangarajan S. Strategies for classifying water quality in the Cauvery River using a federated learning technique. Int J Cognit Comput Eng. 2023;4(March): 187\u2013193. https:\/\/doi.org\/10.1016\/j.ijcce.2023.04.004.","DOI":"10.1016\/j.ijcce.2023.04.004"},{"issue":"1","key":"3813_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e23797","volume":"10","author":"Z Jingfeng","year":"2024","unstructured":"Jingfeng Z, Fan S, Yan L. Spatial difference analysis and dynamic evolution prediction of urban industrial integrated water use efficiency in China. Heliyon. 2024;10(1): e23797. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e23797.","journal-title":"Heliyon"},{"issue":"4","key":"3813_CR17","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.asej.2016.08.004","volume":"8","author":"M Khadr","year":"2017","unstructured":"Khadr M, Elshemy M. Data-driven modeling for water quality prediction case study: the drains system associated with Manzala Lake. Egypt Ain Shams Eng J. 2017;8(4):549\u201357. https:\/\/doi.org\/10.1016\/j.asej.2016.08.004.","journal-title":"Egypt Ain Shams Eng J"},{"issue":"August","key":"3813_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2023.110782","volume":"154","author":"D Li","year":"2023","unstructured":"Li D, Zhao W, Hu J, Zhao S, Liu S. A long-term water quality prediction model for marine ranch based on time-graph convolutional neural network. Ecol Ind. 2023;154(August): 110782. https:\/\/doi.org\/10.1016\/j.ecolind.2023.110782.","journal-title":"Ecol Ind"},{"issue":"3\u20134","key":"3813_CR19","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.mcm.2011.11.021","volume":"58","author":"S Liu","year":"2013","unstructured":"Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y. A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model. 2013;58(3\u20134):458\u201365. https:\/\/doi.org\/10.1016\/j.mcm.2011.11.021.","journal-title":"Math Comput Model"},{"issue":"March","key":"3813_CR20","doi-asserted-by":"publisher","first-page":"29162","DOI":"10.1109\/ACCESS.2023.3260089","volume":"11","author":"W Liu","year":"2023","unstructured":"Liu W, Liu S, Hassan SG, Cao Y, Xu L, Feng D, Cao L, Chen W, Chen Y, Guo J, Liu T, Zhang H. A novel hybrid model to predict dissolved oxygen for efficient water quality in intensive aquaculture. IEEE Access. 2023;11(March):29162\u201374. https:\/\/doi.org\/10.1109\/ACCESS.2023.3260089.","journal-title":"IEEE Access"},{"key":"3813_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswcr.2023.11.005","author":"RP McGehee","year":"2023","unstructured":"McGehee RP, Flanagan DC, Engel BA, Gilley JE. A validation of WEPP water quality routines in uniform and nonuniform agricultural hillslopes. Int Soil Water Conserv Res. 2023. https:\/\/doi.org\/10.1016\/j.iswcr.2023.11.005.","journal-title":"Int Soil Water Conserv Res"},{"issue":"September","key":"3813_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jwpe.2023.104349","volume":"56","author":"H Moeinzadeh","year":"2023","unstructured":"Moeinzadeh H, Jegakumaran P, Yong KT, Withana A. Efficient water quality prediction by synthesizing seven heavy metal parameters using deep neural network. J Water Process Eng. 2023;56(September): 104349. https:\/\/doi.org\/10.1016\/j.jwpe.2023.104349.","journal-title":"J Water Process Eng"},{"key":"3813_CR23","doi-asserted-by":"publisher","first-page":"73561","DOI":"10.1109\/ACCESS.2018.2883702","volume":"6","author":"J Pan","year":"2018","unstructured":"Pan J, Yin Y, Xiong J, Luo W, Gui G, Sari H. Deep learning-based unmanned surveillance systems for observing water levels. IEEE Access. 2018;6:73561\u201371. https:\/\/doi.org\/10.1109\/ACCESS.2018.2883702.","journal-title":"IEEE Access"},{"key":"3813_CR24","doi-asserted-by":"publisher","first-page":"155455","DOI":"10.1109\/ACCESS.2019.2949034","volume":"7","author":"Y Qiu","year":"2019","unstructured":"Qiu Y, Xie H, Sun J, Duan H. A novel spatiotemporal data model for river water quality visualization and analysis. IEEE Access. 2019;7:155455\u201361. https:\/\/doi.org\/10.1109\/ACCESS.2019.2949034.","journal-title":"IEEE Access"},{"issue":"September","key":"3813_CR25","doi-asserted-by":"publisher","first-page":"101055","DOI":"10.1109\/ACCESS.2023.3315649","volume":"11","author":"MA Rahu","year":"2023","unstructured":"Rahu MA, Chandio AF, Aurangzeb K, Karim S, Alhussein M, Anwar MS. Toward design of internet of things and machine learning-enabled frameworks for analysis and prediction of water quality. IEEE Access. 2023;11(September):101055\u201386. https:\/\/doi.org\/10.1109\/ACCESS.2023.3315649.","journal-title":"IEEE Access"},{"key":"3813_CR26","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.proeng.2012.01.1212","volume":"33","author":"A Ramdani","year":"2012","unstructured":"Ramdani A, Djellouli HM, Yala NA, Taleb S, Benghalem A, Mahi C, Khadraoui A. Physico-chemical water quality in some regions of Southern Algeria and pretreatment prediction. Proc Eng. 2012;33:335\u20139. https:\/\/doi.org\/10.1016\/j.proeng.2012.01.1212.","journal-title":"Proc Eng"},{"key":"3813_CR27","doi-asserted-by":"publisher","first-page":"60078","DOI":"10.1109\/ACCESS.2022.3180482","volume":"10","author":"KP Rasheed Abdul Haq","year":"2022","unstructured":"Rasheed Abdul Haq KP, Harigovindan VP. Water quality prediction for smart aquaculture using hybrid deep learning models. IEEE Access. 2022;10:60078\u201398. https:\/\/doi.org\/10.1109\/ACCESS.2022.3180482.","journal-title":"IEEE Access"},{"issue":"May","key":"3813_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2023.139518","volume":"338","author":"G Ravindiran","year":"2023","unstructured":"Ravindiran G, Hayder G, Kanagarathinam K, Alagumalai A, Sonne C. Air quality prediction by machine learning models: a predictive study on the Indian coastal city of Visakhapatnam. Chemosphere. 2023;338(May): 139518. https:\/\/doi.org\/10.1016\/j.chemosphere.2023.139518.","journal-title":"Chemosphere"},{"issue":"December","key":"3813_CR29","doi-asserted-by":"publisher","first-page":"140900","DOI":"10.1109\/ACCESS.2023.3339564","volume":"11","author":"SR Sannasi Chakravarthy","year":"2023","unstructured":"Sannasi Chakravarthy SR, Bharanidharan N, Venkatesan VK, Abbas M, Rajaguru H, Mahesh TR, Venkatesan K. Prediction of water quality using SoftMax-ELM optimized using adaptive crow-search algorithm. IEEE Access. 2023;11(December):140900\u201313. https:\/\/doi.org\/10.1109\/ACCESS.2023.3339564.","journal-title":"IEEE Access"},{"key":"3813_CR30","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1109\/JSTARS.2021.3055798","volume":"14","author":"Z Sun","year":"2021","unstructured":"Sun Z, Chang NB, Chen CF, Mostafiz C, Gao W. Ensemble learning via higher order singular value decomposition for integrating data and classifier fusion in water quality monitoring. IEEE J Sel Top Appl Earth Observ Remote Sens. 2021;14:3345\u201360. https:\/\/doi.org\/10.1109\/JSTARS.2021.3055798.","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"3813_CR31","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.proeng.2012.01.1162","volume":"31","author":"G Tan","year":"2012","unstructured":"Tan G, Yan J, Gao C, Yang S. Prediction of water quality time series data based on least squares support vector machine. Proc Eng. 2012;31:1194\u20139. https:\/\/doi.org\/10.1016\/j.proeng.2012.01.1162.","journal-title":"Proc Eng"},{"issue":"May","key":"3813_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrh.2023.101435","volume":"47","author":"R Tan","year":"2023","unstructured":"Tan R, Wang Z, Wu T, Wu J. A data-driven model for water quality prediction in Tai Lake, China, using secondary modal decomposition with multidimensional external features. J Hydrol Reg Stud. 2023;47(May): 101435. https:\/\/doi.org\/10.1016\/j.ejrh.2023.101435.","journal-title":"J Hydrol Reg Stud"},{"issue":"August 2023","key":"3813_CR33","doi-asserted-by":"publisher","first-page":"119483","DOI":"10.1016\/j.jenvman.2023.119483","volume":"349","author":"S Thorndahl","year":"2024","unstructured":"Thorndahl S, Nielsen JM, Rasmussen MR. Model-based prediction of bathing water quality in a lake polluted by fecal coliform bacteria from combined sewer overflows. J Environ Manag. 2024;349(August 2023):119483. https:\/\/doi.org\/10.1016\/j.jenvman.2023.119483.","journal-title":"J Environ Manag"},{"issue":"August","key":"3813_CR34","doi-asserted-by":"publisher","first-page":"101042","DOI":"10.1109\/ACCESS.2022.3208142","volume":"10","author":"NK Velayudhan","year":"2022","unstructured":"Velayudhan NK, Pradeep P, Rao SN, Devidas AR, Ramesh MV. IoT-enabled water distribution systems\u2014a comparative technological review. IEEE Access. 2022;10(August):101042\u201370. https:\/\/doi.org\/10.1109\/ACCESS.2022.3208142.","journal-title":"IEEE Access"},{"key":"3813_CR35","doi-asserted-by":"publisher","first-page":"27893","DOI":"10.1109\/ACCESS.2019.2901059","volume":"7","author":"D Wang","year":"2019","unstructured":"Wang D, Xiang H. Composite control of post-chlorine dosage during drinking water treatment. IEEE Access. 2019;7:27893\u20138. https:\/\/doi.org\/10.1109\/ACCESS.2019.2901059.","journal-title":"IEEE Access"},{"issue":"December","key":"3813_CR36","doi-asserted-by":"publisher","first-page":"137285","DOI":"10.1109\/ACCESS.2023.3339190","volume":"11","author":"M Wang","year":"2023","unstructured":"Wang M, Xu Q, Cao Y, Hassan SG, Liu W, He M, Liu T, Xu L, Cao L, Liu S, Wu H. An ensemble model for water temperature prediction in intensive aquaculture. IEEE Access. 2023;11(December):137285\u2013302. https:\/\/doi.org\/10.1109\/ACCESS.2023.3339190.","journal-title":"IEEE Access"},{"issue":"March","key":"3813_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2023.110472","volume":"154","author":"Q Wei","year":"2023","unstructured":"Wei Q, Wei Q, Xu J, Liu Z, Chen P, Liu Y, Wei Q, Xu J, Li S, Yang Z, Ding Y, Tan J, Li J. Evaluation of surface water quality in Heilongjiang Province, China: based on different quantities of water quality indicators. Ecol Ind. 2023;154(March): 110472. https:\/\/doi.org\/10.1016\/j.ecolind.2023.110472.","journal-title":"Ecol Ind"},{"issue":"1","key":"3813_CR38","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.wse.2014.11.001","volume":"8","author":"XK Xin","year":"2015","unstructured":"Xin XK, Li KF, Finlayson B, Yin W. Evaluation, prediction, and protection of water quality in Danjiangkou Reservoir, China. Water Sci Eng. 2015;8(1):30\u20139. https:\/\/doi.org\/10.1016\/j.wse.2014.11.001.","journal-title":"Water Sci Eng"},{"issue":"December 2023","key":"3813_CR39","doi-asserted-by":"publisher","first-page":"100549","DOI":"10.1016\/j.ijft.2023.100549","volume":"21","author":"B Xu","year":"2024","unstructured":"Xu B. Prediction of environmental pollution hazard index of water conservancy system based on fuzzy logic. Int J Thermofluids. 2024;21(December 2023):100549. https:\/\/doi.org\/10.1016\/j.ijft.2023.100549.","journal-title":"Int J Thermofluids"},{"issue":"3\u20134","key":"3813_CR40","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.mcm.2012.12.023","volume":"58","author":"L Xu","year":"2013","unstructured":"Xu L, Liu S. Study of short-term water quality prediction model based on wavelet neural network. Math Comput Model. 2013;58(3\u20134):807\u201313. https:\/\/doi.org\/10.1016\/j.mcm.2012.12.023.","journal-title":"Math Comput Model"},{"key":"3813_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ese.2022.100207","volume":"13","author":"B Yang","year":"2023","unstructured":"Yang B, Xiao Z, Meng Q, Yuan Y, Wang W, Wang H, Wang Y, Feng X. Deep learning-based prediction of effluent quality of a constructed wetland. Environ Sci Ecotechnol. 2023;13: 100207. https:\/\/doi.org\/10.1016\/j.ese.2022.100207.","journal-title":"Environ Sci Ecotechnol"},{"issue":"December 2022","key":"3813_CR42","doi-asserted-by":"publisher","first-page":"109768","DOI":"10.1016\/j.ecolind.2022.109768","volume":"146","author":"S Yao","year":"2023","unstructured":"Yao S, Chen C, He M, Cui Z, Mo K, Pang R, Chen Q. Land use as an important indicator for water quality prediction in a region under rapid urbanization. Ecol Indic. 2023;146(December 2022):109768. https:\/\/doi.org\/10.1016\/j.ecolind.2022.109768.","journal-title":"Ecol Indic"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03813-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03813-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03813-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T09:58:52Z","timestamp":1741600732000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03813-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,10]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["3813"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03813-9","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,10]]},"assertion":[{"value":"12 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 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":"All the authors declared no conflict of interest related to this research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"263"}}