{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T06:45:26Z","timestamp":1777963526740,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"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":["Multidim Syst Sign Process"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11045-023-00870-2","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T20:08:47Z","timestamp":1676318927000},"page":"397-421","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Seizure disorders recognition model from EEG signals using new probabilistic particle swarm optimizer and sequential differential evolution"],"prefix":"10.1007","volume":"34","author":[{"given":"Anuradha","family":"Thakare","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed M.","family":"Anter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"870_CR1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","volume":"100","author":"UR Acharya","year":"2018","unstructured":"Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 100, 270\u2013278.","journal-title":"Computers in Biology and Medicine"},{"key":"870_CR2","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1016\/j.future.2021.09.032","volume":"127","author":"AM Anter","year":"2021","unstructured":"Anter, A. M., Abd Elaziz, M., & Zhang, Z. (2021b). Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning. Future Generation Computer Systems, 127, 426\u2013434.","journal-title":"Future Generation Computer Systems"},{"issue":"3","key":"870_CR3","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s00500-019-03988-3","volume":"24","author":"AM Anter","year":"2020","unstructured":"Anter, A. M., & Ali, M. (2020). Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems. Soft Computing, 24(3), 1565\u20131584.","journal-title":"Soft Computing"},{"key":"870_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106677","volume":"96","author":"AM Anter","year":"2020","unstructured":"Anter, A. M., Bhattacharyya, S., & Zhang, Z. (2020d). Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans. Applied Soft Computing, 96, 106677.","journal-title":"Applied Soft Computing"},{"issue":"1","key":"870_CR5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s00500-019-04225-7","volume":"24","author":"AM Anter","year":"2020","unstructured":"Anter, A. M., Gupta, D., & Castillo, O. (2020b). A novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant. Soft Computing, 24(1), 111\u2013129.","journal-title":"Soft Computing"},{"issue":"6","key":"870_CR6","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1109\/TFUZZ.2020.2979150","volume":"28","author":"AM Anter","year":"2020","unstructured":"Anter, A. M., Huang, G., Li, L., Zhang, L., Liang, Z., & Zhang, Z. (2020c). A new type of fuzzy-rule-based system with chaotic swarm intelligence for multiclassification of pain perception from fMRI. IEEE Transactions on Fuzzy Systems, 28(6), 1096\u20131109.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"870_CR7","doi-asserted-by":"publisher","first-page":"104977","DOI":"10.1016\/j.knosys.2019.104977","volume":"188","author":"AM Anter","year":"2020","unstructured":"Anter, A. M., Moemen, Y. S., Darwish, A., & Hassanien, A. E. (2020a). Multi-target QSAR modelling of chemo-genomic data analysis based on extreme learning machine. Knowledge-Based Systems, 188, 104977.","journal-title":"Knowledge-Based Systems"},{"key":"870_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101317","volume":"49","author":"AM Anter","year":"2021","unstructured":"Anter, A. M., Oliva, D., Thakare, A., & Zhang, Z. (2021a). AFCM-LSMA: New intelligent model based on L\u00e9vy slime mould algorithm and adaptive fuzzy C-means for identification of COVID-19 infection from chest X-ray images. Advanced Engineering Informatics, 49, 101317.","journal-title":"Advanced Engineering Informatics"},{"issue":"1\u20132","key":"870_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1504\/IJCAT.2020.107901","volume":"63","author":"AT Azar","year":"2020","unstructured":"Azar, A. T., Anter, A. M., & Fouad, K. M. (2020). Intelligent system for feature selection based on rough set and chaotic binary grey wolf optimisation. International Journal of Computer Applications in Technology, 63(1\u20132), 4\u201324.","journal-title":"International Journal of Computer Applications in Technology"},{"key":"870_CR10","doi-asserted-by":"crossref","unstructured":"Basha, S. H., Anter, A. M., Hassanien, A. E., & Abdalla, A. (2021). Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic. Soft Computing, 1\u201316.","DOI":"10.1007\/s00500-021-06103-7"},{"issue":"11","key":"870_CR11","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1007\/s00500-010-0644-5","volume":"15","author":"J Brest","year":"2011","unstructured":"Brest, J., & Mau\u010dec, M. S. (2011). Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Computing, 15(11), 2157\u20132174.","journal-title":"Soft Computing"},{"issue":"4","key":"870_CR12","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.cose.2004.09.008","volume":"24","author":"S Chebrolu","year":"2005","unstructured":"Chebrolu, S., Abraham, A., & Thomas, J. P. (2005). Feature deduction and ensemble design of intrusion detection systems. Computers & Security, 24(4), 295\u2013307.","journal-title":"Computers & Security"},{"issue":"3","key":"870_CR13","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TEVC.2008.2009457","volume":"13","author":"S Das","year":"2009","unstructured":"Das, S., Abraham, A., Chakraborty, U. K., & Konar, A. (2009). Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation, 13(3), 526\u2013553.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"4","key":"870_CR14","doi-asserted-by":"publisher","first-page":"130","DOI":"10.5626\/JCSE.2017.11.4.130","volume":"11","author":"S Das","year":"2017","unstructured":"Das, S., Chang, C. C., Das, A. K., & Ghosh, A. (2017). Feature selection based on bi-objective differential evolution. Journal of Computing Science and Engineering, 11(4), 130\u2013141.","journal-title":"Journal of Computing Science and Engineering"},{"key":"870_CR15","doi-asserted-by":"publisher","DOI":"10.1201\/9781420011449","volume-title":"Fundamentals of natural computing: Basic concepts, algorithms, and applications","author":"LN De Castro","year":"2006","unstructured":"De Castro, L. N. (2006). Fundamentals of natural computing: Basic concepts, algorithms, and applications. CRC Press."},{"issue":"3","key":"870_CR16","doi-asserted-by":"publisher","first-page":"108","DOI":"10.17148\/IJARCCE.2015.4326","volume":"4","author":"AA Deshprabhu","year":"2015","unstructured":"Deshprabhu, A. A., & Shenvi, N. (2015). Sub-band decomposition of EEG signals and feature extraction for epilepsy classification. International Journal of Advanced Research in Computer and Communication Engineering, 4(3), 108\u2013111.","journal-title":"International Journal of Advanced Research in Computer and Communication Engineering"},{"issue":"4","key":"870_CR17","first-page":"466","volume":"7","author":"MA ElSoud","year":"2016","unstructured":"ElSoud, M. A., & Anter, A. M. (2016). Computational intelligence optimization algorithm based on meta-heuristic social-spider: Case study on CT liver tumor diagnosis. Computational Intelligence, 7(4), 466\u2013475.","journal-title":"Computational Intelligence"},{"issue":"7","key":"870_CR18","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1007\/s00521-017-3170-x","volume":"31","author":"H Ganji","year":"2019","unstructured":"Ganji, H., Khadivi, S., & Ebadzadeh, M. M. (2019). Support vector-based fuzzy classifier with adaptive kernel. Neural Computing and Applications, 31(7), 2117\u20132130.","journal-title":"Neural Computing and Applications"},{"key":"870_CR19","unstructured":"Garde, P., Thakare, A., Biradar, A., & Pawar, N. (2018). Comparative Study of intelligent classifiers for EEG data. International Journal of Scientific Research Engineering & Technology (IJSRET), UGC Approved Journal, 7(9)."},{"issue":"7","key":"870_CR20","doi-asserted-by":"publisher","first-page":"2929","DOI":"10.1007\/s00521-020-04744-7","volume":"32","author":"E Hancer","year":"2020","unstructured":"Hancer, E. (2020). New filter approaches for feature selection using differential evolution and fuzzy rough set theory. Neural Computing and Applications, 32(7), 2929\u20132944.","journal-title":"Neural Computing and Applications"},{"key":"870_CR21","doi-asserted-by":"crossref","unstructured":"Harender, B., & Sharma, R. K. (2017, May). DWT based epileptic seizure detection from EEG signal using k-NN classifier. In 2017 international conference on trends in electronics and informatics (ICEI)\u00a0(pp. 762\u2013765). IEEE.","DOI":"10.1109\/ICOEI.2017.8300806"},{"issue":"1","key":"870_CR22","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s00521-016-2426-1","volume":"29","author":"V Ho-Huu","year":"2018","unstructured":"Ho-Huu, V., Nguyen-Thoi, T., Truong-Khac, T., Le-Anh, L., & Vo-Duy, T. (2018). An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints. Neural Computing and Applications, 29(1), 167\u2013185.","journal-title":"Neural Computing and Applications"},{"issue":"11","key":"870_CR23","doi-asserted-by":"publisher","first-page":"6601","DOI":"10.1007\/s00521-018-3735-3","volume":"32","author":"Z Jin","year":"2020","unstructured":"Jin, Z., Zhou, G., Gao, D., & Zhang, Y. (2020). EEG classification using sparse Bayesian extreme learning machine for brain\u2013computer interface. Neural Computing and Applications, 32(11), 6601\u20136609.","journal-title":"Neural Computing and Applications"},{"key":"870_CR24","doi-asserted-by":"crossref","unstructured":"Kalbhor, S. D., & Harpale, V. K. (2016, August). The review of detection and classification of epilectic seizures using wavelet transform. In 2016 international conference on computing communication control and automation (ICCUBEA)\u00a0(pp. 1\u20135). IEEE.","DOI":"10.1109\/ICCUBEA.2016.7860124"},{"issue":"11","key":"870_CR25","doi-asserted-by":"publisher","first-page":"6443","DOI":"10.1007\/s00521-018-3853-y","volume":"32","author":"J Li","year":"2020","unstructured":"Li, J., Zhang, S., Zhang, L., Lei, C., & Zhang, J. (2020). Unsupervised nonlinear feature selection algorithm via kernel function. Neural Computing and Applications, 32(11), 6443\u20136454.","journal-title":"Neural Computing and Applications"},{"issue":"5","key":"870_CR26","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/s00170-014-5735-5","volume":"72","author":"N Nedic","year":"2014","unstructured":"Nedic, N., Prsic, D., Dubonjic, L., Stojanovic, V., & Djordjevic, V. (2014). Optimal cascade hydraulic control for a parallel robot platform by PSO. The International Journal of Advanced Manufacturing Technology, 72(5), 1085\u20131098.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"870_CR27","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1007\/s11071-015-2252-5","volume":"82","author":"N Nedic","year":"2015","unstructured":"Nedic, N., Stojanovic, V., & Djordjevic, V. (2015). Optimal control of hydraulically driven parallel robot platform based on firefly algorithm. Nonlinear Dynamics, 82(3), 1457\u20131473.","journal-title":"Nonlinear Dynamics"},{"key":"870_CR28","doi-asserted-by":"crossref","unstructured":"Parsopoulos, K. E. (2009, July). Cooperative micro-differential evolution for high-dimensional problems. In Proceedings of the 11th annual conference on Genetic and evolutionary computation\u00a0(pp. 531\u2013538).","DOI":"10.1145\/1569901.1569975"},{"key":"870_CR29","volume-title":"Differential evolution: a practical approach to global optimization","author":"K Price","year":"2006","unstructured":"Price, K., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: a practical approach to global optimization. Berlin: Springer."},{"issue":"4","key":"870_CR30","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/j.bbe.2018.08.002","volume":"38","author":"A Quintero-Rinc\u00f3n","year":"2018","unstructured":"Quintero-Rinc\u00f3n, A., Pereyra, M., d\u2019Giano, C., Risk, M., & Batatia, H. (2018). Fast statistical model-based classification of epileptic EEG signals. Biocybernetics and Biomedical Engineering, 38(4), 877\u2013889.","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"870_CR31","first-page":"167","volume":"10","author":"K Rajesh","year":"2015","unstructured":"Rajesh, K., Sabarinathan, V., Sarath Kumar, V., & Sugumaran, V. (2015). Eye state prediction using EEG signal and C4. 5 decision tree algorithm. International Journal of Applied Engineering Research, 10, 167\u2013171.","journal-title":"International Journal of Applied Engineering Research"},{"issue":"5","key":"870_CR32","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1007\/s00521-018-3581-3","volume":"32","author":"EMA R\u00edos","year":"2020","unstructured":"R\u00edos, E. M. A., S\u00e1nchez, A. S., Lasheras, F. S., & Crespo, M. D. M. S. (2020). Genetic algorithm based on support vector machines for computer vision syndrome classification in health personnel. Neural Computing and Applications, 32(5), 1239\u20131248.","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"870_CR33","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MAP.2011.5773566","volume":"53","author":"P Rocca","year":"2011","unstructured":"Rocca, P., Oliveri, G., & Massa, A. (2011). Differential evolution as applied to electromagnetics. IEEE Antennas and Propagation Magazine, 53(1), 38\u201349.","journal-title":"IEEE Antennas and Propagation Magazine"},{"key":"870_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/6180441","volume":"2022","author":"Y Ru","year":"2022","unstructured":"Ru, Y., Li, J., Chen, H., & Li, J. (2022). Epilepsy detection based on variational mode decomposition and improved sample entropy. Computational Intelligence and Neuroscience, 2022, 1\u201311.","journal-title":"Computational Intelligence and Neuroscience"},{"key":"870_CR35","first-page":"614","volume":"76","author":"E Sathish","year":"2017","unstructured":"Sathish, E., Sivakumaran, N., Simon, S. P., & Raghavan, S. (2017). Genetic algorithm based feature selection for classification of focal and non-focal intracranial electroencephalographic signals. Journal of Scientific & Industrial Research, 76, 614\u2013619.","journal-title":"Journal of Scientific & Industrial Research"},{"key":"870_CR36","doi-asserted-by":"crossref","unstructured":"Shen, S., Gong, W., & Cai, Z. (2016, July). Classification guided differential evolution. In 2016 IEEE congress on evolutionary computation (CEC)\u00a0(pp. 3276\u20133283). IEEE.","DOI":"10.1109\/CEC.2016.7744204"},{"issue":"1","key":"870_CR37","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s00521-017-3003-y","volume":"31","author":"A Subasi","year":"2019","unstructured":"Subasi, A., Kevric, J., & Canbaz, M. A. (2019). Epileptic seizure detection using hybrid machine learning methods. Neural Computing and Applications, 31(1), 317\u2013325.","journal-title":"Neural Computing and Applications"},{"issue":"9","key":"870_CR38","doi-asserted-by":"publisher","first-page":"4139","DOI":"10.1007\/s00521-020-04759-0","volume":"32","author":"W Sufang","year":"2020","unstructured":"Sufang, W. (2020). An adaptive ensemble classification framework for real-time data streams by distributed control systems. Neural Computing & Applications, 32(9), 4139\u20134149.","journal-title":"Neural Computing & Applications"},{"issue":"2","key":"870_CR39","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11045-020-00754-9","volume":"32","author":"H Tao","year":"2021","unstructured":"Tao, H., Li, X., Paszke, W., Stojanovic, V., & Yang, H. (2021). Robust PD-type iterative learning control for discrete systems with multiple time-delays subjected to polytopic uncertainty and restricted frequency-domain. Multidimensional Systems and Signal Processing., 32(2), 671\u2013692.","journal-title":"Multidimensional Systems and Signal Processing."},{"key":"870_CR40","doi-asserted-by":"crossref","unstructured":"Thakare, A., & Gore, S. (2019, September). A methodology for classification of seizure disorder using EEG Signals. In 2019 5th international conference on computing, communication, control and automation (ICCUBEA)\u00a0(pp. 1\u20137). IEEE.","DOI":"10.1109\/ICCUBEA47591.2019.9129298"},{"issue":"8","key":"870_CR41","first-page":"177","volume":"10","author":"A Thakare","year":"2017","unstructured":"Thakare, A., & Kharche, D. (2017). Data clustering using hybrid swarm intelligence method with multi-objective functions. International Journal of Control Theory and Applications, 10(8), 177\u2013183.","journal-title":"International Journal of Control Theory and Applications"},{"issue":"2","key":"870_CR42","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s12553-018-0265-z","volume":"9","author":"KD Tzimourta","year":"2019","unstructured":"Tzimourta, K. D., Tzallas, A. T., Giannakeas, N., Astrakas, L. G., Tsalikakis, D. G., Angelidis, P., & Tsipouras, M. G. (2019). A robust methodology for classification of epileptic seizures in EEG signals. Health and Technology, 9(2), 135\u2013142.","journal-title":"Health and Technology"},{"issue":"11","key":"870_CR43","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1007\/s00500-010-0642-7","volume":"15","author":"H Wang","year":"2011","unstructured":"Wang, H., Wu, Z., & Rahnamayan, S. (2011). Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft Computing, 15(11), 2127\u20132140.","journal-title":"Soft Computing"},{"key":"870_CR44","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3389\/fnhum.2019.00052","volume":"13","author":"X Wang","year":"2019","unstructured":"Wang, X., Gong, G., Li, N., & Qiu, S. (2019). Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization. Frontiers in Human Neuroscience, 13, 52.","journal-title":"Frontiers in Human Neuroscience"},{"key":"870_CR45","doi-asserted-by":"crossref","unstructured":"Xue, B., Fu, W., & Zhang, M. (2014, July). Differential evolution (DE) for multi-objective feature selection in classification. In Proceedings of the companion publication of the 2014 annual conference on genetic and evolutionary computation\u00a0(pp. 83\u201384).","DOI":"10.1145\/2598394.2598493"},{"issue":"1","key":"870_CR46","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s00521-016-2342-4","volume":"28","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Liu, B., Cai, J., & Zhang, S. (2017). Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution. Neural Computing and Applications, 28(1), 259\u2013267.","journal-title":"Neural Computing and Applications"},{"issue":"11","key":"870_CR47","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1007\/s00500-010-0645-4","volume":"15","author":"SZ Zhao","year":"2011","unstructured":"Zhao, S. Z., Suganthan, P. N., & Das, S. (2011). Self-adaptive differential evolution with multi-trajectory search for large-scale optimization. Soft Computing, 15(11), 2175\u20132185.","journal-title":"Soft Computing"}],"container-title":["Multidimensional Systems and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-023-00870-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11045-023-00870-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-023-00870-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T03:24:48Z","timestamp":1728876288000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11045-023-00870-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,13]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["870"],"URL":"https:\/\/doi.org\/10.1007\/s11045-023-00870-2","relation":{},"ISSN":["0923-6082","1573-0824"],"issn-type":[{"value":"0923-6082","type":"print"},{"value":"1573-0824","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,13]]},"assertion":[{"value":"13 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest in this research. This work is not funded by any agency.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}