{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T01:54:38Z","timestamp":1782784478855,"version":"3.54.5"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T00:00:00Z","timestamp":1581552000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T00:00:00Z","timestamp":1581552000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002790","name":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["NSERC RGPIN-2019-04572"],"award-info":[{"award-number":["NSERC RGPIN-2019-04572"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s00521-020-04773-2","type":"journal-article","created":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T07:03:45Z","timestamp":1581577425000},"page":"13639-13649","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Purities prediction in a manufacturing froth flotation plant: the deep learning techniques"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4548-2691","authenticated-orcid":false,"given":"Yuanyuan","family":"Pu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alicja","family":"Szmigiel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5402-7036","authenticated-orcid":false,"given":"Derek B.","family":"Apel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,2,13]]},"reference":[{"key":"4773_CR1","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.minpro.2015.12.002","volume":"146","author":"A Jahedsaravani","year":"2016","unstructured":"Jahedsaravani A et al (2016) Froth-based modeling and control of a batch flotation process. Int J Miner Process 146:90\u201396","journal-title":"Int J Miner Process"},{"key":"4773_CR2","volume-title":"Wills\u2019 mineral processing technology: an introduction to the practical aspects of ore treatment and mineral recovery","author":"BA Wills","year":"2015","unstructured":"Wills BA, Finch J (2015) Wills\u2019 mineral processing technology: an introduction to the practical aspects of ore treatment and mineral recovery. Butterworth-Heinemann, Oxford"},{"key":"4773_CR3","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.minpro.2012.03.003","volume":"110","author":"F Nakhaei","year":"2012","unstructured":"Nakhaei F et al (2012) Recovery and grade accurate prediction of pilot plant flotation column concentrate: neural network and statistical techniques. Int J Miner Process 110:140\u2013154","journal-title":"Int J Miner Process"},{"issue":"7","key":"4773_CR4","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.mineng.2004.10.008","volume":"18","author":"S Vieira","year":"2005","unstructured":"Vieira S, Sousa J, Dur\u00e3o F (2005) Fuzzy modelling strategies applied to a column flotation process. Miner Eng 18(7):725\u2013729","journal-title":"Miner Eng"},{"key":"4773_CR5","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.mineng.2018.12.004","volume":"132","author":"J McCoy","year":"2019","unstructured":"McCoy J, Auret L (2019) Machine learning applications in minerals processing: a review. Miner Eng 132:95\u2013109","journal-title":"Miner Eng"},{"key":"4773_CR6","doi-asserted-by":"publisher","DOI":"10.1080\/10426914.2019.1643476","author":"SS Miriyala","year":"2019","unstructured":"Miriyala SS, Mitra K (2019) Multi-objective optimization of iron ore induration process using optimal neural networks. Mater Manuf Process. https:\/\/doi.org\/10.1080\/10426914.2019.1643476","journal-title":"Mater Manuf Process"},{"issue":"1","key":"4773_CR7","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.ejor.2017.05.026","volume":"264","author":"SS Miriyala","year":"2018","unstructured":"Miriyala SS, Subramanian VR, Mitra K (2018) TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study. Eur J Oper Res 264(1):294\u2013309","journal-title":"Eur J Oper Res"},{"issue":"3","key":"4773_CR8","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.compchemeng.2005.10.007","volume":"30","author":"K Mitra","year":"2006","unstructured":"Mitra K, Ghivari M (2006) Modeling of an industrial wet grinding operation using data-driven techniques. Comput Chem Eng 30(3):508\u2013520","journal-title":"Comput Chem Eng"},{"issue":"5","key":"4773_CR9","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1007\/s12613-010-0353-1","volume":"17","author":"SC Chelgani","year":"2010","unstructured":"Chelgani SC, Shahbazi B, Rezai B (2010) Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network. Int J Miner Metall Mater 17(5):526\u2013534","journal-title":"Int J Miner Metall Mater"},{"issue":"12","key":"4773_CR10","doi-asserted-by":"publisher","first-page":"3149","DOI":"10.1016\/S1003-6326(11)61095-9","volume":"21","author":"C Yang","year":"2011","unstructured":"Yang C et al (2011) Soft sensor of key index for flotation process based on sparse multiple kernels least squares support vector machines. Chin J Nonferr Met 21(12):3149\u20133154","journal-title":"Chin J Nonferr Met"},{"key":"4773_CR11","unstructured":"Kaijun, Z., et al., Flotation recovery prediction based on froth features and LS-SVM [J]. Chinese Journal of Scientific Instrument, 2009. 6"},{"key":"4773_CR12","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.measurement.2017.09.025","volume":"114","author":"SC Chelgani","year":"2018","unstructured":"Chelgani SC, Shahbazi B, Hadavandi E (2018) Support vector regression modeling of coal flotation based on variable importance measurements by mutual information method. Measurement 114:102\u2013108","journal-title":"Measurement"},{"key":"4773_CR13","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1016\/j.colsurfa.2017.07.013","volume":"529","author":"B Shahbazi","year":"2017","unstructured":"Shahbazi B, Chelgani SC, Matin S (2017) Prediction of froth flotation responses based on various conditioning parameters by random forest method. Colloids Surf A 529:936\u2013941","journal-title":"Colloids Surf A"},{"issue":"7553","key":"4773_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436","journal-title":"Nature"},{"key":"4773_CR15","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1016\/j.powtec.2019.10.065","volume":"360","author":"SS Miriyala","year":"2020","unstructured":"Miriyala SS, Mitra K (2020) Deep learning based system identification of industrial integrated grinding circuits. Powder Technol 360:921\u2013936","journal-title":"Powder Technol"},{"key":"4773_CR16","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.minpro.2014.01.002","volume":"127","author":"L Filippov","year":"2014","unstructured":"Filippov L, Severov V, Filippova I (2014) An overview of the beneficiation of iron ores via reverse cationic flotation. Int J Miner Process 127:62\u201369","journal-title":"Int J Miner Process"},{"issue":"2","key":"4773_CR17","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179\u2013211","journal-title":"Cogn Sci"},{"key":"4773_CR18","doi-asserted-by":"crossref","unstructured":"Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. pp 850\u2013855","DOI":"10.1049\/cp:19991218"},{"key":"4773_CR19","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko G (1989) Approximations by superpositions of a sigmoidal function. Math Control Signals Syst 2:183\u2013192","journal-title":"Math Control Signals Syst"},{"key":"4773_CR20","unstructured":"Pascanu R et al (2013) How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026"},{"key":"4773_CR21","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"8","key":"4773_CR22","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1093\/bioinformatics\/bti171","volume":"21","author":"J Hua","year":"2004","unstructured":"Hua J et al (2004) Optimal number of features as a function of sample size for various classification rules. Bioinformatics 21(8):1509\u20131515","journal-title":"Bioinformatics"},{"issue":"3","key":"4773_CR23","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18\u201322","journal-title":"R News"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04773-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-020-04773-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04773-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T00:41:41Z","timestamp":1613090501000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-020-04773-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,13]]},"references-count":23,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["4773"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04773-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,13]]},"assertion":[{"value":"26 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2020","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":"There are no conflicts of interest to disclosure.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}