{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:41:36Z","timestamp":1765546896718},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Internet Things"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, the researchers have perceived the modifications or transformations motivated by the presence of big data on the definition, complexity, and future direction of the real world optimization problems. Big Data visualization is mainly based on the efficient computer system for ingesting actual data and producing graphical representation for understanding large quantity of data in a fraction of seconds. At the same time, clustering is an effective data mining tool used to analyze big data and computational intelligence (CI) techniques can be employed to solve big data classification process. In this aspect, this study develops a novel Computational Intelligence based Clustering with Classification Model for Big Data Visualization on Map Reduce Environment, named CICC-BDVMR technique. The proposed CICC-BDVMR technique intends to perform effective BDV using the clustering and data classification processes on the Map Reduce environment. For clustering process, a grasshopper optimization algorithm (GOA) with kernelized fuzzy c-means (KFCM) technique is used to cluster the big data and the GOA is mainly utilized to determine the initial cluster centers of the KFCM technique. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behaviour of grasshoppers. This algorithm has been shown to be efficient in tackling global unconstrained and constrained optimization problems. Based on the modified GOA, an effective kernel extreme learning machine model for financial stress prediction was created. Besides, big data classification process takes place using the Ridge Regression (RR) and the parameter optimization of the RR model is carried out via the Red Colobuses Monkey (RCM) algorithm. The design of GOA and RCM algorithms for parameter optimization processes for big data classification shows the novelty of the study. A wide ranging simulation analysis is carried out using benchmark big datasets and the comparative results reported the enhanced outcomes of the CICC-BDVMR technique over the recent state of art approaches. The broad comparison research illustrates the CICC-BDVMR approach\u2019s promising performance against contemporary state-of-the-art techniques. As a result, the CICC-BDVMR technique has been demonstrated to be an effective technique for visualising and classifying large amounts of data.<\/jats:p>","DOI":"10.1007\/s43926-022-00022-1","type":"journal-article","created":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T10:02:31Z","timestamp":1652090551000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Computational intelligence based sustainable computing with classification model for big data visualization on map reduce environment"],"prefix":"10.1007","volume":"2","author":[{"given":"Zheng","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1016\/j.apm.2019.10.069","volume":"80","author":"M Abd Elaziz","year":"2020","unstructured":"Abd Elaziz M, Li L, Jayasena KN, Xiong S. Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution. Appl Math Model. 2020;80:929\u201343.","journal-title":"Appl Math Model"},{"key":"22_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2021.168545","volume":"252","author":"N Shelke","year":"2022","unstructured":"Shelke N. An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication. Optik. 2022;252: 168545. https:\/\/doi.org\/10.1016\/j.ijleo.2021.168545.","journal-title":"Optik"},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.comcom.2021.10.020","volume":"181","author":"C Niu","year":"2022","unstructured":"Niu C, Wang L. Big data-driven scheduling optimization algorithm for Cyber-Physical Systems based on a cloud platform. Comput Commun. 2022;181:173\u201381.","journal-title":"Comput Commun"},{"key":"22_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.106053","volume":"88","author":"S Aslan","year":"2020","unstructured":"Aslan S, Karaboga D. A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization. Appl Soft Comput. 2020;88: 106053.","journal-title":"Appl Soft Comput"},{"key":"22_CR5","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MCI.2014.2350953","volume":"9","author":"Z-H Zhou","year":"2014","unstructured":"Zhou Z-H, Chawla NV, Jin Y, Williams GJ. Big data opportunities and challenges: discussions from data analytics perspectives. IEEE Comput Intell Mag. 2014;9:62\u201374.","journal-title":"IEEE Comput Intell Mag"},{"issue":"3","key":"22_CR6","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MC.2015.62","volume":"48","author":"VN Gudivada","year":"2015","unstructured":"Gudivada VN, Baeza-Yates R, Raghavan VV. Big data: promises and problems. Computer. 2015;48(3):20\u20133.","journal-title":"Computer"},{"key":"22_CR7","first-page":"1","volume":"7","author":"C Snijders","year":"2012","unstructured":"Snijders C, Matzat U, Reips UD. \u201dBig Data\u201d: big gaps of knowledge in the field of internet science. Int J Internet Sci. 2012;7:1\u20135.","journal-title":"Int J Internet Sci"},{"issue":"1","key":"22_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s40537-015-0030-3","volume":"2","author":"C-W Tsai","year":"2015","unstructured":"Tsai C-W, Lai C-F, Chao H-C, Vasilakos AV. Big data analytics: a survey. J Big Data. 2015;2(1):21.","journal-title":"J Big Data"},{"key":"22_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/HICSS.2013.645","author":"S Kaisler","year":"2013","unstructured":"Kaisler S, Armour F, Espinosa JA, Money W. Big data: issues and challenges moving forward. Int Conf Syst Sci. 2013. https:\/\/doi.org\/10.1109\/HICSS.2013.645.","journal-title":"Int Conf Syst Sci"},{"issue":"20","key":"22_CR10","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.3390\/math9202627","volume":"9","author":"F Abukhodair","year":"2021","unstructured":"Abukhodair F, Alsaggaf W, Jamal AT, Abdel-Khalek S, Mansour RF. An intelligent metaheuristic binary pigeon optimization-based feature selection and big data classification in a mapreduce environment. Mathematics. 2021;9(20):2627.","journal-title":"Mathematics"},{"issue":"3","key":"22_CR11","doi-asserted-by":"publisher","first-page":"42","DOI":"10.4018\/ijwsr.2021070103","volume":"18","author":"AV Brahmane","year":"2021","unstructured":"Brahmane AV, Krishna CB. Rider chaotic biography optimization-driven deep stacked auto-encoder for big data classification using spark architecture: rider chaotic biography optimization. Int J Web Serv Res. 2021;18(3):42\u201362.","journal-title":"Int J Web Serv Res"},{"issue":"1","key":"22_CR12","doi-asserted-by":"publisher","first-page":"75","DOI":"10.26599\/BDMA.2018.9020007","volume":"1","author":"X Qin","year":"2018","unstructured":"Qin X, Luo Y, Tang N, Li G. Deepeye: an automatic big data visualization framework. Big Data Min Anal. 2018;1(1):75\u201382.","journal-title":"Big Data Min Anal"},{"issue":"1","key":"22_CR13","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/TII.2018.2842234","volume":"15","author":"A Galletta","year":"2018","unstructured":"Galletta A, Carnevale L, Bramanti A, Fazio M. An innovative methodology for big data visualization for telemedicine. IEEE Trans Industr Inf. 2018;15(1):490\u20137.","journal-title":"IEEE Trans Industr Inf"},{"key":"22_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107544","volume":"96","author":"Y Cui","year":"2021","unstructured":"Cui Y, Song X, Hu Q, Li Y, Shanthini A, Vadivel T. Big data visualization using multimodal feedback in education. Comput Electr Eng. 2021;96: 107544.","journal-title":"Comput Electr Eng"},{"issue":"3","key":"22_CR15","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.32604\/iasc.2022.022209","volume":"32","author":"BM Hardas","year":"2022","unstructured":"Hardas BM, Ch T, et al. An automated word embedding with parameter tuned model for web crawling. Intell Autom Soft Comput. 2022;32(3):1617\u201332.","journal-title":"Intell Autom Soft Comput"},{"issue":"2","key":"22_CR16","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s12065-020-00477-7","volume":"14","author":"AK Dubey","year":"2021","unstructured":"Dubey AK, Kumar A, Agrawal R. An efficient ACO-PSO-based framework for data classification and preprocessing in big data. Evol Intel. 2021;14(2):909\u201322.","journal-title":"Evol Intel"},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.procs.2021.02.045","volume":"183","author":"HB Wang","year":"2021","unstructured":"Wang HB, Gao YJ. Research on C4. 5 algorithm improvement strategy based on MapReduce. Proc Comput Sci. 2021;183:160\u20135.","journal-title":"Proc Comput Sci"},{"issue":"1","key":"22_CR18","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.artmed.2004.01.012","volume":"32","author":"DQ Zhang","year":"2004","unstructured":"Zhang DQ, Chen SC. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med. 2004;32(1):37\u201350.","journal-title":"Artif Intell Med"},{"issue":"5","key":"22_CR19","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TSMCB.2011.2124455","volume":"41","author":"L Chen","year":"2011","unstructured":"Chen L, Chen CP, Lu M. A multiple-kernel fuzzy c-means algorithm for image segmentation. IEEE Transact Syst Man Cybern Part B. 2011;41(5):1263\u201374.","journal-title":"IEEE Transact Syst Man Cybern Part B"},{"issue":"4","key":"22_CR20","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s10489-017-1019-8","volume":"48","author":"SZ Mirjalili","year":"2018","unstructured":"Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I. Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell. 2018;48(4):805\u201320.","journal-title":"Appl Intell"},{"issue":"5","key":"22_CR21","first-page":"1570","volume":"48","author":"M Sulaiman","year":"2019","unstructured":"Sulaiman M, Masihullah M, Hussain Z, Ahmad S, Mashwani WK, Jan MA, Khanum RA. Implementation of improved grasshopper optimization algorithm to solve economic load dispatch problems. Hacet J Math Stat. 2019;48(5):1570\u201389.","journal-title":"Hacet J Math Stat"},{"issue":"4","key":"22_CR22","doi-asserted-by":"publisher","first-page":"205027","DOI":"10.1142\/S0219691320500277","volume":"18","author":"D Paulraj","year":"2020","unstructured":"Paulraj D. \u2018A gradient boosted decision tree-based sentiment classification of twitter data. Int J Wavelets Multiresolution Inf Process. 2020;18(4):205027. https:\/\/doi.org\/10.1142\/S0219691320500277.","journal-title":"Int J Wavelets Multiresolution Inf Process"},{"key":"22_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3138915","author":"DK Jain","year":"2022","unstructured":"Jain DK, SahTyagi SKK, Neelakandan S, Prakash M, Natrayan L. Metaheuristic optimization-based resource allocation technique for cybertwin-driven 6G on IoE environment. IEEE Transact Ind Inf. 2022. https:\/\/doi.org\/10.1109\/TII.2021.3138915.","journal-title":"IEEE Transact Ind Inf"},{"key":"22_CR24","volume-title":"Proceedings of the 15th International Conference on Machine Learning, ICML-1998","author":"S Craig","year":"1998","unstructured":"Craig S, Gammerman A, Vovk V. Ridge regression learning algorithm in dual variables. In: Proceedings of the 15th International Conference on Machine Learning, ICML-1998. Burlington: Morgan Kaufmann; 1998."},{"key":"22_CR25","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.procs.2020.05.022","volume":"172","author":"SJ Rayen","year":"2020","unstructured":"Rayen SJ, Arunajsmine J. Social media networks owing to disruptions for effective learning. Proc Comput Sci. 2020;172:145\u201351. https:\/\/doi.org\/10.1016\/j.procs.2020.05.022.","journal-title":"Proc Comput Sci"},{"issue":"1","key":"22_CR26","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.2991\/ijcis.d.210301.004","volume":"14","author":"WJ Al-Kubaisy","year":"2021","unstructured":"Al-Kubaisy WJ, Yousif M, Al-Khateeb B, Mahmood M, Le DN. The red colobuses monkey: a new nature-inspired metaheuristic optimization algorithm. Int J Comput Intell Syst. 2021;14(1):1108\u201318.","journal-title":"Int J Comput Intell Syst"}],"container-title":["Discover Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-022-00022-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43926-022-00022-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-022-00022-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T10:03:43Z","timestamp":1652090623000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43926-022-00022-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,9]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["22"],"URL":"https:\/\/doi.org\/10.1007\/s43926-022-00022-1","relation":{},"ISSN":["2730-7239"],"issn-type":[{"value":"2730-7239","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,9]]},"assertion":[{"value":"15 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2022","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 author declares no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2"}}