{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:54:29Z","timestamp":1777503269496,"version":"3.51.4"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Sichuan\u00a0Science\u00a0and\u00a0Technology\u00a0Innovation\u00a0and\u00a0Venture\u00a0Seedling\u00a0project\u00a0China","award":["No.2020JDRC0081"],"award-info":[{"award-number":["No.2020JDRC0081"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s00521-021-05714-3","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T09:03:21Z","timestamp":1611651801000},"page":"3375-3383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Study on the intelligent identification method of formation lithology by element and gamma spectrum"],"prefix":"10.1007","volume":"34","author":[{"given":"He","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengbo","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialing","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"key":"5714_CR1","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1016\/S1876-3804(16)30124-0","volume":"43","author":"N Liu","year":"2016","unstructured":"Liu N, Wang G (2016) Shale gas sweet spot identification and precise geo-steering drilling in Weiyuan Block of Sichuan Basin, SW China. Pet Explor Dev 43:1067\u20131075. https:\/\/doi.org\/10.1016\/S1876-3804(16)30124-0","journal-title":"Pet Explor Dev"},{"key":"5714_CR2","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1016\/S1876-3804(17)30094-0","volume":"44","author":"X Liu","year":"2017","unstructured":"Liu X (2017) Directional deflection equations for steerable drilling tools and the control mechanism of wellbore trajectory. Pet Explor Dev 44:834\u2013839. https:\/\/doi.org\/10.1016\/S1876-3804(17)30094-0","journal-title":"Pet Explor Dev"},{"key":"5714_CR3","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1049\/iet-cta.2011.0438","volume":"6","author":"N Panchal","year":"2012","unstructured":"Panchal N, Bayliss MT, Whidborne JF (2012) Attitude control system for directional drilling bottom hole assemblies. IET Control Theory Appl 6:884\u2013892. https:\/\/doi.org\/10.1049\/iet-cta.2011.0438","journal-title":"IET Control Theory Appl"},{"key":"5714_CR4","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.perol.2017.10.028","volume":"160","author":"Y Xie","year":"2018","unstructured":"Xie Y, Zhu C, Zhou W, Li Z, Liu X, Tu M (2018) Evaluation of machine learning methods for formation lithology identification: a comparison of tuning processes and model performances. J Pet Sci Eng 160:182\u2013193. https:\/\/doi.org\/10.1016\/j.perol.2017.10.028","journal-title":"J Pet Sci Eng"},{"key":"5714_CR5","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.future.2018.12.068","volume":"95","author":"H Liang","year":"2019","unstructured":"Liang H, Zou J, Li Z, Khan MJ, Lu Y (2019) Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm. Futur Gener Comput Syst 95:454\u2013466. https:\/\/doi.org\/10.1016\/j.future.2018.12.068","journal-title":"Futur Gener Comput Syst"},{"key":"5714_CR6","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.petrol.2016.02.017","volume":"143","author":"S Dong","year":"2016","unstructured":"Dong S, Wang Z, Zeng L (2016) Lithology identification using kernel Fisher discriminant analysis with well logs. J Pet Sci Eng 143:95\u2013102. https:\/\/doi.org\/10.1016\/j.petrol.2016.02.017","journal-title":"J Pet Sci Eng"},{"key":"5714_CR7","doi-asserted-by":"publisher","first-page":"9014","DOI":"10.1364\/oe.23.009014","volume":"23","author":"D G\u00fcrsoy","year":"2015","unstructured":"G\u00fcrsoy D, Bi\u00e7er T, Lanzirotti A, Newville MG, De Carlo F (2015) Hyperspectral image reconstruction for X-ray fluorescence tomography. Opt Express 23:9014. https:\/\/doi.org\/10.1364\/oe.23.009014","journal-title":"Opt Express"},{"key":"5714_CR8","doi-asserted-by":"publisher","unstructured":"Elaidi H, Elhaddar Y, Benabbou Z, Abbar H (2018) An idea of a clustering algorithm using support vector machines based on binary decision tree. In: 2018 international conference on intelligent systems and computer vision, ISCV 2018. 2018-May 2018, pp 1\u20135.https:\/\/doi.org\/10.1109\/ISACV.2018.8354024","DOI":"10.1109\/ISACV.2018.8354024"},{"key":"5714_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-019-01088-z","author":"H Liang","year":"2019","unstructured":"Liang H, Zou J (2019) Rock image segmentation of improved semi-supervised SVM\u2013FCM algorithm based on chaos, circuits. Syst Signal Process. https:\/\/doi.org\/10.1007\/s00034-019-01088-z","journal-title":"Syst Signal Process"},{"key":"5714_CR10","doi-asserted-by":"publisher","unstructured":"Cao S-J, Ding J, Ren C (2020) Sensor deployment strategy using cluster analysis of fuzzy C-means algorithm: towards online control of indoor environment\u2019s safety and health. In: Sustainable cities and society. https:\/\/doi.org\/https:\/\/doi.org\/10.1016\/j.scs.2020.102190.","DOI":"10.1016\/j.scs.2020.102190"},{"key":"5714_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.03.014","author":"A Gosain","year":"2016","unstructured":"Gosain A, Dahiya S (2016) Performance analysis of various fuzzy clustering algorithms: a review. Procedia Comput Sci. https:\/\/doi.org\/10.1016\/j.procs.2016.03.014","journal-title":"Procedia Comput Sci"},{"key":"5714_CR12","doi-asserted-by":"publisher","unstructured":"Qi C, Wang W, Wang S (2015) Application of indoor temperature prediction based on SVM and FCM. In: Proceedings of 2015 27th Chinese control and decision conference CCDC 2015, pp 2883\u20132887. https:\/\/doi.org\/10.1109\/CCDC.2015.7162418","DOI":"10.1109\/CCDC.2015.7162418"},{"key":"5714_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2020.106974","author":"P David","year":"2020","unstructured":"David P (2020) Hofmeyr, degrees of freedom and model selection for K-means clustering. Comput Stat Data Anal. https:\/\/doi.org\/10.1016\/j.csda.2020.106974","journal-title":"Comput Stat Data Anal"},{"key":"5714_CR14","unstructured":"P. Properties, E.B. V All, Rocks\u2014Their Classification and General Properties, (n.d.) 1\u201316"},{"key":"5714_CR15","doi-asserted-by":"publisher","first-page":"20590","DOI":"10.1109\/ACCESS.2017.2756872","volume":"5","author":"Z Ge","year":"2017","unstructured":"Ge Z, Song Z, Ding SX, Huang B (2017) Data mining and analytics in the process industry: the role of machine learning. IEEE Access 5:20590\u201320616. https:\/\/doi.org\/10.1109\/ACCESS.2017.2756872","journal-title":"IEEE Access"},{"key":"5714_CR16","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.procs.2015.12.157","volume":"72","author":"AM Shahiri","year":"2015","unstructured":"Shahiri AM, Husain W, Rashid NA (2015) A review on predicting student\u2019s performance using data mining techniques. Procedia Comput Sci 72:414\u2013422. https:\/\/doi.org\/10.1016\/j.procs.2015.12.157","journal-title":"Procedia Comput Sci"},{"key":"5714_CR17","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.procs.2015.08.220","volume":"60","author":"S Agrawal","year":"2015","unstructured":"Agrawal S, Agrawal J (2015) Survey on anomaly detection using data mining techniques. Procedia Comput Sci 60:708\u2013713. https:\/\/doi.org\/10.1016\/j.procs.2015.08.220","journal-title":"Procedia Comput Sci"},{"key":"5714_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.apsusc.2017.10.167","author":"F Li","year":"2018","unstructured":"Li F, Shi C (2018) NO-sensing performance of vacancy defective monolayer MoS2 predicted by density function theory. Appl Surf Sci. https:\/\/doi.org\/10.1016\/j.apsusc.2017.10.167","journal-title":"Appl Surf Sci"},{"key":"5714_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.molstruc.2016.06.005","author":"M Jamshidi","year":"2016","unstructured":"Jamshidi M, Rezaei O, Belverdi AR, Malekian S, Belverdi AR (2016) A highly selective fluorescent chemosensor for Mg2+ ion in aqueous solution using density function theory calculations. J. Mol Struct. https:\/\/doi.org\/10.1016\/j.molstruc.2016.06.005.","journal-title":"J Mol Struct"},{"key":"5714_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2018.08.002","author":"L Pronzato","year":"2018","unstructured":"Pronzato L, Wynn HP, Zhigljavsky AA (2018) Simplicial variances, potentials and Mahalanobis distances. J Multivar Anal https:\/\/doi.org\/10.1016\/j.jmva.2018.08.002","journal-title":"J Multivar Anal"},{"key":"5714_CR21","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.renene.2018.02.097","volume":"10","author":"RR de la Hermosa Gonz\u00e1lez-Carrato","year":"2018","unstructured":"de la Hermosa Gonz\u00e1lez-Carrato RR (2018) Wind farm monitoring using Mahalanobis distance and fuzzy clustering. Renew Energy 10:526\u2013540","journal-title":"Renew Energy"},{"key":"5714_CR22","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.mechmachtheory.2015.11.010","volume":"98","author":"Y Li","year":"2016","unstructured":"Li Y, Xu M, Zhao H, Huang W (2016) Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis. Mech Mach Theory 98:114\u2013132. https:\/\/doi.org\/10.1016\/j.mechmachtheory.2015.11.010","journal-title":"Mech Mach Theory"},{"key":"5714_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2012.12.048","volume":"113","author":"X Yang","year":"2013","unstructured":"Yang X, Yu Q, He L, Guo T (2013) The one-against-all partition based binary tree support vector machine algorithms for multi-class classification. Neurocomputing 113:1\u20137. https:\/\/doi.org\/10.1016\/j.neucom.2012.12.048","journal-title":"Neurocomputing"},{"key":"5714_CR24","doi-asserted-by":"publisher","first-page":"6067","DOI":"10.1016\/j.patrec.2018.01.012","volume":"103","author":"P Trajdos","year":"2018","unstructured":"Trajdos P, Kurzynski M (2018) Weighting scheme for a pairwise multi-label classifier based on the fuzzy confusion matrix. Pattern Recognit Lett 103:6067. https:\/\/doi.org\/10.1016\/j.patrec.2018.01.012","journal-title":"Pattern Recognit Lett"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05714-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05714-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05714-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T06:30:13Z","timestamp":1645684213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05714-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,26]]},"references-count":24,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["5714"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05714-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,26]]},"assertion":[{"value":"19 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2021","order":3,"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":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}