{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:41:06Z","timestamp":1772044866169,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"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\/s42979-024-03388-x","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T15:54:44Z","timestamp":1731945284000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development of Proposed Model Using Random Forest with Optimization Technique for Classification of Phishing Website"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4277-9320","authenticated-orcid":false,"given":"Prakash","family":"Pathak","sequence":"first","affiliation":[]},{"given":"Akhilesh Kumar","family":"Shrivas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"3388_CR1","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.procs.2017.05.352","volume":"109","author":"HY Abutair","year":"2017","unstructured":"Abutair HY, Belghith A. Using case-based reasoning for phishing detection. Procedia Comput Sci. 2017;109:281\u20138. https:\/\/doi.org\/10.1016\/j.procs.2017.05.352.","journal-title":"Procedia Comput Sci"},{"issue":"6","key":"3388_CR2","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1049\/iet-ifs.2019.0006","volume":"13","author":"W Ali","year":"2019","unstructured":"Ali W, Ahmed AA. Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Inf Secur. 2019;13(6):659\u201369. https:\/\/doi.org\/10.1049\/iet-ifs.2019.0006.","journal-title":"IET Inf Secur"},{"key":"3388_CR3","doi-asserted-by":"publisher","first-page":"116766","DOI":"10.1109\/ACCESS.2020.3003569","volume":"8","author":"W Ali","year":"2020","unstructured":"Ali W, Malebary S. Particle swarm optimization-based feature weighting for improving intelligent phishing website detection. IEEE Access. 2020;8:116766\u201380. https:\/\/doi.org\/10.1109\/ACCESS.2020.3003569.","journal-title":"IEEE Access"},{"issue":"8","key":"3388_CR4","doi-asserted-by":"publisher","first-page":"4649","DOI":"10.3390\/app13084649","volume":"13","author":"S Alnemari","year":"2023","unstructured":"Alnemari S, Alshammari M. Detecting phishing domains using machine learning. Appl Sci. 2023;13(8):4649. https:\/\/doi.org\/10.3390\/app13084649. (pp.1-16).","journal-title":"Appl Sci"},{"key":"3388_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-023-10736-2","author":"BH Alowaimer","year":"2023","unstructured":"Alowaimer BH, Dahiya D. Performance investigation of phishing website detection by improved deep learning techniques. Wirel Person Commun. 2023. https:\/\/doi.org\/10.1007\/s11277-023-10736-2.","journal-title":"Wirel Person Commun"},{"issue":"11","key":"3388_CR6","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.3390\/electronics10111285","volume":"10","author":"M Al-Sarem","year":"2021","unstructured":"Al-Sarem M, Saeed F, Al-Mekhlafi ZG, Mohammed BA, Al-Hadhrami T, Alshammari MT, et al. An optimized stacking ensemble model for phishing websites detection. Electronics. 2021;10(11):1285. https:\/\/doi.org\/10.3390\/electronics10111285. (pp. 1-18).","journal-title":"Electronics"},{"key":"3388_CR7","first-page":"563","volume":"17","author":"YA Alsariera","year":"2022","unstructured":"Alsariera YA, Balogun AO, Adeyemo VE, Tarawneh OH, Mojeed HA. Intelligent tree-based ensemble approaches for phishing website detection. J Eng Sci Technol. 2022;17:563\u201382.","journal-title":"J Eng Sci Technol"},{"key":"3388_CR8","doi-asserted-by":"publisher","first-page":"10459","DOI":"10.1007\/s13369-020-04802-1","volume":"45","author":"YA Alsariera","year":"2020","unstructured":"Alsariera YA, Elijah AV, Balogun AO. Phishing website detection: forest by penalizing attributes algorithm and its enhanced variations. Arab J Sci Eng. 2020;45:10459\u201370. https:\/\/doi.org\/10.1007\/s13369-020-04802-1.","journal-title":"Arab J Sci Eng"},{"key":"3388_CR9","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1016\/j.procs.2024.03.286","volume":"233","author":"S Anirudh","year":"2024","unstructured":"Anirudh S, Nishant PR, Baitha S, Kumar KD. An ensemble classification model for phishing mail detection. Procedia Comput Sci. 2024;233:970\u20138. https:\/\/doi.org\/10.1016\/j.procs.2024.03.286.","journal-title":"Procedia Comput Sci"},{"key":"3388_CR10","unstructured":"APWG T. APWG. Phishing Activity Trends Reports, 2023. Retrieved from https:\/\/apwg.org\/trendsreports\/."},{"issue":"4(76)","key":"3388_CR11","first-page":"861","volume":"18","author":"PA Barot","year":"2023","unstructured":"Barot PA, Patel SA, Jethva HB. Evaluation of performance measures for reliable and secure phishing detection system. Reliabil Theory Appl. 2023;18(4(76)):861\u201370.","journal-title":"Reliabil Theory Appl."},{"key":"3388_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324. (Kluwer Academic Publishers, Manufactured in The Netherlands).","journal-title":"Mach Learn"},{"issue":"1","key":"3388_CR13","doi-asserted-by":"publisher","first-page":"10","DOI":"10.18201\/ijisae.2022.262","volume":"10","author":"A Chawla","year":"2022","unstructured":"Chawla A. Phishing website analysis and detection using machine learning. Int J Intell Syst Appl Eng. 2022;10(1):10\u20136.","journal-title":"Int J Intell Syst Appl Eng"},{"issue":"3","key":"3388_CR14","doi-asserted-by":"publisher","first-page":"213","DOI":"10.3103\/S0146411623030045","volume":"57","author":"MR Davoudi","year":"2023","unstructured":"Davoudi MR, Yari AR. Improving the feature section method based on genetic algorithm to increase the efficiency of detecting phishing websites. Autom Control Comput Sci. 2023;57(3):213\u201321. https:\/\/doi.org\/10.3103\/S0146411623030045.","journal-title":"Autom Control Comput Sci"},{"key":"3388_CR15","doi-asserted-by":"publisher","unstructured":"Dharani M, Badkul S, Gharat K, Vidhate A and Bhosale D. Detection of phishing websites using ensemble machine learning approach. In\u00a0ITM Web of Conference. 2021;40:1\u20135. EDP Sciences. https:\/\/doi.org\/10.1051\/itmconf\/20214003012.","DOI":"10.1051\/itmconf\/20214003012"},{"issue":"10","key":"3388_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0258361","volume":"16","author":"AK Dutta","year":"2021","unstructured":"Dutta AK. Detecting phishing websites using machine learning technique. PLoS ONE. 2021;16(10): e0258361. https:\/\/doi.org\/10.1371\/journal.pone.0258361. (pp. 1-17).","journal-title":"PLoS ONE"},{"key":"3388_CR17","volume-title":"Introduction to machine learning","author":"E Alpaydin","year":"2014","unstructured":"Alpaydin E. Introduction to machine learning. MIT Press; 2014."},{"key":"3388_CR18","doi-asserted-by":"publisher","unstructured":"Elsheh MM and Swayeb K. Phishing website detection using a hybrid approach based on support vector machine and ant colony optimization. In: 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). 2023;402\u20136. IEEE. https:\/\/doi.org\/10.1109\/MI-STA57575.2023.10169464.","DOI":"10.1109\/MI-STA57575.2023.10169464"},{"issue":"1","key":"3388_CR19","doi-asserted-by":"publisher","first-page":"109","DOI":"10.32604\/csse.2022.020414","volume":"41","author":"Z Ghaleb al-Mekhlafi","year":"2022","unstructured":"Ghaleb al-Mekhlafi Z, Abdulkarem Mohammed B, Al-Sarem M, Saeed F, Al-Hadhrami T, Alshammari MT, Alreshidi A, Sarheed Alshammari T. Phishing websites detection by using optimized stacking ensemble model. Comput Syst Sci Eng. 2022;41(1):109\u201325. https:\/\/doi.org\/10.32604\/csse.2022.020414.","journal-title":"Comput Syst Sci Eng"},{"key":"3388_CR20","doi-asserted-by":"publisher","unstructured":"Ghareeb S, Mahyoub M, and Mustafina J. Analysis of feature selection and phishing website classification using machine learning. In: 2023 15th International Conference on Developments in eSystems Engineering (DeSE), January, 2023;178\u201383. IEEE. https:\/\/doi.org\/10.1109\/DeSE58274.2023.10099697.","DOI":"10.1109\/DeSE58274.2023.10099697"},{"key":"3388_CR21","doi-asserted-by":"publisher","DOI":"10.36227\/techrxiv.16863136.v1","author":"A Ghosh","year":"2023","unstructured":"Ghosh A, Kole A. A comparative study of enhanced machine learning algorithms for brain tumor detection and classification. Authorea Preprints. 2023. https:\/\/doi.org\/10.36227\/techrxiv.16863136.v1.","journal-title":"Authorea Preprints"},{"issue":"5","key":"3388_CR22","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1080\/02564602.2022.2158952","volume":"40","author":"D Gountia","year":"2023","unstructured":"Gountia D. reliability issues in state-of-the-art microfluidic biochips: a survey. IETE Tech Rev. 2023;40(5):694\u2013709. https:\/\/doi.org\/10.1080\/02564602.2022.2158952.","journal-title":"IETE Tech Rev"},{"key":"3388_CR23","doi-asserted-by":"publisher","unstructured":"Ishwarya R, Muthumani S, PG S S K and Suriya S. Seperation of phishing emails using probabilistic classifiers. In: 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), 2023;1:1676\u20139. IEEE. https:\/\/doi.org\/10.1109\/ICACCS57279.2023.10112826.","DOI":"10.1109\/ICACCS57279.2023.10112826"},{"key":"3388_CR24","doi-asserted-by":"publisher","unstructured":"Khan SA, Khan W and Hussain A. Phishing attacks and websites classification using machine learning and multiple datasets (a comparative analysis). In: Intelligent Computing Methodologies: 16th International Conference, ICIC 2020, Bari, Italy, October 2\u20135, 2020, Proceedings Springer International Publishing, 2020. Part III 16, pp. 301\u201313. https:\/\/doi.org\/10.1007\/978-3-030-60796-8_26","DOI":"10.1007\/978-3-030-60796-8_26"},{"issue":"4","key":"3388_CR25","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/SURV.2013.032213.00009","volume":"15","author":"M Khonji","year":"2013","unstructured":"Khonji M, Iraqi Y, Jones A. Phishing detection: a literature survey. IEEE Commun Surveys Tutor. 2013;15(4):2091\u2013121. https:\/\/doi.org\/10.1109\/SURV.2013.032213.00009.","journal-title":"IEEE Commun Surveys Tutor"},{"issue":"14","key":"3388_CR26","doi-asserted-by":"publisher","first-page":"6081","DOI":"10.3390\/app1414608","volume":"14","author":"E Kocyigit","year":"2024","unstructured":"Kocyigit E, Korkmaz M, Sahingoz OK, Diri B. Enhanced feature selection using genetic algorithm for machine-learning-based phishing URL detection. Appl Sci. 2024;14(14):6081. https:\/\/doi.org\/10.3390\/app1414608.","journal-title":"Appl Sci"},{"key":"3388_CR27","doi-asserted-by":"publisher","unstructured":"McConnell B, Del Monaco D, Zabihimayvan M, Abdollahzadeh F, and Hamada S. Phishing attack detection: an improved performance through ensemble learning. In: International Conference on Artificial Intelligence and Soft Computing, Vol. 14126. Springer, Cham, 2023;145\u201357. https:\/\/doi.org\/10.1007\/978-3-031-42508-0_14.","DOI":"10.1007\/978-3-031-42508-0_14"},{"issue":"1\u20132","key":"3388_CR28","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1504\/IJICT.2018.089022","volume":"12","author":"A Mishra","year":"2018","unstructured":"Mishra A, Gupta BB. Intelligent phishing detection system using similarity matching algorithms. Int J Inf Commun Technol. 2018;12(1\u20132):51\u201373. https:\/\/doi.org\/10.1504\/IJICT.2018.089022.","journal-title":"Int J Inf Commun Technol"},{"key":"3388_CR29","doi-asserted-by":"publisher","unstructured":"Rami M and Lee M. Phishing Websites. UCI Machine Learning Repository, 2015. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Phishing+Websites. https:\/\/doi.org\/10.24432\/C51W2X.","DOI":"10.24432\/C51W2X"},{"key":"3388_CR30","first-page":"850","volume":"9","author":"C Nalini","year":"2020","unstructured":"Nalini C, Kumari RS, Sudeeptha J. Comparative study on supervised machine learning algorithms for spam mail detection. Int J Sci Technol Res. 2020;9:850\u20133.","journal-title":"Int J Sci Technol Res"},{"key":"3388_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.sciaf.2022.e01165","volume":"16","author":"TO Ojewumi","year":"2022","unstructured":"Ojewumi TO, Ogunleye GO, Oguntunde BO, Folorunsho O, Fashoto SG, Ogbu NJSA. Performance evaluation of machine learning tools for detection of phishing attacks on web pages. Sci Afr. 2022;16: e01165. https:\/\/doi.org\/10.1016\/j.sciaf.2022.e01165. (pp. 1-15).","journal-title":"Sci Afr"},{"key":"3388_CR32","doi-asserted-by":"publisher","unstructured":"Patel D, Saxena AK, Laha S and Ansari GM. A novel scheme for feature selection using filter approach. In: 2022 7th International Conference on Computing. Communication and Security (ICCCS), 2022;1\u20134. IEEE. https:\/\/doi.org\/10.1109\/ICCCS55188.2022.10079604.","DOI":"10.1109\/ICCCS55188.2022.10079604"},{"key":"3388_CR33","unstructured":"Pathak P and Shrivas AK. Phishing website classification using machine learning techniques. National conference on Machine Learning, Deep Learning and IoT (NCMLDLIOT-2023), 2023. Vol. 1, pp. 83\u201396. ISBN No. 978-93-5768-638-9."},{"key":"3388_CR34","doi-asserted-by":"publisher","unstructured":"Priya KS, Chandrika JB and Lakshmi MPP. Machine Learning-Based Phishing Website Detection A Comprehensive Approach for Cyber security. In: 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST). 2024; pp. 344\u20139. IEEE. https:\/\/doi.org\/10.1109\/ICRTCST61793.2024.10578472.","DOI":"10.1109\/ICRTCST61793.2024.10578472"},{"issue":"9","key":"3388_CR35","doi-asserted-by":"publisher","first-page":"145","DOI":"10.31449\/inf.v47i9.5177","volume":"47","author":"MAAAH Qasim","year":"2023","unstructured":"Qasim MAAAH, Flayh NA. Enhancing phishing website detection via feature selection in URL-based analysis. Informatica. 2023;47(9):145\u201356. https:\/\/doi.org\/10.31449\/inf.v47i9.5177.","journal-title":"Informatica"},{"key":"3388_CR36","doi-asserted-by":"publisher","unstructured":"Qiu X, Zhang L, Ren Y, Suganthan PN and Amaratunga G. Ensemble deep learning for regression and time series forecasting. In: 2014 IEEE symposium on computational intelligence in ensemble learning (CIEL), 2014;1\u20136. IEEE. https:\/\/doi.org\/10.1109\/CIEL.2014.7015739.","DOI":"10.1109\/CIEL.2014.7015739"},{"key":"3388_CR37","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.procs.2015.06.017","volume":"54","author":"RS Rao","year":"2015","unstructured":"Rao RS, Ali ST. Phishshield: a desktop application to detect phishing webpages through heuristic approach. Procedia Comput Sci. 2015;54:147\u201356. https:\/\/doi.org\/10.1016\/j.procs.2015.06.017.","journal-title":"Procedia Comput Sci"},{"key":"3388_CR38","doi-asserted-by":"crossref","unstructured":"Sahingoz OK, Baykal SI and Bulut D. Phishing detection from urls by using neural networks. Computer Science & Information Technology (CS & IT), 2018;41\u201354.","DOI":"10.5121\/csit.2018.81705"},{"key":"3388_CR39","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.3103\/S0146411623030045","volume":"171","author":"P Saravanan","year":"2020","unstructured":"Saravanan P, Subramanian S. A framework for detecting phishing websites using GA based feature selection and ARTMAP based website classification. Procedia Comput Sci. 2020;171:1083\u201392. https:\/\/doi.org\/10.3103\/S0146411623030045.","journal-title":"Procedia Comput Sci"},{"issue":"4","key":"3388_CR40","first-page":"588","volume":"11","author":"S Shabudin","year":"2020","unstructured":"Shabudin S, Sani NS, Ariffin KAZ, Aliff M. Feature selection for phishing website classification. Int J Adv Comput Sci Appl. 2020;11(4):588\u201395.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"3388_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103885","volume":"142","author":"S Sheikhi","year":"2024","unstructured":"Sheikhi S, Kostakos PP. Safeguarding cyberspace: enhancing malicious website detection with PSO optimized XGBoost and firefly-based feature selection. Comput Secur. 2024;142: 103885. https:\/\/doi.org\/10.1016\/j.cose.2024.103885. (pp.1-11).","journal-title":"Comput Secur"},{"key":"3388_CR42","doi-asserted-by":"publisher","unstructured":"Singh T, Kumar M and Kumar S. Enhancing phishing website detection using particle swarm optimization and feature selection techniques. In: 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). 2023; pp. 977\u2013982. IEEE. https:\/\/doi.org\/10.1109\/AIC57670.2023.10263814.","DOI":"10.1109\/AIC57670.2023.10263814"},{"issue":"107804","key":"3388_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2020.107804","volume":"112","author":"XF Song","year":"2021","unstructured":"Song XF, Zhang Y, Gong DW, Sun XY. Feature selection using bare-bones particle swarm optimization with mutual information. Pattern Recogn. 2021;112(107804):1\u201317. https:\/\/doi.org\/10.1016\/j.patcog.2020.107804.","journal-title":"Pattern Recogn"},{"key":"3388_CR44","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.procs.2020.02.251","volume":"168","author":"A Subasi","year":"2020","unstructured":"Subasi A, Kremic E. Comparison of adaboost with multiboosting for phishing website detection. Procedia Comput Sci. 2020;168:272\u20138. https:\/\/doi.org\/10.1016\/j.procs.2020.02.251.","journal-title":"Procedia Comput Sci"},{"key":"3388_CR45","doi-asserted-by":"publisher","unstructured":"Subasi A, Molah E, Almkallawi F and Chaudhery TJ. Intelligent phishing website detection using random forest classifier. In: 2017 International conference on electrical and computing technologies and applications (ICECTA), 2017;1\u20135. IEEE. https:\/\/doi.org\/10.1109\/ICECTA.2017.8252051.","DOI":"10.1109\/ICECTA.2017.8252051"},{"key":"3388_CR46","doi-asserted-by":"publisher","first-page":"333","DOI":"10.3103\/S0146411619040102","volume":"53","author":"MT Suleman","year":"2019","unstructured":"Suleman MT, Awan SM. Optimization of URL-based phishing websites detection through genetic algorithms. Autom Control Comput Sci. 2019;53:333\u201341. https:\/\/doi.org\/10.3103\/S0146411619040102.","journal-title":"Autom Control Comput Sci"},{"key":"3388_CR47","doi-asserted-by":"publisher","unstructured":"Talukder AR, Alam F, Mim ST and Al Emon MA. Detecting phishing websites using naive bayes classification. In: 2024 3rd International conference on advancement in electrical and electronic engineering (ICAEEE), 2024;1\u20136. IEEE. https:\/\/doi.org\/10.1109\/ICAEEE62219.2024.10561829.","DOI":"10.1109\/ICAEEE62219.2024.10561829"},{"issue":"1","key":"3388_CR48","first-page":"252","volume":"10","author":"AA Ubing","year":"2019","unstructured":"Ubing AA, Jasmi SKB, Abdullah A, Jhanjhi NZ, Supramaniam M. Phishing website detection: an improved accuracy through feature selection and ensemble learning. Int J Adv Comput Sci Appl. 2019;10(1):252\u20137.","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"1","key":"3388_CR49","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3390\/sym15010180","volume":"15","author":"J Zhou","year":"2023","unstructured":"Zhou J, Cui H, Li X, Yang W, Wu X. A novel phishing website detection model based on LightGBM and domain name features. Symmetry. 2023;15(1):180. https:\/\/doi.org\/10.3390\/sym15010180. (pp. 1-15).","journal-title":"Symmetry"},{"issue":"106505","key":"3388_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.asoc.2020.106505","volume":"95","author":"E Zhu","year":"2020","unstructured":"Zhu E, Ju Y, Chen Z, Liu F, Fang X. DTOF-ANN: an artificial neural network phishing detection model based on decision tree and optimal features. Appl Soft Comput. 2020;95(106505):1\u201314. https:\/\/doi.org\/10.1016\/j.asoc.2020.106505.","journal-title":"Appl Soft Comput"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03388-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03388-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03388-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T16:09:45Z","timestamp":1731946185000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03388-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":50,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3388"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03388-x","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,18]]},"assertion":[{"value":"5 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","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 proposed article has neither been published in any peer-reviewed journal nor under the consideration of any other journal. All the figures, tables, and texts are original and not copyrighted from any other article. No funding was received to assist with the preparation of this manuscript. The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"We agree to consent to every piece of information related to this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and\/or Animals"}}],"article-number":"1059"}}