{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T02:12:09Z","timestamp":1772849529109,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s11042-021-10647-z","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T02:02:35Z","timestamp":1617760955000},"page":"24533-24554","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A new machine learning-based method for android malware detection on imbalanced dataset"],"prefix":"10.1007","volume":"80","author":[{"given":"Diyana Tehrany","family":"Dehkordy","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8668-5650","authenticated-orcid":false,"given":"Abbas","family":"Rasoolzadegan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,7]]},"reference":[{"key":"10647_CR1","doi-asserted-by":"crossref","unstructured":"Aafer Y, Du W, Yin H (2013) Droidapiminer: Mining api-level features for robust malware detection in android. In: International conference on security and privacy in communication systems, pp 86\u2013103, Springer","DOI":"10.1007\/978-3-319-04283-1_6"},{"key":"10647_CR2","doi-asserted-by":"publisher","unstructured":"Agrawal P, Trivedi B (2019) A survey on android malware and their detection techniques. In: 2019 IEEE International conference on electrical, computer and communication technologies (ICECCT), pp 1\u20136, IEEE. https:\/\/doi.org\/10.1109\/ICECCT.2019.8868951","DOI":"10.1109\/ICECCT.2019.8868951"},{"key":"10647_CR3","doi-asserted-by":"publisher","unstructured":"Ahmadi M, Ulyanov D, Semenov S, Trofimov M, Giacinto G (2016) Novel feature extraction, selection and fusion for effective malware family classification. In: Proceedings of the sixth ACM conference on data and application security and privacy, pp 183\u2013194, ACM. https:\/\/doi.org\/10.1145\/2857705.2857713","DOI":"10.1145\/2857705.2857713"},{"key":"10647_CR4","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.cose.2016.11.011","volume":"65","author":"S Alam","year":"2017","unstructured":"Alam S, Qu Z, Riley R, Chen Y, Rastogi V (2017) Droidnative: Automating and optimizing detection of android native code malware variants. Comput Secur 65:230\u2013246. https:\/\/doi.org\/10.1016\/j.cose.2016.11.011","journal-title":"Comput Secur"},{"key":"10647_CR5","doi-asserted-by":"crossref","unstructured":"Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K, Siemens C (2014) Drebin: Effective and explainable detection of android malware in your pocket. In: Ndss, vol. 14, pp 23\u201326","DOI":"10.14722\/ndss.2014.23247"},{"issue":"3","key":"10647_CR6","first-page":"228","volume":"2","author":"Z Aung","year":"2013","unstructured":"Aung Z, Zaw W (2013) Permission-based android malware detection. Int J Sci Technol Res 2(3):228\u2013234","journal-title":"Int J Sci Technol Res"},{"key":"10647_CR7","unstructured":"Apkpure apps store(bangladesh) (2019). https:\/\/apkpure.com\/developer\/Apps%20for%20Bangladesh"},{"key":"10647_CR8","doi-asserted-by":"publisher","unstructured":"Backes M, Nauman M (2017) Luna: quantifying and leveraging uncertainty in android malware analysis through bayesian machine learning. In: 2017 IEEE European symposium on security and privacy (euros&p), pp 204\u2013217, IEEE. https:\/\/doi.org\/10.1109\/EuroSP.2017.24","DOI":"10.1109\/EuroSP.2017.24"},{"issue":"10","key":"10647_CR9","first-page":"1,2, and 4","volume":"3","author":"M Bekkar","year":"2013","unstructured":"Bekkar M, Djemaa HK, Alitouche TA (2013) Evaluation measures for models assessment over imbalanced data sets. J Inf Eng Appl 3(10):1,2, and 4","journal-title":"J Inf Eng Appl"},{"key":"10647_CR10","doi-asserted-by":"publisher","unstructured":"Canfora G, Di Sorbo A, Mercaldo F, Visaggio CA (2015) Obfuscation techniques against signature-based detection: a case study. In: 2015 Mobile systems technologies workshop (MST), pp 21\u201326, IEEE. https:\/\/doi.org\/10.1109\/MST.2015.8","DOI":"10.1109\/MST.2015.8"},{"key":"10647_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J Artif Intell Res"},{"key":"10647_CR12","doi-asserted-by":"publisher","unstructured":"Dong S, Li M, Diao W, Liu X, Liu J, Li Z, Xu F, Chen K, Wang X, Zhang K (2018) Understanding android obfuscation techniques: A large-scale investigation in the wild. In: International conference on security and privacy in communication systems, pp 172\u2013192, Springer. https:\/\/doi.org\/10.1007\/978-3-030-01701-9_10","DOI":"10.1007\/978-3-030-01701-9_10"},{"key":"10647_CR13","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garcia S, Herrera F, Chawla NV (2018) Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Artif Intell Res 61:863\u2013905. https:\/\/doi.org\/10.1613\/jair.1.11192","journal-title":"J Artif Intell Res"},{"key":"10647_CR14","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Galar M, Prati RC, Krawczyk B, Herrera F (2018) Imbalanced classification for big data. In: Learning from imbalanced data sets, pp 327\u2013349, Springer. https:\/\/doi.org\/10.1007\/978-3-319-98074-4_13","DOI":"10.1007\/978-3-319-98074-4_13"},{"issue":"3","key":"10647_CR15","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/3162625","volume":"26","author":"J Garcia","year":"2018","unstructured":"Garcia J, Hammad M, Malek S (2018) Lightweight, obfuscation-resilient detection and family identification of android malware. ACM Trans Softw Eng Methodology (TOSEM) 26(3):11","journal-title":"ACM Trans Softw Eng Methodology (TOSEM)"},{"key":"10647_CR16","doi-asserted-by":"publisher","unstructured":"Grace M, Zhou Y, Zhang Q, Zou S, Jiang X (2012) Riskranker: scalable and accurate zero-day android malware detection. In: Proceedings of the 10th international conference on mobile systems, applications, and services, pp 281\u2013294, ACM. https:\/\/doi.org\/10.1145\/2307636.2307663","DOI":"10.1145\/2307636.2307663"},{"key":"10647_CR17","doi-asserted-by":"crossref","unstructured":"Halimu C, Kasem A, Newaz S (2019) Empirical comparison of area under roc curve (auc) and mathew correlation coefficient (mcc) for evaluating machine learning algorithms on imbalanced datasets for binary classification. In: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing, pp 1\u20136. ACM","DOI":"10.1145\/3310986.3311023"},{"key":"10647_CR18","unstructured":"Huawei apps store(china) (2019). https:\/\/appstore.huawei.com\/"},{"key":"10647_CR19","doi-asserted-by":"publisher","unstructured":"Hung SH, Tu CH, Yeh CW (2016) A cloud-assisted malware detection framework for mobile devices. In: 2016 International computer symposium (ICS), pp 537\u201354, IEEE. https:\/\/doi.org\/10.1109\/ICS.2016.0112","DOI":"10.1109\/ICS.2016.0112"},{"key":"10647_CR20","unstructured":"It threat evolution q3 (2018) statistics \u2014 securelist. https:\/\/securelist.com\/itthreat-evolution-q3-2018-statistics\/88689\/https:\/\/securelist.com\/itthreat-evolution-q3-2018-statistics\/88689\/. [Accessed: 22-Feb-2019]"},{"key":"10647_CR21","unstructured":"Iranapps apps store (2019). https:\/\/iranapps.ir\/"},{"issue":"4","key":"10647_CR22","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","volume":"5","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Progr Artif Intell 5(4):221\u2013232. https:\/\/doi.org\/10.1007\/s13748-016-0094-0","journal-title":"Progr Artif Intell"},{"issue":"2","key":"10647_CR23","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s13748-019-00172-4","volume":"8","author":"LI Kuncheva","year":"2019","unstructured":"Kuncheva LI, Arnaiz-Gonz\u00e1lez \u00c1, D\u00edez-pastor JF, Gunn IA (2019) Instance selection improves geometric mean accuracy: a study on imbalanced data classification. Progr Artif Intell 8(2):215\u2013228. https:\/\/doi.org\/10.1007\/s13748-019-00172-4","journal-title":"Progr Artif Intell"},{"issue":"1","key":"10647_CR24","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N (2018) A survey on addressing high-class imbalance in big data. J Big Data 5(1):42. https:\/\/doi.org\/10.1186\/s40537-018-0151-6","journal-title":"J Big Data"},{"key":"10647_CR25","doi-asserted-by":"publisher","unstructured":"Lei T, Qin Z, Wang Z, Li Q, Ye D (2019) Evedroid: Event-aware android malware detection against model degrading for iot devices. IEEE Internet of Things Journal. https:\/\/doi.org\/10.1109\/JIOT.2019.2909745","DOI":"10.1109\/JIOT.2019.2909745"},{"issue":"7","key":"10647_CR26","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1109\/TII.2017.2789219","volume":"14","author":"J Li","year":"2018","unstructured":"Li J, Sun L, Yan Q, Li Z, Srisa-an W, Ye H (2018) Significant permission identification for machine-learning-based android malware detection. IEEE Trans Industr Inform 14(7):3216\u20133225. https:\/\/doi.org\/10.1109\/TII.2017.2789219","journal-title":"IEEE Trans Industr Inform"},{"issue":"8","key":"10647_CR27","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1109\/TKDE.2006.131","volume":"18","author":"CX Ling","year":"2006","unstructured":"Ling CX, Sheng VS, Yang Q (2006) Test strategies for cost-sensitive decision trees. IEEE Trans Knowl Data Eng 18(8):1055\u20131067. https:\/\/doi.org\/10.1109\/TKDE.2006.131","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10647_CR28","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1016\/j.asoc.2018.11.045","volume":"75","author":"J Liu","year":"2019","unstructured":"Liu J, Zio E (2019) Integration of feature vector selection and support vector machine for classification of imbalanced data. Appl Soft Comput 75:702\u2013711. https:\/\/doi.org\/10.1016\/j.asoc.2018.11.045","journal-title":"Appl Soft Comput"},{"key":"10647_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-019-01423-6","volume":"49","author":"P Lopez-Garcia","year":"2019","unstructured":"Lopez-Garcia P, Masegosa AD, Osaba E, Onieva E, Perallos A (2019) Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics. Appl Intell 49:1\u201316. https:\/\/doi.org\/10.1007\/s10489-019-01423-6","journal-title":"Appl Intell"},{"key":"10647_CR30","doi-asserted-by":"publisher","unstructured":"Lou S, Cheng S, Huang J, Jiang F (2019) Tfdroid: Android malware detection by topics and sensitive data flows using machine learning techniques. In: 2019 IEEE 2Nd international conference on information and computer technologies (ICICT), pp 30\u201336, IEEE. https:\/\/doi.org\/10.1109\/INFOCT.2019.8711179","DOI":"10.1109\/INFOCT.2019.8711179"},{"key":"10647_CR31","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.jpdc.2018.04.008","volume":"119","author":"F Martinelli","year":"2018","unstructured":"Martinelli F, Mercaldo F, Nardone V, Santone A, Sangaiah AK, Cimitile A (2018) Evaluating model checking for cyber threats code obfuscation identification. J Parallel Distrib Comput 119:203\u2013218. https:\/\/doi.org\/10.1016\/j.jpdc.2018.04.008","journal-title":"J Parallel Distrib Comput"},{"key":"10647_CR32","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.inffus.2018.12.006","volume":"52","author":"A Mart\u00edn","year":"2019","unstructured":"Mart\u00edn A, Lara-Cabrera R, Camacho D (2019) Android malware detection through hybrid features fusion and ensemble classifiers: the andropytool framework and the omnidroid dataset. Inform Fusion 52:128\u2013142","journal-title":"Inform Fusion"},{"key":"10647_CR33","doi-asserted-by":"publisher","unstructured":"McGiff J, Hatcher WG, Nguyen J, Yu W, Blasch E, Lu C (2019) Towards multimodal learning for android malware detection. In: 2019 International conference on computing, networking and communications (ICNC), pp 432\u2013436, IEEE. https:\/\/doi.org\/10.1109\/ICCNC.2019.8685502","DOI":"10.1109\/ICCNC.2019.8685502"},{"key":"10647_CR34","doi-asserted-by":"crossref","unstructured":"Odusami M, Abayomi-Alli O, Misra S, Shobayo O, Damasevicius R, Maskeliunas R (2018) Android malware detection: a survey. In: International conference on applied informatics, pp 255\u2013266, Springer","DOI":"10.1007\/978-3-030-01535-0_19"},{"key":"10647_CR35","doi-asserted-by":"publisher","unstructured":"Pekta\u015f A, Acarman T (2019) Learning to detect android malware via opcode sequences. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2018.09.102","DOI":"10.1016\/j.neucom.2018.09.102"},{"key":"10647_CR36","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/j.neucom.2018.09.102","volume":"396","author":"A Pekta\u015f","year":"2020","unstructured":"Pekta\u015f A, Acarman T (2020) Learning to detect android malware via opcode sequences. Neurocomputing 396:599\u2013608","journal-title":"Neurocomputing"},{"key":"10647_CR37","doi-asserted-by":"publisher","unstructured":"Quan D, Zhai L, Yang F, Wang P (2014) Detection of android malicious apps based on the sensitive behaviors. In: 2014 IEEE 13Th international conference on trust, security and privacy in computing and communications, pp 877\u2013883, IEEE. https:\/\/doi.org\/10.1109\/TrustCom.2014.115","DOI":"10.1109\/TrustCom.2014.115"},{"key":"10647_CR38","doi-asserted-by":"publisher","unstructured":"Rout N, Mishra D, Mallick MK (2018) Handling imbalanced data: a survey. In: International proceedings on advances in soft computing, intelligent systems and applications, pp 431\u2013443, Springer. https:\/\/doi.org\/10.1007\/978-981-10-5272-9_39","DOI":"10.1007\/978-981-10-5272-9_39"},{"key":"10647_CR39","doi-asserted-by":"publisher","unstructured":"Samra AAA, Qunoo HN, Al-Rubaie F, El-Talli H (2019) A survey of static android malware detection techniques. In: 2019 IEEE 7Th palestinian international conference on electrical and computer engineering (PICECE), pp 1\u20136, IEEE. https:\/\/doi.org\/10.1109\/PICECE.2019.8747224","DOI":"10.1109\/PICECE.2019.8747224"},{"issue":"1","key":"10647_CR40","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/TDSC.2016.2536605","volume":"15","author":"A Saracino","year":"2016","unstructured":"Saracino A, Sgandurra D, Dini G, Martinelli F (2016) Madam: Effective and efficient behavior-based android malware detection and prevention. IEEE Trans Dependable Secure Comput 15(1):83\u201397","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"10647_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-018-6670-5","volume":"78","author":"G Shrivastava","year":"2019","unstructured":"Shrivastava G, Kumar P (2019) Sensdroid: analysis for malicious activity risk of android application. Multimed Tools Appl 78:1\u201319. https:\/\/doi.org\/10.1007\/s11042-019-07899-1","journal-title":"Multimed Tools Appl"},{"key":"10647_CR42","doi-asserted-by":"publisher","unstructured":"Siddiqui M, Wang MC, Lee J (2008) A survey of data mining techniques for malware detection using file features. In: Proceedings of the 46th annual southeast regional conference on xx, pp 509\u2013510, ACM. https:\/\/doi.org\/10.1145\/1593105.1593239","DOI":"10.1145\/1593105.1593239"},{"key":"10647_CR43","doi-asserted-by":"publisher","unstructured":"Suarez-Tangil G, Dash SK, Ahmadi M, Kinder J, Giacinto G, Cavallaro L (2017) Droidsieve: Fast and accurate classification of obfuscated android malware. In: Proceedings of the Seventh ACM on conference on data and application security and privacy, pp 309\u2013320 ACM. https:\/\/doi.org\/10.1145\/3029806.3029825","DOI":"10.1145\/3029806.3029825"},{"issue":"5","key":"10647_CR44","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1109\/TSMCC.2010.2048428","volume":"40","author":"M Tavallaee","year":"2010","unstructured":"Tavallaee M, Stakhanova N, Ghorbani AA (2010) Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Trans Syst Man Cy Part C (App Rev 40(5):516\u2013524. https:\/\/doi.org\/10.1109\/TSMCC.2010.2048428","journal-title":"IEEE Trans Syst Man Cy Part C (App Rev"},{"key":"10647_CR45","doi-asserted-by":"publisher","unstructured":"Ucci D, Aniello L, Baldoni R (2018) Survey of machine learning techniques for malware analysis. Computers & Security. https:\/\/doi.org\/10.1016\/j.cose.2018.11.001","DOI":"10.1016\/j.cose.2018.11.001"},{"key":"10647_CR46","doi-asserted-by":"publisher","unstructured":"Wang S, Yao X (2009) Diversity analysis on imbalanced data sets by using ensemble models. In: 2009 IEEE Symposium on computational intelligence and data mining, pp 324\u2013331, IEEE. https:\/\/doi.org\/10.1109\/CIDM.2009.4938667","DOI":"10.1109\/CIDM.2009.4938667"},{"key":"10647_CR47","doi-asserted-by":"publisher","unstructured":"Wei F., Li Y., Roy S., Ou X., Zhou W. (2017) Deep ground truth analysis of current android malware. In: International conference on detection of intrusions and malware, and vulnerability assessment (DIMVA\u201917), pp 252\u2013276. Springer, Bonn, Germany. https:\/\/doi.org\/10.1007\/978-3-319-60876-1_12","DOI":"10.1007\/978-3-319-60876-1_12"},{"issue":"3","key":"10647_CR48","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s11219-017-9368-4","volume":"26","author":"P Yan","year":"2018","unstructured":"Yan P, Yan Z (2018) A survey on dynamic mobile malware detection. Softw Qual J 26(3):891\u2013919. https:\/\/doi.org\/10.1007\/s11219-017-9368-4","journal-title":"Softw Qual J"},{"issue":"04","key":"10647_CR49","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1142\/S0219622006002258","volume":"5","author":"Q Yang","year":"2006","unstructured":"Yang Q (2006) Wu, x.: 10 challenging problems in data mining research. Int J Inform Technol Dec Making 5(04):597\u2013604. https:\/\/doi.org\/10.1142\/S0219622006002258","journal-title":"Int J Inform Technol Dec Making"},{"key":"10647_CR50","doi-asserted-by":"crossref","unstructured":"Yang W, Xiao X, Andow B, Li S, Xie T, Enck W (2015) Appcontext: Differentiating malicious and benign mobile app behaviors using context. In: Proceedings of the 37th international conference on software engineering-volume 1, pp 303\u2013313. IEEE Press","DOI":"10.1109\/ICSE.2015.50"},{"issue":"2","key":"10647_CR51","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TCYB.2017.2777960","volume":"49","author":"SY Yerima","year":"2018","unstructured":"Yerima SY, Sezer S (2018) Droidfusion: a novel multilevel classifier fusion approach for android malware detection. IEEE Trans Cybern 49(2):453\u2013466. https:\/\/doi.org\/10.1109\/TCYB.2017.2777960","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"10647_CR52","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TST.2016.7399288","volume":"21","author":"Z Yuan","year":"2016","unstructured":"Yuan Z, Lu Y, Xue Y (2016) Droiddetector: android malware characterization and detection using deep learning. Tsinghua Sci Technol 21(1):114\u2013123. https:\/\/doi.org\/10.1109\/TST.2016.7399288","journal-title":"Tsinghua Sci Technol"},{"key":"10647_CR53","doi-asserted-by":"publisher","unstructured":"Zhao L, Shang Z, Qin A, Zhang T, Zhao L, Wei Y, Tang YY (2019) A cost-sensitive meta-learning classifier: Spfcnn-miner future generation computer systems. https:\/\/doi.org\/10.1016\/j.future.2019.05.080","DOI":"10.1016\/j.future.2019.05.080"},{"issue":"3","key":"10647_CR54","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1007\/s11042-018-6498-z","volume":"78","author":"Q Zhou","year":"2019","unstructured":"Zhou Q, Feng F, Shen Z, Zhou R, Hsieh MY, Li KC (2019) A novel approach for mobile malware classification and detection in android systems. Multimed Tools Appl 78(3):3529\u20133552. https:\/\/doi.org\/10.1007\/s11042-018-6498-z","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10647-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10647-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10647-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T04:14:28Z","timestamp":1625458468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10647-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":54,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10647"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10647-z","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,7]]},"assertion":[{"value":"31 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}