{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:42:00Z","timestamp":1775839320437,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021ZD0112301"],"award-info":[{"award-number":["2021ZD0112301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62021003"],"award-info":[{"award-number":["62021003"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073006"],"award-info":[{"award-number":["62073006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00521-023-09329-8","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T21:22:30Z","timestamp":1704748950000},"page":"5473-5498","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization"],"prefix":"10.1007","volume":"36","author":[{"given":"Runyu","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2270-268X","authenticated-orcid":false,"given":"Jian","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Xiaotong","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Junfei","family":"Qiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"issue":"1","key":"9329_CR1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41467-021-27624-7","volume":"13","author":"A G\u00f3mez-Sanabria","year":"2022","unstructured":"G\u00f3mez-Sanabria A, Kiesewetter G, Klimont Z, Schoepp W, Haberl H (2022) Potential for future reductions of global GHG and air pollutants from circular waste management systems. Nat Commun 13(1):106","journal-title":"Nat Commun"},{"issue":"7","key":"9329_CR2","first-page":"797","volume":"34","author":"A Chen","year":"2019","unstructured":"Chen A, Chen JR, Cui J, Fan C, Han W (2019) Research on risks and countermeasures of \u201ccities besieged by waste\u201d in China-an empirical analysis based on DIIS. Bull Chin Acad Sci 34(7):797\u2013806","journal-title":"Bull Chin Acad Sci"},{"key":"9329_CR3","doi-asserted-by":"publisher","DOI":"10.16383\/j.aas.c220810","author":"J Tang","year":"2023","unstructured":"Tang J, Xia H, Yu W, Qiao JF (2023) Research status and prospects of intelligent optimization control for municipal solid waste incineration process. Acta Autom Sin. https:\/\/doi.org\/10.16383\/j.aas.c220810","journal-title":"Acta Autom Sin"},{"key":"9329_CR4","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.122147","volume":"239","author":"G Vilardi","year":"2022","unstructured":"Vilardi G, Verdone N (2022) Exergy analysis of municipal solid waste incineration processes: the use of O2-enriched air and the oxy-combustion process. Energy 239:122147","journal-title":"Energy"},{"key":"9329_CR5","volume":"318","author":"H Xia","year":"2023","unstructured":"Xia H, Tang J, Aljerf L, Wang TZ, Qiao JF, Xu Q, Ukaogo P (2023) Investigation on dioxins emission characteristic during complete maintenance operating period of municipal solid waste incineration. Environ Pollut 318:120949","journal-title":"Environ Pollut"},{"key":"9329_CR6","volume":"875","author":"WS Li","year":"2023","unstructured":"Li WS, Yan DH, Li L, Wen ZY, Liu MJ, Lu SX, Huang QF (2023) Review of thermal treatments for the degradation of dioxins in municipal solid waste incineration fly ash: proposing a suitable method for large-scale processing. Sci Total Environ 875:162565","journal-title":"Sci Total Environ"},{"issue":"1","key":"9329_CR7","doi-asserted-by":"crossref","first-page":"3861","DOI":"10.1038\/s41467-019-11912-4","volume":"10","author":"B Wang","year":"2019","unstructured":"Wang B, Wang P, Xie LH, Lin RB, Lv J, Li JR, Chen B (2019) A stable zirconium based metal-organic framework for specific recognition of representative polychlorinated dibenzo-p-dioxin molecules. Nat Commun 10(1):3861","journal-title":"Nat Commun"},{"issue":"6","key":"9329_CR8","first-page":"1063","volume":"46","author":"JF Qiao","year":"2020","unstructured":"Qiao JF, Guo ZH, Tang J (2020) Dioxin emission concentration measurement approaches for municipal solid wastes in cineration process: a survey. Acta Autom Sin 46(6):1063\u20131089","journal-title":"Acta Autom Sin"},{"issue":"2","key":"9329_CR9","first-page":"696","volume":"70","author":"J Tang","year":"2019","unstructured":"Tang J, Qiao JF (2019) Soft sensor of dioxin emission concentration in solid waste incineration process based on selective ensemble kernel learning algorithm. J Chem Eng Technol 70(2):696\u2013706","journal-title":"J Chem Eng Technol"},{"key":"9329_CR10","volume-title":"Waste incineration: Best available techniques for integrated pollution prevention and control","author":"HL Hu","year":"2009","unstructured":"Hu HL, Wen XF, Luo QM (2009) Waste incineration: Best available techniques for integrated pollution prevention and control. Chemical Industry Press, Benijing"},{"issue":"8","key":"9329_CR11","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1360\/N112016-00062","volume":"46","author":"TY Chai","year":"2016","unstructured":"Chai TY (2016) Industrial process control systems: research status and development direction. Sci Sin Inf 46(8):1003\u20131015","journal-title":"Sci Sin Inf"},{"issue":"1","key":"9329_CR12","first-page":"1","volume":"13","author":"JH Mart\u00ednez","year":"2022","unstructured":"Mart\u00ednez JH, Romero S, Ramasco JJ, Estrada E (2022) The world-wide waste web. Nat Commun 13(1):1\u201313","journal-title":"Nat Commun"},{"issue":"9","key":"9329_CR13","doi-asserted-by":"crossref","first-page":"5994","DOI":"10.1109\/TIM.2020.2967247","volume":"69","author":"T Wang","year":"2020","unstructured":"Wang T, Leung H, Zhao J, Wang W (2020) Multiseries featural LSTM for partial periodic time-series prediction: a case study for steel industry. IEEE Trans Instrum Meas 69(9):5994\u20136003","journal-title":"IEEE Trans Instrum Meas"},{"key":"9329_CR14","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1007\/s00521-017-3317-9","volume":"31","author":"RK Huda","year":"2019","unstructured":"Huda RK, Banka H (2019) Efficient feature selection and classification algorithm based on PSO and rough sets. Neural Comput Appl 31:4287\u20134303","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9329_CR15","first-page":"110","volume":"38","author":"J Tang","year":"2021","unstructured":"Tang J, Qiao JF, Xu Z, Guo ZH (2021) Soft measuring approach of dioxin emission concentration in municipal solid waste incineration process based on feature reduction and selective ensemble algorithm. Control Theory Appl 38(1):110\u2013120","journal-title":"Control Theory Appl"},{"issue":"22","key":"9329_CR16","doi-asserted-by":"crossref","first-page":"19751","DOI":"10.1007\/s00521-022-07705-4","volume":"34","author":"OO Akinola","year":"2022","unstructured":"Akinola OO, Ezugwu AE, Agushaka JO, Zitar RA, Abualigah L (2022) Multiclass feature selection with metaheuristic optimization algorithms: a review. Neural Comput Appl 34(22):19751\u201319790","journal-title":"Neural Comput Appl"},{"key":"9329_CR17","first-page":"509","volume-title":"Multi-objective semi-supervised feature selection and model selection based on pearson's correlation coefficient","author":"F Coelho","year":"2010","unstructured":"Coelho F, Braga AP, Verleysen M (2010) Multi-objective semi-supervised feature selection and model selection based on pearson\u2019s correlation coefficient. Springer, Berlin, pp 509\u2013516"},{"issue":"4","key":"9329_CR18","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/72.298224","volume":"5","author":"R Battiti","year":"1994","unstructured":"Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 5(4):537\u2013550","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"9329_CR19","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","volume":"24","author":"JR Vergara","year":"2014","unstructured":"Vergara JR, Est\u00e9vez PA (2014) A review of feature selection methods based on mutual information. Neural Comput Appl 24(1):175\u2013186","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9329_CR20","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/34.824819","volume":"22","author":"AK Jain","year":"2000","unstructured":"Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: A review. IEEE Trans Pattern Anal Mach Intell 22(1):4\u201337","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"9329_CR21","first-page":"75","volume":"50","author":"JF Qiao","year":"2021","unstructured":"Qiao JF, Guo ZH, Tang J (2021) Soft sensing of dioxin emission concentration in solid waste incineration process based on multi-layer feature selection. Inf Control 50(1):75\u201387","journal-title":"Inf Control"},{"key":"9329_CR22","first-page":"1531","volume":"5","author":"F Fleuret","year":"2004","unstructured":"Fleuret F (2004) Fast binary feature selection with conditional mutual information. J Mach Learn Res 5:1531\u20131555","journal-title":"J Mach Learn Res"},{"key":"9329_CR23","first-page":"1","volume":"72","author":"S Xie","year":"2023","unstructured":"Xie S, Hua Y, Lu S, Li X (2023) A novel spatio-temporal adaptive prediction modelling strategy for industrial production process. IEEE Trans Instrum Meas 72:1\u201311","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"9329_CR24","first-page":"24","volume":"46","author":"JD Lin","year":"2020","unstructured":"Lin JD, Wu XY, Chai Y, Yin HP (2020) Structure optimization of convolutional neural networks: a survey. Acta Autom Sin 46(1):24\u201337","journal-title":"Acta Autom Sin"},{"key":"9329_CR25","doi-asserted-by":"crossref","unstructured":"Kiranyaz S, Ince T, Hamila R, Gabbouj M (2015) Convolutional neural networks for patient-specific ECG classification. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2608\u20132611","DOI":"10.1109\/EMBC.2015.7318926"},{"key":"9329_CR26","first-page":"1","volume":"72","author":"B Wan","year":"2023","unstructured":"Wan B, Zhou X, Zheng B, Yin H, Zhu Z, Wang H, Yan C (2023) LFRNet: localizing, focus, and refinement network for salient object detection of surface defects. IEEE Trans Instrum Meas 72:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"9329_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-021-09975-1","volume":"54","author":"MM Bejani","year":"2021","unstructured":"Bejani MM, Ghatee M (2021) A systematic review on overfitting control in shallow and deep neural networks. Artif Intell Rev 54:1\u201348","journal-title":"Artif Intell Rev"},{"issue":"4","key":"9329_CR28","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","volume":"69","author":"Y He","year":"2019","unstructured":"He Y, Song K, Meng Q, Yan Y (2019) An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans Instrum Meas 69(4):1493\u20131504","journal-title":"IEEE Trans Instrum Meas"},{"key":"9329_CR29","doi-asserted-by":"crossref","unstructured":"Vinayakumar R, Soman KP, Poornachandran P (2017) Secure shell (ssh) traffic analysis with flow based features using shallow and deep networks. In: 2017 International conference on advances in computing, communications and informatics (ICACCI), pp 2026\u20132032","DOI":"10.1109\/ICACCI.2017.8126143"},{"key":"9329_CR30","first-page":"1","volume":"71","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Qin N, Huang D, Du J (2022) Precise diagnosis of unknown fault of high-speed train bogie using novel FBM-net. IEEE Trans Instrum Meas 71:1\u201311","journal-title":"IEEE Trans Instrum Meas"},{"issue":"10","key":"9329_CR31","doi-asserted-by":"crossref","first-page":"8136","DOI":"10.1109\/TIE.2018.2886789","volume":"66","author":"O Abdeljaber","year":"2018","unstructured":"Abdeljaber O, Sassi S, Avci O, Kiranyaz S, Ibrahim AA, Gabbouj M (2018) Fault detection and severity identification of ball bearings by online condition monitoring. IEEE Trans Industr Electron 66(10):8136\u20138147","journal-title":"IEEE Trans Industr Electron"},{"issue":"2","key":"9329_CR32","doi-asserted-by":"crossref","DOI":"10.1088\/2631-8695\/ac769e","volume":"4","author":"D Yang","year":"2022","unstructured":"Yang D, Lu J, Zhou Y, Dong H (2022) Establishment of leakage detection model for oil and gas pipeline based on VMD-MD-1DCNN. Eng Res Express 4(2):025051","journal-title":"Eng Res Express"},{"key":"9329_CR33","doi-asserted-by":"crossref","unstructured":"Cao J, He Z, Wang J, Yu P (2020) An anti-noise fault diagnosis method of bearing based on multi-scale 1DCNN. Preprints.org","DOI":"10.20944\/preprints202008.0164.v1"},{"key":"9329_CR34","doi-asserted-by":"crossref","first-page":"17569","DOI":"10.1007\/s00521-020-04926-3","volume":"32","author":"F Mtibaa","year":"2020","unstructured":"Mtibaa F, Nguyen KK, Azam M, Papachristou A, Venne JS, Cheriet M (2020) LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings. Neural Comput Appl 32:17569\u201317585","journal-title":"Neural Comput Appl"},{"issue":"8","key":"9329_CR35","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"9329_CR36","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.jhydrol.2018.04.065","volume":"561","author":"J Zhang","year":"2018","unstructured":"Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918\u2013929","journal-title":"J Hydrol"},{"issue":"1","key":"9329_CR37","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","volume":"10","author":"W Kong","year":"2017","unstructured":"Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2017) Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid 10(1):841\u2013851","journal-title":"IEEE Trans Smart Grid"},{"key":"9329_CR38","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3247993","author":"E Chondrodima","year":"2023","unstructured":"Chondrodima E, Pelekis N, Pikrakis A, Theodoridis Y (2023) An efficient LSTM neural network-based framework for vessel location forecasting. IEEE Trans Intell Trans Syst. https:\/\/doi.org\/10.1109\/TITS.2023.3247993","journal-title":"IEEE Trans Intell Trans Syst"},{"issue":"13","key":"9329_CR39","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.3390\/s20133743","volume":"20","author":"I Pisa","year":"2020","unstructured":"Pisa I, Morell A, Vicario JL, Vilanova R (2020) Denoising autoencoders and LSTM-based artificial neural networks data processing for its application to internal model control in industrial environments\u2014the wastewater treatment plant control case. Sensors 20(13):3743","journal-title":"Sensors"},{"key":"9329_CR40","doi-asserted-by":"crossref","first-page":"124889","DOI":"10.1016\/j.energy.2022.124889","volume":"260","author":"WS Zha","year":"2022","unstructured":"Zha WS, Liu YP, Wan YJ, Luo RL, Li DL, Yang S, Xu Y (2022) Forecasting monthly gas field production based on the CNN-LSTM model. Energy 260:124889","journal-title":"Energy"},{"issue":"9","key":"9329_CR41","doi-asserted-by":"crossref","first-page":"6979","DOI":"10.1007\/s00521-022-08104-5","volume":"35","author":"CL Cui","year":"2023","unstructured":"Cui CL, Tang J, Xia H, Qiao JF, Yu W (2023) Virtual sample generation method based on generative adversarial fuzzy neural network. Neural Comput Appl 35(9):6979\u20137001","journal-title":"Neural Comput Appl"},{"key":"9329_CR42","doi-asserted-by":"crossref","first-page":"9307","DOI":"10.1007\/s00521-021-05691-7","volume":"33","author":"J Tang","year":"2021","unstructured":"Tang J, Xia H, Zhang J, Qiao JF, Yu W (2021) Deep forest regression based on cross-layer full connection. Neural Neural Comput Appl 33:9307\u20139328","journal-title":"Neural Neural Comput Appl"},{"key":"9329_CR43","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1007\/s00521-021-06809-7","volume":"34","author":"H Xia","year":"2022","unstructured":"Xia H, Tang J, Qiao JF, Zhang J, Yu W (2022) DF classification algorithm for constructing a small sample size of data-oriented DF regression model. Neural Comput Appl 34:2785\u20132810","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9329_CR44","first-page":"343","volume":"49","author":"H Xia","year":"2023","unstructured":"Xia H, Tang J, Cui CL, Qiao JF (2023) Soft sensing method of dioxin emission in municipal solid waste incineration process based on broad hybrid forest regression. Acta Autom Sin 49(2):343\u2013365","journal-title":"Acta Autom Sin"},{"key":"9329_CR45","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2022.3227918","author":"JF Qiao","year":"2022","unstructured":"Qiao JF, Sun J, Meng X (2022) Event-triggered adaptive model predictive control of oxygen content for municipal solid waste incineration process. IEEE Trans Autom Sci Eng. https:\/\/doi.org\/10.1109\/TASE.2022.3227918","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"9329_CR46","volume":"17","author":"JF Ruma","year":"2023","unstructured":"Ruma JF, Adnan MSG, Dewan A, Rahman RM (2023) Particle swarm optimization based LSTM networks for water level forecasting: a case study on Bangladesh river network. Res Eng 17:100951","journal-title":"Res Eng"},{"key":"9329_CR47","doi-asserted-by":"publisher","DOI":"10.3390\/w14132018","author":"X Yang","year":"2022","unstructured":"Yang X, Maihemuti B, Simayi Z, Saydi M, Na L (2022) Prediction of glacially derived runoff in the muzati river watershed based on the PSO-LSTM model. Water. https:\/\/doi.org\/10.3390\/w14132018","journal-title":"Water"},{"key":"9329_CR48","doi-asserted-by":"crossref","unstructured":"Zhou H, Zuo Y, Li T, Li S (2021) Application of PSO-LSTM for forecasting of ship traffic flow. In: 2021 International conference on security, pattern analysis, and cybernetics (SPAC), pp 298\u2013302","DOI":"10.1109\/SPAC53836.2021.9539945"},{"issue":"3","key":"9329_CR49","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TEVC.2005.857610","volume":"10","author":"JJ Liang","year":"2006","unstructured":"Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281\u2013295","journal-title":"IEEE Trans Evol Comput"},{"issue":"x","key":"9329_CR50","first-page":"1","volume":"45","author":"DD Wang","year":"2022","unstructured":"Wang DD, Tang J, Xia H, Qiao JF (2022) Virtual sample generation method based on hybrid optimization with multi-objective PSO. Acta Autom Sin 45(x):1\u201322","journal-title":"Acta Autom Sin"},{"key":"9329_CR51","doi-asserted-by":"crossref","DOI":"10.1016\/j.fuel.2022.123826","volume":"320","author":"JB Zhuang","year":"2022","unstructured":"Zhuang JB, Tang J, Aljerf L (2022) Comprehensive review on mechanism analysis and numerical simulation of municipal solid waste incineration process based on mechanical grate. Fuel 320:123826","journal-title":"Fuel"},{"key":"9329_CR52","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1039\/tf9514700164","volume":"47","author":"JR Arthur","year":"1951","unstructured":"Arthur JR (1951) Reactions between carbon and oxygen. Trans Faraday Soc 47:164\u2013178","journal-title":"Trans Faraday Soc"},{"key":"9329_CR53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0045-6535(89)90372-X","volume":"19","author":"W Hasberg","year":"1989","unstructured":"Hasberg W, May H, Dorn I (1989) Description of the residence-time behaviour and burnout of PCDD, PCDF and other higher chlorinated aromatic hydrocarbons in industrial waste incineration plants. Chemosphere 19:1\u20136","journal-title":"Chemosphere"},{"key":"9329_CR54","unstructured":"Blake CL, Merz CJ (1998) UCI repository of machine learning databases, Dept. Inf. Comput. Sci., Univ. California, Irvine, Irvine, CA, USA, vol. 55. [Online], available: http:\/\/archive.ics.uci.edu\/ml\/datasets.html, January 1, 2022"},{"issue":"6","key":"9329_CR55","first-page":"251","volume":"43","author":"W Xu","year":"2022","unstructured":"Xu W, Tang J, Xia H, Qiao JF (2022) Soft sensor of dioxin emission concentration based on Bagging semi-supervised deep forest regression. Chin J Sci Instrum 43(6):251\u2013259","journal-title":"Chin J Sci Instrum"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09329-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09329-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09329-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T20:12:07Z","timestamp":1709842327000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09329-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":55,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9329"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09329-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]},"assertion":[{"value":"14 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 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 no conflict of interest. The work is considered for publication on the understanding that the article has neither been published nor will be published anywhere else before being published in Neural Computing and Applications.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}