{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T19:21:18Z","timestamp":1773343278596,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Department of Science and Technology","award":["SEED\/WS\/018\/2015"],"award-info":[{"award-number":["SEED\/WS\/018\/2015"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s00521-021-06099-z","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T09:03:12Z","timestamp":1621846992000},"page":"14583-14602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["A novel feature selection framework based on grey wolf optimizer for mammogram image analysis"],"prefix":"10.1007","volume":"33","author":[{"given":"B.","family":"Sathiyabhama","sequence":"first","affiliation":[]},{"given":"S. Udhaya","family":"Kumar","sequence":"additional","affiliation":[]},{"given":"J.","family":"Jayanthi","sequence":"additional","affiliation":[]},{"given":"T.","family":"Sathiya","sequence":"additional","affiliation":[]},{"given":"A. K.","family":"Ilavarasi","sequence":"additional","affiliation":[]},{"given":"V.","family":"Yuvarajan","sequence":"additional","affiliation":[]},{"given":"Konga","family":"Gopikrishna","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"6099_CR1","unstructured":"American Cancer Society (2021) How Common Is Breast Cancer? https:\/\/www.cancer.org\/cancer\/breast-cancer\/about\/how-common-is-breast-cancer.html"},{"key":"6099_CR2","unstructured":"The National Cancer Registry Programme (2020). https:\/\/www.ncdirindia.org\/All_Reports\/Report_2020\/default.aspx"},{"key":"6099_CR3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.cmpb.2016.02.019","volume":"130","author":"YN Guo","year":"2016","unstructured":"Guo YN, Dong M, Yang Z, Gao X, Wang K, Luo C, Zhang J (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput Methods Prog Biomed 130:31\u201345","journal-title":"Comput Methods Prog Biomed"},{"issue":"5","key":"6099_CR4","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s10278-015-9778-4","volume":"28","author":"M Dong","year":"2015","unstructured":"Dong M, Lu X, Ma Y, Guo Y, Ma Y, Wang K (2015) An efficient approach for automated mass segmentation and classification in mammograms. J Digit Imaging 28(5): 613\u2013625","journal-title":"J Digit Imaging"},{"key":"6099_CR5","doi-asserted-by":"publisher","first-page":"2967","DOI":"10.1016\/S0031-3203(03)00192-4","volume":"36","author":"HD Cheng","year":"2003","unstructured":"Cheng HD, Cai X, Chen X, Hu L, Lou X (2003) Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recogn 36:2967\u20132991","journal-title":"Pattern Recogn"},{"key":"6099_CR6","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.eswa.2016.11.024","volume":"71","author":"F Pacheco","year":"2017","unstructured":"Pacheco F, Cerrada M, S\u00e1nchez RV, Cabrera D, Li C, de Oliveira JV (2017) Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery. Expert Syst Appl 71:69\u201386","journal-title":"Expert Syst Appl"},{"issue":"3","key":"6099_CR7","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3233\/IDA-1997-1302","volume":"1","author":"M Dash","year":"1997","unstructured":"Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(3):131\u2013156","journal-title":"Intell Data Anal"},{"key":"6099_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/NECO_a_00798","volume":"28","author":"SU Kumar","year":"2016","unstructured":"Kumar SU, Inbarani HH (2016) PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Comput Appl 28:1\u201320","journal-title":"Neural Comput Appl"},{"key":"6099_CR9","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1016\/j.asoc.2016.03.014","volume":"46","author":"G Jothi","year":"2016","unstructured":"Jothi G, Inbarani HH (2016) Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification. Appl Soft Comput 46:639\u2013651","journal-title":"Appl Soft Comput"},{"key":"6099_CR10","doi-asserted-by":"publisher","unstructured":"Bazan JG, Peters JF, Skowron A (2005) Behavioral pattern identification through rough set modelling. In: \u015al\u0119zak D., Yao J., Peters J.F., Ziarko W., Hu X. (eds) Rough sets, fuzzy sets, data mining, and granular computing (RSFDGrC), Lecture Notes in Computer Science, vol 3642. Springer, Berlin, Heidelberg, pp 688\u2013697. https:\/\/doi.org\/10.1007\/11548706_73","DOI":"10.1007\/11548706_73"},{"key":"6099_CR11","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.procs.2015.03.216","volume":"47","author":"SU Kumar","year":"2015","unstructured":"Kumar SU, Inbarani HH (2015) A novel neighborhood rough set based classification approach for medical diagnosis. Procedia Comput Sci 47:351\u2013359","journal-title":"Procedia Comput Sci"},{"key":"6099_CR12","first-page":"1157","volume":"3","author":"A Guyon","year":"2003","unstructured":"Guyon A (2003) Elisseeff, an introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"key":"6099_CR13","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.cmpb.2013.10.007","volume":"113","author":"HH Inbarani","year":"2014","unstructured":"Inbarani HH, Azar AT, Jothi G (2014) Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput Methods Prog Biomed 113:175\u2013185","journal-title":"Comput Methods Prog Biomed"},{"key":"6099_CR14","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1007\/s00500-013-1150-3","volume":"18","author":"J Si-Yuan","year":"2014","unstructured":"Si-Yuan J (2014) A hybrid genetic algorithm for feature subset selection in rough set theory. Soft Comput 18:1373\u20131382","journal-title":"Soft Comput"},{"key":"6099_CR15","first-page":"1","volume":"4","author":"MA El Aziz","year":"2016","unstructured":"El Aziz MA, Hassanien AE (2016) Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput Appl 4:1\u201310","journal-title":"Neural Comput Appl"},{"key":"6099_CR16","first-page":"1","volume":"21","author":"Y Chen","year":"2016","unstructured":"Chen Y, Zeng Z, Lu J (2016) Neighborhood rough set reduction with fish swarm algorithm. Soft Comput 21:1\u201312","journal-title":"Soft Comput"},{"key":"6099_CR17","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.compag.2017.02.026","volume":"136","author":"AE Hassanien","year":"2017","unstructured":"Hassanien AE, Gaber T, Mokhtar U, Hefny H (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86\u201396","journal-title":"Comput Electron Agric"},{"key":"6099_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/NECO_a_00798","volume":"28","author":"NF Abubacker","year":"2016","unstructured":"Abubacker NF, Azman A, Doraisamy S, Murad MAA (2016) An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification. Neural Comput Appl 28:1\u201314","journal-title":"Neural Comput Appl"},{"issue":"2","key":"6099_CR19","doi-asserted-by":"publisher","first-page":"39","DOI":"10.5565\/rev\/elcvia.216","volume":"8","author":"L Martins","year":"2009","unstructured":"Martins L, Junior GB, Silva AC, de Paiva AC, Gattass M (2009) Detection of masses in digital mammograms using K-means and support vector machine. ELCVIA Electron Lett Comput Vis Image Anal 8(2):39\u201350","journal-title":"ELCVIA Electron Lett Comput Vis Image Anal"},{"key":"6099_CR20","doi-asserted-by":"publisher","unstructured":"Jaleel JA, Salim S, Archana S (2014) Textural features based computer aided diagnostic system for mammogram mass classification. In: IEEE 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT). pp 806\u2013811. https:\/\/doi.org\/10.1109\/ICCICCT.2014.6993069","DOI":"10.1109\/ICCICCT.2014.6993069"},{"key":"6099_CR21","first-page":"340","volume":"9","author":"J Jona","year":"2012","unstructured":"Jona J, Nagaveni N (2012) A hybrid swarm optimization approach for feature set reduction in digital mammograms. WSEAS Trans Inf Sci Appl 9:340\u2013349","journal-title":"WSEAS Trans Inf Sci Appl"},{"issue":"1","key":"6099_CR22","first-page":"867","volume":"9","author":"P Gorgel","year":"2012","unstructured":"Gorgel P, Sertba\u015f A, Kilic N, Osman O et al (2012) Mammographic mass classification using wavelet based support vector machine. IU-J Electr Electron Eng 9(1):867\u2013875","journal-title":"IU-J Electr Electron Eng"},{"key":"6099_CR23","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.jbi.2014.01.010","volume":"49","author":"J Dheeba","year":"2014","unstructured":"Dheeba J, Albert Singh N, Tamil Selvi S (2014) Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inform 49:45\u201352","journal-title":"J Biomed Inform"},{"issue":"1","key":"6099_CR24","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.cmpb.2014.01.014","volume":"114","author":"DC Pereira","year":"2014","unstructured":"Pereira DC, Ramos RP, DoNascimento MZ (2014) Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Prog Biomed 114(1):88\u2013101","journal-title":"Comput Methods Prog Biomed"},{"key":"6099_CR25","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.sigpro.2013.12.010","volume":"99","author":"P Agrawal","year":"2014","unstructured":"Agrawal P, Vatsa M, Singh R (2014) Saliency based mass detection from screening mammograms. Signal Process 99:29\u201347","journal-title":"Signal Process"},{"key":"6099_CR26","first-page":"43","volume":"72","author":"C Muramatsu","year":"2016","unstructured":"Muramatsu C, Hara T, Endo T, Fujita H (2016) Breast mass classification on mammograms using radial local ternary patterns. Comput Methods Prog Biomed 72:43\u201353","journal-title":"Comput Methods Prog Biomed"},{"key":"6099_CR27","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.neucom.2016.02.060","volume":"197","author":"Z Jiao","year":"2016","unstructured":"Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197:221\u2013231","journal-title":"Neurocomputing"},{"key":"6099_CR28","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.asoc.2016.03.003","volume":"43","author":"N Gedik","year":"2016","unstructured":"Gedik N, Atasoy A, Sevim Y (2016) Investigation of wave atom transform by using the classification of mammograms. Appl Soft Comp 43:546\u2013552","journal-title":"Appl Soft Comp"},{"issue":"2","key":"6099_CR29","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.compbiomed.2005.12.006","volume":"37","author":"C Varela","year":"2007","unstructured":"Varela C, Tahoces PG, M\u00e9ndez AJ, Souto M, Vidal JJ (2007) Computerized detection of breast masses in digitized mammograms. Comput Biol Med 37(2):214\u2013226","journal-title":"Comput Biol Med"},{"issue":"2","key":"6099_CR30","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s00521-014-1731-9","volume":"26","author":"E El-Baz","year":"2015","unstructured":"El-Baz E (2015) Hybrid intelligent system-based rough set and ensemble classifier for breast cancer diagnosis. Neural Comput Appl 26(2):437\u2013446","journal-title":"Neural Comput Appl"},{"issue":"5","key":"6099_CR31","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1007\/s00521-012-1324-4","volume":"24","author":"AT Azar","year":"2014","unstructured":"Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24(5):1163\u20131177","journal-title":"Neural Comput Appl"},{"issue":"5","key":"6099_CR32","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341\u2013356","journal-title":"Int J Comput Inf Sci"},{"key":"6099_CR33","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ins.2006.06.006","volume":"77","author":"Z Pawlak","year":"2007","unstructured":"Pawlak Z, Skowron A (2007) Rough sets: some extensions. Inform Sci 77:28\u201340","journal-title":"Inform Sci"},{"key":"6099_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-3534-4","volume-title":"Rough sets: theoretical aspects of reasoning about data","author":"Z Pawlak","year":"1991","unstructured":"Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishing, Dordrecht"},{"key":"6099_CR35","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/S0167-8655(02)00196-4","volume":"24","author":"RW Swiniarski","year":"2003","unstructured":"Swiniarski RW, Kowron A (2003) Rough set methods in feature selection and recognition. Pattern Recogn Lett 24:833\u2013849","journal-title":"Pattern Recogn Lett"},{"key":"6099_CR36","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 SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"6099_CR37","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.eswa.2015.10.039","volume":"47","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106\u2013119","journal-title":"Expert Syst Appl"},{"key":"6099_CR38","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.bspc.2017.01.003","volume":"34","author":"E Daniel","year":"2017","unstructured":"Daniel E, Anitha J, Kamaleshwaran KK, Rani I (2017) Optimum spectrum mask based medical image fusion using gray wolf optimization. Biomed Signal Proces 34:36\u201343","journal-title":"Biomed Signal Proces"},{"key":"6099_CR39","unstructured":"Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S, Taylor P (2015) Mammographic image analysis society (MIAS) database v1."},{"key":"6099_CR40","first-page":"361","volume":"9","author":"K Thangavel","year":"2012","unstructured":"Thangavel K, Roselin R (2012) Fuzzy-rough feature selection with \u03c0-membership function for mammogram classification. Int J Comput Sci Issues 9:361\u2013371","journal-title":"Int J Comput Sci Issues"},{"issue":"1","key":"6099_CR41","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu N (1979) A threshold selection method from grey level histogram. IEEE Trans Syst Man Cybern 9(1):62\u201366","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"6099_CR42","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1016\/j.procs.2015.02.117","volume":"46","author":"S Sreedevi","year":"2015","unstructured":"Sreedevi S, Sherly E (2015) A novel approach for removal of pectoral muscles in digital mammogram. Procedia Comput Sci 46:1724\u20131731","journal-title":"Procedia Comput Sci"},{"issue":"6","key":"6099_CR43","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugam K (1973) Dinstein, textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610\u2013621","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"3","key":"6099_CR44","first-page":"193","volume":"9","author":"C Velayutham","year":"2011","unstructured":"Velayutham C, Thangavel K (2011) Unsupervised quick reduct algorithm using rough set theory. J Electron Sci Technol 9(3):193\u2013201","journal-title":"J Electron Sci Technol"},{"key":"6099_CR45","first-page":"1","volume":"21","author":"SU Kumar","year":"2016","unstructured":"Kumar SU, Inbarani HH (2016) Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases. Soft Comput 21:1\u201313","journal-title":"Soft Comput"},{"key":"6099_CR46","doi-asserted-by":"crossref","unstructured":"Vibha L, Harshavardhan GM, Pranaw K, Deepa Shenoy P, Venugopal KR, Patnaik LM (2006) Classification of mammograms using decision trees. In: Proceedings of the 10th IEEE international database engineering and applications symposium (IDEAS\u201906). pp 263\u2013266","DOI":"10.1109\/IDEAS.2006.14"},{"key":"6099_CR47","first-page":"8274","volume":"3","author":"S Krishnaveni","year":"2014","unstructured":"Krishnaveni S, Bhanumathi R, Pugazharasan T (2014) Study of mammogram microcalcification to aid tumour detection using Naive Bayes classifier. Int J Adv Res Electr Electron Instrum Eng 3:8274\u20138282","journal-title":"Int J Adv Res Electr Electron Instrum Eng"},{"key":"6099_CR48","doi-asserted-by":"crossref","unstructured":"Yamany W, Emary E, Hassanien AE (2015) New rough set attribute reduction algorithm based on grey wolf optimization. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28\u201330, Beni Suef, Egypt. Springer, pp 241\u2013251","DOI":"10.1007\/978-3-319-26690-9_22"},{"key":"6099_CR49","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.medntd.2019.100004","volume":"1","author":"V Nagarajan","year":"2019","unstructured":"Nagarajan V, Britto EC, Veeraputhiran SM (2019) Feature extraction based on empirical mode decomposition for automatic mass classification of mammogram images. Med Novel Technol Devices 1:100004","journal-title":"Med Novel Technol Devices"},{"key":"6099_CR50","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol 1 (NIPS'12). Curran Associates Inc., Red Hook, NY, USA, pp 1097\u20131105."},{"key":"6099_CR51","doi-asserted-by":"publisher","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision \u2013 ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8689. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"6099_CR52","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06099-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06099-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06099-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T15:44:46Z","timestamp":1675352686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06099-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":52,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["6099"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06099-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,24]]},"assertion":[{"value":"9 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 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 declared that they have no conflicts of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}