{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:43:14Z","timestamp":1775119394329,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University","doi-asserted-by":"publisher","award":["TURSP-2020\/79"],"award-info":[{"award-number":["TURSP-2020\/79"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>COVID - 19 affected severely worldwide. The pandemic has caused many causalities in a very short span. The IoT-cloud-based healthcare model requirement is utmost in this situation to provide a better decision in the covid-19 pandemic. In this paper, an attempt has been made to perform predictive analytics regarding the disease using a machine learning classifier. This research proposed an enhanced KNN (k NearestNeighbor) algorithm eKNN, which did not randomly choose the value of k. However, it used a mathematical function of the dataset\u2019s sample size while determining the k value. The enhanced KNN algorithm eKNN has experimented on 7 benchmark COVID-19 datasets of different size, which has been gathered from standard data cloud of different countries (Brazil, Mexico, etc.). It appeared that the enhanced KNN classifier performs significantly better than ordinary KNN. The second research question augmented the enhanced KNN algorithm with feature selection using ACO (Ant Colony Optimization). Results indicated that the enhanced KNN classifier along with the feature selection mechanism performed way better than enhanced KNN without feature selection. This paper involves proposing an improved KNN attempting to find an optimal value of k and studying IoT-cloud-based COVID - 19 detection.\n<\/jats:p>","DOI":"10.1007\/s00607-021-00951-9","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T05:02:27Z","timestamp":1619758947000},"page":"849-869","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach"],"prefix":"10.1007","volume":"105","author":[{"given":"Rajendrani","family":"Mukherjee","sequence":"first","affiliation":[]},{"given":"Aurghyadip","family":"Kundu","sequence":"additional","affiliation":[]},{"given":"Indrajit","family":"Mukherjee","sequence":"additional","affiliation":[]},{"given":"Deepak","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2851-4260","authenticated-orcid":false,"given":"Prayag","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Ashish","family":"Khanna","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Shorfuzzaman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"issue":"August","key":"951_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ymeth.2016.08.014","volume":"111","author":"L Wang","year":"2016","unstructured":"Wang L, Wang Y, Chang Q (2016) Feature selection methods for big data bioinformatics: a survey from the search perspective. Methods 111(August):21\u201331. https:\/\/doi.org\/10.1016\/j.ymeth.2016.08.014","journal-title":"Methods"},{"issue":"2","key":"951_CR2","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s10115-017-1140-3","volume":"56","author":"V Bol\u00f3n-Canedo","year":"2018","unstructured":"Bol\u00f3n-Canedo V, Rego-Fern\u00e1ndez D, Peteiro-Barral D, Alonso-Betanzos A, Guijarro-Berdi\u00f1as B, S\u00e1nchez-Maro\u00f1o N (2018) On the scalability of feature selection methods on high-dimensional data. Knowl Inf Syst 56(2):395\u2013442. https:\/\/doi.org\/10.1007\/s10115-017-1140-3","journal-title":"Knowl Inf Syst"},{"key":"951_CR3","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.cmpb.2016.08.010","volume":"136","author":"X Li","year":"2016","unstructured":"Li X, Hu B, Sun S, Cai H (2016) EEG-based mild depressive detection using feature selection methods and classifiers. Comput Methods Programs Biomed 136:151\u2013161. https:\/\/doi.org\/10.1016\/j.cmpb.2016.08.010","journal-title":"Comput Methods Programs Biomed"},{"issue":"February","key":"951_CR4","doi-asserted-by":"publisher","first-page":"103375","DOI":"10.1016\/j.compbiomed.2019.103375","volume":"112","author":"B Remeseiro","year":"2019","unstructured":"Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Biol Med 112(February):103375. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103375","journal-title":"Comput Biol Med"},{"issue":"4","key":"951_CR5","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/MNET.011.2000458","volume":"34","author":"MS Hossain","year":"2020","unstructured":"Hossain MS, Muhammad G, Guizani N (2020) Explainable AI and mass surveillance system-based healthcare framework to combat COVID-I9 like pandemics. IEEE Network 34(4):126\u2013132","journal-title":"IEEE Network"},{"issue":"2021","key":"951_CR6","doi-asserted-by":"publisher","first-page":"107700","DOI":"10.1016\/j.patcog.2020.107700","volume":"113","author":"M Shorfuzzaman","year":"2021","unstructured":"Shorfuzzaman M, Hossain MS (2021) MetaCOVID: a Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients. Pattern Recogn 113(2021):107700","journal-title":"Pattern Recogn"},{"key":"951_CR7","doi-asserted-by":"publisher","unstructured":"Somasekar J, Pavan Kumar P, Sharma A, Ramesh G (2020) Machine learning and image analysis applications in the fight against COVID-19 pandemic: Datasets, research directions, challenges and opportunities. Materials Today: Proceedings 3\u20136. https:\/\/doi.org\/10.1016\/j.matpr.2020.09.352","DOI":"10.1016\/j.matpr.2020.09.352"},{"key":"951_CR8","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.idm.2020.08.008","volume":"5","author":"LA Amar","year":"2020","unstructured":"Amar LA, Taha AA, Mohamed MY (2020) Prediction of the final size for COVID-19 epidemic using machine learning: a case study of Egypt. Infect Diseas Model 5:622\u2013634. https:\/\/doi.org\/10.1016\/j.idm.2020.08.008","journal-title":"Infect Diseas Model"},{"issue":"5","key":"951_CR9","doi-asserted-by":"publisher","first-page":"100074","DOI":"10.1016\/j.patter.2020.100074","volume":"1","author":"M Nemati","year":"2020","unstructured":"Nemati M, Ansary J, Nemati N (2020) Machine-learning approaches in covid-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns 1(5):100074. https:\/\/doi.org\/10.1016\/j.patter.2020.100074","journal-title":"Patterns"},{"key":"951_CR10","unstructured":"Ndiaye BM, Tendeng L, Seck D (2020) Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting. ArXiv"},{"key":"951_CR11","unstructured":"Yan et al. (2020) Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. MedRxiv, 2020.02.27.20028027"},{"key":"951_CR12","doi-asserted-by":"publisher","unstructured":"Souza FSH, Hojo-Souza NS, Santos EB, Silva CM, Guidoni DL (2020) Predicting the disease outcome in COVID-19 positive patients through Machine Learning: a retrospective cohort study with Brazilian data. 2, 1-20. https:\/\/doi.org\/10.1101\/2020.06.26.20140764","DOI":"10.1101\/2020.06.26.20140764"},{"issue":"10","key":"951_CR13","doi-asserted-by":"publisher","first-page":"3350","DOI":"10.3390\/jcm9103350","volume":"9","author":"L Flesia","year":"2020","unstructured":"Flesia L, Monaro M, Mazza C, Fietta V, Colicino E, Segatto B, Roma P (2020) Predicting perceived stress related to the Covid-19 outbreak through stable psychological traits and machine learning models. J Clin Med 9(10):3350. https:\/\/doi.org\/10.3390\/jcm9103350","journal-title":"J Clin Med"},{"issue":"1","key":"951_CR14","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/JSYST.2015.2470644","volume":"11","author":"MS Hossain","year":"2017","unstructured":"Hossain MS (2017) Cloud-supported cyber-physical localization framework for patients monitoring. IEEE Syst J 11(1):118\u2013127","journal-title":"IEEE Syst J"},{"issue":"4","key":"951_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0232391","volume":"15","author":"GS Randhawa","year":"2020","unstructured":"Randhawa GS, Soltysiak MPM, El Roz H, de Souza CPE, Hill KA, Kari L (2020) Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS ONE 15(4):1\u201324. https:\/\/doi.org\/10.1371\/journal.pone.0232391","journal-title":"PLoS ONE"},{"key":"951_CR16","doi-asserted-by":"publisher","unstructured":"Cosenza DN, Korhonen L, Maltamo M, Packalen P, Strunk J L, N\u00e6sset E, Gobakken T, Soares P, Tom\u00e9 M (2020) Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock. Forestry. An International Journal of Forest Research, 1-13. https:\/\/doi.org\/10.1093\/forestry\/cpaa034","DOI":"10.1093\/forestry\/cpaa034"},{"key":"951_CR17","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1007\/978-3-540-39964-3_62","volume":"2888","author":"G Guo","year":"2003","unstructured":"Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. Lecture Notes Comput Sci 2888:986\u2013996. https:\/\/doi.org\/10.1007\/978-3-540-39964-3_62","journal-title":"Lecture Notes Comput Sci"},{"key":"951_CR18","doi-asserted-by":"publisher","unstructured":"Ali N, Neagu D, Trundle P (2019) Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets. SN Appl Sci, 1(12). https:\/\/doi.org\/10.1007\/s42452-019-1356-9","DOI":"10.1007\/s42452-019-1356-9"},{"key":"951_CR19","doi-asserted-by":"publisher","unstructured":"Sowmiya C, Sumitra P (2020) A hybrid approach for mortality prediction for heart patients using ACO-HKNN. J Ambient Intell Humanized Comput 0123456789. https:\/\/doi.org\/10.1007\/s12652-020-02027-6","DOI":"10.1007\/s12652-020-02027-6"},{"key":"951_CR20","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neucom.2016.08.159","volume":"326\u2013327","author":"D Mateos-Garc\u00eda","year":"2019","unstructured":"Mateos-Garc\u00eda D, Garc\u00eda-Guti\u00e9rrez J, Riquelme-Santos JC (2019) On the evolutionary weighting of neighbours and features in the k-nearest neighbour rule. Neurocomputing 326\u2013327:54\u201360. https:\/\/doi.org\/10.1016\/j.neucom.2016.08.159","journal-title":"Neurocomputing"},{"key":"951_CR21","doi-asserted-by":"crossref","unstructured":"Tiwari P, Melucci M (2018) Towards a quantum-inspired framework for binary classification. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 1815-1818)","DOI":"10.1145\/3269206.3269304"},{"issue":"2","key":"951_CR22","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/JSAC.2020.3020654","volume":"39","author":"G Muhammad","year":"2020","unstructured":"Muhammad G, Hossain MS, Kumar N (2020) EEG-based pathology detection for home health monitoring. IEEE J Sel Areas Commun 39(2):603\u2013610. https:\/\/doi.org\/10.1109\/JSAC.2020.3020654","journal-title":"IEEE J Sel Areas Commun"},{"key":"951_CR23","doi-asserted-by":"publisher","unstructured":"Rahman MA, Hossain MS (2021) An Internet of Medical Things-Enabled Edge Computing Framework for Tackling COVID-19. IEEE Intern Things J. https:\/\/doi.org\/10.1109\/JIOT.2021.3051080","DOI":"10.1109\/JIOT.2021.3051080"},{"key":"951_CR24","doi-asserted-by":"publisher","first-page":"42354","DOI":"10.1109\/ACCESS.2019.2904624","volume":"7","author":"P Tiwari","year":"2019","unstructured":"Tiwari P, Melucci M (2019) Towards a quantum-inspired binary classifier. IEEE Access 7:42354\u201342372","journal-title":"IEEE Access"},{"issue":"6","key":"951_CR25","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1049\/iet-com.2016.0961","volume":"11","author":"MT Van","year":"2017","unstructured":"Van MT, van Tuan N, Son TT, Le-Minh H, Burton A (2017) Weighted k-nearest neighbour model for indoor VLC positioning. IET Commun 11(6):864\u2013871. https:\/\/doi.org\/10.1049\/iet-com.2016.0961","journal-title":"IET Commun"},{"key":"951_CR26","doi-asserted-by":"publisher","unstructured":"Okfalisa, Gazalba, I, Mustakim, Reza NGI (2018) Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. Proceedings - 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017, 2018-January, 294-298. https:\/\/doi.org\/10.1109\/ICITISEE.2017.8285514","DOI":"10.1109\/ICITISEE.2017.8285514"},{"key":"951_CR27","doi-asserted-by":"publisher","unstructured":"Song G, Rochas J, El Beze LE, Huet F, Magoul\u00e8s F (2016) K Nearest Neighbour Joins for Big Data on MapReduce: A Theoretical and Experimental Analysis. IEEE Transac Knowled Data Eng, 28(9), 2376-2392. https:\/\/doi.org\/10.1109\/TKDE.2016.2562627","DOI":"10.1109\/TKDE.2016.2562627"},{"key":"951_CR28","unstructured":"Uprety S, Dehdashti S, Hossain MS (2020) TermInformer: unsupervised term mining and analysis in biomedical literature. Neural Comput Appl 1\u201314"},{"key":"951_CR29","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/s41597-020-00688-8","volume":"7","author":"J Hasell","year":"2020","unstructured":"Hasell J, Mathieu E, Beltekian D et al (2020) A cross-country database of COVID-19 testing. Sci Data 7:345","journal-title":"Sci Data"},{"key":"951_CR30","first-page":"65","volume":"9","author":"K Menghour","year":"2016","unstructured":"Menghour K, Souici-Meslati L (2016) Hybrid ACO-PSO based approaches for feature selection. Int J Intell Eng Syst 9:65\u201379","journal-title":"Int J Intell Eng Syst"},{"key":"951_CR31","unstructured":"Vincent, Pascal, Bengio Y (2002) K-Local hyperplane and convex distance nearest neighbor algorithms. Adv Neural Inf Proc Syst"},{"key":"951_CR32","doi-asserted-by":"crossref","unstructured":"Sowmiya C, Sumitra P (2020) A hybrid approach for mortality prediction for heart patients using ACO-HKNN. J Ambient Intell Humanized Comput","DOI":"10.1007\/s12652-020-02027-6"},{"key":"951_CR33","doi-asserted-by":"publisher","unstructured":"Abdulsalam Y. Hossain, MS. (2020) COVID-19 Networking demand: an auction-based mechanism for automated selection of edge computing services. IEEE Transac Net Sci Eng. https:\/\/doi.org\/10.1109\/TNSE.2020.3026637","DOI":"10.1109\/TNSE.2020.3026637"},{"key":"951_CR34","doi-asserted-by":"publisher","first-page":"110059","DOI":"10.1016\/j.chaos.2020.110059","volume":"139","author":"S Lalmuanawma","year":"2020","unstructured":"Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review. Chaos, Solitons Fractals 139:110059. https:\/\/doi.org\/10.1016\/j.chaos.2020.110059","journal-title":"Chaos, Solitons Fractals"},{"key":"951_CR35","doi-asserted-by":"publisher","unstructured":"Khanday AMUD, Rabani ST, Khan QR, Rouf N, Din Mohi Ud, M. (2020) Machine learning based approaches for detecting COVID-19 using clinical text data. Int J Inf Technol (Singapore) 12(3):731\u2013739. https:\/\/doi.org\/10.1007\/s41870-020-00495-9","DOI":"10.1007\/s41870-020-00495-9"},{"key":"951_CR36","doi-asserted-by":"publisher","unstructured":"Case Study for Epileptic Seizure Detection and Monitoring (2018) Alhussein, M. et al. (2018). Cognitive IoT-Cloud Integration for Smart Healthcare. Mobile Netw Appl 23:1624\u20131635. https:\/\/doi.org\/10.1007\/s11036-018-1113-0A","DOI":"10.1007\/s11036-018-1113-0A"},{"key":"951_CR37","doi-asserted-by":"publisher","unstructured":"Hossain MS. Muhammad, G. (2020) Deep learning based pathology detection for smart connected healthcares. IEEE Netw 34(6):120\u2013125. https:\/\/doi.org\/10.1109\/MNET.011.2000064","DOI":"10.1109\/MNET.011.2000064"},{"key":"951_CR38","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.protcy.2013.12.340","volume":"10","author":"MA Jabbar","year":"2013","unstructured":"Jabbar MA, Deekshatulu BL, Chandra P (2013) Classification of heart disease using K- Nearest Neighbor and genetic algorithm. Procedia Technol 10:85\u201394. https:\/\/doi.org\/10.1016\/j.protcy.2013.12.340","journal-title":"Procedia Technol"},{"key":"951_CR39","doi-asserted-by":"publisher","unstructured":"Amin UA et al. (2019) Multilevel weighted feature fusion using convolutional neural networks for eeg motor imagery classification. IEEE Access.7, 18940-18950 https:\/\/doi.org\/10.1109\/ACCESS.2019.2895688.","DOI":"10.1109\/ACCESS.2019.2895688."},{"key":"951_CR40","doi-asserted-by":"crossref","unstructured":"Tan W, et al (2020) Multimodal medical image fusion algorithm in the era of big data. Neural Comput Appl, 1-21","DOI":"10.1007\/s00521-020-05173-2"},{"key":"951_CR41","doi-asserted-by":"publisher","unstructured":"ahmedMedjahed, S., Ait Saadi, T., & Benyettou, A. (2013) Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. Int J Comput Appl 62(1):1\u20135. https:\/\/doi.org\/10.5120\/10041-4635","DOI":"10.5120\/10041-4635"},{"key":"951_CR42","doi-asserted-by":"publisher","unstructured":"Lin H et al (2020) Privacy-enhanced data fusion for COVID-19 applications in intelligent internet of medical things. IEEE Inter Things J. https:\/\/doi.org\/10.1109\/JIOT.2020.3033129","DOI":"10.1109\/JIOT.2020.3033129"},{"key":"951_CR43","doi-asserted-by":"crossref","unstructured":"Jaiswal AK, et al. (2020) Covidpen: A novel covid-19 detection model using chest x-rays and ct scans. medRxiv","DOI":"10.1101\/2020.07.08.20149161"},{"key":"951_CR44","doi-asserted-by":"publisher","unstructured":"Shaban WM, et al (2020) A new COVID-19 patients detection strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowled Based Syst.205(1), https:\/\/doi.org\/10.1016\/j.knosys.2020.106270","DOI":"10.1016\/j.knosys.2020.106270"},{"key":"951_CR45","doi-asserted-by":"crossref","unstructured":"Chouhan V, et al (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci, 10(2), 559","DOI":"10.3390\/app10020559"},{"key":"951_CR46","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.measurement.2019.05.076","volume":"145","author":"AK Jaiswal","year":"2019","unstructured":"Jaiswal AK et al (2019) Identifying pneumonia in chest X-rays: a deep learning approach. Measurement 145:511\u2013518","journal-title":"Measurement"},{"issue":"10","key":"951_CR47","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1109\/TMM.2015.2463226","volume":"17","author":"W Min","year":"2015","unstructured":"Min W et al (2015) Cross-platform multi-modal topic modeling for personalized inter-platform recommendation. IEEE Trans Multimedia 17(10):1787\u20131801","journal-title":"IEEE Trans Multimedia"},{"key":"951_CR48","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.inffus.2021.02.013","volume":"72","author":"G Muhammad","year":"2021","unstructured":"Muhammad G, Hossain MS (2021) COVID-19 and non-COVID-19 classification using multi-layers fusion from lung ultrasound images. Inform Fusion 72:80\u201388","journal-title":"Inform Fusion"},{"key":"951_CR49","doi-asserted-by":"crossref","unstructured":"Alanazi S et al. (2020) Measuring and preventing COVID-19 using the SIR model and machine learning in smart health care. J Healthcare Eng, Article ID 8857346","DOI":"10.1155\/2020\/8857346"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00951-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-021-00951-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00951-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:55:17Z","timestamp":1679882117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-021-00951-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,30]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["951"],"URL":"https:\/\/doi.org\/10.1007\/s00607-021-00951-9","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,30]]},"assertion":[{"value":"25 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2021","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 authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}