{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T21:06:47Z","timestamp":1773090407645,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:00:00Z","timestamp":1609632000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T00:00:00Z","timestamp":1609632000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s11042-020-10317-6","type":"journal-article","created":{"date-parts":[[2021,1,3]],"date-time":"2021-01-03T10:02:42Z","timestamp":1609668162000},"page":"10881-10900","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Automated malware identification method using image descriptors and singular value decomposition"],"prefix":"10.1007","volume":"80","author":[{"given":"Turker","family":"Tuncer","sequence":"first","affiliation":[]},{"given":"Fatih","family":"Ertam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9677-5684","authenticated-orcid":false,"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,3]]},"reference":[{"key":"10317_CR1","unstructured":"Agarap AF (2017) Towards building an intelligent anti-malware system: a deep learning approach using support vector machine (SVM) for malware classification. arXiv Prepr arXiv180100318"},{"key":"10317_CR2","doi-asserted-by":"crossref","unstructured":"Akarsh S, Simran K, Poornachandran P et al (2019) Deep learning framework and visualization for malware classification. In: 2019 5th international conference on advanced computing and communication systems, ICACCS 2019. IEEE, pp 1059\u20131063","DOI":"10.1109\/ICACCS.2019.8728471"},{"key":"10317_CR3","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/0378-4266(94)90007-8","volume":"18","author":"EI Altman","year":"1994","unstructured":"Altman EI, Marco G, Varetto F (1994) Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). J Bank Financ 18:505\u2013529. https:\/\/doi.org\/10.1016\/0378-4266(94)90007-8","journal-title":"J Bank Financ"},{"key":"10317_CR4","unstructured":"Aquilina JM, Casey E, Malin CH (2008) Malware forensics: investigating and analyzing malicious code. Elsevier"},{"key":"10317_CR5","doi-asserted-by":"crossref","unstructured":"Banin S, Dyrkolbotn GO (2018) Multinomial malware classification via low-level features. In: Proceedings of the digital forensic research conference, DFRWS 2018 USA, pp S107\u2013S117","DOI":"10.1016\/j.diin.2018.04.019"},{"key":"10317_CR6","first-page":"7207","volume":"12","author":"JJA Barriga","year":"2017","unstructured":"Barriga JJA, Yoo SG (2017) Malware detection and evasion with machine learning techniques: a survey. Int J Appl Eng Res 12:7207\u20137214","journal-title":"Int J Appl Eng Res"},{"key":"10317_CR7","unstructured":"Basu I, Sinha N, Bhagat D, Goswami S (2016) Malware detection based on source data using data mining : a survey. Am J Adv Comput III:18\u201337"},{"key":"10317_CR8","doi-asserted-by":"crossref","unstructured":"Boero L, Marchese M, Zappatore S (2017) Support vector machine meets software defined networking in IDS domain. In: 2017 29th international teletraffic congress (ITC 29). IEEE, pp 25\u201330","DOI":"10.23919\/ITC.2017.8065806"},{"key":"10317_CR9","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1016\/j.patcog.2003.09.004","volume":"37","author":"S Chen","year":"2004","unstructured":"Chen S, Zhu Y (2004) Subpattern-based principle component analysis. Pattern Recogn 37:1081\u20131083. https:\/\/doi.org\/10.1016\/j.patcog.2003.09.004","journal-title":"Pattern Recogn"},{"key":"10317_CR10","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.diin.2018.09.006","volume":"27","author":"Y Dai","year":"2018","unstructured":"Dai Y, Li H, Qian Y, Lu X (2018) A malware classification method based on memory dump grayscale image. Digit Investig 27:30\u201337. https:\/\/doi.org\/10.1016\/j.diin.2018.09.006","journal-title":"Digit Investig"},{"key":"10317_CR11","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","author":"L De Lathauwer","year":"2000","unstructured":"De Lathauwer L, De Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21:1253\u20131278","journal-title":"SIAM J Matrix Anal Appl"},{"key":"10317_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2089125.2089126","volume":"44","author":"M Egele","year":"2012","unstructured":"Egele M, Scholte T, Kirda E, Kruegel C (2012) A survey on automated dynamic malware-analysis techniques and tools. ACM Comput Surv 44:1\u201342. https:\/\/doi.org\/10.1145\/2089125.2089126","journal-title":"ACM Comput Surv"},{"key":"10317_CR13","doi-asserted-by":"publisher","first-page":"29","DOI":"10.14257\/ijsia.2013.7.5.03","volume":"7","author":"AAE Elhadi","year":"2013","unstructured":"Elhadi AAE, Maarof MA, Barry BIA (2013) Improving the detection of malware behaviour using simplified data dependent API call graph. Int J Secur Appl 7:29\u201342. https:\/\/doi.org\/10.14257\/ijsia.2013.7.5.03","journal-title":"Int J Secur Appl"},{"key":"10317_CR14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861\u2013874. https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recogn Lett"},{"key":"10317_CR15","unstructured":"Flach PA, Kull M (2015) Precision-Recall-Gain curves: PR analysis done right. Adv Neural Inf Process Syst 2015-January, pp 838\u2013846"},{"key":"10317_CR16","unstructured":"Golub GH, Reinsch C (1970) Singular value decomposition and least squares solutions. In: Numerische Mathematik. Springer, pp 403\u2013420"},{"key":"10317_CR17","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.protcy.2014.09.007","volume":"15","author":"J G\u00fcnther","year":"2014","unstructured":"G\u00fcnther J, Pilarski PM, Helfrich G, Shen H, Diepold K (2014) First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning. Procedia Technol 15:474\u2013483. https:\/\/doi.org\/10.1016\/j.protcy.2014.09.007","journal-title":"Procedia Technol"},{"key":"10317_CR18","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1016\/j.jnca.2012.10.004","volume":"36","author":"R Islam","year":"2013","unstructured":"Islam R, Tian R, Batten LM, Versteeg S (2013) Classification of malware based on integrated static and dynamic features. J Netw Comput Appl 36:646\u2013656","journal-title":"J Netw Comput Appl"},{"key":"10317_CR19","unstructured":"Kim K (2009) Face recognition using principle component analysis. In: International conference on computer vision and pattern recognition, pp 1\u20137"},{"key":"10317_CR20","doi-asserted-by":"crossref","unstructured":"Kruczkowski M, Niewiadomska-Szynkiewicz E (2014) Support vector machine for malware analysis and classification. In: proceedings - 2014 IEEE\/WIC\/ACM international joint conference on web intelligence and intelligent agent technology - workshops, WI-IAT 2014","DOI":"10.1109\/WI-IAT.2014.127"},{"key":"10317_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.2174\/1872212111666170808104744","volume":"12","author":"P Kumar","year":"2018","unstructured":"Kumar P, Quadri MZ, Sharma K, Gia NN, Ranjan P (2018) Persistent cellular telephony: enhanced secure GSM architecture. Recent Patents Eng 12:23\u201329. https:\/\/doi.org\/10.2174\/1872212111666170808104744","journal-title":"Recent Patents Eng"},{"key":"10317_CR22","doi-asserted-by":"publisher","first-page":"291","DOI":"10.7763\/IJIET.2016.V6.702","volume":"6","author":"G Liang","year":"2016","unstructured":"Liang G, Pang J, Dai C (2016) A behavior-based malware variant classification technique. Int J Inf Educ Technol 6:291\u2013295. https:\/\/doi.org\/10.7763\/IJIET.2016.V6.702","journal-title":"Int J Inf Educ Technol"},{"key":"10317_CR23","doi-asserted-by":"publisher","first-page":"965","DOI":"10.6688\/JISE.2015.31.3.11","volume":"31","author":"CT Lin","year":"2015","unstructured":"Lin CT, Wang NJ, Xiao H, Eckert C (2015) Feature selection and extraction for malware classification. J Inf Sci Eng 31:965\u2013992. https:\/\/doi.org\/10.6688\/JISE.2015.31.3.11","journal-title":"J Inf Sci Eng"},{"key":"10317_CR24","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/0167-8655(87)90072-9","volume":"5","author":"ID Longstaff","year":"1987","unstructured":"Longstaff ID, Cross JF (1987) A pattern recognition approach to understanding the multi-layer perception. Pattern Recogn Lett 5:315\u2013319. https:\/\/doi.org\/10.1016\/0167-8655(87)90072-9","journal-title":"Pattern Recogn Lett"},{"key":"10317_CR25","unstructured":"Machado JAT, Lopes AM (2017) Computational complexity. John Wiley and Sons Ltd."},{"key":"10317_CR26","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.cose.2015.04.001","volume":"52","author":"A Mohaisen","year":"2015","unstructured":"Mohaisen A, Alrawi O, Mohaisen M (2015) AMAL: high-fidelity, behavior-based automated malware analysis and classification. Comput Secur 52:251\u2013266. https:\/\/doi.org\/10.1016\/j.cose.2015.04.001","journal-title":"Comput Secur"},{"key":"10317_CR27","doi-asserted-by":"publisher","unstructured":"Nataraj L, Karthikeyan S, Jacob G, Manjunath BS (2011) Malware images: visualization and automatic classification. ACM Int Conf Proceeding Ser. https:\/\/doi.org\/10.1145\/2016904.2016908","DOI":"10.1145\/2016904.2016908"},{"key":"10317_CR28","doi-asserted-by":"crossref","unstructured":"Ojala T, Pietik\u00e4inen M, M\u00e4enp\u00e4\u00e4 T (2001) A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp 399\u2013408","DOI":"10.1007\/3-540-44732-6_41"},{"key":"10317_CR29","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietik\u00e4inen M, M\u00e4enp\u00e4\u00e4 T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971\u2013987","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10317_CR30","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s11416-016-0265-3","volume":"13","author":"S Pai","year":"2017","unstructured":"Pai S, Di Troia F, Visaggio CA et al (2017) Clustering for malware classification. J Comput Virol Hacking Tech 13:95\u2013107. https:\/\/doi.org\/10.1007\/s11416-016-0265-3","journal-title":"J Comput Virol Hacking Tech"},{"key":"10317_CR31","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.diin.2013.08.006","volume":"10","author":"A Provataki","year":"2013","unstructured":"Provataki A, Katos V (2013) Differential malware forensics. Digit Investig 10:311\u2013322. https:\/\/doi.org\/10.1016\/j.diin.2013.08.006","journal-title":"Digit Investig"},{"key":"10317_CR32","doi-asserted-by":"crossref","unstructured":"Raff E, Nicholas C (2017) An alternative to NCD for large sequences, Lempel-Ziv Jaccard distance. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, NY, USA, pp 1007\u20131015","DOI":"10.1145\/3097983.3098111"},{"key":"10317_CR33","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.future.2018.12.009","volume":"94","author":"L Ren","year":"2019","unstructured":"Ren L, Cheng X, Wang X, Cui J, Zhang L (2019) Multi-scale dense gate recurrent unit networks for bearing remaining useful life prediction. Futur Gener Comput Syst 94:601\u2013609. https:\/\/doi.org\/10.1016\/j.future.2018.12.009","journal-title":"Futur Gener Comput Syst"},{"key":"10317_CR34","doi-asserted-by":"crossref","unstructured":"Rieck K, Holz T, Willems C et al (2008) Learning and classification of malware behavior. In: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Springer, pp 108\u2013125","DOI":"10.1007\/978-3-540-70542-0_6"},{"key":"10317_CR35","doi-asserted-by":"publisher","first-page":"639","DOI":"10.3233\/JCS-2010-0410","volume":"19","author":"K Rieck","year":"2011","unstructured":"Rieck K, Trinius P, Willems C, Holz T (2011) Automatic analysis of malware behavior using machine learning. J Comput Secur 19:639\u2013668. https:\/\/doi.org\/10.3233\/JCS-2010-0410","journal-title":"J Comput Secur"},{"key":"10317_CR36","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1109\/COMST.2016.2636078","volume":"19","author":"EM Rudd","year":"2017","unstructured":"Rudd EM, Rozsa A, G\u00fcnther M, Boult TE (2017) A survey of stealth malware attacks, mitigation measures, and steps toward autonomous open world solutions. IEEE Commun Surv Tutorials 19:1145\u20131172. https:\/\/doi.org\/10.1109\/COMST.2016.2636078","journal-title":"IEEE Commun Surv Tutorials"},{"key":"10317_CR37","first-page":"944","volume":"5","author":"M Sahu","year":"2014","unstructured":"Sahu M, Ahirwar M, Hemlata A (2014) A review of malware detection based on pattern matching technique. Int J Comput Sci Inf Technol 5:944\u2013947","journal-title":"Int J Comput Sci Inf Technol"},{"key":"10317_CR38","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ins.2011.08.020","volume":"231","author":"I Santos","year":"2013","unstructured":"Santos I, Brezo F, Ugarte-Pedrero X, Bringas PG (2013) Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf Sci (Ny) 231:64\u201382. https:\/\/doi.org\/10.1016\/j.ins.2011.08.020","journal-title":"Inf Sci (Ny)"},{"key":"10317_CR39","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ins.2011.08.020","volume":"231","author":"I Santos","year":"2013","unstructured":"Santos I, Brezo F, Ugarte-Pedrero X, Bringas PG (2013) Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf Sci (Ny) 231:64\u201382. https:\/\/doi.org\/10.1016\/j.ins.2011.08.020","journal-title":"Inf Sci (Ny)"},{"key":"10317_CR40","doi-asserted-by":"crossref","unstructured":"Saxe J, Berlin K (2015) Deep neural network based malware detection using two dimensional binary program features. In: 2015 10th international conference on malicious and unwanted software (MALWARE). IEEE, pp 11\u201320","DOI":"10.1109\/MALWARE.2015.7413680"},{"key":"10317_CR41","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.istr.2009.03.003","volume":"14","author":"A Shabtai","year":"2009","unstructured":"Shabtai A, Moskovitch R, Elovici Y, Glezer C (2009) Detection of malicious code by applying machine learning classifiers on static features: a state-of-the-art survey. Inf Secur Tech Rep 14:16\u201329. https:\/\/doi.org\/10.1016\/j.istr.2009.03.003","journal-title":"Inf Secur Tech Rep"},{"key":"10317_CR42","doi-asserted-by":"publisher","first-page":"iii","DOI":"10.12694\/scpe.v18i3.1299","volume":"18","author":"K Sharma","year":"2017","unstructured":"Sharma K, Bala S, Bansal H, Shrivastava G (2017) Introduction to the special issue on secure solutions for network in scalable computing. Scalable Comput Pract Exp 18:iii\u2013iv. https:\/\/doi.org\/10.12694\/scpe.v18i3.1299","journal-title":"Scalable Comput Pract Exp"},{"key":"10317_CR43","doi-asserted-by":"crossref","unstructured":"Shrivastava G (2012) Forensic computing models: technical overview. In: Computer Science & Information Technology (CS & IT). Academy & Industry Research Collaboration Center (AIRCC), pp 207\u2013216","DOI":"10.5121\/csit.2012.2222"},{"key":"10317_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cosrev.2019.01.002","volume":"32","author":"S Sibi Chakkaravarthy","year":"2019","unstructured":"Sibi Chakkaravarthy S, Sangeetha D, Vaidehi V (2019) A survey on malware analysis and mitigation techniques. Comput Sci Rev 32:1\u201323. https:\/\/doi.org\/10.1016\/j.cosrev.2019.01.002","journal-title":"Comput Sci Rev"},{"key":"10317_CR45","doi-asserted-by":"publisher","unstructured":"Sinha A, Shrivastava G, Kumar P, Gupta D (2020) A community-based hierarchical user authentication scheme for industry 4.0. Softw Pract Exp. https:\/\/doi.org\/10.1002\/spe.2832","DOI":"10.1002\/spe.2832"},{"key":"10317_CR46","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s13673-018-0125-x","volume":"8","author":"A Souri","year":"2018","unstructured":"Souri A, Hosseini R (2018) A state-of-the-art survey of malware detection approaches using data mining techniques. Human-centric Comput Inf Sci 8:3. https:\/\/doi.org\/10.1186\/s13673-018-0125-x","journal-title":"Human-centric Comput Inf Sci"},{"key":"10317_CR47","doi-asserted-by":"publisher","first-page":"26","DOI":"10.2174\/2210327908666180413154130","volume":"8","author":"PK Srivastava","year":"2018","unstructured":"Srivastava PK, Ojha RP, Sharma K, Awasthi S, Sanyal G (2018) Effect of quarantine and recovery on infectious nodes in wireless sensor network. Int J Sensors, Wirel Commun Control 8:26\u201336. https:\/\/doi.org\/10.2174\/2210327908666180413154130","journal-title":"Int J Sensors, Wirel Commun Control"},{"key":"10317_CR48","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.1109\/TIP.2009.2033625","volume":"19","author":"X Tan","year":"2010","unstructured":"Tan X, Triggs W (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19:1635\u20131650","journal-title":"IEEE Trans Image Process"},{"key":"10317_CR49","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.cose.2018.11.001","volume":"81","author":"D Ucci","year":"2019","unstructured":"Ucci D, Aniello L, Baldoni R (2019) Survey of machine learning techniques for malware analysis. Comput Secur 81:123\u2013147. https:\/\/doi.org\/10.1016\/j.cose.2018.11.001","journal-title":"Comput Secur"},{"key":"10317_CR50","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.cose.2018.11.001","volume":"81","author":"D Ucci","year":"2019","unstructured":"Ucci D, Aniello L, Baldoni R (2019) Survey of machine learning techniques for malware analysis. Comput Secur 81:123\u2013147. https:\/\/doi.org\/10.1016\/j.cose.2018.11.001","journal-title":"Comput Secur"},{"key":"10317_CR51","doi-asserted-by":"publisher","first-page":"46717","DOI":"10.1109\/ACCESS.2019.2906934","volume":"7","author":"R Vinayakumar","year":"2019","unstructured":"Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Venkatraman S (2019) Robust intelligent malware detection using deep learning. IEEE Access 7:46717\u201346738. https:\/\/doi.org\/10.1109\/ACCESS.2019.2906934","journal-title":"IEEE Access"},{"key":"10317_CR52","doi-asserted-by":"crossref","unstructured":"W\u00fcchner T, Ochoa M, Pretschner A (2015) Robust and effective malware detection through quantitative data flow graph metrics. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp 98\u2013118","DOI":"10.1007\/978-3-319-20550-2_6"},{"key":"10317_CR53","unstructured":"Ye J, Janardan R, Li Q (2005) Two-dimensional linear discriminant analysis. In: Advances in neural information processing systems, pp 1569\u20131576"},{"key":"10317_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3073559","volume":"50","author":"Y Ye","year":"2017","unstructured":"Ye Y, Li T, Adjeroh D, Iyengar SS (2017) A survey on malware detection using data mining techniques. ACM Comput Surv 50:1\u201340. https:\/\/doi.org\/10.1145\/3073559","journal-title":"ACM Comput Surv"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10317-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-020-10317-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10317-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T00:17:14Z","timestamp":1616717834000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-020-10317-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,3]]},"references-count":54,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["10317"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10317-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,3]]},"assertion":[{"value":"30 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}