{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T20:30:25Z","timestamp":1767990625312,"version":"3.49.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2017,12,16]],"date-time":"2017-12-16T00:00:00Z","timestamp":1513382400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61232001,61420106009,61622213,81371474"],"award-info":[{"award-number":["61232001,61420106009,61622213,81371474"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s11042-017-5470-7","type":"journal-article","created":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T22:06:53Z","timestamp":1513375613000},"page":"29651-29667","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["MMM: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning"],"prefix":"10.1007","volume":"77","author":[{"given":"Jin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangrong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaosheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,16]]},"reference":[{"issue":"1","key":"5470_CR1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s11263-016-0925-2","volume":"121","author":"S Alchatzidis","year":"2017","unstructured":"Alchatzidis S, Sotiras A, Zacharaki EI, Paragios N (2017) A discrete mrf framework for integrated multi-atlas registration and segmentation. Int J Comput Vis 121(1):169\u2013181","journal-title":"Int J Comput Vis"},{"key":"5470_CR2","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/j.neuroimage.2015.10.019","volume":"125","author":"JL Andersson","year":"2016","unstructured":"Andersson JL, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion mr imaging. Neuroimage 125:1063\u20131078","journal-title":"Neuroimage"},{"issue":"Jan","key":"5470_CR3","first-page":"27","volume":"13","author":"G Brown","year":"2012","unstructured":"Brown G, Pocock A, Zhao MJ, Luj\u00e1n M (2012) Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res 13(Jan):27\u201366","journal-title":"J Mach Learn Res"},{"key":"5470_CR4","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2013.02.037","volume":"123","author":"AC Carli","year":"2014","unstructured":"Carli AC, Figueiredo MA, Bicego M, Murino V (2014) Generative embeddings based on rician mixtures for kernel-based classification of magnetic resonance images. Neurocomputing 123:49\u201359","journal-title":"Neurocomputing"},{"issue":"3","key":"5470_CR5","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s00702-011-0693-7","volume":"119","author":"U Castellani","year":"2012","unstructured":"Castellani U, Rossato E, Murino V, Bellani M, Rambaldelli G, Perlini C, Tomelleri L, Tansella M, Brambilla P (2012) Classification of schizophrenia using feature-based morphometry. J Neural Transm 119(3):395\u2013404","journal-title":"J Neural Transm"},{"issue":"3","key":"5470_CR6","first-page":"27","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"3","key":"5470_CR7","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"5470_CR8","doi-asserted-by":"crossref","unstructured":"Cui Z, Zhong S, Xu P, He Y, Gong G (2013) PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 7:42","DOI":"10.3389\/fnhum.2013.00042"},{"issue":"3","key":"5470_CR9","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan RS, S\u00e9gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT et al (2006) An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest. Neuroimage 31(3):968\u2013980","journal-title":"Neuroimage"},{"issue":"1","key":"5470_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroimage.2010.06.010","volume":"53","author":"C Destrieux","year":"2010","unstructured":"Destrieux C, Fischl B, Dale A, Halgren E (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1):1\u201315","journal-title":"Neuroimage"},{"issue":"8","key":"5470_CR11","doi-asserted-by":"crossref","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(8):861\u2013874","journal-title":"Pattern Recogn Lett"},{"issue":"10","key":"5470_CR12","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1017\/S0033291716000994","volume":"46","author":"S Guo","year":"2016","unstructured":"Guo S, Palaniyappan L, Liddle PF, Feng J (2016) Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a mri-derived cortical thickness study. Psychol Med 46(10):2201\u20132214","journal-title":"Psychol Med"},{"key":"5470_CR13","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"1","key":"5470_CR14","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389\u2013422","journal-title":"Mach Learn"},{"key":"5470_CR15","doi-asserted-by":"crossref","unstructured":"Hertz T, Hillel AB, Weinshall D (2006) Learning a kernel function for classification with small training samples. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 401\u2013408","DOI":"10.1145\/1143844.1143895"},{"key":"5470_CR16","unstructured":"Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University"},{"key":"5470_CR17","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.neucom.2016.05.081","volume":"220","author":"R Hu","year":"2017","unstructured":"Hu R, Zhu X, Cheng D, He W, Yan Y, Song J, Zhang S (2017) Graph self-representation method for unsupervised feature selection. Neurocomputing 220:130\u2013137","journal-title":"Neurocomputing"},{"issue":"1","key":"5470_CR18","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neuroimage.2007.07.053","volume":"39","author":"K Hua","year":"2008","unstructured":"Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA, Pekar JJ, van Zijl PC, Mori S (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39(1):336\u2013347","journal-title":"Neuroimage"},{"issue":"1","key":"5470_CR19","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.media.2015.06.012","volume":"24","author":"JE Iglesias","year":"2015","unstructured":"Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24(1):205\u2013219","journal-title":"Med Image Anal"},{"issue":"3","key":"5470_CR20","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.pscychresns.2015.03.004","volume":"232","author":"E Janousova","year":"2015","unstructured":"Janousova E, Schwarz D, Kasparek T (2015) Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition. Psychiatry Res Neuroimaging 232(3):237\u2013249","journal-title":"Psychiatry Res Neuroimaging"},{"issue":"9","key":"5470_CR21","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1017\/S0033291716000593","volume":"46","author":"K J\u00f8rgensen","year":"2016","unstructured":"J\u00f8rgensen K, Nerland S, Norbom L, Doan N, Nesv\u00e5g R, M\u00f8rch-Johnsen L, Haukvik U, Melle I, Andreassen O, Westlye L et al (2016) Increased mri-based cortical grey\/white-matter contrast in sensory and motor regions in schizophrenia and bipolar disorder. Psychol Med 46(9):1971\u20131985","journal-title":"Psychol Med"},{"issue":"3","key":"5470_CR22","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1016\/j.neuroimage.2011.03.029","volume":"56","author":"J Koikkalainen","year":"2011","unstructured":"Koikkalainen J, L\u00f6tj\u00f6nen J, Thurfjell L, Rueckert D, Waldemar G, Soininen H, Initiative ADN et al. (2011) Multi-template tensor-based morphometry: application to analysis of alzheimer\u2019s disease. NeuroImage 56(3):1134\u20131144","journal-title":"NeuroImage"},{"issue":"3","key":"5470_CR23","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1093\/bioinformatics\/btw639","volume":"33","author":"W Lan","year":"2016","unstructured":"Lan W, Li M, Zhao K, Liu J, Wu FX, Pan Y, Wang J (2016) Ldap: a web server for lncrna-disease association prediction. Bioinformatics 33(3):458\u2013460","journal-title":"Bioinformatics"},{"key":"5470_CR24","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2016.03.080","volume":"206","author":"W Lan","year":"2016","unstructured":"Lan W, Wang J, Li M, Liu J, Li Y, Wu FX, Pan Y (2016) Predicting drug\u2013target interaction using positive-unlabeled learning. Neurocomputing 206:50\u201357","journal-title":"Neurocomputing"},{"key":"5470_CR25","doi-asserted-by":"publisher","unstructured":"Lan W, Wang J, Li M, Liu J, Wu F, Pan Y (2016) Predicting microrna-disease associations based on improved microrna and disease similarities. IEEE\/ACM Trans Comput Biol Bioinform. https:\/\/doi.org\/10.1109\/TCBB.2016.2586190","DOI":"10.1109\/TCBB.2016.2586190"},{"issue":"2","key":"5470_CR26","first-page":"231","volume":"143","author":"SH Lee","year":"2013","unstructured":"Lee SH, Kubicki M, Asami T, Seidman LJ, Goldstein JM, Mesholam-Gately RI, McCarley RW, Shenton ME (2013) Extensive white matter abnormalities in patients with first-episode schizophrenia: a diffusion tensor imaging (dti) study. Schizophr Res 143(2):231\u2013238","journal-title":"Schizophr Res"},{"issue":"6","key":"5470_CR27","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TST.2014.6961028","volume":"19","author":"J Liu","year":"2014","unstructured":"Liu J, Li M, Wang J, Wu F, Liu T, Pan Y (2014) A survey of mri-based brain tumor segmentation methods. Tsinghua Sci Technol 19(6):578\u2013595","journal-title":"Tsinghua Sci Technol"},{"key":"5470_CR28","doi-asserted-by":"publisher","unstructured":"Liu J, Li M, Lan W, Wu F, Pan Y, Wang J (2016) Classification of alzheimer\u2019s disease using whole brain hierarchical network. IEEE\/ACM Trans Comput Biol Bioinform. https:\/\/doi.org\/10.1109\/TCBB.2016.2635144","DOI":"10.1109\/TCBB.2016.2635144"},{"key":"5470_CR29","doi-asserted-by":"crossref","unstructured":"Liu J, Li M, Pan Y, Lan W, Zheng R, Wu FX, Wang J (2017) Complex brain network analysis and its applications to brain disorders: a survey. Complexity 2017:8362741","DOI":"10.1155\/2017\/8362741"},{"issue":"7","key":"5470_CR30","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TNB.2017.2751074","volume":"16","author":"J Liu","year":"2017","unstructured":"Liu J, Li M, Pan Y, Wu FX, Chen X, Wang J (2017) Classification of schizophrenia based on individual hierarchical brain networks constructed from structural mri images. IEEE Trans NanoBiosci 16(7):600\u2013608","journal-title":"IEEE Trans NanoBiosci"},{"issue":"99","key":"5470_CR31","first-page":"1","volume":"PP","author":"J Liu","year":"2017","unstructured":"Liu J, Pan Y, Li M, Chen Z, Tang L, Lu C, Wang J (2017) Applications of deep learning to mri images: a survey. Big Data Mining and Analytics PP(99):1\u20131","journal-title":"Big Data Mining and Analytics"},{"issue":"6","key":"5470_CR32","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1109\/TNB.2017.2707139","volume":"16","author":"J Liu","year":"2017","unstructured":"Liu J, Wang J, Bin H, Wu FX, Pan Y (2017) Alzheimer\u2019s disease classification based on individual hierarchical networks constructed with 3d texture features. IEEE Trans NanoBiosci 16(6):428\u2013437","journal-title":"IEEE Trans NanoBiosci"},{"key":"5470_CR33","doi-asserted-by":"publisher","unstructured":"Liu J, Wang J, Tang Z, Hu B, Wu F, Pan Y (2017) Improving alzheimeres disease classification by combining multiple measures. IEEE\/ACM Trans Comput Biol Bioinform. https:\/\/doi.org\/10.1109\/TCBB.2017.2731849","DOI":"10.1109\/TCBB.2017.2731849"},{"issue":"30","key":"5470_CR34","doi-asserted-by":"crossref","first-page":"e3973","DOI":"10.1097\/MD.0000000000003973","volume":"95","author":"X Lu","year":"2016","unstructured":"Lu X, Yang Y, Wu F, Gao M, Xu Y, Zhang Y, Yao Y, Du X, Li C, Wu L et al (2016) Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural mri images. Medicine 95(30):e3973","journal-title":"Medicine"},{"issue":"10","key":"5470_CR35","doi-asserted-by":"crossref","first-page":"5052","DOI":"10.1002\/hbm.22531","volume":"35","author":"R Min","year":"2014","unstructured":"Min R, Wu G, Cheng J, Wang Q, Shen D (2014) Multi-atlas based representations for alzheimer\u2019s disease diagnosis. Hum Brain Mapp 35(10):5052\u20135070","journal-title":"Hum Brain Mapp"},{"key":"5470_CR36","volume-title":"MRI Atlas of human white matter","author":"S Mori","year":"2005","unstructured":"Mori S, Wakana S, Van Zijl PC, Nagae-Poetscher L (2005) MRI Atlas of human white matter. Elsevier, Amsterdam"},{"issue":"10","key":"5470_CR37","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1038\/npp.2013.93","volume":"38","author":"A Nazeri","year":"2013","unstructured":"Nazeri A, Chakravarty MM, Felsky D, Lobaugh NJ, Rajji TK, Mulsant BH, Voineskos AN (2013) Alterations of superficial white matter in schizophrenia and relationship to cognitive performance. Neuropsychopharmacology 38(10):1954\u20131962","journal-title":"Neuropsychopharmacology"},{"issue":"6","key":"5470_CR38","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1503\/jpn.110119","volume":"37","author":"L Palaniyappan","year":"2012","unstructured":"Palaniyappan L, Liddle PF (2012) Aberrant cortical gyrification in schizophrenia: a surface-based morphometry study. J Psychiatry Neurosci: JPN 37(6):399","journal-title":"J Psychiatry Neurosci: JPN"},{"issue":"8","key":"5470_CR39","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"5470_CR40","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.neuroimage.2010.07.020","volume":"53","author":"M Reuter","year":"2010","unstructured":"Reuter M, Rosas HD, Fischl B (2010) Highly accurate inverse consistent registration: a robust approach. Neuroimage 53(4):1181\u20131196","journal-title":"Neuroimage"},{"key":"5470_CR41","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-61461-8_2","volume-title":"Ranking to learn: feature ranking and selection via eigenvector centrality","author":"G Roffo","year":"2017","unstructured":"Roffo G, Melzi S (2017) Ranking to learn: feature ranking and selection via eigenvector centrality. Springer International Publishing, Cham, pp 19\u201335. https:\/\/doi.org\/10.1007\/978-3-319-61461-8_2"},{"key":"5470_CR42","doi-asserted-by":"publisher","unstructured":"Roffo G, Melzi S, Cristani M (2015) Infinite feature selection. In: 2015 IEEE International conference on computer vision (ICCV), pp 4202\u20134210. https:\/\/doi.org\/10.1109\/ICCV.2015.478","DOI":"10.1109\/ICCV.2015.478"},{"issue":"3","key":"5470_CR43","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1016\/j.neuroimage.2004.03.032","volume":"22","author":"F S\u00e9gonne","year":"2004","unstructured":"S\u00e9gonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004) A hybrid approach to the skull stripping problem in mri. Neuroimage 22(3):1060\u20131075","journal-title":"Neuroimage"},{"issue":"1","key":"5470_CR44","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/42.668698","volume":"17","author":"JG Sled","year":"1998","unstructured":"Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in mri data. IEEE Trans Med Imaging 17(1):87\u201397","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"5470_CR45","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1093\/schbul\/sbw220","volume":"43","author":"W Tschacher","year":"2017","unstructured":"Tschacher W, Giersch A, Friston K (2017) Embodiment and schizophrenia: a review of implications and applications. Schizophr Bull 43(4):745\u2013753","journal-title":"Schizophr Bull"},{"issue":"1","key":"5470_CR46","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage 15(1):273\u2013289","journal-title":"Neuroimage"},{"issue":"6","key":"5470_CR47","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.biopsych.2015.02.008","volume":"78","author":"A Vita","year":"2015","unstructured":"Vita A, De Peri L, Deste G, Barlati S, Sacchetti E (2015) The effect of antipsychotic treatment on cortical gray matter changes in schizophrenia: does the class matter? a meta-analysis and meta-regression of longitudinal magnetic resonance imaging studies. Biol Psychiatry 78(6):403\u2013412","journal-title":"Biol Psychiatry"},{"issue":"3","key":"5470_CR48","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.neuroimage.2007.02.049","volume":"36","author":"S Wakana","year":"2007","unstructured":"Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P et al (2007) Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36(3):630\u2013644","journal-title":"Neuroimage"},{"issue":"3","key":"5470_CR49","first-page":"43","volume":"8","author":"S Zhang","year":"2017","unstructured":"Zhang S, Li X, Zong M, Zhu X, Cheng D (2017) Learning k for knn classification. ACM Trans Intell Syst Technol 8(3):43","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"6","key":"5470_CR50","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TNNLS.2016.2521602","volume":"28","author":"X Zhu","year":"2017","unstructured":"Zhu X, Li X, Zhang S, Ju C, Wu X (2017) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Transactions on Neural Networks and Learning Systems 28(6):1263\u20131275","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"5470_CR51","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.media.2015.10.008","volume":"38","author":"X Zhu","year":"2017","unstructured":"Zhu X, Suk HI, Wang L, Lee SW, Shen D, Initiative ADN et al. (2017) A novel relational regularization feature selection method for joint regression and classification in ad diagnosis. Med Image Anal 38:205\u2013214","journal-title":"Med Image Anal"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-017-5470-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5470-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5470-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T19:42:29Z","timestamp":1693338149000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-017-5470-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,16]]},"references-count":51,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["5470"],"URL":"https:\/\/doi.org\/10.1007\/s11042-017-5470-7","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,16]]}}}