{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:59:47Z","timestamp":1743037187568,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030510992"},{"type":"electronic","value":"9783030511005"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-51100-5_34","type":"book-chapter","created":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T14:02:39Z","timestamp":1595080959000},"page":"382-393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiple Frame CT Image Sequencing Big Data Batch Clustering Method"],"prefix":"10.1007","author":[{"given":"Xiao-yan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Guo-hui","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Zheng-wei","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jin-gang","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,19]]},"reference":[{"issue":"12","key":"34_CR1","first-page":"22","volume":"23","author":"S Bricq","year":"2017","unstructured":"Bricq, S., Collet, C.H., Armspach, J.P.: Unifying framework for multimodal brain MRI segmentation based on hidden markov chains. Med. Image 23(12), 22\u201325 (2017)","journal-title":"Med. Image"},{"key":"34_CR2","unstructured":"Gilbert: Ultrasonic detection based on computer simulation technology, 36(4), 94\u201395 (2017)"},{"issue":"2","key":"34_CR3","first-page":"60","volume":"11","author":"AWC Liew","year":"2017","unstructured":"Liew, A.W.C., Yan, H.: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imaging 11(2), 60\u201362 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"34_CR4","first-page":"37","volume":"19","author":"D-Q Zhang","year":"2017","unstructured":"Zhang, D.-Q., Chen, S.-C.: A novel kemelized fuzzy c-means algorithm with application in medical image sementation. Artif. Intell. Med. 19(2), 37\u201338 (2017)","journal-title":"Artif. Intell. Med."},{"issue":"10","key":"34_CR5","first-page":"123","volume":"3","author":"KS Chuang","year":"2017","unstructured":"Chuang, K.S., Tzang, H.L., Chen, T.-J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 3(10), 123\u2013124 (2017)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"4","key":"34_CR6","doi-asserted-by":"publisher","first-page":"1740004","DOI":"10.1142\/S0218348X17400047","volume":"25","author":"S Liu","year":"2017","unstructured":"Liu, S., Pan, Z., Cheng, X.: A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface. Fractals 25(4), 1740004 (2017)","journal-title":"Fractals"},{"issue":"12","key":"34_CR7","first-page":"34","volume":"42","author":"H Tang","year":"2017","unstructured":"Tang, H., Dillenseger, J.L., Bao, X.D., Luo, L.M.: A vectorial image soft segmentation method based on neighborhood weighted gaussian mixture model. Comput. Med. Imaging Graph. 42(12), 34\u201336 (2017)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"9","key":"34_CR8","first-page":"25","volume":"3","author":"A Raghuvira Pratap","year":"2017","unstructured":"Raghuvira Pratap, A., Vani, K.S., Rama Devi, J., KnageeswaraRao, V.A.: An efficient density based improved K-medoids clustering algorithm. Int. J. Adv. Comput. Sci. Appl. 3(9), 25\u201327 (2017)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"issue":"34","key":"34_CR9","first-page":"57","volume":"5","author":"F Chabat","year":"2017","unstructured":"Chabat, F., Yang, G.-Z., Hansell, D.-M.: Obstructive lung diseases: texture classification for differentiation at CT. Radiology 5(34), 57\u201358 (2017)","journal-title":"Radiology"},{"key":"34_CR10","unstructured":"Kuo, W.-F., Lin, C.-Y., Sun, Y.-N.: MR images segmentation. In: Computer Society Conference on Computer Vision and Pattern Recognition, vol. 27, no. 5, pp. 67\u201369 (2017)"},{"issue":"5","key":"34_CR11","first-page":"1","volume":"34","author":"J Yang","year":"2018","unstructured":"Yang, J., Xie, Y., Guo, Y.: Panel data clustering analysis based on composite PCC: a parametric approach. Cluster Comput. 34(5), 1\u201311 (2018)","journal-title":"Cluster Comput."},{"key":"34_CR12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2018.05.072","volume":"311","author":"S Huang","year":"2018","unstructured":"Huang, S., Ren, Y., Xu, Z.: Robust multi-view data clustering with multi-view capped-norm K-means. Neurocomputing 311, 197\u2013208 (2018)","journal-title":"Neurocomputing"},{"key":"34_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-018-1960-2","volume":"7330","author":"Y Bo","year":"2018","unstructured":"Bo, Y.: The data clustering based dynamic risk identification of biological immune system: mechanism, method and simulation. Cluster Comput. 7330, 1\u201314 (2018). https:\/\/doi.org\/10.1007\/s10586-018-1960-2","journal-title":"Cluster Comput."},{"key":"34_CR14","doi-asserted-by":"publisher","first-page":"1850015","DOI":"10.1142\/S2010132518500153","volume":"26","author":"WV Payne","year":"2018","unstructured":"Payne, W.V., Heo, J., Domanski, P.A.: A data-clustering technique for fault detection and diagnostics in field-assembled air conditioners. Int. J. Air-Cond. Refrig. 26, 1850015 (2018)","journal-title":"Int. J. Air-Cond. Refrig."},{"issue":"99","key":"34_CR15","first-page":"1","volume":"PP","author":"P Das","year":"2018","unstructured":"Das, P., Das, D.K., Dey, S.: A new class topper optimization algorithm with an application to data clustering. IEEE Trans. Emerg. Top. Comput. PP(99), 1 (2018)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"issue":"1","key":"34_CR16","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3847\/1538-4365\/aab76f","volume":"236","author":"R Ma","year":"2018","unstructured":"Ma, R., Angryk, R.A., Riley, P., et al.: Coronal mass ejection data clustering and visualization of decision trees. Astrophys. J. Suppl. 236(1), 14 (2018)","journal-title":"Astrophys. J. Suppl."},{"issue":"3","key":"34_CR17","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.2217\/epi-2018-0057","volume":"10","author":"P Karpinski","year":"2018","unstructured":"Karpinski, P., Patai, A., Hap, W., et al.: Multilevel omic data clustering reveals variable contribution of methylator phenotype to integrative cancer subtypes. Epigenomics 10(3), 1289\u20131299 (2018). epi-2018-0057","journal-title":"Epigenomics"},{"issue":"99","key":"34_CR18","first-page":"1","volume":"PP","author":"W Fan","year":"2018","unstructured":"Fan, W., Bouguila, N., Du, J.X., et al.: Axially symmetric data clustering through Dirichlet process mixture models of Watson distributions. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1\u201312 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Popova, O., Kuznetsova, E., Sazonova, T.: Algorithms of data clustering in assessing the transport infrastructure of the region. In: MATEC Web of Conferences, vol. 170, no. 3, pp. 123\u2013130 (2018)","DOI":"10.1051\/matecconf\/201817005006"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Sriadhi, S., Gultom, S., Martiano, M., et al.: K-means method with linear search algorithm to reduce Means Square Error (MSE) within data clustering. In: IOP Conference Series Materials Science and Engineering, vol. 434, no. 3, p. 012032 (2018)","DOI":"10.1088\/1757-899X\/434\/1\/012032"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Multimedia Technology and Enhanced Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-51100-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T02:02:44Z","timestamp":1619229764000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-51100-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030510992","9783030511005"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-51100-5_34","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICMTEL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Technology and Enhanced Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leicester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icmtel2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icmtel.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"confyplus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"158","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic all papers were presented in YouTubeLive.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}