{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:15:35Z","timestamp":1762956935809,"version":"3.37.3"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Grid Computing"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10723-022-09609-y","type":"journal-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T08:02:52Z","timestamp":1655366572000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Randomized Convolutional Neural Network Architecture for Eyewitness Tweet Identification During Disaster"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9367-7069","authenticated-orcid":false,"given":"Abhinav","family":"Kumar","sequence":"first","affiliation":[]},{"given":"Jyoti Prakash","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Amit Kumar","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"issue":"2","key":"9609_CR1","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.prrv.2010.10.004","volume":"12","author":"N Al Khushi","year":"2011","unstructured":"Al Khushi, N., C\u00f4t\u00e9, A.: Apparent life-threatening events: assessment, risks, reality. Paediatr. Respir. Rev. 12(2), 124\u2013132 (2011)","journal-title":"Paediatr. Respir. Rev."},{"key":"9609_CR2","doi-asserted-by":"publisher","unstructured":"Bianchi, F.M., Scardapane, S., L\u00f8kse, S, Jenssen, R.: Reservoir computing approaches for representation and classification of multivariate time series. IEEE Transactions on Neural Networks and Learning Systems, 1\u201311. https:\/\/doi.org\/10.1109\/TNNLS.2020.3001377 (2020)","DOI":"10.1109\/TNNLS.2020.3001377"},{"key":"9609_CR3","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neunet.2016.06.001","volume":"81","author":"J Cao","year":"2016","unstructured":"Cao, J., Zhang, K., Luo, M., Yin, C., Lai, X.: Extreme learning machine and adaptive sparse representation for image classification. Neural Netw. 81, 91\u2013102 (2016)","journal-title":"Neural Netw."},{"key":"9609_CR4","unstructured":"Chang, H., Futagami, K.: Convolutional reservoir computing for world models. arXiv:1907.08040 (2019)"},{"key":"9609_CR5","doi-asserted-by":"crossref","unstructured":"Darabian, H., Homayounoot, S., Dehghantanha, A., Hashemi, S., Karimipour, H., Parizi, R.M., Choo, K.K.R.: Detecting cryptomining malware: a deep learning approach for static and dynamic analysis. J. Grid. Comput., 1\u201311 (2020)","DOI":"10.1007\/s10723-020-09510-6"},{"key":"9609_CR6","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"key":"9609_CR7","doi-asserted-by":"crossref","unstructured":"Doggett, E., Cantarero, A.: Identifying eyewitness news-worthy events on twitter. In: Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media, 7\u201313 (2016)","DOI":"10.18653\/v1\/W16-6202"},{"key":"9609_CR8","doi-asserted-by":"crossref","unstructured":"Fang, R., Nourbakhsh, A., Liu, X., Shah, S., Li, Q.: Witness identification in twitter. In: Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media, 65\u201373 (2016)","DOI":"10.18653\/v1\/W16-6210"},{"key":"9609_CR9","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1613\/jair.4992","volume":"57","author":"Y Goldberg","year":"2016","unstructured":"Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345\u2013420 (2016)","journal-title":"J. Artif. Intell. Res."},{"issue":"2","key":"9609_CR10","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s10723-018-9462-2","volume":"17","author":"A Gupta","year":"2019","unstructured":"Gupta, A., Sahu, H., Nanecha, N., Kumar, P., Roy, P.P., Chang, V.: Enhancing text using emotion detected from eeg signals. J. Grid. Comput. 17(2), 325\u2013340 (2019)","journal-title":"J. Grid. Comput."},{"issue":"1","key":"9609_CR11","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10479-017-2600-6","volume":"277","author":"CF Huang","year":"2019","unstructured":"Huang, C.F.: Evaluation of system reliability for a stochastic delivery-flow distribution network with inventory. Ann. Oper. Res. 277(1), 33\u201345 (2019)","journal-title":"Ann. Oper. Res."},{"issue":"2","key":"9609_CR12","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2011","unstructured":"Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics) 42(2), 513\u2013529 (2011)","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics)"},{"issue":"1-3","key":"9609_CR13","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1-3), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"issue":"4","key":"9609_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2771588","volume":"47","author":"M Imran","year":"2015","unstructured":"Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey. ACM Computing Surveys (CSUR) 47(4), 1\u201338 (2015)","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"9609_CR15","doi-asserted-by":"publisher","unstructured":"Imran, M., Ofli, F., Caragea, D., Torralba, A.: Using ai and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions Information Processing & Management 57(5). https:\/\/doi.org\/10.1016\/j.ipm.2020.102261 (2020)","DOI":"10.1016\/j.ipm.2020.102261"},{"issue":"34","key":"9609_CR16","first-page":"13","volume":"148","author":"H Jaeger","year":"2001","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany:, German National Research Center for Information Technology GMD Technical Report 148(34), 13 (2001)","journal-title":"Bonn, Germany:, German National Research Center for Information Technology GMD Technical Report"},{"key":"9609_CR17","doi-asserted-by":"crossref","unstructured":"Jayawardene, I., Venayagamoorthy, G.K.: Comparison of Echo State Network and Extreme Learning Machine for Pv Power Prediction. In: 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), 1\u20138 (2014)","DOI":"10.1109\/CIASG.2014.7011546"},{"key":"9609_CR18","unstructured":"Katuwal, R., Suganthan, P.N., Tanveer, M.: Random vector functional link neural network based ensemble deep learning. arXiv:1907.00350 (2019)"},{"key":"9609_CR19","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1016\/j.asoc.2017.09.020","volume":"70","author":"R Katuwal","year":"2018","unstructured":"Katuwal, R., Suganthan, P.N., Zhang, L.: An ensemble of decision trees with random vector functional link networks for multi-class classification. Appl. Soft Comput. 70, 1146\u20131153 (2018)","journal-title":"Appl. Soft Comput."},{"key":"9609_CR20","unstructured":"Kingma, D.P., Ba, J.: Adam:, A method for stochastic optimization. arXiv:1412.6980 (2014)"},{"key":"9609_CR21","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.ijdrr.2018.10.021","volume":"33","author":"A Kumar","year":"2019","unstructured":"Kumar, A., Singh, J.P.: Location reference identification from tweets during emergencies: a deep learning approach. Int. J. Disaster Risk Reduction 33, 365\u2013375 (2019)","journal-title":"Int. J. Disaster Risk Reduction"},{"key":"9609_CR22","doi-asserted-by":"publisher","unstructured":"Kumar, A., Singh, J.P., Dwivedi, Y.K., Rana, N.P.: A deep multi-modal neural network for informative twitter content classification during emergencies. Annals of Operations Research 1\u201332. https:\/\/doi.org\/10.1007\/s10479-020-03514-x (2020)","DOI":"10.1007\/s10479-020-03514-x"},{"key":"9609_CR23","doi-asserted-by":"crossref","unstructured":"Kumar, A., Singh, J.P., Saumya, S.: A Comparative Analysis of Machine Learning Techniques for Disaster-Related Tweet Classification IEEE Region 10 Humanitarian Technology Conference, 222\u2013227 (2019)","DOI":"10.1109\/R10-HTC47129.2019.9042443"},{"issue":"5","key":"9609_CR24","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/TNNLS.2012.2188414","volume":"23","author":"D Li","year":"2012","unstructured":"Li, D., Han, M., Wang, J.: Chaotic time series prediction based on a novel robust echo state network. IEEE Transactions on Neural Networks and Learning Systems 23(5), 787\u2013799 (2012)","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"2","key":"9609_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10723-021-09564-0","volume":"19","author":"D Liu","year":"2021","unstructured":"Liu, D., Chen, L., Wang, Z., Diao, G.: Speech expression multimodal emotion recognition based on deep belief network. J. Grid. Comput. 19(2), 1\u201313 (2021)","journal-title":"J. Grid. Comput."},{"key":"9609_CR26","doi-asserted-by":"crossref","unstructured":"Loyola-Gonz\u00e1lez, O., Medina-P\u00e9rez, M.A., Choo, K.K.R.: A review of supervised classification based on contrast patterns: Applications, trends, and challenges. J. Grid. Comput., 1\u201349 (2020)","DOI":"10.1007\/s10723-020-09526-y"},{"key":"9609_CR27","volume-title":"A Practical guide to applying echo state networks, 659\u2013686","author":"M Luko\u0161evi\u010dius","year":"2012","unstructured":"Luko\u0161evi\u010dius, M.: A Practical guide to applying echo state networks, 659\u2013686. Springer, Berlin (2012)"},{"key":"9609_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2016.08.081","volume":"373","author":"Q Ma","year":"2016","unstructured":"Ma, Q., Shen, L., Chen, W., Wang, J., Wei, J., Yu, Z.: Functional echo state network for time series classification. Inform. Sci. 373, 1\u201320 (2016)","journal-title":"Inform. Sci."},{"key":"9609_CR29","doi-asserted-by":"publisher","unstructured":"Morstatter, F., Lubold, N., Pon-Barry, H., Pfeffer, J., Liu, H.: Finding eyewitness tweets during crises. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, 23-27. Association for Computational Linguistics, Baltimore, MD, USA. https:\/\/doi.org\/10.3115\/v1\/W14-2509 (2014)","DOI":"10.3115\/v1\/W14-2509"},{"issue":"1","key":"9609_CR30","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s10479-018-2918-8","volume":"283","author":"S Nilsang","year":"2019","unstructured":"Nilsang, S., Yuangyai, C., Cheng, C.Y., Janjarassuk, U.: Locating an ambulance base by using social media: a case study in bangkok. Ann. Oper. Res. 283(1), 497\u2013516 (2019)","journal-title":"Ann. Oper. Res."},{"issue":"1","key":"9609_CR31","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1002\/asi.24208","volume":"71","author":"V Pekar","year":"2020","unstructured":"Pekar, V., Binner, J., Najafi, H., Hale, C., Schmidt, V.: Early detection of heterogeneous disaster events using social media. J. Assoc. Inf. Sci. Technol. 71(1), 43\u201354 (2020)","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"9609_CR32","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.knosys.2018.01.015","volume":"145","author":"X Qiu","year":"2018","unstructured":"Qiu, X., Suganthan, P.N., Amaratunga, G.A.: Ensemble incremental learning random vector functional link network for short-term electric load forecasting. Knowl.-Based Syst. 145, 182\u2013196 (2018)","journal-title":"Knowl.-Based Syst."},{"key":"9609_CR33","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1016\/j.neucom.2015.11.009","volume":"175","author":"BY Qu","year":"2016","unstructured":"Qu, B.Y., Lang, B., Liang, J.J., Qin, A.K., Crisalle, O.D.: Two-hidden-layer extreme learning machine for regression and classification. Neurocomputing 175, 826\u2013834 (2016)","journal-title":"Neurocomputing"},{"issue":"2","key":"9609_CR34","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/s10723-019-09482-2","volume":"17","author":"Z Saeed","year":"2019","unstructured":"Saeed, Z., Abbasi, R.A., Maqbool, O., Sadaf, A., Razzak, I., Daud, A., Aljohani, N.R., Xu, G.: What\u2019s happening around the world? a survey and framework on event detection techniques on twitter. J. Grid. Comput. 17(2), 279\u2013312 (2019)","journal-title":"J. Grid. Comput."},{"issue":"1","key":"9609_CR35","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s10479-017-2522-3","volume":"283","author":"JP Singh","year":"2019","unstructured":"Singh, J.P., Dwivedi, Y.K., Rana, N.P., Kumar, A., Kapoor, K.K.: Event classification and location prediction from tweets during disasters. Ann. Oper. Res. 283(1), 737\u2013757 (2019)","journal-title":"Ann. Oper. Res."},{"key":"9609_CR36","unstructured":"Stefan, I., Rebedea, T., Caragea, D.: Classification of Eyewitness Tweets in Emergency Situations. In: RoCHI, 46\u201352 (2019)"},{"key":"9609_CR37","doi-asserted-by":"crossref","unstructured":"Subasi, A., Khateeb, K., Brahimi, T., Sarirete, A.: Human Activity Recognition Using Machine Learning Methods in a Smart Healthcare Environment. In: Innovation in Health Informatics, 123\u2013144. Elsevier (2020)","DOI":"10.1016\/B978-0-12-819043-2.00005-8"},{"key":"9609_CR38","unstructured":"Tanev, H., Zavarella, V., Steinberger, J.: Monitoring Disaster Impact: Detecting Micro-Events and Eyewitness Reports in Mainstream and Social Media. In: ISCRAM (2017)"},{"issue":"4","key":"9609_CR39","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","volume":"27","author":"J Tang","year":"2015","unstructured":"Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809\u2013821 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"9609_CR40","doi-asserted-by":"crossref","unstructured":"Tanisaro, P., Heidemann, G.: Time series classification using time warping invariant echo state networks. In: 2016 15Th IEEE International Conference on Machine Learning and Applications (ICMLA), 831\u2013836. IEEE (2016)","DOI":"10.1109\/ICMLA.2016.0149"},{"issue":"3","key":"9609_CR41","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1093\/comjnl\/bxp032","volume":"53","author":"S Timotheou","year":"2010","unstructured":"Timotheou, S.: The random neural network: a survey. Comput. J. 53(3), 251\u2013267 (2010)","journal-title":"Comput. J."},{"issue":"3","key":"9609_CR42","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.neunet.2007.04.013","volume":"20","author":"MH Tong","year":"2007","unstructured":"Tong, M.H., Bickett, A.D., Christiansen, E.M., Cottrell, G.W.: Learning grammatical structure with echo state networks. Neural Netw. 20(3), 424\u2013432 (2007)","journal-title":"Neural Netw."},{"key":"9609_CR43","doi-asserted-by":"crossref","unstructured":"Tong, Z., Tanaka, G.: Reservoir Computing with Untrained Convolutional Neural Networks for Image Recognition. In: 2018 24Th International Conference on Pattern Recognition (ICPR), 1289\u20131294. IEEE (2018)","DOI":"10.1109\/ICPR.2018.8545471"},{"issue":"4","key":"9609_CR44","doi-asserted-by":"publisher","first-page":"120","DOI":"10.3390\/ijgi6040120","volume":"6","author":"M Truelove","year":"2017","unstructured":"Truelove, M., Khoshelham, K., McLean, S., Winter, S., Vasardani, M.: Identifying witness accounts from social media using imagery. ISPRS International Journal of Geo-Information 6(4), 120 (2017)","journal-title":"ISPRS International Journal of Geo-Information"},{"issue":"3","key":"9609_CR45","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s10708-014-9556-8","volume":"80","author":"M Truelove","year":"2015","unstructured":"Truelove, M., Vasardani, M., Winter, S.: Towards credibility of micro-blogs: characterising witness accounts. GeoJournal 80(3), 339\u2013359 (2015)","journal-title":"GeoJournal"},{"issue":"1\u20132","key":"9609_CR46","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10479-017-2545-9","volume":"283","author":"SF Wamba","year":"2019","unstructured":"Wamba, S.F., Edwards, A., Akter, S.: Social media adoption and use for improved emergency services operations: the case of the nsw ses. Ann. Oper. Res. 283(1\u20132), 225\u2013245 (2019)","journal-title":"Ann. Oper. Res."},{"key":"9609_CR47","unstructured":"Yin, Y.: Deep learning with the random neural network and its applications. arXiv:1810.08653 (2018)"},{"key":"9609_CR48","doi-asserted-by":"publisher","unstructured":"Yin, Y.: Random neural network methods and deep learning. Probability in the Engineering and Informational Sciences, 1\u201331. https:\/\/doi.org\/10.1017\/S026996481800058X (2019)","DOI":"10.1017\/S026996481800058X"},{"issue":"1","key":"9609_CR49","doi-asserted-by":"publisher","first-page":"102107","DOI":"10.1016\/j.ipm.2019.102107","volume":"57","author":"K Zahra","year":"2020","unstructured":"Zahra, K., Imran, M., Ostermann, F.O.: Automatic identification of eyewitness messages on twitter during disasters. Inf. Process. Manag. 57(1), 102107 (2020)","journal-title":"Inf. Process. Manag."},{"key":"9609_CR50","unstructured":"Zahra, K., Imran, M., Ostermann, F.O., Boersma, K., Tomaszewski, B.: Understanding eyewitness reports on twitter during disasters. In: Proceedings of the of the ISCRAM (2018) (2018)"},{"key":"9609_CR51","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.ins.2015.09.025","volume":"367","author":"L Zhang","year":"2016","unstructured":"Zhang, L., Suganthan, P.N.: A comprehensive evaluation of random vector functional link networks. Inf. Sci. 367, 1094\u20131105 (2016)","journal-title":"Inf. Sci."},{"key":"9609_CR52","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ins.2016.01.039","volume":"364","author":"L Zhang","year":"2016","unstructured":"Zhang, L., Suganthan, P.N.: A survey of randomized algorithms for training neural networks. Infor. Sci. 364, 146\u2013155 (2016)","journal-title":"Infor. Sci."},{"issue":"6","key":"9609_CR53","doi-asserted-by":"publisher","first-page":"102312","DOI":"10.1016\/j.ipm.2020.102312","volume":"57","author":"P Zola","year":"2020","unstructured":"Zola, P., Ragno, C., Cortez, P.: A google trends spatial clustering approach for a worldwide twitter user geolocation. Inf. Process. Manag. 57(6), 102312 (2020). https:\/\/doi.org\/10.1016\/j.ipm.2020.102312","journal-title":"Inf. Process. Manag."}],"container-title":["Journal of Grid Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10723-022-09609-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10723-022-09609-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10723-022-09609-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,6]],"date-time":"2022-10-06T07:14:10Z","timestamp":1665040450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10723-022-09609-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":53,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["9609"],"URL":"https:\/\/doi.org\/10.1007\/s10723-022-09609-y","relation":{},"ISSN":["1570-7873","1572-9184"],"issn-type":[{"type":"print","value":"1570-7873"},{"type":"electronic","value":"1572-9184"}],"subject":[],"published":{"date-parts":[[2022,6,16]]},"assertion":[{"value":"19 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"20"}}