{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T13:18:23Z","timestamp":1786281503114,"version":"3.56.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"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":["New Gener. Comput."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00354-022-00190-2","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T17:03:53Z","timestamp":1662656633000},"page":"1241-1279","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Design and Development of Modified Ensemble Learning with Weighted RBM Features for Enhanced Multi-disease Prediction Model"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1340-3258","authenticated-orcid":false,"given":"A. S.","family":"Prakaash","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Sivakumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Surendiran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Jagatheswari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Kalaiarasi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"190_CR1","doi-asserted-by":"publisher","first-page":"209278","DOI":"10.1109\/ACCESS.2020.3037710","volume":"8","author":"V Gupta","year":"2020","unstructured":"Gupta, V., Sachdeva, S., Bhalla, S.: A novel deep similarity learning approach to electronic health records data. IEEE Access 8, 209278\u2013209295 (2020)","journal-title":"IEEE Access"},{"issue":"5","key":"190_CR2","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/TBME.2017.2731158","volume":"65","author":"Z Huang","year":"2018","unstructured":"Huang, Z., Dong, W., Duan, H., Liu, J.: A regularized deep learning approach for clinical risk prediction of acute coronary syndrome using electronic health records. IEEE Trans. Biomed. Eng. 65(5), 956\u2013968 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"3","key":"190_CR3","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1109\/TETC.2020.2975251","volume":"9","author":"T Gangavarapu","year":"2021","unstructured":"Gangavarapu, T., Krishnan, G.S., Jeganathan, S.K.S.J.: FarSight: long-term disease prediction using unstructured clinical nursing notes. IEEE Trans. Emerg. Top. Comput. 9(3), 1151\u20131169 (2021)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"190_CR4","doi-asserted-by":"publisher","first-page":"123672","DOI":"10.1109\/ACCESS.2021.3110336","volume":"9","author":"K Davagdorj","year":"2021","unstructured":"Davagdorj, K., Bae, J.-W., Pham, V.-H., Theera-Umpon, N., Ryu, K.H.: Explainable artificial intelligence based framework for non-communicable diseases prediction. IEEE Access 9, 123672\u2013123688 (2021)","journal-title":"IEEE Access"},{"key":"190_CR5","doi-asserted-by":"publisher","first-page":"65947","DOI":"10.1109\/ACCESS.2020.2985646","volume":"8","author":"SA Ali","year":"2020","unstructured":"Ali, S.A., et al.: An optimally configured and improved deep belief network (OCI-DBN) approach for heart disease prediction based on Ruzzo-Tompa and stacked genetic algorithm. IEEE Access 8, 65947\u201365958 (2020)","journal-title":"IEEE Access"},{"key":"190_CR6","doi-asserted-by":"publisher","first-page":"131094","DOI":"10.1109\/ACCESS.2019.2940644","volume":"7","author":"W Hong","year":"2019","unstructured":"Hong, W., Xiong, Z., Zheng, N., Weng, Y.: A medical-history-based potential disease prediction algorithm. IEEE Access 7, 131094\u2013131101 (2019)","journal-title":"IEEE Access"},{"key":"190_CR7","doi-asserted-by":"publisher","first-page":"159790","DOI":"10.1109\/ACCESS.2020.3020579","volume":"8","author":"K Wang","year":"2020","unstructured":"Wang, K., Zhang, X., Huang, S., Chen, F., Zhang, X., Huangfu, L.: Learning to recognize thoracic disease in chest X-rays with knowledge-guided deep zoom neural networks. IEEE Access 8, 159790\u2013159805 (2020)","journal-title":"IEEE Access"},{"key":"190_CR8","doi-asserted-by":"publisher","first-page":"36955","DOI":"10.1109\/ACCESS.2021.3063129","volume":"9","author":"SB Shuvo","year":"2021","unstructured":"Shuvo, S.B., Ali, S.N., Swapnil, S.I., Al-Rakhami, M.S., Gumaei, A.: CardioXNet: a novel lightweight deep learning framework for cardiovascular disease classification using heart sound recordings. IEEE Access 9, 36955\u201336967 (2021)","journal-title":"IEEE Access"},{"issue":"2","key":"190_CR9","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TNNLS.2020.2979486","volume":"32","author":"Q Song","year":"2021","unstructured":"Song, Q., Zheng, Y.-J., Sheng, W.-G., Yang, J.: Tridirectional transfer learning for predicting gastric cancer morbidity. IEEE Trans. Neural Netw. Learn. Syst. 32(2), 561\u2013574 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"190_CR10","doi-asserted-by":"publisher","first-page":"395","DOI":"10.5373\/JARDCS\/V11SP11\/20193047","volume":"11","author":"AS Prakaash","year":"2019","unstructured":"Prakaash, A.S., Sivakumar, K.: Data analytics and predictive modelling in the application of big data: a systematic review. J. Adv. Res. Dyn. Control Syst. 11, 395\u2013399 (2019)","journal-title":"J. Adv. Res. Dyn. Control Syst."},{"key":"190_CR11","doi-asserted-by":"publisher","first-page":"82493","DOI":"10.1109\/ACCESS.2020.2991750","volume":"8","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Li, L., Yan, J., Yao, Y.: Approaching high-accuracy side effect prediction of traditional Chinese medicine compound prescription using network embedding and deep learning. IEEE Access 8, 82493\u201382499 (2020)","journal-title":"IEEE Access"},{"key":"190_CR12","doi-asserted-by":"crossref","unstructured":"Shanmuga Priya, S., Saran Raj, S., Surendiran, B., Arulmurugaselvi, N.: Brain tumour detection in MRI using deep learning. In: Advances in Intelligent Systems and Computing, pp. 395\u2013403 (2021)","DOI":"10.1007\/978-981-15-5788-0_38"},{"issue":"9","key":"190_CR13","doi-asserted-by":"publisher","first-page":"6539","DOI":"10.1109\/TII.2021.3057683","volume":"17","author":"SJ Tang","year":"2021","unstructured":"Tang, S.J., Wang, C.J., Nie, J.T., Kumar, N., Zhang, Y., Xiong, Z.H., Barnawi, A., et al.: EDL-COVID: ensemble deep learning for COVID-19 case detection from chest X-ray images. IEEE Trans. Ind. Inf. 17(9), 6539\u20136549 (2021)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"190_CR14","first-page":"626","volume":"12","author":"AS Prakaash","year":"2018","unstructured":"Prakaash, A.S., Sivakumar, K.: A precipitation prediction model exploitation artificial neural network. J. Adv. Res. Dyn. Control Syst. 12, 626\u2013633 (2018)","journal-title":"J. Adv. Res. Dyn. Control Syst."},{"key":"190_CR15","doi-asserted-by":"publisher","first-page":"24032","DOI":"10.1109\/ACCESS.2017.2766758","volume":"5","author":"G Li","year":"2017","unstructured":"Li, G., Luo, J., Xiao, Q., Liang, C., Ding, P., Cao, B.: Predicting microRNA-disease associations using network topological similarity based on DeepWalk. IEEE Access 5, 24032\u201324039 (2017)","journal-title":"IEEE Access"},{"key":"190_CR16","doi-asserted-by":"publisher","first-page":"135210","DOI":"10.1109\/ACCESS.2021.3116974","volume":"9","author":"T Amarbayasgalan","year":"2021","unstructured":"Amarbayasgalan, T., Pham, V.-H., Theera-Umpon, N., Piao, Y., Ryu, K.H.: An efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. IEEE Access 9, 135210\u2013135223 (2021)","journal-title":"IEEE Access"},{"key":"190_CR17","doi-asserted-by":"publisher","first-page":"58006","DOI":"10.1109\/ACCESS.2020.2981337","volume":"8","author":"RJS Raj","year":"2020","unstructured":"Raj, R.J.S., Shobana, S.J., Pustokhina, I.V., Pustokhin, D.A., Gupta, D., Shankar, K.: Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8, 58006\u201358017 (2020)","journal-title":"IEEE Access"},{"key":"190_CR18","doi-asserted-by":"publisher","first-page":"86984","DOI":"10.1109\/ACCESS.2020.2992063","volume":"8","author":"H Sadr","year":"2020","unstructured":"Sadr, H., Pedram, M.M., Teshnehlab, M.: Multi-View deep network: a deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE Access 8, 86984\u201386997 (2020)","journal-title":"IEEE Access"},{"issue":"02","key":"190_CR19","doi-asserted-by":"publisher","first-page":"2050074","DOI":"10.1142\/S0219691320500745","volume":"19","author":"AS Prakaash","year":"2021","unstructured":"Prakaash, A.S., Sivakumar, K.: Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: scope towards diverse applications. Int. J. Wavel. Multiresolut. Inf. Process. 19(02), 2050074 (2021)","journal-title":"Int. J. Wavel. Multiresolut. Inf. Process."},{"issue":"8","key":"190_CR20","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1109\/JBHI.2019.2962366","volume":"24","author":"T Wang","year":"2020","unstructured":"Wang, T., Tian, Y., Qiu, R.G.: Long short-term memory recurrent neural networks for multiple diseases risk prediction by leveraging longitudinal medical records. IEEE J. Biomed. Health Inf. 24(8), 2337\u20132346 (2020)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"190_CR21","doi-asserted-by":"crossref","unstructured":"Dubey, A.K.: Optimized hybrid learning for multi disease prediction enabled by lion with butterfly optimization algorithm. S\u0101dhan\u0101 46(63), (2021)","DOI":"10.1007\/s12046-021-01574-8"},{"key":"190_CR22","doi-asserted-by":"publisher","first-page":"3715","DOI":"10.1007\/s12652-019-01652-0","volume":"12","author":"K Harimoorthy","year":"2021","unstructured":"Harimoorthy, K., Thangavelu, M.: Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system. J. Ambient Intell. Humaniz. Comput. 12, 3715\u20133723 (2021)","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"190_CR23","doi-asserted-by":"publisher","first-page":"114905","DOI":"10.1016\/j.eswa.2021.114905","volume":"177","author":"L Men","year":"2021","unstructured":"Men, L., Ilk, N., Tang, X., Liu, Y.: Multi-disease prediction using LSTM recurrent neural networks. Expert Syst. Appl. 177, 114905 (2021)","journal-title":"Expert Syst. Appl."},{"key":"190_CR24","doi-asserted-by":"publisher","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","volume":"7","author":"S Mohan","year":"2019","unstructured":"Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7, 81542\u201381554 (2019)","journal-title":"IEEE Access"},{"key":"190_CR25","doi-asserted-by":"publisher","first-page":"133034","DOI":"10.1109\/ACCESS.2020.3010511","volume":"8","author":"NL Fitriyani","year":"2020","unstructured":"Fitriyani, N.L., Syafrudin, M., Alfian, G., Rhee, J.: HDPM: an effective heart disease prediction model for a clinical decision support system. IEEE Access 8, 133034\u2013133050 (2020)","journal-title":"IEEE Access"},{"key":"190_CR26","doi-asserted-by":"publisher","first-page":"144777","DOI":"10.1109\/ACCESS.2019.2945129","volume":"7","author":"NL Fitriyani","year":"2019","unstructured":"Fitriyani, N.L., Syafrudin, M., Alfian, G., Rhee, J.: Development of disease prediction model based on ensemble learning approach for diabetes and hypertension. IEEE Access 7, 144777\u2013144789 (2019)","journal-title":"IEEE Access"},{"key":"190_CR27","doi-asserted-by":"publisher","first-page":"119252","DOI":"10.1109\/ACCESS.2020.3005614","volume":"8","author":"IM El-Hasnony","year":"2020","unstructured":"El-Hasnony, I.M., Barakat, S.I., Mostafa, R.R.: Optimized ANFIS model using hybrid metaheuristic algorithms for Parkinson\u2019s disease prediction in IoT environment. IEEE Access 8, 119252\u2013119270 (2020)","journal-title":"IEEE Access"},{"issue":"10","key":"190_CR28","first-page":"2020","volume":"8","author":"BA Tama","year":"1814","unstructured":"Tama, B.A., Lim, S.: A comparative performance evaluation of classification algorithms for clinical decision support systems. Mathematics 8(10), 2020 (1814)","journal-title":"Mathematics"},{"key":"190_CR29","doi-asserted-by":"crossref","unstructured":"Tama, B.A., Im, S. and Lee, S.: Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. BioMed Res. Int. (2020)","DOI":"10.1155\/2020\/9816142"},{"key":"190_CR30","doi-asserted-by":"crossref","unstructured":"Ramesh, D., Jose, D., Keerthana, R., Krishnaveni, V.: Detection of pulmonary nodules using thresholding and fractal analysis. In: Computational Vision and Bio Inspired Computing, pp.937\u2013946 (2018)","DOI":"10.1007\/978-3-319-71767-8_80"},{"key":"190_CR31","unstructured":"Tabjula, J., Kalyani, S., Rajagopal, P., Srinivasan, B.: Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves. Struct. Health Monit. (2021)"},{"key":"190_CR32","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1007\/s12652-020-02030-x","volume":"12","author":"B Illuri","year":"2021","unstructured":"Illuri, B., Jose, D.: Design and implementation of hybrid integration of cognitive learning and chaotic countermeasures for side channel attacks. J. Ambient Intell. Humaniz. Comput. 12, 5427\u20135441 (2021)","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"issue":"8","key":"190_CR33","first-page":"3921","volume":"17","author":"D Jose","year":"2014","unstructured":"Jose, D., Kumar, P.N., Shirley, J.A., Ghayathrrie, S.: Implementation of genetic algorithm framework for fault-tolerant system on chip. Int. Inf. Inst. (Tokyo) 17(8), 3921\u20133945 (2014). (Information; Koganei)","journal-title":"Int. Inf. Inst. (Tokyo)"},{"key":"190_CR34","first-page":"353","volume":"1172","author":"D Menaga","year":"2020","unstructured":"Menaga, D., Revathi, S.: Probabilistic principal component Analysis (PPCA) based dimensionality reduction and deep learning for cancer classification. Intell. Comput. Appl. 1172, 353\u2013368 (2020)","journal-title":"Intell. Comput. Appl."},{"key":"190_CR35","doi-asserted-by":"crossref","unstructured":"Nawar, N., El-Gayar, O., Ambati, L.S., Bojja, G.R.: Social media for exploring adverse drug events associated with multiple sclerosis. Hawaii International Conference on System Sciences (HICSS), In: Proceedings of the 55th Hawaii International Conference on System Sciences, pp. 4217\u20134226 (2022)","DOI":"10.24251\/HICSS.2022.515"},{"key":"190_CR36","unstructured":"Bojja, G.R., Ofori, M., Liu, J., Ambati, L.S.: Early public outlook on the coronavirus disease (COVID-19): a social media study. In: Social Media Analysis on Coronavirus (COVID-19), (2020)"},{"issue":"1","key":"190_CR37","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s13534-021-00209-5","volume":"12","author":"MYB Murthy","year":"2022","unstructured":"Murthy, M.Y.B., Koteswararao, A., Babu, M.S.: Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis. Biomed. Eng. Lett 12, 37\u201358 (2022)","journal-title":"Biomed. Eng. Lett"},{"key":"190_CR38","unstructured":"Ambati, L.S., Narukonda, K., Bojja, G.R., Bishop, D.: Factors influencing the adoption of artificial intelligence in organizations-from an employee's perspective. In: Adoption of AI in organization from employee perspective (2020)"},{"key":"190_CR39","doi-asserted-by":"publisher","first-page":"889","DOI":"10.4028\/www.scientific.net\/KEM.594-595.889","volume":"594-595","author":"MN Noor","year":"2013","unstructured":"Noor, M.N., Yahaya, A.S., Ramli, N.A., Al Bakri, A.M.M.: Filling missing data using interpolation methods: study on the effect of fitting distribution. Key Eng. Mater. 594\u2013595, 889\u2013895 (2013)","journal-title":"Key Eng. Mater."},{"key":"190_CR40","first-page":"889","volume":"594\u2013595","author":"MN Noor","year":"2014","unstructured":"Noor, M.N., Yahaya, A.S., Ramli, N.A., Bakri, A.M.: Filling Missing Data Using Interpolation Methods: Study On The Effect Of Fitting Distribution. Key Eng. Mater. 594\u2013595, 889\u2013895 (2014)","journal-title":"Key Eng. Mater."},{"key":"190_CR41","doi-asserted-by":"publisher","first-page":"e2690","DOI":"10.1002\/stc.2690","volume":"28","author":"JL Tabjula","year":"2021","unstructured":"Tabjula, J.L., Kanakambaran, S., Kalyani, S., Rajagopal, P., Srinivasan, B.: Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring. Struct. Control Health Monit. 28, e2690 (2021)","journal-title":"Struct. Control Health Monit."},{"key":"190_CR42","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s12065-020-00505-6","volume":"15","author":"T Vaisakh","year":"2020","unstructured":"Vaisakh, T., Jayabarathi, R.: Analysis on intelligent machine learning enabled with meta-heuristic algorithms for solar irradiance prediction. Evol. Intell. 15, 235\u2013254 (2020)","journal-title":"Evol. Intell."},{"key":"190_CR43","doi-asserted-by":"publisher","first-page":"5011","DOI":"10.1007\/s00521-020-05296-6","volume":"33","author":"MA Al-Betar","year":"2021","unstructured":"Al-Betar, M.A., Alyasseri, Z.A.A., Awadallah, M.A., Doush, I.A.: Coronavirus herd immunity optimizer (CHIO). Neural Comput. Appl. 33, 5011\u20135042 (2021)","journal-title":"Neural Comput. Appl."},{"key":"190_CR44","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014)","journal-title":"Adv. Eng. Softw."},{"key":"190_CR45","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s12065-020-00477-7","volume":"14","author":"AK Dubey","year":"2021","unstructured":"Dubey, A.K., Kumar, A., Agrawal, R.: An efficient ACO-PSO-based framework for data classification and preprocessing in big data. Evol. Intell. 14, 909\u2013922 (2021)","journal-title":"Evol. Intell."},{"issue":"06","key":"190_CR46","doi-asserted-by":"publisher","first-page":"1950040","DOI":"10.1142\/S1793962319500405","volume":"10","author":"I Sudha","year":"2019","unstructured":"Sudha, I., Nedunchelian, R.: A secure data protection technique for healthcare data in the cloud using homomorphic encryption and Jaya-Whale optimization algorithm. Int. J. Model. Simul. Sci. Comput. 10(06), 1950040 (2019)","journal-title":"Int. J. Model. Simul. Sci. Comput."},{"key":"190_CR47","first-page":"19","volume":"7","author":"R Venkata Rao","year":"2016","unstructured":"Venkata Rao, R.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19\u201334 (2016)","journal-title":"Int. J. Ind. Eng. Comput."}],"container-title":["New Generation Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-022-00190-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00354-022-00190-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-022-00190-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:20:53Z","timestamp":1744204853000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00354-022-00190-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,8]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["190"],"URL":"https:\/\/doi.org\/10.1007\/s00354-022-00190-2","relation":{},"ISSN":["0288-3635","1882-7055"],"issn-type":[{"value":"0288-3635","type":"print"},{"value":"1882-7055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,8]]},"assertion":[{"value":"27 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}