{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T17:01:55Z","timestamp":1769533315718,"version":"3.49.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Program of National Natural Science Foundation of China","award":["51634002"],"award-info":[{"award-number":["51634002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773101"],"award-info":[{"award-number":["61773101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s13042-024-02137-z","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T10:01:51Z","timestamp":1714384911000},"page":"4093-4118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A real-valued label noise cleaning method based on ensemble iterative filtering with noise score"],"prefix":"10.1007","volume":"15","author":[{"given":"Chuang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2658-3297","authenticated-orcid":false,"given":"Zhizhong","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxing","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"issue":"5","key":"2137_CR1","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1109\/TCYB.2018.2887094","volume":"50","author":"Z Kang","year":"2020","unstructured":"Kang Z, Pan H, Hoi SCH et al (2020) Robust graph learning from noisy data. IEEE Trans Cybernet 50(5):1833\u20131843","journal-title":"IEEE Trans Cybernet"},{"key":"2137_CR2","doi-asserted-by":"crossref","first-page":"99754","DOI":"10.1109\/ACCESS.2019.2930355","volume":"7","author":"JA S\u00e1ez","year":"2019","unstructured":"S\u00e1ez JA, Corchado E (2019) KSUFS: a novel unsupervised feature selection method based on statistical tests for standard and big data problems. IEEE Access 7:99754\u201399770","journal-title":"IEEE Access"},{"issue":"5","key":"2137_CR3","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Frenay","year":"2014","unstructured":"Frenay B, Verleysen M (2014) Classification in the presence of label noise: a survey. IEEE Trans Neural Netw Learn Syst 25(5):845\u2013869","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2137_CR4","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10462-004-0751-8","volume":"22","author":"X Zhu","year":"2004","unstructured":"Zhu X, Wu X (2004) Class noise vs. attribute noise: a quantitative study. Artif Intell Rev 22:177\u2013210","journal-title":"Artif Intell Rev"},{"key":"2137_CR5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10115-012-0570-1","volume":"38","author":"JA S\u00e1ez","year":"2014","unstructured":"S\u00e1ez JA, Galar M, Luengo J et al (2014) Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition. Knowl Inf Syst 38:179\u2013206","journal-title":"Knowl Inf Syst"},{"key":"2137_CR6","doi-asserted-by":"crossref","unstructured":"Gamberger D, Lavrac N, Dzeroski S (1996) Noise elimination in inductive concept learning: a case study in medical diagnosis. In: proceedings of the 7th international workshop on algorithmic learning theory, pp 199\u2013212","DOI":"10.1007\/3-540-61863-5_47"},{"key":"2137_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2015.12.006","volume":"98","author":"S Garc\u00eda","year":"2016","unstructured":"Garc\u00eda S, Luengo J, Herrera F (2016) Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowl Based Syst 98:1\u201329","journal-title":"Knowl Based Syst"},{"key":"2137_CR8","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.inffus.2015.04.002","volume":"27","author":"JA S\u00e1ez","year":"2016","unstructured":"S\u00e1ez JA, Galar M, Luengo J et al (2016) INFFC: an iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Inform Fusion 27:19\u201332","journal-title":"Inform Fusion"},{"key":"2137_CR9","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.knosys.2017.10.026","volume":"140","author":"J Luengo","year":"2018","unstructured":"Luengo J, Shim SO, Alshomrani S et al (2018) CNC-NOS: class noise cleaning by ensemble filtering and noise scoring. Knowl Based Syst 140:27\u201349","journal-title":"Knowl Based Syst"},{"key":"2137_CR10","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106428","volume":"94","author":"Z Nematzadeh","year":"2020","unstructured":"Nematzadeh Z, Ibrahim R, Selamat A (2020) Improving class noise detection and classification performance: a new two-filter CNDC model. Appl Soft Comput 94:106428","journal-title":"Appl Soft Comput"},{"key":"2137_CR11","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.knosys.2016.06.003","volume":"107","author":"C Li","year":"2016","unstructured":"Li C, Sheng VS, Jiang L et al (2016) Noise filtering to improve data and model quality for crowdsourcing. Knowl Based Syst 107:96\u2013103","journal-title":"Knowl Based Syst"},{"key":"2137_CR12","doi-asserted-by":"crossref","first-page":"297","DOI":"10.20965\/jaciii.2010.p0297","volume":"14","author":"P Jeatrakul","year":"2010","unstructured":"Jeatrakul P, Wong KW, Fung CC (2010) Data cleaning for classification using misclassification analysis. J Adv Comput Intell Intell Inf 14:297\u2013302","journal-title":"J Adv Comput Intell Intell Inf"},{"key":"2137_CR13","volume":"215","author":"G Algan","year":"2020","unstructured":"Algan G, Ulusoy I (2020) Image classification with deep learning in the presence of noisy labels: a survey. Knowl Based Syst 215:106771","journal-title":"Knowl Based Syst"},{"key":"2137_CR14","doi-asserted-by":"crossref","unstructured":"Wang Y, Liu W, Ma X, et al (2018) Iterative learning with open-set noisy labels. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8688\u20138696","DOI":"10.1109\/CVPR.2018.00906"},{"key":"2137_CR15","unstructured":"Daiki T, Daiki I, Toshihiko Y et al (2018) Joint optimization framework for learning with noisy labels. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5552\u20135560"},{"key":"2137_CR16","unstructured":"Yu X, Han B, Yao J et al (2019) How does disagreement help generalization against label corruption? In: international conference on machine learning, pp 7164\u20137173"},{"key":"2137_CR17","doi-asserted-by":"crossref","unstructured":"Kordos M, Blachnik M (2012) Instance selection with neural networks for regression problems. In: international conference on artificial neural networks, pp 263\u2013270","DOI":"10.1007\/978-3-642-33266-1_33"},{"key":"2137_CR18","doi-asserted-by":"crossref","first-page":"145800","DOI":"10.1109\/ACCESS.2021.3123151","volume":"9","author":"J Mart\u00edn","year":"2021","unstructured":"Mart\u00edn J, S\u00e1ez JA, Corchado E (2021) On the regressand noise problem: model robustness and synergy with regression-adapted noise filters. IEEE Access 9:145800\u2013145816","journal-title":"IEEE Access"},{"key":"2137_CR19","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eswa.2015.12.046","volume":"54","author":"AA Gonz\u00e1lez","year":"2016","unstructured":"Gonz\u00e1lez AA, Pastor JFD, Rodr\u00edguez JJ et al (2016) Instance selection for regression by discretization. Expert Syst Appl 54:340\u2013350","journal-title":"Expert Syst Appl"},{"key":"2137_CR20","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.neucom.2016.04.003","volume":"201","author":"AA Gonz\u00e1lez","year":"2016","unstructured":"Gonz\u00e1lez AA, Pastor JFD, Rodr\u00edguez JJ et al (2016) Instance selection for regression: adapting DROP. Neurocomputing 201:66\u201381","journal-title":"Neurocomputing"},{"key":"2137_CR21","first-page":"317","volume-title":"Multiple classifier systems","author":"V Sofie","year":"2003","unstructured":"Sofie V, Assche AV (2003) Ensemble methods for noise elimination in classification problems. Multiple classifier systems. Springer, Berlin, pp 317\u2013325"},{"key":"2137_CR22","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s11390-007-9054-2","volume":"22","author":"TM Khoshgoftaar","year":"2007","unstructured":"Khoshgoftaar TM, Rebours P (2007) Improving software quality prediction by noise filtering techniques. J Comput Sci Technol 22:387\u2013396","journal-title":"J Comput Sci Technol"},{"issue":"2","key":"2137_CR23","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1080\/088395100117124","volume":"14","author":"D Gamberger","year":"2000","unstructured":"Gamberger D, Lavrac N, Dzeroski S (2000) Noise detection and elimination in data preprocessing: experiments in medical domains. Appl Artif Intell 14(2):205\u2013223","journal-title":"Appl Artif Intell"},{"key":"2137_CR24","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2020.103936","volume":"96","author":"T Berghout","year":"2020","unstructured":"Berghout T, Mouss LH, Kadri O et al (2020) Aircraft engines remaining useful life prediction with an adaptive denoising online sequential extreme learning machine. Eng Appl Artif Intel 96:103936","journal-title":"Eng Appl Artif Intel"},{"issue":"6","key":"2137_CR25","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1109\/TITS.2020.2983763","volume":"22","author":"M Lv","year":"2020","unstructured":"Lv M, Hong Z, Chen L et al (2020) Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans Intell Transp Syst 22(6):3337\u20133348","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2137_CR26","doi-asserted-by":"crossref","first-page":"3491","DOI":"10.1007\/s10489-020-02054-y","volume":"51","author":"L Ge","year":"2020","unstructured":"Ge L, Wu K, Zeng Y et al (2020) Multi-scale spatiotemporal graph convolution network for air quality prediction. Appl Intell 51:3491\u20133505","journal-title":"Appl Intell"},{"key":"2137_CR27","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.apenergy.2019.05.103","volume":"250","author":"P Shine","year":"2019","unstructured":"Shine P, Scully T, Upton J et al (2019) Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine. Appl Energy 250:1110\u20131119","journal-title":"Appl Energy"},{"key":"2137_CR28","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.asoc.2015.09.034","volume":"38","author":"F Kara","year":"2016","unstructured":"Kara F, Aslanta\u015f K, \u00c7i\u00e7ek A (2016) Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network. Appl Soft Comput 38:64\u201374","journal-title":"Appl Soft Comput"},{"key":"2137_CR29","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1109\/69.404034","volume":"7","author":"RY Wang","year":"1995","unstructured":"Wang RY, Storey VC, Firth CP (1995) A framework for analysis of data quality research. IEEE Trans Knowl Data Eng 7:623\u2013640","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"7","key":"2137_CR30","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.engappai.2007.11.008","volume":"21","author":"JMM Fernandez","year":"2008","unstructured":"Fernandez JMM, Cabal VA, Montequin VR et al (2008) Online estimation of electric arc furnace tap temperature by using fuzzy neural networks. Eng Appl Artif Intel 21(7):1001\u20131012","journal-title":"Eng Appl Artif Intel"},{"issue":"1","key":"2137_CR31","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1613\/jair.606","volume":"11","author":"CE Brodley","year":"1999","unstructured":"Brodley CE, Friedl MA (1999) Identifying mislabeled training data. J Artif Intell Res 11(1):131\u2013167","journal-title":"J Artif Intell Res"},{"key":"2137_CR32","doi-asserted-by":"crossref","unstructured":"Sun J, Zhao F, Wang C et al (2007) Identifying and correcting mislabeled training instances. In: proceedings of the future generation communication and networking, pp 244\u2013250","DOI":"10.1109\/FGCN.2007.146"},{"issue":"6","key":"2137_CR33","first-page":"448","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek I (1976) An experiment with the edited nearest-neighbor rule. IEEE Trans Syst Man Cybernet 6(6):448\u2013452","journal-title":"IEEE Trans Syst Man Cybernet"},{"key":"2137_CR34","first-page":"37","volume":"6","author":"DW Aha","year":"1991","unstructured":"Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37\u201366","journal-title":"Mach Learn"},{"key":"2137_CR35","first-page":"1","volume":"22","author":"G Jiang","year":"2021","unstructured":"Jiang G, Wang W, Qian Y et al (2021) A unified sample selection framework for output noise filtering: an error-bound perspective. J Mach Learn Res 22:1\u201366","journal-title":"J Mach Learn Res"},{"key":"2137_CR36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.inffus.2015.12.002","volume":"30","author":"AA Gonz\u00e1lez","year":"2016","unstructured":"Gonz\u00e1lez AA, Blachnik M, Kordos M et al (2016) Fusion of instance selection methods in regression tasks. Inform Fusion 30:69\u201379","journal-title":"Inform Fusion"},{"key":"2137_CR37","doi-asserted-by":"crossref","unstructured":"Angelova A, Mostafam YA, Perona P (2005) Pruning training sets for learning of object categories. In proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 494\u2013501","DOI":"10.1109\/CVPR.2005.283"},{"issue":"2\u20133","key":"2137_CR38","first-page":"255","volume":"17","author":"JA Fdez","year":"2011","unstructured":"Fdez JA, Fernandez A, Luengo J et al (2011) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Logic Soft Comput 17(2\u20133):255\u2013287","journal-title":"J Mult Valued Logic Soft Comput"},{"key":"2137_CR39","unstructured":"Dheeru D, Graff C (2017) UCI Machine learning repository. http:\/\/archive.ics.uci.edu\/ml. Accessed 2017"},{"issue":"3","key":"2137_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339823","volume":"5","author":"L Zhao","year":"2019","unstructured":"Zhao L, Gkountouna O, Pfoser D (2019) Spatial auto-regressive dependency interpretable learning based on spatial topological constraints. ACM Trans Spat Algorithms Syst 5(3):1\u201328","journal-title":"ACM Trans Spat Algorithms Syst"},{"key":"2137_CR41","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1007\/s11227-015-1384-1","volume":"71","author":"CI Ac\u0131","year":"2015","unstructured":"Ac\u0131 CI, Akay MF (2015) A hybrid congestion control algorithm for broadcast-based architectures with multiple input queues. J Supercomput 71:1907\u20131931","journal-title":"J Supercomput"},{"key":"2137_CR42","doi-asserted-by":"crossref","unstructured":"Zhou F, Claire Q, King RD (2014) Predicting the geographical origin of music. In proceedings of the IEEE international conference on data mining, pp 1115\u20131120","DOI":"10.1109\/ICDM.2014.73"},{"issue":"6","key":"2137_CR43","doi-asserted-by":"crossref","first-page":"4783","DOI":"10.3906\/elk-1807-87","volume":"27","author":"H Kaya","year":"2019","unstructured":"Kaya H, T\u00fcfekci P, Uzun E (2019) Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS. Turk J Electr Eng Comput Sci 27(6):4783\u20134796","journal-title":"Turk J Electr Eng Comput Sci"},{"issue":"9","key":"2137_CR44","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.1016\/j.jbusres.2016.02.010","volume":"69","author":"S Moro","year":"2016","unstructured":"Moro S, Rita P, Vala B (2016) Predicting social media performance metrics and evaluation of the impact on brand building: a data mining approach. J Bus Res 69(9):3341\u20133351","journal-title":"J Bus Res"},{"key":"2137_CR45","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.snb.2012.01.074","volume":"166","author":"A Vergara","year":"2012","unstructured":"Vergara A, Vembu S, Ayhan T et al (2012) Chemical gas sensor drift compensation using classifier ensembles. Sens Actuators B Chem 166:320\u2013329","journal-title":"Sens Actuators B Chem"},{"key":"2137_CR46","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.chemolab.2013.10.012","volume":"130","author":"IR Lujan","year":"2014","unstructured":"Lujan IR, Fonollosa J, Vergara A et al (2014) On the calibration of sensor arrays for pattern recognition using the minimal number of experiments. Chemom Intell Lab Syst 130:123\u2013134","journal-title":"Chemom Intell Lab Syst"},{"key":"2137_CR47","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.eswa.2019.03.029","volume":"129","author":"E Hoseinzade","year":"2019","unstructured":"Hoseinzade E, Haratizadeh S (2019) CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273\u2013285","journal-title":"Expert Syst Appl"},{"issue":"2","key":"2137_CR48","doi-asserted-by":"crossref","first-page":"04015066","DOI":"10.1061\/(ASCE)CO.1943-7862.0001047","volume":"142","author":"MH Rafiei","year":"2016","unstructured":"Rafiei MH, Adeli H (2016) A novel machine learning model for estimation of sale prices of real estate units. J Constr Eng Manag 142(2):04015066","journal-title":"J Constr Eng Manag"},{"issue":"2","key":"2137_CR49","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.snb.2007.09.060","volume":"129","author":"SDE Vito","year":"2008","unstructured":"Vito SDE, Massera E, Piga M et al (2008) On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens Actuators B Chem 129(2):750\u2013757","journal-title":"Sens Actuators B Chem"},{"issue":"2","key":"2137_CR50","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s13748-013-0040-3","volume":"2","author":"TH Fanaee","year":"2014","unstructured":"Fanaee TH, Gama J (2014) Event labeling combining ensemble detectors and background knowledge. Prog Artif Intell 2(2):113\u2013127","journal-title":"Prog Artif Intell"},{"issue":"1","key":"2137_CR51","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"key":"2137_CR52","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"issue":"10","key":"2137_CR53","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J et al (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inform Sci 180(10):2044\u20132064","journal-title":"Inform Sci"},{"key":"2137_CR54","first-page":"65","volume":"6","author":"S Holm","year":"1979","unstructured":"Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65\u201370","journal-title":"Scand J Stat"},{"issue":"3","key":"2137_CR55","doi-asserted-by":"crossref","first-page":"2000395","DOI":"10.1002\/srin.202000395","volume":"92","author":"T Hay","year":"2020","unstructured":"Hay T, Visuri VV, Aula M et al (2020) A review of mathematical process models for the electric arc furnace process. Steel Res Int 92(3):2000395","journal-title":"Steel Res Int"},{"issue":"8","key":"2137_CR56","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1177\/01423312211052213","volume":"44","author":"C Li","year":"2022","unstructured":"Li C, Mao Z (2022) Generative adversarial network\u2013based real-time temperature prediction model for heating stage of electric arc furnace. Trans Inst Meas Control 44(8):1669\u20131684","journal-title":"Trans Inst Meas Control"},{"issue":"10","key":"2137_CR57","first-page":"7","volume":"18","author":"P Yuan","year":"2006","unstructured":"Yuan P, Wang F, Mao Z (2006) Endpoint prediction of EAF based on G-SVM. J Iron Steel Res Int 18(10):7\u201310","journal-title":"J Iron Steel Res Int"},{"issue":"7","key":"2137_CR58","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1080\/00207160701798749","volume":"86","author":"JMM Fernandez","year":"2009","unstructured":"Fernandez JMM, Menendez C, Ortega FA et al (2009) A smart modelling for the casting temperature prediction in an electric arc furnace. Int J Comput Math 86(7):1182\u20131193","journal-title":"Int J Comput Math"},{"key":"2137_CR59","doi-asserted-by":"crossref","unstructured":"Sismanis P (2019) Prediction of productivity and energy consumption in a consteel furnace using data-science models. In: proceedings of the 22th international conference on business information systems, pp 85\u201399","DOI":"10.1007\/978-3-030-20485-3_7"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02137-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02137-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02137-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T08:32:43Z","timestamp":1723883563000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02137-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":59,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["2137"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02137-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"17 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}